Take home Exercise 2

Author

Wyz

Take-home Exercise 2: DataVis Makeover A Look into Singapore Students’ Performance Using PISA 2022——FU WEIMING

1 Overview

1.1 Setting the scene

OECD education director Andreas Schleicher shared in a BBC article that “Singapore managed to achieve excellence without wide differences between children from wealthy and disadvantaged families.” (2016) Furthermore, several Singapore’s Minister for Education also started an “every school a good school” slogan. In fact, according to a recent Straits Times article (2023), Singapore students ranked the best of all countries in all 3 domains in PISA 2022.

The general public, however, strongly belief that there are still disparities that exist, especially between the elite schools and neighborhood school, between students from families with higher socioeconomic status and those with relatively lower socioeconomic status and immigration and non-immigration families.

1.2 Our task

The 2022 Programme for International Student Assessment (PISA) data was released on December 5, 2022. PISA global education survey every three years to assess the education systems worldwide through testing 15 year old students in the subjects of mathematics, reading, and science.

In this exercise, we are required to use Exploratory Data Analysis (EDA) methods and ggplot2 data visualisation functions to reveal:

the distribution of Singapore students’ performance in mathematics, reading, and science, and
the relationship between these performances with schools, gender and socioeconomic status of the students.

1.3 The Data

The PISA 2022 database contains the full set of responses from individual students, school principals and parents. There are a total of five data files and their contents are as follows:

Student questionnaire data file
School questionnaire data file
Teacher questionnaire data file
Cognitive item data file
Questionnaire timing data file

In this exercise, we will use the Student questionnaire data file only.

2 Data Preparation

2.1 Loading R packages

The pacman::p_load() function is used to install and load the required R packages into the R environment, as below.

tidyverse: collection of R packages designed for data science
haven: an R package to read and write data formats used by other statistical packages, e.g. SAS, SPSS
ggrepel: an R package provides geoms for ggplot2 to repel overlapping text labels.
ggthemes: an R package provides some extra themes, geoms, and scales for ‘ggplot2’.
hrbrthemes: an R package provides typography-centric themes and theme components for ggplot2.
patchwork: an R package for preparing composite figure created using ggplot2.
DT: an R package to render interactive DataTables
knitr: an R package for dynamic report generation
kableExtra: an R package to build complex HTML tables using kable()
ggridges: an R package for plotting Ridgeline plots
qqplotr: an R package which extends ggplot2 Q-Q plot functionalities
rstatix: an R package for performaing basic statistical tests
ggdist: an R package that provides a flexible set of ggplot2 geoms and stats for visualising distributions and uncertainties
ggstatsplot: an R package that extends ggplot2 to create graphics with details from statistical tests

pacman::p_load(tidyverse, haven, ggrepel, ggthemes, hrbrthemes, patchwork, DT, knitr, kableExtra, ggridges, qqplotr, rstatix, ggdist, ggstatsplot)

2.2 Importing PISA data

The code chunk below uses read_sas() of haven to import PISA data into R environment.

stu_qqq <- read_sas("THE1/data/cy08msp_stu_qqq.sas7bdat")

Filtering the entire data file for records belonging to Singapore students.

stu_qqq_SG <- stu_qqq %>%
  filter(CNT == "SGP")

Saving the records belonging to Singapore students into “stu_qqq_SG.rds” file for easier loading (no need to extract the records from the entire dataset repeatedly).

write_rds(stu_qqq_SG,
          "THE1/data/stu_qqq_SG.rds")

Loading the extract records from the saved “stu_qqq_SG.rds” file.

sfu_qqq_SG <- read_rds("THE1/data/stu_qqq_SG.rds")

2.3 Summary statistics of PISA data

Below code chunks detail initial observations and summary statistics relating to Singapore students:

kable(head(sfu_qqq_SG, 8), "html")

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SGP	702	70200052	70200001	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	4	10	10	2006	1	2	1	1	1	1	7020002	7020002	2	1	3	3	3	3	9999997	9999997	7	2	1	3	2	1	3	7	2	2	4	4	3	3	2	4	4	2	2	2	1	2	1	2	2	2	2	1	2	1	NA	NA	1	1	1	NA	1	1	3	4	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	NA	2	NA	2	3	NA	3	2	4	NA	2	4	2	3	1	1	1	1	1	1	1	1	1	2	2	2	2	2	2	2	2	2	5	1	6	1	1	6	5	6	1	2	3	4	2	1	NA	8	NA	NA	NA	NA	NA	NA	5	5	NA	2	1	1	4	NA	4	2	NA	2	2	NA	NA	NA	NA	3	NA	3	NA	NA	3	4	2	NA	5	NA	NA	5	NA	5	5	NA	NA	5	4	NA	NA	2	3	4	1	NA	NA	NA	NA	5	5	5	NA	NA	NA	NA	3	5	NA	4	3	3	NA	3	NA	NA	NA	4	5	5	5	NA	NA	5	1	NA	NA	NA	NA	3	4	3	4	2	NA	NA	3	5	2	1	3	NA	NA	NA	NA	NA	NA	2	2	1	3	NA	NA	NA	1	NA	1	2	2	NA	NA	4	55	1	1	2	3	NA	3	NA	NA	4	4	3	3	3	2	2	3	NA	3	NA	NA	2	3	1	4	NA	NA	NA	3	3	3	2	4	NA	NA	NA	2	2	2	NA	1	NA	4	NA	NA	NA	4	NA	NA	NA	3	3	3	3	2	3	3	2	3	3	3	3	3	NA	3	NA	3	4	NA	NA	2	4	3	3	3	NA	NA	NA	NA	NA	3	2	2	5	NA	NA	NA	NA	5	NA	4	NA	1	NA	NA	NA	4	NA	3	2	2	NA	4	2	2	3	2	NA	3	0	0	0	1	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	NA	NA	4	2	4	NA	NA	4	1	1	NA	2	1	1	3	3	3	2	NA	NA	NA	NA	2	NA	NA	2	7020003	3	3	NA	4	NA	NA	3	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	8	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4	4	1	4	2	2	4	4	5	1	5	2	4	3	3	3	3	3	3	3	3	3	3	1	2	6	2	4	3	2	1	1	2	2	2	2	2	2	2	1	3	3	4	4	4	3	4	4	3	4	1	1	3	2	3	2	2	2	1	3	2	3	2	2	3	1	1	1	3	2	2	1	3	3	3	2	3	3	3	1	NA	NA	NA	NA	3	3	3	4	4	4	4	4	4	4	4	4	4	4	2	2	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	9	9701	83	2634	07020002	15.50	344	1	070200	070200	070200	998	0	0	1	4	0	10	7	0	0	0	3	85.85	1	-0.2606	0.2442	-1.2280	-0.7560	-0.6386	0.4369	2.7951	-0.0319	1.3979	-0.2970	0.9777	1.2321	NA	NA	-0.3780	0.3884	-0.5635	-0.4092	0.1666	0.3356	-0.7746	0.1429	0.4317	1.3180	0.3729	-0.1305	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	7	8	16.0	17.00	30.34	30.34	0.1940	0.7524	0.1836	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.4062	7	0.3346	6	0.3623	-0.1315	-0.3767	-0.4038	0.2260	-0.4469	-0.3452	-0.1855	0.2929	NA	0.5764	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.35943	8.20073	2.63139	8.03388	8.18690	7.89416	8.04452	2.67796	2.72897	7.74437	2.67796	2.73358	8.18690	8.20073	2.58146	2.68151	2.63139	2.73358	7.89416	2.72897	8.03388	7.89416	2.63139	8.03388	7.88647	7.89416	8.36312	2.67796	2.62882	8.03388	2.78196	2.73358	8.18690	8.20073	2.67796	2.68151	2.73358	2.63139	7.89416	2.62882	8.03388	8.20073	2.63139	8.03388	8.18690	7.89416	8.04452	2.67796	2.72897	7.74437	2.67796	2.73358	8.18690	8.20073	2.58146	2.68151	2.63139	2.73358	7.89416	2.72897	8.03388	7.89416	2.63139	8.03388	7.88647	7.89416	8.36312	2.67796	2.62882	8.03388	2.78196	2.73358	8.18690	8.20073	2.67796	2.68151	2.73358	2.63139	7.89416	2.62882	8.03388	1	70	639.004	601.251	621.480	631.596	579.276	591.791	600.709	587.322	618.131	581.973	676.298	692.247	690.981	643.067	627.908	684.676	661.380	674.070	666.282	657.387	710.634	618.739	591.623	659.770	635.892	646.901	603.569	621.352	659.674	649.719	649.392	575.372	603.792	656.162	625.136	582.174	665.104	599.694	616.165	628.181	615.137	538.617	587.580	686.280	589.078	554.608	682.886	574.889	601.470	627.403	686.193	656.043	650.782	643.126	675.389	591.001	684.203	617.908	601.076	645.941	597.328	564.341	655.238	638.884	604.706	576.996	672.527	599.424	604.423	664.795	604.382	575.460	534.443	571.301	675.638	566.880	582.805	558.696	662.795	640.998	518.732	557.279	497.254	615.386	615.007	591.702	595.836	540.481	600.664	613.118	602.757	571.184	646.605	679.914	685.582	637.760	645.213	577.579	661.673	670.254	537.068	614.320	583.272	620.093	634.054	602.552	595.217	603.353	611.942	663.352	0.63867	01MAY23:14:19:45	218
SGP	702	70200134	70200002	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	45	10	6	2006	2	1	1	1	1	1	7020001	7020001	1	4	3	3	4	2	9999997	9999997	8	3	2	2	3	5	2	4	4	5	5	3	5	3	2	4	4	2	2	2	2	1	1	2	2	2	2	1	1	1	NA	NA	1	1	1	NA	1	1	5	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	4	3	NA	2	3	NA	4	NA	4	2	NA	3	2	3	1	1	1	1	2	1	1	1	1	1	1	1	1	1	2	2	2	2	4	5	2	1	3	4	4	2	1	3	2	2	3	1	1	1	NA	NA	NA	NA	NA	NA	6	1	5	5	1	NA	NA	5	4	NA	5	NA	NA	3	NA	3	NA	5	3	NA	4	NA	NA	3	NA	5	NA	NA	NA	NA	4	5	NA	2	3	4	NA	2	NA	NA	3	4	3	5	NA	NA	2	4	NA	4	2	NA	NA	NA	4	NA	NA	3	NA	3	NA	3	2	NA	3	NA	NA	NA	NA	4	3	5	4	4	2	NA	NA	NA	4	NA	4	NA	3	4	4	NA	4	NA	NA	2	5	NA	NA	4	NA	NA	3	4	NA	4	NA	4	NA	4	1	1	1	NA	NA	10	45	3	2	3	5	NA	4	3	4	4	NA	NA	4	2	2	2	1	1	NA	NA	2	2	2	NA	NA	3	NA	3	3	3	3	2	NA	NA	NA	3	4	4	NA	NA	4	NA	NA	2	NA	NA	3	2	NA	3	3	NA	NA	4	4	3	3	3	2	3	4	4	4	NA	3	NA	3	3	NA	3	NA	3	NA	3	3	NA	NA	3	NA	3	3	NA	NA	NA	NA	5	NA	NA	NA	5	5	5	NA	5	NA	5	5	NA	5	NA	NA	4	2	2	NA	3	3	1	1	0	1	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	3	3	NA	3	5	1	1	NA	1	1	1	3	3	NA	NA	NA	1	1	1	NA	NA	1	1	9999999	3	NA	NA	NA	2	1	NA	1	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	10	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4	4	4	4	4	4	4	2	4	1	4	1	1	4	4	4	4	4	4	4	4	4	4	2	4	6	3	3	3	3	3	3	3	3	3	3	4	3	3	3	2	2	2	3	2	1	2	3	3	3	3	3	2	3	3	3	3	3	3	3	3	3	3	3	3	3	3	2	3	3	3	3	2	3	3	2	2	2	2	1	NA	NA	NA	NA	3	2	3	4	4	4	4	4	4	4	4	2	4	4	4	4	4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	8	31	21	9705	07020002	15.83	344	1	070200	070200	070200	998	0	0	4	7	0	2	7	1	0	0	2	NA	0	1.2437	-0.0437	-0.2016	1.1246	0.1810	0.4540	0.3058	0.1187	0.1290	-0.1734	-0.7402	-0.5609	NA	NA	-0.5969	1.1687	0.1475	-0.7102	0.0024	-1.2147	-0.5240	-0.2874	0.7644	4.7588	0.6647	0.6178	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	7	7	14.5	37.83	77.10	77.10	0.6249	0.7842	0.8261	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.4062	7	0.3346	6	2.8889	0.5048	0.3109	0.4297	-0.8080	0.4182	0.3311	0.9444	-0.4797	NA	0.7781	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.74318	9.88533	10.36607	10.33466	3.44489	3.29511	3.44489	3.29830	10.36607	3.29511	9.89489	3.29830	9.88533	10.33466	10.36607	9.89489	3.45536	3.45536	9.88533	3.29830	3.44489	9.88533	10.36607	10.33466	3.44489	3.29511	3.44489	3.29830	10.36607	3.29511	9.89489	3.29830	9.88533	10.33466	10.36607	9.89489	3.45536	3.45536	9.88533	3.29830	3.44489	9.88533	10.36607	10.33466	3.44489	3.29511	3.44489	3.29830	10.36607	3.29511	9.89489	3.29830	9.88533	10.33466	10.36607	9.89489	3.45536	3.45536	9.88533	3.29830	3.44489	9.88533	10.36607	10.33466	3.44489	3.29511	3.44489	3.29830	10.36607	3.29511	9.89489	3.29830	9.88533	10.33466	10.36607	9.89489	3.45536	3.45536	9.88533	3.29830	3.44489	1	53	697.191	754.277	671.940	657.300	621.126	655.729	747.934	694.365	742.732	656.934	625.585	686.716	663.147	567.435	614.500	604.745	669.375	623.735	649.579	571.261	670.646	748.839	635.443	639.735	608.385	670.662	734.807	639.748	716.768	655.670	636.431	674.370	716.787	616.569	701.626	686.793	665.775	729.194	715.697	688.830	661.706	762.401	712.182	685.954	722.402	677.709	691.586	688.120	704.436	692.040	664.374	660.937	692.654	654.111	644.941	699.918	653.602	629.891	696.030	663.634	655.345	732.821	737.658	651.654	690.450	666.422	673.451	728.294	701.038	650.797	705.040	710.217	713.023	679.747	661.754	718.268	613.478	643.541	640.522	655.268	763.661	729.497	714.971	753.899	719.492	715.191	702.035	704.257	664.705	705.987	733.566	744.273	758.913	695.003	714.181	716.221	663.813	662.428	640.743	768.695	706.337	672.767	651.949	620.759	645.072	677.174	634.813	648.907	641.203	644.001	0.80357	01MAY23:14:19:44	218
SGP	702	70200112	70200003	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	8	10	7	2006	2	1	1	2	1	1	7020001	7020002	2	1	3	3	2	1	9999997	9999997	7	2	2	2	2	1	3	4	5	2	2	3	5	3	4	5	2	2	2	2	2	2	2	2	2	2	2	2	2	1	NA	NA	1	2	1	NA	2	1	8	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	3	3	3	NA	3	NA	4	NA	3	2	NA	2	2	3	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	2	2	6	2	1	1	1	6	3	1	1	3	3	2	2	2	5	7	NA	NA	NA	NA	NA	NA	3	3	2	2	NA	2	NA	4	3	3	NA	2	NA	NA	3	NA	5	NA	3	NA	3	4	NA	NA	1	NA	NA	NA	3	NA	NA	3	3	NA	4	3	3	NA	NA	NA	3	3	NA	NA	4	2	NA	4	NA	4	2	NA	NA	3	3	NA	NA	3	NA	3	NA	3	NA	2	NA	4	NA	3	2	4	NA	4	3	NA	NA	2	NA	NA	3	3	NA	NA	2	3	NA	4	NA	4	NA	NA	3	3	3	NA	NA	2	3	NA	3	2	NA	NA	2	NA	2	2	3	NA	NA	8	56	2	3	3	5	NA	3	3	NA	3	4	3	NA	3	2	2	3	NA	2	3	NA	1	NA	3	2	NA	NA	NA	1	3	NA	1	NA	2	2	2	NA	NA	2	1	NA	NA	2	1	NA	NA	NA	NA	2	1	NA	3	4	3	3	2	3	3	2	2	4	4	4	NA	3	NA	2	4	3	4	NA	NA	2	NA	2	2	NA	3	NA	NA	1	NA	NA	5	NA	4	NA	NA	3	NA	NA	5	NA	2	NA	4	3	3	NA	4	NA	NA	2	2	3	3	3	2	NA	0	0	1	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	NA	2	NA	2	NA	3	NA	1	1	1	NA	3	1	3	3	3	NA	1	3	NA	NA	NA	1	3	NA	NA	7020003	NA	NA	2	3	2	2	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	9	7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	5	5	1	5	5	4	4	4	4	3	5	4	4	3	3	3	4	3	3	4	4	5	4	6	1	4	3	4	1	1	1	3	3	3	4	2	1	1	5	4	3	5	4	2	4	1	1	3	3	1	4	2	3	2	2	2	1	5	2	2	2	1	2	1	2	2	3	2	2	3	2	3	3	2	3	3	2	1	NA	NA	NA	NA	2	3	3	3	5	2	4	3	3	2	1	1	4	2	1	1	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	8	9701	9704	9704	07020002	15.75	344	1	070200	970200	070200	998	0	0	2	3	0	0	6	0	1	0	NA	NA	0	0.7190	-0.6137	-1.2280	0.4417	-0.6386	-0.4017	-0.6563	-0.6986	-0.2087	-0.4816	-0.2053	0.5777	NA	NA	-1.0537	0.2002	-0.5635	-0.6541	-0.5604	0.6908	0.0949	0.2226	-0.4779	0.4736	0.0510	-0.3993	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4	4	4	12.0	17.00	NA	17.00	-0.3987	0.0666	-1.0357	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.4062	7	0.3346	6	1.2313	0.9454	-0.1658	-0.4292	0.1088	-0.3710	-0.7926	0.2941	0.0811	NA	-0.8446	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.77500	10.16250	10.16250	10.16250	3.38750	3.38750	3.38750	3.38750	10.16250	3.38750	10.16250	3.38750	10.16250	10.16250	10.16250	10.16250	3.38750	3.38750	10.16250	3.38750	3.38750	3.38750	3.38750	3.38750	10.16250	10.16250	10.16250	10.16250	3.38750	10.16250	3.38750	10.16250	3.38750	3.38750	3.38750	3.38750	10.16250	10.16250	3.38750	10.16250	10.16250	3.38750	3.38750	3.38750	10.16250	10.16250	10.16250	10.16250	3.38750	10.16250	3.38750	10.16250	3.38750	3.38750	3.38750	3.38750	10.16250	10.16250	3.38750	10.16250	10.16250	10.16250	10.16250	10.16250	3.38750	3.38750	3.38750	3.38750	10.16250	3.38750	10.16250	3.38750	10.16250	10.16250	10.16250	10.16250	3.38750	3.38750	10.16250	3.38750	3.38750	1	2	693.710	654.450	696.938	646.187	678.119	644.019	720.531	671.425	694.085	668.304	620.116	559.078	554.767	587.026	591.806	570.547	599.078	545.610	610.466	590.758	666.095	604.771	704.217	687.659	690.974	617.175	692.886	630.900	656.620	649.087	645.218	680.260	711.792	633.124	711.941	749.576	692.915	648.755	721.097	657.165	569.292	647.507	660.431	569.983	637.599	652.780	636.887	611.227	685.701	604.016	655.070	612.146	646.904	590.767	727.077	648.958	623.166	677.449	654.643	649.460	658.932	648.412	672.524	614.591	664.442	687.078	708.829	640.191	732.526	619.894	676.642	705.385	585.184	670.486	645.880	760.958	731.917	676.092	684.182	702.866	690.547	728.787	633.737	703.501	654.486	734.709	737.481	685.489	665.867	727.280	682.130	692.729	647.770	629.600	693.276	660.979	684.474	656.617	687.070	648.410	630.753	694.543	604.546	614.087	603.798	644.046	710.851	656.938	690.323	664.134	0.80736	01MAY23:14:19:45	218
SGP	702	70200004	70200004	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	40	10	2	2006	2	2	1	1	1	1	7020002	7020002	1	2	3	3	2	1	9999997	9999997	6	2	1	2	1	1	3	3	2	1	1	2	2	1	2	2	4	2	2	2	2	2	1	4	NA	NA	NA	NA	1	5	NA	NA	1	1	1	NA	2	1	4	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	3	NA	2	NA	3	3	NA	1	2	3	3	2	2	NA	1	1	1	1	1	1	1	1	1	2	2	2	2	2	2	2	2	2	1	1	6	4	6	6	6	6	4	1	2	3	2	1	1	1	NA	NA	NA	NA	NA	NA	5	5	5	1	NA	1	NA	NA	4	2	NA	2	2	4	NA	NA	NA	NA	NA	NA	NA	3	4	4	4	3	4	NA	NA	4	NA	4	4	3	NA	NA	NA	3	NA	2	2	4	2	NA	NA	NA	NA	4	NA	4	2	NA	NA	NA	4	3	4	2	NA	4	NA	NA	NA	1	NA	4	NA	NA	3	4	2	4	NA	3	NA	NA	NA	NA	3	3	3	3	NA	3	3	3	NA	NA	3	NA	3	3	NA	NA	3	NA	3	3	3	NA	NA	NA	3	NA	2	3	2	NA	NA	10	11	3	1	2	2	NA	2	NA	NA	3	2	4	4	2	2	2	2	3	3	3	NA	NA	3	NA	3	NA	NA	3	NA	3	3	NA	3	3	NA	NA	2	4	2	NA	3	1	NA	NA	2	NA	NA	NA	2	NA	2	2	2	NA	2	3	3	2	2	2	4	4	4	3	3	NA	NA	NA	NA	2	2	2	2	NA	2	NA	NA	NA	NA	2	2	2	NA	NA	NA	1	NA	4	NA	NA	NA	5	4	4	4	5	NA	NA	NA	NA	3	5	5	2	2	2	NA	2	2	1	0	0	1	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	3	3	3	NA	NA	NA	NA	3	3	3	3	NA	3	3	3	3	3	3	NA	3	NA	NA	NA	3	3	NA	3	9999999	NA	NA	2	NA	2	NA	2	2	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	10	10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	4	1	1	1	1	1	1	5	1	5	1	1	4	4	4	4	4	4	4	4	4	1	1	1	1	3	3	3	3	3	3	3	3	3	3	1	2	1	2	1	1	1	2	2	1	1	3	5	4	1	1	2	2	2	2	2	2	2	1	1	1	1	1	1	1	1	1	4	1	1	2	2	2	2	2	2	2	2	2	NA	NA	NA	NA	4	4	4	3	3	3	3	3	3	3	3	3	3	3	3	3	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	41	9705	2411	07020002	16.17	344	1	070200	070200	070200	998	0	0	5	5	6	10	NA	0	0	0	3	76.65	1	-0.2194	-1.1147	-1.2280	-0.7560	-0.6386	0.5617	0.1778	0.0849	-0.1344	-0.1538	0.2201	-0.0563	NA	NA	-0.8521	-0.1219	-0.1002	-0.0820	0.1868	-0.2194	0.4959	-0.9344	-0.5515	0.0594	0.6387	1.8158	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	6	6	12.0	43.33	NA	43.33	-0.9028	-0.9300	-0.9606	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.4062	7	0.3346	6	2.8889	-2.0101	0.3109	-0.6744	-1.2894	-0.5032	-3.5000	-0.6148	-0.8360	NA	-0.5172	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.43200	7.98824	2.66275	8.31429	7.98824	2.56226	2.56226	2.66275	2.66275	7.98824	2.66275	8.31429	2.66275	7.68679	7.68679	7.98824	8.31429	2.66275	2.77143	7.68679	2.77143	2.66275	7.98824	2.77143	2.66275	7.68679	7.68679	7.98824	7.98824	2.66275	7.98824	2.77143	7.98824	2.56226	2.56226	2.66275	2.77143	7.98824	8.31429	2.56226	8.31429	2.66275	7.98824	2.77143	2.66275	7.68679	7.68679	7.98824	7.98824	2.66275	7.98824	2.77143	7.98824	2.56226	2.56226	2.66275	2.77143	7.98824	8.31429	2.56226	8.31429	7.98824	2.66275	8.31429	7.98824	2.56226	2.56226	2.66275	2.66275	7.98824	2.66275	8.31429	2.66275	7.68679	7.68679	7.98824	8.31429	2.66275	2.77143	7.68679	2.77143	1	27	427.317	410.376	423.586	388.935	330.962	379.988	398.535	422.127	375.354	453.348	381.495	400.815	374.911	367.484	336.009	324.630	396.242	374.723	314.704	342.956	340.308	329.889	411.353	327.974	292.183	355.423	400.182	317.518	298.893	362.702	437.613	498.958	402.737	407.151	450.975	487.698	391.175	426.641	366.883	435.455	403.429	419.847	418.996	375.158	433.093	453.028	435.661	441.660	430.417	382.344	397.737	336.048	438.551	347.416	420.278	401.609	413.761	299.027	373.784	363.817	393.019	393.366	384.864	379.824	501.245	453.339	425.168	407.016	421.738	421.415	401.548	389.686	390.502	434.343	366.385	401.969	496.875	353.233	476.154	342.948	421.798	467.856	414.444	445.029	389.460	414.555	489.032	363.830	419.104	394.856	407.066	381.339	364.773	406.470	433.901	406.423	489.700	432.277	512.069	374.502	378.730	400.807	407.607	336.451	317.742	349.040	450.198	392.060	447.422	382.088	0.64732	01MAY23:14:19:45	218
SGP	702	70200152	70200005	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	42	10	9	2006	1	2	1	1	1	1	7020002	7020002	2	2	2	2	1	4	9999997	9999997	7	2	3	2	1	NA	4	2	4	1	1	2	2	1	2	4	4	2	2	2	2	1	2	2	2	2	2	1	2	1	NA	NA	1	1	1	NA	1	1	8	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	1	2	NA	3	NA	3	3	4	NA	1	4	1	1	4	NA	4	1	1	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	1	4	1	1	6	6	5	6	1	5	6	5	4	5	5	2	NA	NA	NA	NA	NA	NA	4	5	NA	5	4	4	4	NA	3	NA	NA	5	NA	NA	4	5	4	5	NA	NA	4	NA	NA	5	NA	4	5	1	4	5	4	NA	NA	NA	NA	NA	NA	NA	4	NA	2	5	NA	5	NA	2	2	NA	NA	NA	NA	3	NA	3	4	3	NA	NA	NA	3	NA	NA	2	2	5	5	NA	5	4	5	NA	5	NA	5	5	NA	NA	5	2	NA	NA	NA	3	3	5	3	NA	NA	NA	3	5	NA	2	NA	2	NA	NA	4	NA	4	4	NA	4	NA	1	1	1	NA	NA	5	30	4	3	4	4	NA	4	NA	4	3	3	NA	2	1	1	1	1	3	4	2	5	NA	5	NA	NA	NA	NA	NA	5	NA	5	5	5	NA	5	NA	2	2	NA	1	2	NA	2	NA	NA	2	2	2	NA	2	NA	2	NA	NA	3	4	4	1	3	2	4	4	4	NA	2	NA	2	2	NA	NA	2	4	NA	2	2	NA	NA	2	NA	NA	2	2	NA	NA	1	NA	NA	NA	4	3	NA	4	2	NA	5	5	NA	NA	5	NA	NA	5	3	1	1	NA	1	1	1	1	1	1	1	1	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	NA	5	5	5	5	NA	1	1	NA	1	1	2	3	3	3	3	NA	3	NA	2	NA	NA	2	NA	9999999	4	4	NA	2	NA	NA	NA	4	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10	10	10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	5	6	5	4	6	5	4	4	1	4	1	1	4	4	3	3	4	4	4	4	4	5	1	5	6	3	5	5	3	3	5	5	3	3	3	5	5	1	2	5	5	5	5	5	5	5	5	1	1	1	1	4	6	4	4	4	4	5	4	6	4	4	4	4	1	2	2	4	2	4	3	3	3	3	3	3	3	2	2	NA	NA	NA	NA	2	2	3	4	4	4	4	4	4	4	4	4	4	4	5	5	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10	10	23	83	21	07020002	15.58	344	1	070200	070200	070200	998	1	1	9	7	0	5	6	0	0	0	NA	79.49	1	1.0726	2.1143	-1.2280	1.1246	-0.6386	-0.5954	0.3406	0.8806	-0.5172	0.2470	-0.9940	-0.8597	NA	NA	1.7459	0.1166	1.5558	0.5051	2.3426	0.5478	0.4121	-0.9322	-0.5730	0.8973	2.5026	1.1353	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	7	7	14.5	75.54	30.34	75.54	0.2514	-0.8949	0.0856	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.6647	5	0.3346	6	1.8870	0.6775	1.0316	0.2776	2.9804	1.3145	0.8948	0.8255	0.4191	NA	1.0613	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.82353	9.00000	8.48571	2.82857	9.00000	8.48571	8.48571	9.00000	3.00000	2.82857	8.48571	2.82857	2.82857	9.00000	8.48571	3.00000	2.82857	3.00000	3.00000	9.00000	3.00000	9.00000	8.48571	2.82857	9.00000	8.48571	8.48571	9.00000	3.00000	2.82857	8.48571	2.82857	2.82857	9.00000	8.48571	3.00000	2.82857	3.00000	3.00000	9.00000	3.00000	2.82857	3.00000	9.00000	2.82857	3.00000	3.00000	2.82857	8.48571	9.00000	3.00000	9.00000	9.00000	2.82857	3.00000	8.48571	9.00000	8.48571	8.48571	2.82857	8.48571	2.82857	3.00000	9.00000	2.82857	3.00000	3.00000	2.82857	8.48571	9.00000	3.00000	9.00000	9.00000	2.82857	3.00000	8.48571	9.00000	8.48571	8.48571	2.82857	8.48571	1	35	436.462	453.450	392.315	439.986	443.125	452.648	396.970	459.945	438.166	448.084	448.199	560.636	365.478	469.970	503.664	481.215	436.800	531.226	480.997	478.578	456.333	453.400	498.937	532.324	508.231	504.461	404.572	549.457	411.062	473.613	474.516	521.987	434.209	432.725	392.901	444.589	382.733	497.778	439.754	486.126	432.794	494.698	429.147	464.070	446.069	441.193	417.452	474.220	443.916	476.528	429.063	437.136	451.101	464.708	338.774	453.538	366.873	469.776	438.499	484.175	429.756	491.067	445.708	510.568	403.908	412.346	419.173	576.384	392.309	461.595	437.563	474.378	433.958	442.276	522.369	417.482	444.665	464.823	434.014	458.216	454.383	401.375	453.331	414.743	443.411	391.559	386.903	452.824	478.373	407.059	414.746	399.365	447.814	454.758	442.892	463.269	423.967	464.304	435.375	495.469	364.784	399.972	452.831	394.357	409.755	428.787	375.015	414.975	441.178	421.531	0.69397	01MAY23:14:19:44	218
SGP	702	70200043	70200006	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	15	10	9	2006	1	2	1	1	1	1	7020001	7020002	2	1	2	3	2	1	9999997	9999997	7	2	2	2	2	1	2	2	1	2	2	5	5	5	2	1	3	2	2	2	2	1	1	2	2	1	1	1	1	1	NA	NA	2	2	2	1	2	1	3	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	2	4	3	3	NA	3	4	NA	NA	4	2	2	NA	2	4	1	1	1	1	1	1	1	1	1	1	2	2	2	1	2	2	2	2	1	6	3	1	1	6	6	4	1	2	3	2	1	1	2	5	NA	NA	NA	NA	NA	NA	6	5	4	NA	6	2	NA	3	NA	NA	NA	3	NA	4	5	4	4	NA	NA	4	NA	4	NA	NA	2	4	4	NA	4	NA	4	NA	4	NA	4	NA	NA	1	NA	1	1	NA	NA	5	5	NA	1	1	NA	5	1	NA	NA	NA	NA	3	NA	3	5	NA	NA	5	NA	3	5	NA	NA	4	NA	5	1	NA	2	5	NA	NA	NA	2	3	4	NA	5	1	NA	NA	NA	4	2	NA	5	5	NA	NA	3	NA	1	NA	NA	1	1	1	NA	NA	1	4	1	1	NA	NA	7	26	1	1	1	2	NA	3	3	NA	3	3	NA	3	3	1	1	2	1	NA	1	NA	5	NA	5	5	NA	4	NA	4	NA	NA	3	4	NA	4	2	2	2	NA	NA	NA	NA	2	1	NA	2	NA	2	NA	NA	2	2	2	NA	3	3	3	3	3	2	4	4	4	NA	NA	3	NA	4	2	4	NA	4	NA	NA	2	2	NA	2	3	2	NA	NA	NA	NA	NA	4	NA	5	5	NA	5	5	NA	NA	4	NA	5	NA	5	NA	NA	5	3	2	4	NA	4	4	2	0	0	0	1	0	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	5	5	NA	NA	NA	NA	NA	5	5	1	1	NA	1	1	1	3	3	3	NA	3	3	NA	NA	NA	NA	3	3	9999999	NA	3	4	NA	3	2	NA	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	10	7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	3	6	3	3	3	3	3	5	5	5	5	1	5	4	4	4	4	4	4	3	3	3	5	3	3	6	2	4	1	3	2	2	2	3	3	3	4	5	3	3	3	4	4	4	4	4	4	3	4	4	3	3	4	4	4	4	4	1	1	4	4	4	4	4	1	1	4	2	4	4	4	4	4	2	4	1	3	3	4	1	NA	NA	NA	NA	2	2	3	4	4	4	4	3	4	4	4	4	4	4	2	3	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	9701	34	22	07020002	15.58	344	3	970200	970200	970200	998	0	1	1	10	0	5	7	0	0	0	4	76.98	1	1.1296	0.5159	-1.2280	0.1413	0.1810	-0.1554	0.3247	4.8203	-0.5881	-0.8113	0.2183	NA	NA	NA	1.7327	-0.1006	0.1475	0.2635	0.7819	0.5515	0.4936	0.2637	-0.2489	2.2322	-0.7358	0.9918	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	9	9	16.0	17.00	57.64	57.64	-0.4733	-0.5988	0.1268	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-0.8411	6	0.3346	6	1.6615	0.6012	-0.0477	0.9257	0.3464	0.4565	0.4976	1.9301	0.2147	NA	0.3941	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.69931	2.68850	8.76685	8.34850	8.40156	2.89878	9.60179	2.92228	8.76685	3.05511	2.89878	3.02482	2.80052	8.02740	8.34850	2.58510	8.40156	8.69635	2.89878	2.78283	8.34850	2.92228	8.06550	7.73009	9.16534	2.89878	8.76685	2.92228	8.76685	2.80052	2.89878	2.78283	2.80052	8.02740	8.34850	2.80052	8.40156	9.48693	2.89878	2.78283	9.07446	9.07446	2.78283	2.92228	2.89878	8.40156	2.57670	8.34850	2.78283	8.02740	8.40156	8.06550	8.69635	3.05511	2.92228	9.48693	2.89878	2.80052	8.40156	8.76685	2.92228	8.34850	3.02482	3.20060	2.67580	8.40156	2.78283	8.34850	2.78283	8.69635	8.40156	8.76685	8.69635	3.05511	2.92228	8.69635	2.89878	2.58510	8.40156	8.76685	2.68850	2	50	569.982	539.609	531.648	534.368	465.815	528.509	514.326	521.029	472.382	503.387	469.441	500.350	375.703	377.452	470.781	415.448	448.547	434.381	411.703	410.846	475.158	470.030	461.218	504.199	486.930	493.011	469.950	464.012	440.113	495.410	471.365	546.743	581.279	567.093	498.145	519.416	521.639	513.726	519.125	544.408	526.659	580.938	577.745	622.873	557.871	488.815	602.295	578.260	541.486	572.958	466.035	498.416	502.442	517.999	452.092	494.653	463.116	498.289	441.962	411.098	491.327	522.747	486.597	524.480	483.108	505.925	501.566	506.734	499.849	442.838	528.852	604.877	498.130	511.479	555.593	555.839	553.932	571.562	520.155	609.277	493.759	538.097	485.088	465.568	493.251	545.850	504.961	532.093	568.115	485.863	459.876	490.634	386.584	499.082	441.024	544.965	475.647	504.819	459.819	481.600	523.219	536.264	454.319	501.514	498.824	523.705	497.360	508.547	499.691	580.387	0.67917	01MAY23:14:19:45	218
SGP	702	70200049	70200007	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	13	10	3	2006	2	1	1	1	1	1	7020001	7020002	2	1	3	3	2	4	9999997	9999997	8	2	2	3	2	1	4	4	1	1	2	2	1	1	2	4	2	2	2	2	2	2	1	4	2	2	2	2	2	1	NA	NA	1	1	1	NA	1	1	4	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	3	1	1	1	NA	NA	NA	1	2	2	3	3	NA	3	2	4	1	1	1	2	1	1	1	1	1	3	3	1	2	2	2	2	1	1	4	1	1	6	1	5	1	3	3	2	1	2	4	9	NA	NA	NA	NA	NA	NA	5	5	1	NA	1	2	4	NA	NA	NA	NA	1	5	5	NA	2	5	NA	2	5	NA	NA	2	NA	NA	4	4	NA	1	NA	NA	NA	NA	4	5	4	5	NA	3	3	NA	NA	NA	NA	2	2	5	NA	NA	NA	3	3	1	2	NA	NA	5	1	NA	NA	NA	2	4	NA	1	NA	4	NA	NA	NA	3	4	2	4	2	4	3	NA	NA	NA	NA	2	4	NA	3	5	NA	3	NA	NA	NA	NA	5	2	5	2	NA	1	NA	2	NA	NA	3	NA	2	1	1	NA	NA	20	75	1	1	1	2	NA	2	2	NA	1	2	NA	4	2	2	2	3	1	NA	NA	3	NA	4	NA	4	4	1	1	NA	NA	3	1	3	NA	NA	3	NA	NA	1	3	NA	NA	2	4	NA	2	2	2	2	NA	NA	4	NA	NA	4	3	4	4	4	3	4	4	4	NA	NA	4	NA	NA	4	4	3	4	3	3	NA	NA	4	4	NA	NA	3	NA	NA	NA	NA	5	5	NA	5	NA	5	5	NA	NA	3	NA	NA	NA	1	NA	3	1	2	4	4	4	4	1	NA	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	NA	NA	4	NA	3	NA	2	1	1	1	NA	1	1	1	1	1	NA	3	3	NA	3	2	2	NA	NA	NA	9999999	3	NA	NA	3	NA	NA	2	1	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	8	8	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	5	1	5	1	3	3	5	5	1	5	4	5	3	3	1	3	3	2	3	2	4	3	5	3	6	1	3	1	3	1	1	5	4	3	1	3	3	1	1	3	1	1	3	3	4	5	4	1	1	3	6	4	1	3	3	2	2	1	6	1	2	2	1	1	1	2	2	4	2	2	3	2	3	4	2	3	3	1	1	NA	NA	NA	NA	3	4	4	4	3	3	2	3	3	4	2	2	4	4	2	1	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	8	11	31	2512	07020002	16.08	344	1	070200	070200	070200	998	0	0	2	0	0	7	9	0	0	0	3	74.66	1	-1.0841	-0.7185	1.0692	0.0936	0.1810	0.4375	0.0134	-0.7118	-1.2602	-0.1040	0.4922	0.8646	NA	NA	-0.8815	-0.8709	-0.3322	0.1421	-0.5560	-0.1287	0.2869	1.2000	1.4010	3.6192	-0.9755	-1.5183	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	2	6	12.0	70.34	40.54	70.34	0.9904	0.0975	-0.0154	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.4062	7	0.3346	6	-0.2086	0.5918	-0.0210	-0.3548	-0.4834	-0.2504	-1.0853	0.3353	0.1939	NA	-0.5344	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4.94118	2.43429	2.50909	2.50588	7.30286	7.51765	2.43429	7.52727	7.30588	2.43429	2.43529	7.30588	7.51765	7.51765	7.30588	2.50909	7.52727	2.43529	7.30286	2.50909	2.50588	2.43429	2.50909	2.50588	7.30286	7.51765	2.43429	7.52727	7.30588	2.43429	2.43529	7.30588	7.51765	7.51765	7.30588	2.50909	7.52727	2.43529	7.30286	2.50909	2.50588	7.52727	7.30286	7.30588	2.50909	2.43529	7.52727	2.43429	2.50588	7.52727	7.51765	2.50588	2.43529	2.43529	2.50588	7.30286	2.43429	7.51765	2.50909	7.30286	7.30588	7.52727	7.30286	7.30588	2.50909	2.43529	7.52727	2.43429	2.50588	7.52727	7.51765	2.50588	2.43529	2.43529	2.50588	7.30286	2.43429	7.51765	2.50909	7.30286	7.30588	2	65	771.624	672.817	653.746	734.817	727.529	729.831	597.222	772.277	694.391	725.298	744.539	679.852	635.126	725.555	731.162	684.683	646.022	756.805	653.765	784.710	693.800	626.981	627.388	676.792	661.846	618.396	602.072	653.976	645.474	662.553	718.528	708.196	707.955	720.423	736.853	670.624	749.410	729.058	696.433	779.263	703.817	707.636	703.294	631.382	673.710	645.381	740.089	757.451	721.617	715.842	637.843	712.634	702.262	702.844	663.192	632.845	727.207	719.259	635.915	774.625	650.786	721.049	663.995	680.657	753.988	667.512	746.336	801.521	638.295	734.227	692.250	678.493	711.292	711.104	706.254	749.436	690.873	708.684	719.640	671.520	702.292	662.039	670.400	658.479	653.682	699.759	697.650	692.704	656.502	698.151	691.438	671.611	737.979	731.602	737.844	763.672	782.202	769.825	686.190	708.430	756.812	700.122	687.556	701.049	724.612	680.763	697.147	736.499	734.282	727.392	0.58883	01MAY23:14:19:45	218
SGP	702	70200107	70200008	08MS	070200	SGP01	7020000	70200	2	313	313	NA	NA	NA	1	39	10	4	2006	2	1	1	1	1	1	7020001	7020002	1	1	3	3	3	1	9999997	9999997	8	2	5	3	3	5	3	5	3	5	5	4	4	5	2	4	2	2	NA	1	1	1	1	2	NA	NA	1	1	1	1	NA	NA	2	2	2	1	2	4	8	5	1	1	1	1	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	1	1	NA	NA	3	3	3	3	NA	3	1	NA	3	2	3	4	3	3	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	6	6	1	1	1	6	6	1	1	1	8	5	NA	3	5	7	NA	NA	NA	NA	NA	NA	5	5	5	5	NA	1	5	NA	4	3	NA	3	NA	NA	NA	1	4	NA	4	NA	NA	NA	1	4	2	NA	NA	NA	NA	5	NA	NA	5	2	5	5	NA	4	NA	1	NA	1	1	2	NA	NA	2	NA	5	NA	NA	NA	5	5	NA	5	4	NA	NA	1	NA	1	5	1	NA	NA	NA	5	5	5	NA	5	NA	5	NA	3	2	NA	NA	NA	4	NA	1	1	5	NA	4	NA	4	NA	3	NA	NA	4	NA	1	NA	2	NA	2	4	2	NA	NA	3	3	3	NA	NA	18	75	2	2	2	4	NA	3	2	3	3	NA	3	NA	2	2	2	2	1	5	NA	NA	NA	5	NA	5	5	NA	NA	4	5	NA	4	5	5	NA	NA	NA	NA	1	1	1	1	NA	1	NA	1	NA	1	1	NA	NA	4	4	NA	4	2	3	4	2	2	4	3	3	3	NA	3	NA	NA	NA	4	4	4	NA	4	4	NA	4	4	4	NA	NA	NA	NA	NA	NA	5	NA	5	5	5	NA	NA	NA	5	NA	3	NA	1	1	NA	3	NA	5	4	NA	4	4	4	4	0	0	1	0	1	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	NA	NA	NA	5	5	4	5	NA	NA	1	1	NA	1	1	1	1	1	3	NA	NA	2	NA	NA	2	NA	2	NA	7020003	2	NA	2	NA	2	NA	NA	2	3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10	10	10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	3	6	5	5	5	5	5	5	5	5	5	5	5	3	3	3	3	3	3	3	3	3	4	4	4	4	1	4	4	4	4	1	4	4	1	4	5	5	5	5	5	5	5	5	5	5	5	5	1	1	1	1	3	3	3	1	1	1	3	3	3	3	1	1	1	3	2	2	3	2	2	3	2	3	3	3	3	3	3	1	NA	NA	NA	NA	2	2	3	4	4	4	4	1	4	4	4	4	4	1	1	4	4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	10	10	23	21	2310	07020002	16.00	344	3	970200	970200	970200	998	0	0	0	10	0	0	9	1	1	1	NA	85.41	1	-0.3538	-0.9290	0.8814	1.1246	-0.6386	0.8254	2.0864	-1.3496	1.2934	-1.0742	-0.4546	1.0424	NA	NA	1.0172	-0.2865	-0.1002	1.6989	1.5350	2.1323	1.0454	0.6614	2.2551	4.8471	-2.3868	-0.6406	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	9	8	9	16.0	75.54	80.78	80.78	1.1035	0.7991	1.1558	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-0.8411	6	0.3346	6	0.3623	0.7732	0.3744	2.9420	2.9804	-0.5555	-0.7088	0.2941	0.6984	NA	-0.1735	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7.45313	11.26250	3.72656	12.06696	11.17969	4.02232	3.51953	4.02232	3.75417	11.92500	3.72656	10.55859	3.50735	11.17969	11.26250	11.26250	11.17969	3.97500	3.72656	10.52206	3.51953	3.72656	11.26250	3.50735	3.75417	10.52206	11.92500	10.52206	11.17969	3.51953	11.26250	3.97500	12.06696	3.75417	3.72656	3.72656	3.75417	10.55859	11.26250	4.02232	11.92500	11.17969	3.70221	11.92500	11.10662	3.97500	3.50735	3.97500	3.72656	11.80078	3.70221	10.52206	3.49653	11.10662	11.17969	11.17969	11.10662	3.93359	3.70221	10.48958	3.50735	3.70221	11.17969	3.49653	3.72656	10.48958	11.80078	10.48958	11.10662	3.50735	11.17969	3.93359	11.92500	3.72656	3.70221	3.70221	3.72656	10.52206	11.17969	3.97500	11.80078	1	1	567.724	630.957	601.015	628.501	666.768	661.167	596.705	611.592	620.112	619.595	526.947	562.617	492.219	509.621	556.488	533.708	487.191	527.884	501.552	545.783	583.164	635.482	504.824	627.459	584.111	567.106	556.817	532.212	586.551	554.037	628.317	690.041	583.671	651.162	681.114	656.790	630.309	603.415	616.384	744.633	628.330	645.092	545.678	597.876	687.305	628.140	566.607	590.176	539.028	701.336	532.071	623.793	593.161	559.257	661.017	530.236	597.293	581.571	610.877	583.064	461.765	638.860	552.672	592.829	670.762	638.462	617.124	561.769	538.557	716.829	596.677	697.988	670.218	595.541	667.307	687.567	624.655	645.881	622.716	639.228	588.867	663.858	634.698	612.223	655.242	671.400	647.752	680.585	603.823	642.875	546.587	663.982	653.562	553.418	587.555	713.430	607.447	686.587	639.914	647.539	586.140	659.576	653.323	572.491	613.450	672.383	588.179	628.349	627.019	599.225	0.88817	01MAY23:14:19:45	218

glimpse(sfu_qqq_SG)

Rows: 6,606
Columns: 1,279
$ CNT          <chr> "SGP", "SGP", "SGP", "SGP", "SGP", "SGP", "SGP", "SGP", "…
$ CNTRYID      <dbl> 702, 702, 702, 702, 702, 702, 702, 702, 702, 702, 702, 70…
$ CNTSCHID     <dbl> 70200052, 70200134, 70200112, 70200004, 70200152, 7020004…
$ CNTSTUID     <dbl> 70200001, 70200002, 70200003, 70200004, 70200005, 7020000…
$ CYC          <chr> "08MS", "08MS", "08MS", "08MS", "08MS", "08MS", "08MS", "…
$ NatCen       <chr> "070200", "070200", "070200", "070200", "070200", "070200…
$ STRATUM      <chr> "SGP01", "SGP01", "SGP01", "SGP01", "SGP01", "SGP01", "SG…
$ SUBNATIO     <chr> "7020000", "7020000", "7020000", "7020000", "7020000", "7…
$ REGION       <dbl> 70200, 70200, 70200, 70200, 70200, 70200, 70200, 70200, 7…
$ OECD         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ADMINMODE    <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ LANGTEST_QQQ <dbl> 313, 313, 313, 313, 313, 313, 313, 313, 313, 313, 313, 31…
$ LANGTEST_COG <dbl> 313, 313, 313, 313, 313, 313, 313, 313, 313, 313, 313, 31…
$ LANGTEST_PAQ <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ Option_CT    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ Option_FL    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ Option_ICTQ  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ Option_WBQ   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Option_PQ    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Option_TQ    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Option_UH    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ BOOKID       <dbl> 4, 45, 8, 40, 42, 15, 13, 39, 14, 7, 20, 17, 38, 24, 19, …
$ ST001D01T    <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1…
$ ST003D02T    <dbl> 10, 6, 7, 2, 9, 9, 3, 4, 8, 6, 10, 7, 9, 11, 5, 10, 11, 4…
$ ST003D03T    <dbl> 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 200…
$ ST004D01T    <dbl> 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, …
$ ST250Q01JA   <dbl> 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, …
$ ST250Q02JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST250Q03JA   <dbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST250Q04JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST250Q05JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST250D06JA   <chr> "7020002", "7020001", "7020001", "7020002", "7020002", "7…
$ ST250D07JA   <chr> "7020002", "7020001", "7020002", "7020002", "7020002", "7…
$ ST251Q01JA   <dbl> 2, 1, 2, 1, 2, 2, 2, 1, 3, 3, 1, 2, 2, 1, 2, 2, 1, 2, 3, …
$ ST251Q02JA   <dbl> 1, 4, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, …
$ ST251Q03JA   <dbl> 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 2, 2, 3, 2, 3, 3, 2, 3, 4, …
$ ST251Q04JA   <dbl> 3, 3, 3, 3, 2, 3, 3, 3, 4, 3, 2, 2, 3, 2, 3, 3, 2, 3, 4, …
$ ST251Q06JA   <dbl> 3, 4, 2, 2, 1, 2, 2, 3, 4, 1, 3, 3, 1, 2, 2, 4, 4, 1, 2, …
$ ST251Q07JA   <dbl> 3, 2, 1, 1, 4, 1, 4, 1, 4, 3, 1, 4, 1, 1, 4, 4, 1, 4, 1, …
$ ST251D08JA   <chr> "9999997", "9999997", "9999997", "9999997", "9999997", "9…
$ ST251D09JA   <chr> "9999997", "9999997", "9999997", "9999997", "9999997", "9…
$ ST253Q01JA   <dbl> 7, 8, 7, 6, 7, 7, 8, 8, 8, 7, 7, 8, 5, 7, 7, 8, 5, 7, 7, …
$ ST254Q01JA   <dbl> 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 1, 3, 2, 2, 2, 2, …
$ ST254Q02JA   <dbl> 1, 2, 2, 1, 3, 2, 2, 5, 2, 2, 1, 3, 2, 1, 2, 2, 2, 2, 2, …
$ ST254Q03JA   <dbl> 3, 2, 2, 2, 2, 2, 3, 3, 3, 4, 2, 3, 2, 2, 3, 3, 2, 3, 2, …
$ ST254Q04JA   <dbl> 2, 3, 2, 1, 1, 2, 2, 3, 3, 2, 1, 3, 2, 2, 3, 3, 2, 3, 2, …
$ ST254Q05JA   <dbl> 1, 5, 1, 1, NA, 1, 1, 5, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1,…
$ ST254Q06JA   <dbl> 3, 2, 3, 3, 4, 2, 4, 3, 4, 4, 3, 3, 2, 3, 3, 4, 3, 4, 3, …
$ ST255Q01JA   <dbl> 7, 4, 4, 3, 2, 2, 4, 5, 7, 4, 3, 7, 4, 4, 2, 4, 5, 4, 4, …
$ ST256Q01JA   <dbl> 2, 4, 5, 2, 4, 1, 1, 3, 4, 4, 1, 4, 2, 2, 2, 3, 2, 2, 1, …
$ ST256Q02JA   <dbl> 2, 5, 2, 1, 1, 2, 1, 5, 4, 2, 2, 4, 1, 2, 2, 2, 3, 5, 1, …
$ ST256Q03JA   <dbl> 4, 5, 2, 1, 1, 2, 2, 5, 5, 1, 2, 4, 1, 3, 1, 4, 4, 5, 1, …
$ ST256Q06JA   <dbl> 4, 3, 3, 2, 2, 5, 2, 4, 4, 1, 2, 4, 2, 2, 2, 3, 2, 2, 3, …
$ ST256Q07JA   <dbl> 3, 5, 5, 2, 2, 5, 1, 4, 3, 1, 2, 4, 1, 2, 2, 2, 1, 5, 1, …
$ ST256Q08JA   <dbl> 3, 3, 3, 1, 1, 5, 1, 5, 5, 2, 2, 4, 1, 1, 1, 3, 1, 5, 2, …
$ ST256Q09JA   <dbl> 2, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, NA,…
$ ST256Q10JA   <dbl> 4, 4, 5, 2, 4, 1, 4, 4, 4, 3, 2, 4, 4, 1, 2, 4, 3, 3, 3, …
$ ST230Q01JA   <dbl> 4, 4, 2, 4, 4, 3, 2, 2, 3, 4, 1, 3, 4, 1, 4, 3, 2, 3, 2, …
$ ST005Q01JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST006Q01JA   <dbl> 2, 2, 2, 2, 2, 2, 2, NA, 2, 2, NA, 2, 1, 2, 2, 2, 2, 2, 2…
$ ST006Q02JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, NA, 1, 1, 2, 2, 1, 2, 2, 2,…
$ ST006Q03JA   <dbl> 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, NA, 1, 1, 2, 2, 1, 1, 1, 2,…
$ ST006Q04JA   <dbl> 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, NA, 2, 1, 2, 2, 2, 1, 2, 2,…
$ ST006Q05JA   <dbl> 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, NA, 1, 2, 1, 2, 1, 1, 2, 1,…
$ ST007Q01JA   <dbl> 2, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST008Q01JA   <dbl> 2, 2, 2, NA, 2, 2, 2, NA, 2, 2, NA, 2, 1, 2, 2, 2, 2, 2, …
$ ST008Q02JA   <dbl> 2, 2, 2, NA, 2, 1, 2, NA, 2, 2, NA, 1, 1, 1, 2, 2, 1, 2, …
$ ST008Q03JA   <dbl> 2, 2, 2, NA, 2, 1, 2, 1, 1, 2, NA, 1, 1, 2, 2, 2, 1, 1, 2…
$ ST008Q04JA   <dbl> 1, 1, 2, NA, 1, 1, 2, 1, 1, 1, NA, 2, 1, 2, 2, 2, 1, 2, 2…
$ ST008Q05JA   <dbl> 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, NA, 1, 2, 2, 1, 2, 1, 2, 1,…
$ ST258Q01JA   <dbl> 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, …
$ ST259Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST259Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST019AQ01T   <dbl> 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, …
$ ST019BQ01T   <dbl> 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, …
$ ST019CQ01T   <dbl> 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, …
$ ST021Q01TA   <dbl> NA, NA, NA, NA, NA, 1, NA, 1, NA, NA, NA, NA, NA, 7, NA, …
$ ST022Q01TA   <dbl> 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, …
$ ST226Q01JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST125Q01NA   <dbl> 3, 5, 8, 4, 8, 3, 4, 8, 8, 4, 8, 2, 7, 2, 3, 5, 8, 8, 3, …
$ ST126Q01TA   <dbl> 4, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 4, 5, 5, 4, 5, …
$ ST127Q01TA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST127Q02TA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST127Q03TA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST260Q01JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, …
$ ST260Q02JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST260Q03JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST261Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA…
$ ST261Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA…
$ ST261Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST261Q11JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, NA…
$ ST062Q01TA   <dbl> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST062Q02TA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, …
$ ST062Q03TA   <dbl> 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST267Q01JA   <dbl> NA, 4, 3, 3, NA, 4, 3, NA, 4, NA, NA, 4, 3, 3, 3, NA, 3, …
$ ST267Q02JA   <dbl> 2, 3, 3, NA, 3, 3, 1, NA, 4, NA, 3, 4, 1, NA, 4, 2, 1, 4,…
$ ST267Q03JA   <dbl> NA, NA, 3, 2, NA, 3, 1, 3, NA, 3, NA, 4, NA, 3, NA, 3, NA…
$ ST267Q04JA   <dbl> 2, 2, NA, NA, 3, NA, 1, 3, 2, 2, 2, NA, 3, 1, 4, 1, 2, NA…
$ ST267Q05JA   <dbl> 3, 3, 3, 3, 3, 3, NA, 3, NA, NA, 3, 4, NA, NA, NA, 4, 3, …
$ ST267Q06JA   <dbl> NA, NA, NA, 3, 4, 4, NA, 3, 4, 3, NA, NA, 2, 3, 3, 4, 4, …
$ ST267Q07JA   <dbl> 3, 4, 4, NA, NA, NA, NA, NA, 4, 3, 3, 4, 1, 3, 4, NA, NA,…
$ ST267Q08JA   <dbl> 2, NA, NA, 1, 1, NA, 1, 3, NA, 2, 2, NA, NA, NA, NA, NA, …
$ ST034Q01TA   <dbl> 4, 4, 3, 2, 4, 4, 2, 1, 3, 3, 3, 4, 2, 4, 3, 4, 3, 4, 3, …
$ ST034Q02TA   <dbl> NA, 2, 2, 3, 1, 2, 2, NA, 1, 2, NA, 1, 3, 1, 4, 2, 3, 2, …
$ ST034Q03TA   <dbl> 2, NA, NA, 3, 1, 2, 3, 3, 2, NA, 2, 1, 3, 1, 2, NA, 2, NA…
$ ST034Q04TA   <dbl> 4, 3, 2, 2, 4, NA, 3, 2, NA, 3, 3, 4, 2, 4, 2, 4, NA, 4, …
$ ST034Q05TA   <dbl> 2, 2, 2, 2, NA, 2, NA, 3, 2, 2, 2, NA, 3, 1, NA, 2, 2, 3,…
$ ST034Q06TA   <dbl> 3, 3, 3, NA, 4, 4, 3, 4, 3, 3, 3, 4, NA, NA, 3, 4, 3, 4, …
$ ST038Q03NA   <dbl> 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, …
$ ST038Q04NA   <dbl> 1, 1, 1, 1, 1, 1, 4, 3, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, …
$ ST038Q05NA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST038Q06NA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, …
$ ST038Q07NA   <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, …
$ ST038Q08NA   <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, …
$ ST038Q09JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST038Q10JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, …
$ ST038Q11JA   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST265Q01JA   <dbl> 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 3, 1, 2, 1, 2, 1, 2, …
$ ST265Q02JA   <dbl> 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 3, 1, 2, 1, 2, 1, 2, …
$ ST265Q03JA   <dbl> 2, 1, 2, 2, 1, 2, 3, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, …
$ ST265Q04JA   <dbl> 2, 1, 2, 2, 1, 2, 3, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, …
$ ST266Q01JA   <dbl> 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, …
$ ST266Q02JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST266Q03JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST266Q04JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST266Q05JA   <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ST294Q01JA   <dbl> 5, 4, 6, 1, 1, 1, 1, 6, 6, 1, 5, 1, 1, 1, 4, 3, 6, 6, 1, …
$ ST294Q02JA   <dbl> 1, 5, 2, 1, 4, 6, 1, 6, 1, 1, 1, 1, 6, 1, 5, 1, 1, 1, 6, …
$ ST294Q03JA   <dbl> 6, 2, 1, 6, 1, 3, 4, 1, 1, 1, 1, 1, 6, 1, 6, 1, 3, 1, 1, …
$ ST294Q04JA   <dbl> 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST294Q05JA   <dbl> 1, 3, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, …
$ ST295Q01JA   <dbl> 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 6, 3, 6, 6, …
$ ST295Q02JA   <dbl> 5, 4, 3, 6, 5, 6, 1, 6, 6, 4, 6, 6, 6, 1, 5, 6, 6, 6, 6, …
$ ST295Q03JA   <dbl> 6, 2, 1, 6, 6, 4, 5, 1, 1, 5, 3, 3, 6, 1, 6, 1, 4, 6, 1, …
$ ST295Q04JA   <dbl> 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST295Q05JA   <dbl> 2, 3, 3, 1, 5, 2, 3, 1, 4, 6, 2, 3, 6, 3, 3, 1, 3, 4, 1, …
$ ST326Q01JA   <dbl> 3, 2, 3, 2, 6, 3, 3, 8, 4, 3, 4, 4, 3, 7, 5, 3, 7, 6, 2, …
$ ST326Q02JA   <dbl> 4, 2, 2, 3, 5, 2, 2, 5, 5, 5, 5, 3, 5, 2, 3, 3, 6, 4, 4, …
$ ST326Q03JA   <dbl> 2, 3, 2, 2, 4, 1, 1, NA, 4, 4, 6, 5, 8, 1, 4, 3, 9, 4, 2,…
$ ST326Q04JA   <dbl> 1, 1, 2, 1, 5, 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, 2, 3, 2, 1, …
$ ST326Q05JA   <dbl> NA, 1, 5, 1, 5, 2, 4, 5, 3, 5, 5, 5, 1, 9, 3, 1, 4, 6, 5,…
$ ST326Q06JA   <dbl> 8, 1, 7, 1, 2, 5, 9, 7, 4, 5, 4, 7, 2, 9, 5, 1, 7, 7, 6, …
$ ST326Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST326Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST326Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST326Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST326Q11JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST326Q12JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST322Q01JA   <dbl> 5, 6, 3, 5, 4, 6, 5, 5, 4, NA, 4, 5, 5, 1, 2, 5, NA, 4, 5…
$ ST322Q02JA   <dbl> 5, 1, 3, 5, 5, 5, 5, 5, NA, 4, 2, 5, 5, NA, NA, 5, 4, 3, …
$ ST322Q03JA   <dbl> NA, 5, 2, 5, NA, 4, 1, 5, 3, 5, NA, 4, 3, 4, 4, NA, 4, 4,…
$ ST322Q04JA   <dbl> 2, 5, 2, 1, 5, NA, NA, 5, 3, 3, 4, 5, NA, 5, 5, 2, 2, NA,…
$ ST322Q06JA   <dbl> 1, 1, NA, NA, 4, 6, 1, NA, 6, 4, 1, NA, 1, 1, 4, 1, 3, 4,…
$ ST322Q07JA   <dbl> 1, NA, 2, 1, 4, 2, 2, 1, 1, 5, 2, 3, 1, 1, 1, 1, 5, 3, 1,…
$ ST307Q01JA   <dbl> 4, NA, NA, NA, 4, NA, 4, 5, NA, NA, 5, NA, 4, NA, NA, 4, …
$ ST307Q02JA   <dbl> NA, 5, 4, NA, NA, 3, NA, NA, 5, NA, NA, 5, NA, 4, NA, NA,…
$ ST307Q03JA   <dbl> 4, 4, 3, 4, 3, NA, NA, 4, 2, 3, 4, 5, NA, NA, 4, 5, NA, N…
$ ST307Q04JA   <dbl> 2, NA, 3, 2, NA, NA, NA, 3, 4, 4, NA, NA, NA, 1, 4, 2, 3,…
$ ST307Q05JA   <dbl> NA, 5, NA, NA, NA, NA, NA, NA, NA, NA, 3, 4, NA, 4, 4, NA…
$ ST307Q06JA   <dbl> 2, NA, 2, 2, 5, 3, 1, 3, NA, NA, NA, NA, 2, NA, 3, NA, NA…
$ ST307Q07JA   <dbl> 2, NA, NA, 2, NA, NA, 5, NA, 4, 4, 2, 1, 2, 1, NA, 2, NA,…
$ ST307Q08JA   <dbl> NA, 3, NA, 4, NA, 4, 5, NA, NA, 4, NA, NA, NA, NA, NA, 4,…
$ ST307Q09JA   <dbl> NA, NA, 3, NA, 4, 5, NA, NA, 4, NA, 4, 4, 4, 5, 3, NA, 3,…
$ ST307Q10JA   <dbl> NA, 3, NA, NA, 5, 4, 2, 1, NA, 3, NA, NA, 3, NA, NA, NA, …
$ ST309Q01JA   <dbl> NA, NA, 5, NA, 4, 4, 5, 4, 5, NA, 4, NA, 5, NA, 5, NA, 4,…
$ ST309Q02JA   <dbl> 3, 5, NA, NA, 5, NA, NA, NA, 4, NA, NA, 2, 5, NA, NA, NA,…
$ ST309Q03JA   <dbl> NA, 3, 3, NA, NA, NA, 2, 4, 1, 4, 2, 1, 1, 5, 2, NA, NA, …
$ ST309Q04JA   <dbl> 3, NA, NA, NA, NA, 4, 5, NA, NA, NA, NA, NA, NA, 4, NA, N…
$ ST309Q05JA   <dbl> NA, 4, 3, NA, 4, NA, NA, NA, 5, 4, NA, NA, NA, NA, 4, 1, …
$ ST309Q06JA   <dbl> NA, NA, 4, 3, NA, 4, NA, NA, 4, NA, NA, 1, 3, NA, NA, 3, …
$ ST309Q07JA   <dbl> 3, NA, NA, 4, NA, NA, 2, 1, NA, 4, 3, 1, NA, 4, 4, 3, NA,…
$ ST309Q08JA   <dbl> 4, 3, NA, 4, 5, NA, NA, 4, NA, 4, 4, NA, NA, 2, 5, NA, 3,…
$ ST309Q09JA   <dbl> 2, NA, 1, 4, NA, 2, NA, 2, NA, NA, 3, NA, 2, 4, NA, 4, NA…
$ ST309Q10JA   <dbl> NA, 5, NA, 3, 4, 4, 4, NA, NA, 3, NA, 5, NA, NA, NA, 3, 4…
$ ST301Q01JA   <dbl> 5, NA, NA, 4, 5, 4, 4, NA, 3, 5, NA, NA, 3, NA, NA, NA, N…
$ ST301Q02JA   <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, 4, 4, 5, NA, NA, NA, 5…
$ ST301Q03JA   <dbl> NA, NA, 3, NA, 4, 4, 1, NA, 2, NA, 2, 4, NA, NA, 4, 2, 3,…
$ ST301Q04JA   <dbl> 5, NA, NA, 4, 5, NA, NA, 5, NA, 4, NA, 5, NA, 4, NA, 4, N…
$ ST301Q05JA   <dbl> NA, 4, NA, NA, 4, 4, NA, NA, 5, NA, 4, 4, 3, NA, NA, 4, 3…
$ ST301Q06JA   <dbl> 5, 5, 3, 4, NA, NA, NA, NA, 3, 4, 4, 5, 2, 4, 5, NA, 3, 4…
$ ST301Q07JA   <dbl> 5, NA, 3, 4, NA, 4, NA, 5, NA, NA, NA, NA, 2, 4, NA, 4, N…
$ ST301Q08JA   <dbl> NA, 2, NA, 3, NA, NA, 4, 2, 3, 2, NA, NA, NA, 2, 2, NA, 3…
$ ST301Q09JA   <dbl> NA, 3, 4, NA, NA, 4, 5, 5, NA, NA, 5, NA, NA, 4, 5, NA, N…
$ ST301Q10JA   <dbl> 5, 4, 3, NA, NA, NA, 4, 5, NA, NA, NA, NA, 3, NA, 4, NA, …
$ ST343Q01JA   <dbl> 4, NA, 3, NA, NA, NA, 5, NA, 5, NA, NA, 5, 4, NA, NA, NA,…
$ ST343Q02JA   <dbl> NA, 2, NA, 3, NA, 1, NA, 4, NA, 3, NA, 2, NA, 4, NA, NA, …
$ ST343Q03JA   <dbl> NA, NA, NA, NA, 4, NA, 3, NA, NA, NA, 4, 5, NA, NA, NA, N…
$ ST343Q04JA   <dbl> 2, NA, NA, 2, NA, 1, 3, 1, NA, 2, NA, NA, 3, NA, 3, 2, 2,…
$ ST343Q05JA   <dbl> 3, 3, 3, 2, 2, 1, NA, NA, 1, NA, 2, NA, NA, NA, 5, 2, 4, …
$ ST343Q06JA   <dbl> 4, 4, 3, 4, 5, NA, NA, 1, 5, 5, 4, 5, 4, 5, NA, 5, 3, NA,…
$ ST343Q07JA   <dbl> 1, 3, NA, 2, NA, NA, NA, 1, NA, 3, 3, NA, NA, 3, 1, 3, NA…
$ ST343Q08JA   <dbl> NA, 5, NA, NA, 5, 5, NA, 2, 5, NA, NA, NA, NA, 2, 3, NA, …
$ ST343Q09JA   <dbl> NA, NA, 4, NA, NA, 5, 2, NA, 5, NA, NA, 5, 3, 3, 3, NA, N…
$ ST343Q10JA   <dbl> NA, NA, 2, NA, 2, NA, 2, NA, NA, 2, 3, NA, 3, NA, NA, 4, …
$ ST311Q01JA   <dbl> NA, 2, NA, NA, 2, 1, 5, 2, NA, NA, 2, 1, 2, 2, NA, 2, NA,…
$ ST311Q02JA   <dbl> 5, 4, 4, 4, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA,…
$ ST311Q03JA   <dbl> 5, NA, NA, NA, NA, NA, NA, 5, 5, 5, NA, NA, NA, NA, 5, NA…
$ ST311Q04JA   <dbl> 5, 4, 4, 4, NA, 5, NA, NA, NA, NA, 4, 5, 4, NA, 4, NA, 3,…
$ ST311Q05JA   <dbl> NA, 2, 2, 2, NA, 1, 3, NA, NA, 1, NA, NA, NA, 2, 1, 2, NA…
$ ST311Q06JA   <dbl> NA, NA, NA, NA, 3, NA, 3, NA, 5, NA, 3, 5, NA, NA, NA, NA…
$ ST311Q07JA   <dbl> NA, NA, NA, NA, NA, NA, 1, 5, NA, NA, NA, 3, 3, 2, NA, 1,…
$ ST311Q08JA   <dbl> NA, NA, 3, NA, 3, NA, 2, 5, 5, 4, 3, 5, 3, 4, 4, 5, NA, N…
$ ST311Q09JA   <dbl> 3, 4, 3, 4, 4, NA, NA, NA, 5, 4, NA, NA, 4, NA, NA, 4, NA…
$ ST311Q10JA   <dbl> 5, NA, NA, 3, 3, 3, NA, 5, 5, 4, 3, NA, NA, 4, NA, NA, 3,…
$ ST315Q01JA   <dbl> NA, NA, NA, 4, NA, NA, 5, 4, NA, NA, 3, 4, 4, NA, NA, NA,…
$ ST315Q02JA   <dbl> 4, 3, 3, 2, NA, 3, 1, NA, 5, NA, NA, 5, NA, NA, NA, 5, NA…
$ ST315Q03JA   <dbl> 3, NA, NA, NA, NA, 5, NA, NA, NA, 4, NA, NA, NA, 2, 2, NA…
$ ST315Q04JA   <dbl> 3, 3, 3, 4, 3, NA, NA, 1, NA, 4, 4, NA, 3, 2, NA, NA, NA,…
$ ST315Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, NA, 1, 2, 3, 4, NA,…
$ ST315Q06JA   <dbl> 3, 3, 3, NA, NA, 5, 2, 1, NA, NA, 3, NA, NA, NA, NA, 5, N…
$ ST315Q07JA   <dbl> NA, 2, NA, NA, 2, NA, 4, 5, 1, NA, 3, NA, NA, 3, NA, 1, N…
$ ST315Q08JA   <dbl> NA, NA, 2, 1, 2, 3, NA, 1, 3, 4, NA, NA, 3, NA, 4, 5, 2, …
$ ST315Q09JA   <dbl> NA, 3, NA, NA, 5, 5, 1, NA, 4, 4, 3, 5, NA, 3, 3, 5, 4, 3…
$ ST315Q10JA   <dbl> 4, NA, 4, 4, 5, NA, NA, NA, NA, NA, NA, 5, 4, NA, 3, NA, …
$ ST303Q01JA   <dbl> 5, NA, NA, NA, NA, NA, 4, NA, 5, 4, 4, 5, NA, NA, NA, NA,…
$ ST303Q02JA   <dbl> 5, NA, 3, NA, 5, 4, NA, 5, NA, NA, 4, 5, NA, 4, NA, NA, 3…
$ ST303Q03JA   <dbl> 5, NA, 2, 3, 4, NA, NA, 5, 5, 4, NA, NA, 3, 4, 3, 5, 3, N…
$ ST303Q04JA   <dbl> NA, 4, 4, 4, 5, 5, NA, 5, 5, NA, 4, 5, 3, 4, 5, 5, NA, NA…
$ ST303Q05JA   <dbl> NA, 3, NA, 2, NA, 1, 3, NA, NA, 4, NA, 1, NA, 2, 4, 1, NA…
$ ST303Q06JA   <dbl> 5, 5, 4, 4, 5, NA, 4, 5, 5, 4, 4, NA, 3, NA, 5, 5, 3, 4, …
$ ST303Q07JA   <dbl> 1, 4, 3, NA, NA, 2, 2, NA, 3, 3, 3, NA, 3, NA, NA, 1, NA,…
$ ST303Q08JA   <dbl> NA, 4, NA, 3, 5, 5, 4, 5, NA, NA, NA, 5, 3, 4, 4, NA, 3, …
$ ST305Q01JA   <dbl> NA, 2, NA, NA, 5, NA, 2, NA, 5, NA, NA, 5, NA, 5, 2, 5, 2…
$ ST305Q02JA   <dbl> NA, NA, 2, NA, NA, NA, 4, 3, NA, 3, 4, 3, 2, NA, NA, 5, N…
$ ST305Q03JA   <dbl> NA, NA, NA, NA, NA, NA, 3, 2, 5, 4, 4, NA, 3, NA, 2, NA, …
$ ST305Q04JA   <dbl> 3, NA, NA, NA, 5, 2, NA, NA, NA, NA, NA, 5, NA, NA, NA, 1…
$ ST305Q05JA   <dbl> 4, 4, 3, 3, 2, 3, NA, NA, NA, 4, NA, NA, 2, 5, 2, NA, 3, …
$ ST305Q06JA   <dbl> 3, NA, 3, 3, NA, 4, NA, NA, 5, NA, 4, NA, NA, 4, NA, 5, 3…
$ ST305Q07JA   <dbl> 4, 4, NA, 3, NA, NA, NA, 4, 3, 4, 3, 1, NA, NA, 4, NA, 3,…
$ ST305Q08JA   <dbl> 2, NA, NA, 3, NA, 5, 2, NA, NA, NA, NA, NA, 3, 3, NA, NA,…
$ ST305Q09JA   <dbl> NA, 3, 2, NA, 3, 1, 4, 1, 3, NA, 3, 4, NA, NA, 2, 3, NA, …
$ ST305Q10JA   <dbl> NA, 4, 3, 3, 3, NA, NA, 1, NA, 4, NA, NA, 3, 4, NA, NA, N…
$ ST345Q01JA   <dbl> 3, 4, NA, 3, 5, NA, 3, 5, 4, 4, NA, 3, 5, NA, NA, NA, 4, …
$ ST345Q02JA   <dbl> 5, NA, 4, 3, 3, NA, 5, NA, NA, NA, NA, 3, 2, NA, 4, NA, 3…
$ ST345Q03JA   <dbl> 2, 4, NA, NA, NA, 4, NA, 4, NA, NA, NA, NA, 5, 2, NA, NA,…
$ ST345Q04JA   <dbl> 1, NA, 4, NA, NA, 2, 3, NA, 4, 3, 4, 3, NA, NA, NA, 2, 3,…
$ ST345Q05JA   <dbl> 3, NA, NA, 3, NA, NA, NA, 4, 4, NA, NA, 3, 1, NA, 3, NA, …
$ ST345Q06JA   <dbl> NA, 2, NA, NA, 3, 5, NA, NA, 5, 4, NA, NA, NA, 5, 3, 4, N…
$ ST345Q07JA   <dbl> NA, 5, 3, 3, 5, 5, NA, 3, NA, 5, 5, NA, NA, 1, 5, NA, 5, …
$ ST345Q08JA   <dbl> NA, NA, 3, 3, NA, NA, NA, NA, NA, 3, 4, 5, NA, NA, NA, 5,…
$ ST345Q09JA   <dbl> NA, NA, 3, NA, 2, NA, 5, NA, 5, NA, 4, NA, 1, 5, 2, 5, NA…
$ ST345Q10JA   <dbl> NA, 4, NA, NA, NA, 3, 2, 4, NA, NA, 4, NA, NA, 2, NA, 1, …
$ ST313Q01JA   <dbl> NA, NA, NA, 3, 2, NA, 5, NA, NA, 4, NA, 5, NA, 4, NA, 3, …
$ ST313Q02JA   <dbl> 2, NA, 2, NA, NA, 1, 2, 1, 4, NA, NA, 1, 3, NA, NA, 3, NA…
$ ST313Q03JA   <dbl> 2, 3, 3, 3, NA, NA, NA, NA, 3, NA, NA, NA, 2, 2, NA, NA, …
$ ST313Q04JA   <dbl> 1, 4, NA, 3, 4, NA, 1, 2, 2, NA, 2, NA, NA, NA, 4, 3, 1, …
$ ST313Q05JA   <dbl> 3, NA, 3, 3, NA, 1, NA, NA, 4, NA, 4, 4, 1, 5, 3, NA, 3, …
$ ST313Q06JA   <dbl> NA, 4, 2, NA, 4, 1, 2, 2, NA, 2, 4, 2, 5, 2, 5, NA, 1, 2,…
$ ST313Q07JA   <dbl> NA, NA, NA, NA, 4, 1, NA, 4, 3, 4, NA, NA, NA, NA, 3, NA,…
$ ST313Q08JA   <dbl> NA, 4, NA, NA, NA, NA, NA, 2, NA, NA, 2, 1, NA, NA, NA, 3…
$ ST313Q09JA   <dbl> 1, NA, 2, 3, 4, NA, 3, NA, NA, 4, 3, NA, 3, NA, NA, 3, 1,…
$ ST313Q10JA   <dbl> NA, 4, NA, NA, NA, 1, NA, NA, NA, 3, NA, NA, NA, 2, 4, NA…
$ ST263Q02JA   <dbl> 1, 1, 2, 2, 1, 4, 2, 3, 3, 3, 3, 2, 4, 3, 2, 2, 2, 2, 3, …
$ ST263Q04JA   <dbl> 2, 1, 2, 3, 1, 1, 1, 3, 2, 3, 1, 1, 3, 3, 2, 2, 1, 2, 3, …
$ ST263Q06JA   <dbl> 2, 1, 3, 2, 1, 1, 1, 3, 2, 3, 1, 1, 3, 3, 2, 2, 1, 2, 3, …
$ ST263Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST016Q01NA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST059Q01TA   <dbl> 4, 10, 8, 10, 5, 7, 20, 18, 6, 3, 9, 10, 5, 12, 5, 7, 8, …
$ ST059Q02JA   <dbl> 55, 45, 56, 11, 30, 26, 75, 75, 28, 15, 51, 63, 40, 75, 3…
$ ST296Q01JA   <dbl> 1, 3, 2, 3, 4, 1, 1, 2, 1, 3, 3, 4, 3, 1, 2, 1, 3, 3, 2, …
$ ST296Q02JA   <dbl> 1, 2, 3, 1, 3, 1, 1, 2, 1, 3, 3, 3, 2, 1, 4, 1, 3, 1, 1, …
$ ST296Q03JA   <dbl> 2, 3, 3, 2, 4, 1, 1, 2, 1, 2, 3, 4, 3, 1, 3, 1, 3, 3, 2, …
$ ST296Q04JA   <dbl> 3, 5, 5, 2, 4, 2, 2, 4, 4, 6, 4, 6, 6, 1, 5, 3, 4, 4, 3, …
$ ST272Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST273Q01JA   <dbl> 3, 4, 3, 2, 4, 3, 2, 3, 2, 3, 3, 3, NA, 2, NA, NA, 4, 4, …
$ ST273Q02JA   <dbl> NA, 3, 3, NA, NA, 3, 2, 2, 2, 3, 3, NA, 4, 2, 4, 4, NA, 3…
$ ST273Q03JA   <dbl> NA, 4, NA, NA, 4, NA, NA, 3, 3, 4, NA, 4, 4, NA, 4, 1, 4,…
$ ST273Q04JA   <dbl> 4, 4, 3, 3, 3, 3, 1, 3, NA, NA, 3, 4, 4, 2, 4, 4, 4, NA, …
$ ST273Q05JA   <dbl> 4, NA, 4, 2, 3, 3, 2, NA, 3, 3, 3, NA, 4, NA, 2, NA, 4, 4…
$ ST273Q06JA   <dbl> 3, NA, 3, 4, NA, NA, NA, 3, NA, 3, 3, 4, NA, 2, NA, 1, NA…
$ ST273Q07JA   <dbl> 3, 4, NA, 4, 2, 3, 4, NA, 3, NA, NA, 4, 4, 2, 2, 1, 4, 4,…
$ ST270Q01JA   <dbl> 3, 2, 3, 2, 1, 3, 2, 2, 1, 2, 2, 1, 3, 3, 1, 1, 2, 1, 4, …
$ ST270Q02JA   <dbl> 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 3, 1, 1, 1, 2, 1, 2, …
$ ST270Q03JA   <dbl> 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 3, 2, 1, 1, 2, 1, 1, …
$ ST270Q04JA   <dbl> 3, 1, 3, 2, 1, 2, 3, 2, 1, 2, 2, 1, 4, 2, 1, 1, 2, 1, 3, …
$ ST285Q01JA   <dbl> NA, 1, NA, 3, 3, 1, 1, 1, NA, NA, 2, 1, 1, 1, 4, 5, NA, 2…
$ ST285Q02JA   <dbl> 3, NA, 2, 3, 4, NA, NA, 5, 5, 4, NA, NA, 3, NA, 4, 5, NA,…
$ ST285Q03JA   <dbl> NA, NA, 3, 3, 2, 1, NA, NA, 4, 4, 3, NA, NA, 1, 4, 5, NA,…
$ ST285Q04JA   <dbl> NA, 2, NA, NA, 5, NA, 3, NA, NA, 4, NA, 5, 3, NA, 4, NA, …
$ ST285Q05JA   <dbl> 2, 2, 1, NA, NA, 5, NA, NA, NA, NA, 2, NA, NA, 2, NA, NA,…
$ ST285Q06JA   <dbl> 3, 2, NA, 3, 5, NA, 4, 5, 5, NA, 2, 5, NA, NA, NA, NA, 4,…
$ ST285Q07JA   <dbl> 1, NA, 3, NA, NA, 5, NA, NA, 5, 2, 2, 5, 4, 2, NA, 5, 4, …
$ ST285Q08JA   <dbl> 4, NA, 2, 3, NA, 5, 4, 5, 5, NA, NA, 5, 5, NA, NA, NA, 4,…
$ ST285Q09JA   <dbl> NA, 3, NA, NA, NA, NA, 4, 5, NA, 4, NA, NA, NA, 1, 4, 5, …
$ ST283Q01JA   <dbl> NA, NA, NA, NA, NA, 4, 1, NA, NA, 3, 2, 5, NA, NA, NA, 4,…
$ ST283Q02JA   <dbl> NA, 3, NA, 3, NA, NA, 1, NA, 3, NA, 3, 5, NA, NA, NA, NA,…
$ ST283Q03JA   <dbl> 3, 3, 1, NA, 5, 4, NA, 4, 5, 3, NA, 5, 3, 1, 4, 2, 4, NA,…
$ ST283Q04JA   <dbl> 3, 3, 3, 3, NA, NA, NA, 5, NA, 3, NA, 5, NA, NA, 4, 5, NA…
$ ST283Q05JA   <dbl> 3, 3, NA, 3, 5, NA, 3, NA, 5, 3, NA, NA, 4, NA, NA, NA, N…
$ ST283Q06JA   <dbl> 2, 2, 1, NA, 5, 3, 1, 4, NA, NA, 2, NA, NA, 1, 4, NA, 4, …
$ ST283Q07JA   <dbl> 4, NA, NA, 3, 5, 4, 3, 5, 5, NA, 3, NA, 3, 1, 4, NA, NA, …
$ ST283Q08JA   <dbl> NA, NA, 2, 3, NA, NA, NA, 5, NA, NA, NA, 5, 2, 1, NA, 5, …
$ ST283Q09JA   <dbl> NA, NA, 2, NA, 5, 4, NA, NA, 5, 3, 3, NA, 3, 1, 4, 5, 4, …
$ ST275Q01WA   <dbl> NA, 3, 2, NA, NA, 2, 3, NA, 3, NA, 3, NA, NA, 4, 2, 1, 3,…
$ ST275Q02WA   <dbl> 2, 4, NA, 2, 2, 2, NA, NA, NA, 3, NA, 1, NA, NA, NA, 3, 3…
$ ST275Q03WA   <dbl> 2, 4, NA, 4, 2, 2, NA, NA, 2, 3, 3, NA, 3, 4, 1, 4, NA, N…
$ ST275Q04WA   <dbl> 2, NA, 2, 2, NA, NA, 1, 1, 2, NA, NA, 1, NA, NA, 3, NA, N…
$ ST275Q05WA   <dbl> NA, NA, 1, NA, 1, NA, 3, 1, NA, 2, NA, 1, 3, 2, 1, 1, 1, …
$ ST275Q06WA   <dbl> 1, 4, NA, 3, 2, NA, NA, 1, 1, NA, NA, 1, NA, NA, 2, NA, N…
$ ST275Q07WA   <dbl> NA, NA, NA, 1, NA, NA, NA, 1, 1, 2, 4, NA, 3, NA, NA, NA,…
$ ST275Q08WA   <dbl> 4, NA, 2, NA, 2, 2, 2, NA, NA, NA, 2, 1, 4, 2, NA, NA, NA…
$ ST275Q09WA   <dbl> NA, 2, 1, NA, NA, 1, 4, 1, NA, 2, 4, NA, 3, 2, NA, 1, 1, …
$ ST276Q01JA   <dbl> NA, NA, NA, 2, NA, NA, NA, NA, NA, 2, 2, 1, 2, 2, 1, NA, …
$ ST276Q02JA   <dbl> NA, NA, NA, NA, 2, 2, 2, 1, NA, NA, 2, NA, NA, NA, NA, NA…
$ ST276Q03JA   <dbl> 4, 3, NA, NA, 2, NA, 2, NA, NA, NA, NA, 1, NA, 3, 1, 2, 3…
$ ST276Q04JA   <dbl> NA, 2, NA, NA, 2, 2, 2, 1, 1, 2, NA, NA, NA, NA, NA, 2, 2…
$ ST276Q05JA   <dbl> NA, NA, 2, 2, NA, NA, 2, 1, 2, NA, NA, 1, NA, 2, NA, 2, N…
$ ST276Q06JA   <dbl> NA, 3, 1, NA, 2, NA, NA, NA, 1, 2, 2, NA, 1, NA, 1, NA, 2…
$ ST276Q07JA   <dbl> 3, 3, NA, 2, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, 2, 2,…
$ ST276Q08JA   <dbl> 3, NA, 3, 2, 2, 2, 4, 4, 4, NA, 3, 1, 4, NA, NA, NA, 4, 4…
$ ST276Q09JA   <dbl> 3, NA, 4, 2, NA, 2, NA, 4, 1, 3, 2, 1, 3, 2, 3, 4, NA, NA…
$ ST276Q10JA   <dbl> 3, 4, 3, NA, NA, NA, NA, NA, NA, 2, NA, NA, 3, 2, NA, NA,…
$ ST268Q01JA   <dbl> 2, 4, 3, 2, 3, 3, 4, 4, 2, 3, 3, 4, 3, 4, 1, 3, 2, 3, 2, …
$ ST268Q02JA   <dbl> 3, 3, 2, 3, 4, 3, 3, 2, 2, 2, 2, 3, 2, 2, 4, 3, 2, 3, 2, …
$ ST268Q03JA   <dbl> 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 4, 2, 4, 2, 2, 2, 3, 3, …
$ ST268Q04JA   <dbl> 2, 3, 3, 2, 1, 3, 4, 4, 2, 3, 4, 4, 3, 4, 1, 3, 2, 2, 2, …
$ ST268Q05JA   <dbl> 3, 2, 2, 2, 3, 3, 4, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 2, 2, …
$ ST268Q06JA   <dbl> 3, 3, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 4, 1, 2, 2, 2, 3, …
$ ST268Q07JA   <dbl> 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, …
$ ST268Q08JA   <dbl> 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, …
$ ST268Q09JA   <dbl> 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, …
$ ST290Q01WA   <dbl> NA, NA, NA, 3, NA, NA, NA, 3, NA, 3, 4, 4, NA, 4, 2, NA, …
$ ST290Q02WA   <dbl> 3, 3, 3, 3, 2, NA, NA, NA, 4, NA, 4, 4, 3, 4, NA, NA, NA,…
$ ST290Q03WA   <dbl> NA, NA, NA, NA, NA, 3, 4, 3, NA, NA, NA, 4, NA, 4, 2, NA,…
$ ST290Q04WA   <dbl> 3, 3, 2, NA, 2, NA, NA, NA, 3, 3, NA, 4, 2, NA, 2, 3, 2, …
$ ST290Q05WA   <dbl> 4, 3, 4, NA, 2, 4, NA, NA, NA, 3, 4, NA, 4, NA, NA, NA, N…
$ ST290Q06WA   <dbl> NA, NA, 3, NA, NA, 2, 4, NA, NA, NA, 4, NA, 4, 4, NA, 4, …
$ ST290Q07WA   <dbl> NA, 3, 4, 2, NA, 4, 4, 4, 4, 3, NA, NA, 4, NA, 2, 4, 4, N…
$ ST290Q08WA   <dbl> 2, NA, NA, 2, 2, NA, 3, 4, 4, NA, NA, NA, NA, 4, 2, 4, NA…
$ ST290Q09WA   <dbl> 4, 3, NA, 2, 4, 4, 4, 4, 4, 3, 4, NA, NA, NA, NA, 4, 4, 4…
$ ST291Q01JA   <dbl> 3, NA, 2, 2, NA, NA, 3, NA, 4, NA, 4, NA, 3, 4, NA, 4, NA…
$ ST291Q02JA   <dbl> 3, 3, NA, NA, 2, NA, 3, 4, 4, 3, NA, NA, 4, NA, 2, NA, 2,…
$ ST291Q03JA   <dbl> 3, 3, 2, 2, 2, 2, NA, 4, 4, NA, 4, NA, NA, NA, NA, NA, 2,…
$ ST291Q04JA   <dbl> NA, NA, 2, NA, NA, 2, NA, NA, NA, NA, NA, 4, NA, NA, NA, …
$ ST291Q05JA   <dbl> NA, NA, NA, NA, NA, NA, 4, 4, NA, NA, NA, 4, 2, 3, 2, 3, …
$ ST291Q06JA   <dbl> NA, 3, 3, NA, 2, 2, 4, 4, 4, 2, 4, NA, NA, 4, 2, 3, NA, N…
$ ST291Q07JA   <dbl> NA, NA, NA, NA, NA, 3, NA, 4, 4, 2, NA, NA, 2, 3, 2, NA, …
$ ST291Q08JA   <dbl> NA, 3, NA, 2, NA, 2, NA, NA, NA, NA, 3, 4, NA, 2, 2, 2, N…
$ ST291Q09JA   <dbl> 3, 3, 1, 2, 2, NA, 3, NA, NA, 2, 3, 4, NA, NA, NA, NA, NA…
$ ST291Q10JA   <dbl> 2, NA, NA, 2, 2, NA, NA, NA, NA, 3, NA, 4, 2, NA, NA, NA,…
$ ST289Q01WA   <dbl> 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA, NA, 2, NA, …
$ ST289Q02JA   <dbl> 5, NA, 5, NA, NA, NA, NA, NA, 5, NA, NA, 5, 3, 5, NA, NA,…
$ ST289Q03WA   <dbl> NA, NA, NA, NA, 1, NA, NA, NA, NA, 1, NA, 5, 1, NA, 1, NA…
$ ST289Q04JA   <dbl> NA, 5, 4, 1, NA, 4, 5, 5, 5, NA, NA, 5, NA, NA, NA, NA, N…
$ ST289Q05WA   <dbl> NA, NA, NA, NA, NA, NA, 5, NA, NA, 4, NA, NA, NA, NA, 4, …
$ ST289Q06JA   <dbl> NA, NA, NA, 4, NA, 5, NA, 5, 5, NA, 5, NA, NA, 5, NA, 5, …
$ ST289Q07JA   <dbl> 5, NA, 3, NA, 4, 5, 5, 5, NA, NA, NA, 5, NA, NA, 4, NA, N…
$ ST289Q08WA   <dbl> NA, 5, NA, NA, 3, NA, NA, 5, NA, 2, NA, NA, 1, NA, 4, 2, …
$ ST289Q09WA   <dbl> 4, 5, NA, NA, NA, 5, 5, NA, NA, NA, 5, NA, 4, NA, NA, 5, …
$ ST289Q10WA   <dbl> NA, 5, 5, 5, 4, 5, 5, NA, 5, 5, NA, NA, NA, 5, NA, NA, NA…
$ ST289Q11WA   <dbl> 1, NA, NA, 4, 2, NA, NA, NA, NA, NA, 1, NA, NA, 2, NA, 2,…
$ ST289Q14JA   <dbl> NA, 5, 2, 4, NA, NA, NA, 5, 5, 5, 4, 5, 1, NA, 2, NA, 3, …
$ ST293Q01JA   <dbl> NA, NA, NA, 4, 5, 4, 3, NA, 5, 3, 4, 5, 3, NA, NA, NA, NA…
$ ST293Q02JA   <dbl> NA, 5, 4, 5, 5, NA, NA, 3, NA, NA, NA, 5, 4, 2, 4, 3, 5, …
$ ST293Q03JA   <dbl> 4, 5, 3, NA, NA, 5, NA, NA, NA, 3, 5, NA, 4, NA, NA, NA, …
$ ST293Q04JA   <dbl> NA, NA, 3, NA, NA, NA, NA, 1, 1, NA, NA, NA, 2, NA, 4, NA…
$ ST293Q05JA   <dbl> 3, 5, NA, NA, 5, 5, 1, 1, 5, 4, 5, 5, NA, 1, NA, NA, 3, 2…
$ ST293Q06JA   <dbl> 2, NA, 4, NA, NA, NA, NA, NA, NA, NA, 5, 5, NA, 1, 5, 5, …
$ ST293Q07JA   <dbl> 2, NA, NA, 3, NA, NA, 3, 3, 2, NA, NA, 1, NA, 4, NA, 3, N…
$ ST293Q08JA   <dbl> NA, 4, NA, 5, 5, 5, 1, NA, 5, 4, NA, NA, 2, NA, 4, 5, 2, …
$ ST293Q09JA   <dbl> 4, 2, 2, 5, 3, 3, 2, 5, NA, 3, 5, NA, NA, 5, 3, 4, NA, NA…
$ ST292Q01JA   <dbl> 2, 2, 2, 2, 1, 2, 4, 4, 2, 2, 3, 2, 2, 3, 1, NA, 3, 2, 3,…
$ ST292Q02JA   <dbl> 2, NA, 3, 2, 1, 4, 4, NA, 3, 2, 3, 2, 3, 3, 1, 3, 3, 3, 3…
$ ST292Q03JA   <dbl> 3, 3, 3, 2, NA, NA, 4, 4, 3, 2, NA, 3, 2, 3, 1, 4, 3, 3, …
$ ST292Q04JA   <dbl> 2, 3, 3, NA, 1, 4, 4, 4, 3, 2, 3, 3, 2, 3, 1, 4, 3, 3, 3,…
$ ST292Q05JA   <dbl> NA, 1, 2, 2, 1, 4, 1, 4, NA, NA, 2, 2, 1, 3, NA, 4, NA, N…
$ ST292Q06JA   <dbl> 3, 1, NA, 2, 1, 2, NA, 4, 3, 1, 3, NA, NA, NA, 1, 2, 1, 2…
$ ST297Q01JA   <dbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, …
$ ST297Q03JA   <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
$ ST297Q05JA   <dbl> 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, …
$ ST297Q06JA   <dbl> 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
$ ST297Q07JA   <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, …
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$ ST334Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST334Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST335Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST336Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST337Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST338Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST339Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST339Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST340Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST341Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST341Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST341Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST341Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST341Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST342Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST300Q01JA   <dbl> NA, NA, NA, NA, NA, 5, NA, 5, NA, NA, 3, 5, NA, NA, NA, N…
$ ST300Q02JA   <dbl> 5, NA, 5, 3, NA, 5, 5, NA, 5, 5, 5, 5, NA, NA, NA, NA, 5,…
$ ST300Q03JA   <dbl> NA, NA, NA, 3, NA, 5, NA, NA, NA, NA, NA, 5, 1, NA, 4, NA…
$ ST300Q04JA   <dbl> NA, 2, 2, 3, 5, NA, NA, NA, 1, NA, 1, NA, 1, NA, 3, NA, N…
$ ST300Q05JA   <dbl> 4, NA, NA, NA, NA, NA, 4, 5, 5, 5, NA, NA, NA, 1, 3, 1, 2…
$ ST300Q06JA   <dbl> 2, 3, 2, NA, 5, NA, NA, 5, 5, 5, 3, NA, 1, NA, 2, 1, NA, …
$ ST300Q07JA   <dbl> 4, 3, NA, NA, 5, NA, 3, 4, NA, 5, NA, NA, NA, 4, NA, 5, 4…
$ ST300Q08JA   <dbl> NA, NA, 3, NA, 5, NA, NA, 5, 5, NA, 3, 5, 1, 4, NA, 1, 2,…
$ ST300Q09JA   <dbl> NA, 3, NA, 3, 5, 5, 2, NA, NA, NA, NA, NA, 1, 4, NA, 5, N…
$ ST300Q10JA   <dbl> 4, 5, 1, 3, NA, 5, 1, NA, NA, 5, NA, 5, NA, 4, 2, NA, 1, …
$ ST327Q01JA   <dbl> 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST327Q02JA   <dbl> 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST327Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST327Q04JA   <dbl> 2, 1, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST327Q05JA   <dbl> 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 2, 1, 1, 1, …
$ ST327Q06JA   <dbl> 1, 1, 3, 3, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ ST327Q07JA   <dbl> 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, NA, 3, 3,…
$ ST327Q08JA   <dbl> 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 2, 1, 1, 1, 1, NA, 3, 3,…
$ ST330Q01WA   <dbl> 3, NA, NA, 3, 3, 3, NA, 3, 3, NA, 3, 3, 3, 1, NA, NA, NA,…
$ ST330Q02WA   <dbl> 2, NA, 1, NA, 3, NA, 3, NA, 3, 3, NA, NA, 1, 1, 2, NA, NA…
$ ST330Q03WA   <dbl> NA, NA, 3, 3, NA, 3, 3, NA, NA, NA, NA, 1, 3, NA, 3, 1, 3…
$ ST330Q04WA   <dbl> NA, 1, NA, NA, 3, 3, NA, 2, NA, 1, NA, NA, NA, NA, NA, 3,…
$ ST330Q05WA   <dbl> NA, 1, NA, NA, NA, NA, 3, NA, NA, NA, NA, 2, NA, 3, 3, NA…
$ ST330Q06WA   <dbl> NA, 1, NA, NA, 2, NA, 2, NA, 3, 1, 3, 1, NA, NA, 2, 1, NA…
$ ST330Q07WA   <dbl> 2, NA, 1, 3, NA, NA, 2, 2, 2, 1, NA, NA, NA, NA, NA, NA, …
$ ST330Q08WA   <dbl> NA, NA, 3, 3, NA, NA, NA, NA, NA, NA, 3, NA, 3, 1, NA, NA…
$ ST330Q09WA   <dbl> NA, 1, NA, NA, 2, 3, NA, 2, NA, NA, 2, NA, NA, NA, NA, 1,…
$ ST330Q11WA   <dbl> 2, 1, NA, 3, NA, 3, NA, NA, NA, 1, 2, NA, NA, 3, NA, NA, …
$ ST330D10WA   <chr> "7020003", "9999999", "7020003", "9999999", "9999999", "9…
$ ST324Q02JA   <dbl> 3, 3, NA, NA, 4, NA, 3, 2, 3, 3, 2, 3, 4, 3, 3, 4, NA, NA…
$ ST324Q04JA   <dbl> 3, NA, NA, NA, 4, 3, NA, NA, NA, NA, 3, 4, 3, NA, NA, 3, …
$ ST324Q05JA   <dbl> NA, NA, 2, 2, NA, 4, NA, 2, 3, 1, 2, 4, NA, 3, 4, NA, 3, …
$ ST324Q07JA   <dbl> 4, NA, 3, NA, 2, NA, 3, NA, NA, 3, NA, 2, NA, NA, 3, 4, 3…
$ ST324Q10JA   <dbl> NA, 2, 2, 2, NA, 3, NA, 2, 1, 2, NA, NA, NA, 3, 3, NA, 3,…
$ ST324Q11JA   <dbl> NA, 1, 2, NA, NA, 2, NA, NA, NA, NA, NA, NA, 2, NA, NA, N…
$ ST324Q12JA   <dbl> 3, NA, 3, 2, NA, NA, 2, NA, 4, NA, NA, 3, NA, 3, NA, NA, …
$ ST324Q13JA   <dbl> NA, 1, NA, 2, 4, 3, 1, 2, 4, 3, 3, NA, 3, NA, 3, 1, 2, 3,…
$ ST324Q14JA   <dbl> 2, 3, NA, 2, 2, NA, 2, 3, NA, NA, 3, NA, 2, 3, NA, 2, 2, …
$ ST347Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST347Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST348Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST349Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST350Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST351Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST352Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST353Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST354Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST355Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST356Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ST331Q01JA   <dbl> 7, 8, 8, 8, 10, 6, 7, 10, 7, 8, 10, 9, 7, 6, 8, 4, 7, 8, …
$ ST331Q02JA   <dbl> 8, 10, 9, 10, 10, 10, 8, 10, 10, 10, 10, 10, 9, 7, 10, 10…
$ ST331Q03JA   <dbl> 8, 8, 7, 10, 10, 7, 8, 10, 9, 8, 9, 10, 10, 6, 10, 10, 8,…
$ FL150Q01TA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL150Q02TA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL150Q03TA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q06HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q07HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q08HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q09HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q10HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q11HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q12HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q13HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q14HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q15HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL164Q16HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q06HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL166Q07HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL174Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL167Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL170Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL159Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL159Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL159Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL159Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL160Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL160Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL160Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL160Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL161Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL161Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL161Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL162Q06HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL163Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL163Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL163Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL163Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL163Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q11JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL171Q12JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL169Q11JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL172Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL172Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL172Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FL172Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IC170Q01JA   <dbl> 4, 4, 1, 2, 5, 3, 5, 3, 4, 4, 5, 2, 1, 5, 4, 4, 4, 1, 3, …
$ IC170Q02JA   <dbl> 4, 4, 1, 4, 5, 6, 5, 6, 2, 5, 5, 4, 1, 5, 5, 1, 3, 4, 5, …
$ IC170Q03JA   <dbl> 1, 4, 5, 1, 6, 3, 1, 5, 1, 6, 6, 4, 4, 5, 5, 1, 1, 4, 1, …
$ IC170Q04JA   <dbl> 4, 4, 5, 1, 5, 3, 5, 5, 1, 4, 5, 4, 5, 5, 5, 4, 1, 4, 5, …
$ IC170Q05JA   <dbl> 2, 4, 1, 1, 4, 3, 1, 5, 2, 4, 5, 4, 5, 5, 1, 4, 1, 1, 5, …
$ IC170Q06JA   <dbl> 2, 4, 5, 1, 6, 3, 3, 5, 3, 1, 3, 4, 2, 5, 5, 2, 2, 4, 1, …
$ IC170Q07JA   <dbl> 4, 4, 5, 1, 5, 3, 3, 5, 5, 4, 5, 4, 4, 5, 5, 4, 5, 3, 3, …
$ IC171Q01JA   <dbl> 4, 2, 4, 1, 4, 5, 5, 5, 4, 5, 5, 4, 3, 5, 2, 5, 1, 4, 5, …
$ IC171Q02JA   <dbl> 5, 4, 4, 5, 4, 5, 5, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5, 4, 5, …
$ IC171Q03JA   <dbl> 1, 1, 4, 1, 1, 5, 1, 5, 4, 1, 6, 4, 4, 5, 5, 6, 1, 4, 1, …
$ IC171Q04JA   <dbl> 5, 4, 4, 5, 4, 5, 5, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5, 4, 5, …
$ IC171Q05JA   <dbl> 2, 1, 3, 1, 1, 1, 4, 5, 3, 1, 4, 4, 2, 5, 5, 1, 1, 4, 3, …
$ IC171Q06JA   <dbl> 4, 1, 5, 1, 1, 5, 5, 5, 4, 5, 4, 4, 1, 5, 2, 5, 1, 4, 3, …
$ IC172Q01JA   <dbl> 3, 4, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 3, 3, 4, 4, 3, 3, 3, …
$ IC172Q02JA   <dbl> 3, 4, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 3, 3, 4, 4, 3, 3, 2, …
$ IC172Q03JA   <dbl> 3, 4, 3, 4, 3, 4, 1, 3, 3, 2, 4, 2, 3, 3, 4, 4, 3, 3, 2, …
$ IC172Q04JA   <dbl> 3, 4, 3, 4, 3, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 4, 3, 3, 2, …
$ IC172Q05JA   <dbl> 3, 4, 3, 4, 4, 4, 3, 3, 4, 3, 3, 4, 3, 3, 4, 4, 3, 3, 3, …
$ IC172Q06JA   <dbl> 3, 4, 4, 4, 4, 4, 2, 3, 4, 3, 3, 4, 4, 3, 4, 4, 3, 3, 2, …
$ IC172Q07JA   <dbl> 3, 4, 3, 4, 4, 3, 3, 3, 4, 3, 3, 4, 3, 3, 4, 4, 3, 3, 3, …
$ IC172Q08JA   <dbl> 3, 4, 3, 4, 4, 3, 2, 3, 3, 3, 3, 4, 2, 3, 4, 4, 3, 3, 3, …
$ IC172Q09JA   <dbl> 3, 4, 4, 4, 4, 3, 4, 3, 4, 3, 3, 4, 3, 3, 4, 4, 3, 3, 2, …
$ IC173Q01JA   <dbl> 3, 4, 4, 1, 5, 5, 3, 4, 5, 4, 3, 2, 3, 4, 5, 3, 5, 3, 2, …
$ IC173Q02JA   <dbl> 1, 2, 5, 1, 1, 3, 5, 4, 3, 1, 1, 3, 1, 2, 2, 2, 2, 4, 1, …
$ IC173Q03JA   <dbl> 2, 4, 4, 1, 5, 3, 3, 4, 2, 3, 3, 2, 3, 2, 2, 1, 2, 2, 1, …
$ IC173Q04JA   <dbl> 6, 6, 6, 1, 6, 6, 6, 4, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 6, …
$ IC174Q01JA   <dbl> 2, 3, 1, 3, 3, 2, 1, 1, 2, 3, NA, 4, 1, 1, 3, 2, 2, 1, 1,…
$ IC174Q02JA   <dbl> 4, 3, 4, 3, 5, 4, 3, 4, 4, 4, NA, 5, 4, 3, 1, 4, 4, 4, 4,…
$ IC174Q03JA   <dbl> 3, 3, 3, 3, 5, 1, 1, 4, 3, 4, 2, 4, 1, 3, 4, 4, 2, 4, 4, …
$ IC174Q04JA   <dbl> 2, 3, 4, 3, 3, 3, 3, 4, 3, 4, 2, 5, 3, 3, 2, 1, 2, 3, 1, …
$ IC174Q05JA   <dbl> 1, 3, 1, 3, 3, 2, 1, 4, 3, 2, 2, 3, 1, 2, 1, 1, 2, 1, 1, …
$ IC174Q06JA   <dbl> 1, 3, 1, 3, 5, 2, 1, 1, 3, 1, 3, 3, 1, 2, 1, 2, 2, 1, 1, …
$ IC174Q07JA   <dbl> 2, 3, 1, 3, 5, 2, 5, 4, 3, 4, 3, 5, 4, 2, 1, 3, 2, 1, 1, …
$ IC174Q08JA   <dbl> 2, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 5, 3, 2, 5, 3, 3, 1, 1, …
$ IC174Q09JA   <dbl> 2, 3, 3, 3, 3, 3, 3, 1, 3, 4, 3, 4, 1, 3, 1, 4, 3, 1, 1, …
$ IC174Q10JA   <dbl> 2, 3, 3, 3, 3, 3, 1, 4, 3, 3, 1, 4, 1, 1, 1, 1, 2, 1, 1, …
$ IC175Q01JA   <dbl> 2, 4, 4, 1, 5, 4, 3, 5, 4, 4, 3, 4, 3, 3, 4, 5, 1, 3, 3, …
$ IC175Q02JA   <dbl> 2, 3, 2, 2, 5, 5, 3, 5, 4, 4, 3, 4, 1, 3, 1, 5, 1, 1, 3, …
$ IC175Q03JA   <dbl> 2, 3, 1, 1, 1, 3, 1, 5, 2, 4, 1, 4, 1, 3, 4, 5, 1, 1, 2, …
$ IC175Q05JA   <dbl> 1, 3, 1, 2, 2, 3, 1, 5, 3, 4, 1, 5, 3, 3, 4, 3, 1, 1, 3, …
$ IC176Q01JA   <dbl> 3, 2, 5, 1, 5, 3, 3, 5, 3, 5, 3, 4, 1, 3, 4, 4, 3, 4, 3, …
$ IC176Q02JA   <dbl> 3, 2, 4, 1, 5, 4, 1, 5, 5, 5, 5, 5, 4, 3, 5, 5, 3, 3, 3, …
$ IC176Q03JA   <dbl> 4, 2, 3, 1, 5, 4, 1, 5, 4, 5, 4, 5, 1, 3, 5, 5, 3, 4, 3, …
$ IC176Q04JA   <dbl> 4, 3, 5, 2, 5, 4, 3, 5, 5, 5, 5, 5, 5, 3, 5, 5, 3, 5, 4, …
$ IC176Q05JA   <dbl> 4, 2, 4, 2, 5, 4, 3, 5, 5, 4, 4, 5, 3, 3, 3, 5, 3, 5, 3, …
$ IC176Q06JA   <dbl> 3, 1, 2, 1, 5, 4, 4, 5, 5, 2, 3, 5, 1, 3, 3, 3, 4, 5, 1, …
$ IC176Q07JA   <dbl> 4, 2, 4, 1, 5, 4, 5, 5, 5, 4, 5, 5, 4, 3, 5, 3, 4, 5, 5, …
$ IC176Q08JA   <dbl> 4, 3, 1, 3, 5, 3, 4, 5, 4, 5, 5, 4, 1, 3, 5, 4, 4, 5, 3, …
$ IC184Q01JA   <dbl> 3, 3, 1, 5, 1, 4, 1, 1, 3, 4, 4, 5, 1, 5, 1, 4, 3, 5, 1, …
$ IC184Q02JA   <dbl> 4, 3, 3, 4, 1, 4, 1, 1, 3, 4, 4, 5, 1, 5, 4, 4, 3, 4, 3, …
$ IC184Q03JA   <dbl> 1, 3, 3, 1, 1, 3, 3, 1, 3, 6, 6, 4, 3, 5, 1, 1, 3, 3, 1, …
$ IC184Q04JA   <dbl> 1, 3, 1, 1, 1, 3, 6, 1, 2, 6, 6, 2, 6, 1, 1, 1, 3, 6, 1, …
$ IC177Q01JA   <dbl> 3, 2, 4, 2, 4, 4, 4, 3, 1, 5, 3, 2, 1, 4, 4, 3, 4, 3, 1, …
$ IC177Q02JA   <dbl> 2, 3, 2, 2, 6, 4, 1, 3, 2, 5, 3, 3, 2, 2, 4, 3, 3, 5, 3, …
$ IC177Q03JA   <dbl> 3, 3, 3, 2, 4, 4, 3, 3, 2, 5, 3, 3, 4, 4, 5, 3, 2, 4, 3, …
$ IC177Q04JA   <dbl> 2, 3, 2, 2, 4, 4, 3, 1, 2, 3, 2, 2, 1, 3, 5, 2, 1, 3, 2, …
$ IC177Q05JA   <dbl> 2, 3, 2, 2, 4, 4, 2, 1, 2, 2, 2, 4, 2, 2, 5, 1, 4, 4, 1, …
$ IC177Q06JA   <dbl> 2, 3, 2, 2, 4, 1, 2, 1, 2, 4, 2, 2, 1, 3, 5, 3, 1, 3, 2, …
$ IC177Q07JA   <dbl> 1, 3, 1, 2, 5, 1, 1, 3, 1, 3, 1, 2, 1, 1, 5, 2, 1, 1, 1, …
$ IC178Q01JA   <dbl> 3, 3, 5, 1, 4, 4, 6, 3, 1, 6, 3, 3, 1, 5, 5, 4, 4, 4, 1, …
$ IC178Q02JA   <dbl> 2, 3, 2, 1, 6, 4, 1, 3, 2, 6, 3, 4, 3, 2, 5, 3, 4, 5, 3, …
$ IC178Q03JA   <dbl> 3, 3, 2, 1, 4, 4, 2, 3, 2, 6, 3, 4, 5, 5, 5, 3, 1, 4, 2, …
$ IC178Q04JA   <dbl> 2, 3, 2, 1, 4, 4, 2, 1, 2, 2, 2, 3, 1, 2, 5, 2, 1, 3, 2, …
$ IC178Q05JA   <dbl> 2, 3, 1, 1, 4, 4, 1, 1, 2, 5, 1, 4, 2, 2, 5, 2, 1, 4, 1, …
$ IC178Q06JA   <dbl> 3, 3, 2, 1, 4, 1, 1, 1, 2, 3, 3, 4, 1, 2, 5, 2, 1, 3, 2, …
$ IC178Q07JA   <dbl> 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 1, 2, 1, 1, 5, 2, 1, 1, 1, …
$ IC179Q01JA   <dbl> 1, 3, 2, 1, 2, 4, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 2, 3, 3, …
$ IC179Q02JA   <dbl> 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3, 1, …
$ IC179Q03JA   <dbl> 3, 3, 3, 4, 4, 4, 4, 3, 3, 4, 3, 4, 3, 4, 3, 4, 2, 2, 3, …
$ IC179Q04JA   <dbl> 2, 3, 2, 1, 2, 4, 2, 2, 4, 1, 2, 4, 1, 3, 3, 3, 2, 2, 2, …
$ IC179Q05JA   <dbl> 2, 3, 2, 1, 4, 4, 2, 2, 4, 1, 3, 4, 1, 3, 4, 3, 2, 2, 3, …
$ IC179Q06JA   <dbl> 1, 3, 3, 2, 3, 4, 3, 3, 4, 1, 3, 1, 1, 1, 2, 1, 2, 2, 3, …
$ IC180Q01JA   <dbl> 3, 2, 2, 2, 3, 4, 2, 2, 2, 3, 2, 2, 2, 3, 2, 1, 3, 2, 2, …
$ IC180Q02JA   <dbl> 3, 3, 3, 2, 3, 2, 3, 3, 2, 3, 3, 4, 3, 3, 4, 4, 2, 3, 3, …
$ IC180Q03JA   <dbl> 3, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 4, 3, 3, 4, 4, 2, 3, 3, …
$ IC180Q04JA   <dbl> 2, 2, 2, 2, 3, 1, 2, 3, 3, 3, 3, 4, 1, 1, 1, 2, 2, 3, 2, …
$ IC180Q05JA   <dbl> 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 4, 1, 1, NA, 2, 2, 3, 3,…
$ IC180Q06JA   <dbl> 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 4, 1, 1, 3, 2, 2, 3, 2, …
$ IC180Q07JA   <dbl> 3, 2, 2, 2, 2, 4, 1, 3, 3, 3, 2, 2, 3, 2, 1, 4, 2, 3, 2, …
$ IC180Q08JA   <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, …
$ IC181Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IC181Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IC181Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IC181Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IC182Q01JA   <dbl> 3, 3, 2, 4, 2, 2, 3, 2, 2, 3, 3, 4, 3, 3, 3, 4, 2, 3, 2, …
$ IC182Q02JA   <dbl> 3, 2, 3, 4, 2, 2, 4, 2, 2, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, …
$ IC182Q03JA   <dbl> 3, 3, 3, 4, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 4, 4, 2, 3, 3, …
$ IC183Q01JA   <dbl> 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, …
$ IC183Q02JA   <dbl> 4, 4, 5, 3, 4, 4, 3, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, …
$ IC183Q03JA   <dbl> 4, 4, 2, 3, 4, 4, 3, 4, 4, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, …
$ IC183Q04JA   <dbl> 4, 4, 4, 3, 4, 4, 2, 4, 4, 4, 4, 4, 3, 4, 2, 4, 4, 3, 4, …
$ IC183Q05JA   <dbl> 4, 4, 3, 3, 4, 3, 3, 1, 4, 4, 3, 4, 3, 4, 3, 4, 3, 3, 4, …
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$ PA177Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q03HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q04HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q05HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q06HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q07HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA177Q08HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA180Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA182Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA175Q01HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA175Q02HA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA175Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA175Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA185Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q07JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA186Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA187Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA187Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q08JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA188Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q02JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q03JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q04JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q05JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q06JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q09JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA189Q10JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA194Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA195Q01JA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA041Q01TA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PA042Q01TA   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ EFFORT1      <dbl> 7, 8, 7, NA, 10, NA, 7, 10, 7, 7, 10, 9, 7, 6, 8, NA, 6, …
$ EFFORT2      <dbl> 9, 8, 8, NA, 10, NA, 8, 10, 10, 10, 10, 10, 10, 7, 10, NA…
$ OCOD1        <chr> "9701", "31", "9701", "41", "23", "9701", "11", "23", "1"…
$ OCOD2        <chr> "83", "21", "9704", "9705", "83", "34", "31", "21", "14",…
$ OCOD3        <chr> "2634", "9705", "9704", "2411", "21", "22", "2512", "2310…
$ PROGN        <chr> "07020002", "07020002", "07020002", "07020002", "07020002…
$ AGE          <dbl> 15.50, 15.83, 15.75, 16.17, 15.58, 15.58, 16.08, 16.00, 1…
$ GRADE        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ISCEDP       <dbl> 344, 344, 344, 344, 344, 344, 344, 344, 344, 344, 344, 34…
$ IMMIG        <dbl> 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 3, 3, 1, …
$ COBN_S       <chr> "070200", "070200", "070200", "070200", "070200", "970200…
$ COBN_M       <chr> "070200", "070200", "970200", "070200", "070200", "970200…
$ COBN_F       <chr> "070200", "070200", "070200", "070200", "070200", "970200…
$ LANGN        <dbl> 998, 998, 998, 998, 998, 998, 998, 998, 998, 998, 998, 99…
$ REPEAT       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ MISSSC       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
$ SKIPPING     <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ TARDYSD      <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ EXERPRAC     <dbl> 1, 4, 2, 5, 9, 1, 2, 0, 3, 5, 1, 2, 5, 2, 4, 0, 2, 3, 2, …
$ STUDYHMW     <dbl> 4, 7, 3, 5, 7, 10, 0, 10, 5, 3, 5, 5, 10, 0, 8, 5, 5, 5, …
$ WORKPAY      <dbl> 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ WORKHOME     <dbl> 10, 2, 0, 10, 5, 5, 7, 0, 0, 4, 2, 2, 10, 0, 10, 0, 5, 5,…
$ EXPECEDU     <dbl> 7, 7, 6, NA, 6, 7, 9, 9, 7, 6, 7, 8, 9, 9, 9, 9, 7, 7, 7,…
$ MATHPREF     <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
$ MATHEASE     <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, …
$ MATHMOT      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ DURECEC      <dbl> 3, 2, NA, 3, NA, 4, 3, NA, NA, 3, NA, 5, NA, 5, 3, 2, NA,…
$ BSMJ         <dbl> 85.85, NA, NA, 76.65, 79.49, 76.98, 74.66, 85.41, 24.79, …
$ SISCO        <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ RELATST      <dbl> -0.2606, 1.2437, 0.7190, -0.2194, 1.0726, 1.1296, -1.0841…
$ BELONG       <dbl> 0.2442, -0.0437, -0.6137, -1.1147, 2.1143, 0.5159, -0.718…
$ BULLIED      <dbl> -1.2280, -0.2016, -1.2280, -1.2280, -1.2280, -1.2280, 1.0…
$ FEELSAFE     <dbl> -0.7560, 1.1246, 0.4417, -0.7560, 1.1246, 0.1413, 0.0936,…
$ SCHRISK      <dbl> -0.6386, 0.1810, -0.6386, -0.6386, -0.6386, 0.1810, 0.181…
$ PERSEVAGR    <dbl> 0.4369, 0.4540, -0.4017, 0.5617, -0.5954, -0.1554, 0.4375…
$ CURIOAGR     <dbl> 2.7951, 0.3058, -0.6563, 0.1778, 0.3406, 0.3247, 0.0134, …
$ COOPAGR      <dbl> -0.0319, 0.1187, -0.6986, 0.0849, 0.8806, 4.8203, -0.7118…
$ EMPATAGR     <dbl> 1.3979, 0.1290, -0.2087, -0.1344, -0.5172, -0.5881, -1.26…
$ ASSERAGR     <dbl> -0.2970, -0.1734, -0.4816, -0.1538, 0.2470, -0.8113, -0.1…
$ STRESAGR     <dbl> 0.9777, -0.7402, -0.2053, 0.2201, -0.9940, 0.2183, 0.4922…
$ EMOCOAGR     <dbl> 1.2321, -0.5609, 0.5777, -0.0563, -0.8597, NA, 0.8646, 1.…
$ GROSAGR      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ INFOSEEK     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FAMSUP       <dbl> -0.3780, -0.5969, -1.0537, -0.8521, 1.7459, 1.7327, -0.88…
$ DISCLIM      <dbl> 0.3884, 1.1687, 0.2002, -0.1219, 0.1166, -0.1006, -0.8709…
$ TEACHSUP     <dbl> -0.5635, 0.1475, -0.5635, -0.1002, 1.5558, 0.1475, -0.332…
$ COGACRCO     <dbl> -0.4092, -0.7102, -0.6541, -0.0820, 0.5051, 0.2635, 0.142…
$ COGACMCO     <dbl> 0.1666, 0.0024, -0.5604, 0.1868, 2.3426, 0.7819, -0.5560,…
$ EXPOFA       <dbl> 0.3356, -1.2147, 0.6908, -0.2194, 0.5478, 0.5515, -0.1287…
$ EXPO21ST     <dbl> -0.7746, -0.5240, 0.0949, 0.4959, 0.4121, 0.4936, 0.2869,…
$ MATHEFF      <dbl> 0.1429, -0.2874, 0.2226, -0.9344, -0.9322, 0.2637, 1.2000…
$ MATHEF21     <dbl> 0.4317, 0.7644, -0.4779, -0.5515, -0.5730, -0.2489, 1.401…
$ FAMCON       <dbl> 1.3180, 4.7588, 0.4736, 0.0594, 0.8973, 2.2322, 3.6192, 4…
$ ANXMAT       <dbl> 0.3729, 0.6647, 0.0510, 0.6387, 2.5026, -0.7358, -0.9755,…
$ MATHPERS     <dbl> -0.1305, 0.6178, -0.3993, 1.8158, 1.1353, 0.9918, -1.5183…
$ CREATEFF     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATSCH     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATFAM     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATAS      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATOOS     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATOP      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ OPENART      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ IMAGINE      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ SCHSUST      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ LEARRES      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PROBSELF     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FAMSUPSL     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FEELLAH      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ SDLEFF       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ MISCED       <dbl> 8, 7, 4, 6, 7, 7, 6, 9, 8, 8, 4, 9, 10, 6, 4, 9, 8, 8, 6,…
$ FISCED       <dbl> 7, 7, 4, 6, 7, 9, 2, 8, 8, 7, 4, 9, 10, 9, 6, 4, 9, 8, 6,…
$ HISCED       <dbl> 8, 7, 4, 6, 7, 9, 6, 9, 8, 8, 4, 9, 10, 9, 6, 9, 9, 8, 6,…
$ PAREDINT     <dbl> 16.0, 14.5, 12.0, 12.0, 14.5, 16.0, 12.0, 16.0, 16.0, 16.…
$ BMMJ1        <dbl> 17.00, 37.83, 17.00, 43.33, 75.54, 17.00, 70.34, 75.54, 6…
$ BFMJ2        <dbl> 30.34, 77.10, NA, NA, 30.34, 57.64, 40.54, 80.78, 43.85, …
$ HISEI        <dbl> 30.34, 77.10, 17.00, 43.33, 75.54, 57.64, 70.34, 80.78, 6…
$ ICTRES       <dbl> 0.1940, 0.6249, -0.3987, -0.9028, 0.2514, -0.4733, 0.9904…
$ HOMEPOS      <dbl> 0.7524, 0.7842, 0.0666, -0.9300, -0.8949, -0.5988, 0.0975…
$ ESCS         <dbl> 0.1836, 0.8261, -1.0357, -0.9606, 0.0856, 0.1268, -0.0154…
$ FCFMLRTY     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FLSCHOOL     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FLMULTSB     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FLFAMILY     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ACCESSFP     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FLCONFIN     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FLCONICT     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ACCESSFA     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ATTCONFM     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ FRINFLFM     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ICTSCH       <dbl> 0.4062, 0.4062, 0.4062, 0.4062, -1.6647, -0.8411, 0.4062,…
$ ICTAVSCH     <dbl> 7, 7, 7, 7, 5, 6, 7, 6, 7, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, …
$ ICTHOME      <dbl> 0.3346, 0.3346, 0.3346, 0.3346, 0.3346, 0.3346, 0.3346, 0…
$ ICTAVHOM     <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 5, 6, 6, 6, …
$ ICTQUAL      <dbl> 0.3623, 2.8889, 1.2313, 2.8889, 1.8870, 1.6615, -0.2086, …
$ ICTSUBJ      <dbl> -0.1315, 0.5048, 0.9454, -2.0101, 0.6775, 0.6012, 0.5918,…
$ ICTENQ       <dbl> -0.3767, 0.3109, -0.1658, 0.3109, 1.0316, -0.0477, -0.021…
$ ICTFEED      <dbl> -0.4038, 0.4297, -0.4292, -0.6744, 0.2776, 0.9257, -0.354…
$ ICTOUT       <dbl> 0.2260, -0.8080, 0.1088, -1.2894, 2.9804, 0.3464, -0.4834…
$ ICTWKDY      <dbl> -0.4469, 0.4182, -0.3710, -0.5032, 1.3145, 0.4565, -0.250…
$ ICTWKEND     <dbl> -0.3452, 0.3311, -0.7926, -3.5000, 0.8948, 0.4976, -1.085…
$ ICTREG       <dbl> -0.1855, 0.9444, 0.2941, -0.6148, 0.8255, 1.9301, 0.3353,…
$ ICTINFO      <dbl> 0.2929, -0.4797, 0.0811, -0.8360, 0.4191, 0.2147, 0.1939,…
$ ICTDISTR     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ICTEFFIC     <dbl> 0.5764, 0.7781, -0.8446, -0.5172, 1.0613, 0.3941, -0.5344…
$ STUBMI       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ BODYIMA      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ SOCONPA      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ LIFESAT      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PSYCHSYM     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ SOCCON       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ EXPWB        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CURSUPP      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PQMIMP       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PQMCAR       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PARINVOL     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PQSCHOOL     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PASCHPOL     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ATTIMMP      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ PAREXPT      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATHME     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATACT     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATOPN     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ CREATOR      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ W_FSTUWT     <dbl> 5.35943, 6.74318, 6.77500, 5.43200, 5.82353, 5.69931, 4.9…
$ W_FSTURWT1   <dbl> 8.20073, 9.88533, 10.16250, 7.98824, 9.00000, 2.68850, 2.…
$ W_FSTURWT2   <dbl> 2.63139, 10.36607, 10.16250, 2.66275, 8.48571, 8.76685, 2…
$ W_FSTURWT3   <dbl> 8.03388, 10.33466, 10.16250, 8.31429, 2.82857, 8.34850, 2…
$ W_FSTURWT4   <dbl> 8.18690, 3.44489, 3.38750, 7.98824, 9.00000, 8.40156, 7.3…
$ W_FSTURWT5   <dbl> 7.89416, 3.29511, 3.38750, 2.56226, 8.48571, 2.89878, 7.5…
$ W_FSTURWT6   <dbl> 8.04452, 3.44489, 3.38750, 2.56226, 8.48571, 9.60179, 2.4…
$ W_FSTURWT7   <dbl> 2.67796, 3.29830, 3.38750, 2.66275, 9.00000, 2.92228, 7.5…
$ W_FSTURWT8   <dbl> 2.72897, 10.36607, 10.16250, 2.66275, 3.00000, 8.76685, 7…
$ W_FSTURWT9   <dbl> 7.74437, 3.29511, 3.38750, 7.98824, 2.82857, 3.05511, 2.4…
$ W_FSTURWT10  <dbl> 2.67796, 9.89489, 10.16250, 2.66275, 8.48571, 2.89878, 2.…
$ W_FSTURWT11  <dbl> 2.73358, 3.29830, 3.38750, 8.31429, 2.82857, 3.02482, 7.3…
$ W_FSTURWT12  <dbl> 8.18690, 9.88533, 10.16250, 2.66275, 2.82857, 2.80052, 7.…
$ W_FSTURWT13  <dbl> 8.20073, 10.33466, 10.16250, 7.68679, 9.00000, 8.02740, 7…
$ W_FSTURWT14  <dbl> 2.58146, 10.36607, 10.16250, 7.68679, 8.48571, 8.34850, 7…
$ W_FSTURWT15  <dbl> 2.68151, 9.89489, 10.16250, 7.98824, 3.00000, 2.58510, 2.…
$ W_FSTURWT16  <dbl> 2.63139, 3.45536, 3.38750, 8.31429, 2.82857, 8.40156, 7.5…
$ W_FSTURWT17  <dbl> 2.73358, 3.45536, 3.38750, 2.66275, 3.00000, 8.69635, 2.4…
$ W_FSTURWT18  <dbl> 7.89416, 9.88533, 10.16250, 2.77143, 3.00000, 2.89878, 7.…
$ W_FSTURWT19  <dbl> 2.72897, 3.29830, 3.38750, 7.68679, 9.00000, 2.78283, 2.5…
$ W_FSTURWT20  <dbl> 8.03388, 3.44489, 3.38750, 2.77143, 3.00000, 8.34850, 2.5…
$ W_FSTURWT21  <dbl> 7.89416, 9.88533, 3.38750, 2.66275, 9.00000, 2.92228, 2.4…
$ W_FSTURWT22  <dbl> 2.63139, 10.36607, 3.38750, 7.98824, 8.48571, 8.06550, 2.…
$ W_FSTURWT23  <dbl> 8.03388, 10.33466, 3.38750, 2.77143, 2.82857, 7.73009, 2.…
$ W_FSTURWT24  <dbl> 7.88647, 3.44489, 10.16250, 2.66275, 9.00000, 9.16534, 7.…
$ W_FSTURWT25  <dbl> 7.89416, 3.29511, 10.16250, 7.68679, 8.48571, 2.89878, 7.…
$ W_FSTURWT26  <dbl> 8.36312, 3.44489, 10.16250, 7.68679, 8.48571, 8.76685, 2.…
$ W_FSTURWT27  <dbl> 2.67796, 3.29830, 10.16250, 7.98824, 9.00000, 2.92228, 7.…
$ W_FSTURWT28  <dbl> 2.62882, 10.36607, 3.38750, 7.98824, 3.00000, 8.76685, 7.…
$ W_FSTURWT29  <dbl> 8.03388, 3.29511, 10.16250, 2.66275, 2.82857, 2.80052, 2.…
$ W_FSTURWT30  <dbl> 2.78196, 9.89489, 3.38750, 7.98824, 8.48571, 2.89878, 2.4…
$ W_FSTURWT31  <dbl> 2.73358, 3.29830, 10.16250, 2.77143, 2.82857, 2.78283, 7.…
$ W_FSTURWT32  <dbl> 8.18690, 9.88533, 3.38750, 7.98824, 2.82857, 2.80052, 7.5…
$ W_FSTURWT33  <dbl> 8.20073, 10.33466, 3.38750, 2.56226, 9.00000, 8.02740, 7.…
$ W_FSTURWT34  <dbl> 2.67796, 10.36607, 3.38750, 2.56226, 8.48571, 8.34850, 7.…
$ W_FSTURWT35  <dbl> 2.68151, 9.89489, 3.38750, 2.66275, 3.00000, 2.80052, 2.5…
$ W_FSTURWT36  <dbl> 2.73358, 3.45536, 10.16250, 2.77143, 2.82857, 8.40156, 7.…
$ W_FSTURWT37  <dbl> 2.63139, 3.45536, 10.16250, 7.98824, 3.00000, 9.48693, 2.…
$ W_FSTURWT38  <dbl> 7.89416, 9.88533, 3.38750, 8.31429, 3.00000, 2.89878, 7.3…
$ W_FSTURWT39  <dbl> 2.62882, 3.29830, 10.16250, 2.56226, 9.00000, 2.78283, 2.…
$ W_FSTURWT40  <dbl> 8.03388, 3.44489, 10.16250, 8.31429, 3.00000, 9.07446, 2.…
$ W_FSTURWT41  <dbl> 8.20073, 9.88533, 3.38750, 2.66275, 2.82857, 9.07446, 7.5…
$ W_FSTURWT42  <dbl> 2.63139, 10.36607, 3.38750, 7.98824, 3.00000, 2.78283, 7.…
$ W_FSTURWT43  <dbl> 8.03388, 10.33466, 3.38750, 2.77143, 9.00000, 2.92228, 7.…
$ W_FSTURWT44  <dbl> 8.18690, 3.44489, 10.16250, 2.66275, 2.82857, 2.89878, 2.…
$ W_FSTURWT45  <dbl> 7.89416, 3.29511, 10.16250, 7.68679, 3.00000, 8.40156, 2.…
$ W_FSTURWT46  <dbl> 8.04452, 3.44489, 10.16250, 7.68679, 3.00000, 2.57670, 7.…
$ W_FSTURWT47  <dbl> 2.67796, 3.29830, 10.16250, 7.98824, 2.82857, 8.34850, 2.…
$ W_FSTURWT48  <dbl> 2.72897, 10.36607, 3.38750, 7.98824, 8.48571, 2.78283, 2.…
$ W_FSTURWT49  <dbl> 7.74437, 3.29511, 10.16250, 2.66275, 9.00000, 8.02740, 7.…
$ W_FSTURWT50  <dbl> 2.67796, 9.89489, 3.38750, 7.98824, 3.00000, 8.40156, 7.5…
$ W_FSTURWT51  <dbl> 2.73358, 3.29830, 10.16250, 2.77143, 9.00000, 8.06550, 2.…
$ W_FSTURWT52  <dbl> 8.18690, 9.88533, 3.38750, 7.98824, 9.00000, 8.69635, 2.4…
$ W_FSTURWT53  <dbl> 8.20073, 10.33466, 3.38750, 2.56226, 2.82857, 3.05511, 2.…
$ W_FSTURWT54  <dbl> 2.58146, 10.36607, 3.38750, 2.56226, 3.00000, 2.92228, 2.…
$ W_FSTURWT55  <dbl> 2.68151, 9.89489, 3.38750, 2.66275, 8.48571, 9.48693, 7.3…
$ W_FSTURWT56  <dbl> 2.63139, 3.45536, 10.16250, 2.77143, 9.00000, 2.89878, 2.…
$ W_FSTURWT57  <dbl> 2.73358, 3.45536, 10.16250, 7.98824, 8.48571, 2.80052, 7.…
$ W_FSTURWT58  <dbl> 7.89416, 9.88533, 3.38750, 8.31429, 8.48571, 8.40156, 2.5…
$ W_FSTURWT59  <dbl> 2.72897, 3.29830, 10.16250, 2.56226, 2.82857, 8.76685, 7.…
$ W_FSTURWT60  <dbl> 8.03388, 3.44489, 10.16250, 8.31429, 8.48571, 2.92228, 7.…
$ W_FSTURWT61  <dbl> 7.89416, 9.88533, 10.16250, 7.98824, 2.82857, 8.34850, 7.…
$ W_FSTURWT62  <dbl> 2.63139, 10.36607, 10.16250, 2.66275, 3.00000, 3.02482, 7…
$ W_FSTURWT63  <dbl> 8.03388, 10.33466, 10.16250, 8.31429, 9.00000, 3.20060, 7…
$ W_FSTURWT64  <dbl> 7.88647, 3.44489, 3.38750, 7.98824, 2.82857, 2.67580, 2.5…
$ W_FSTURWT65  <dbl> 7.89416, 3.29511, 3.38750, 2.56226, 3.00000, 8.40156, 2.4…
$ W_FSTURWT66  <dbl> 8.36312, 3.44489, 3.38750, 2.56226, 3.00000, 2.78283, 7.5…
$ W_FSTURWT67  <dbl> 2.67796, 3.29830, 3.38750, 2.66275, 2.82857, 8.34850, 2.4…
$ W_FSTURWT68  <dbl> 2.62882, 10.36607, 10.16250, 2.66275, 8.48571, 2.78283, 2…
$ W_FSTURWT69  <dbl> 8.03388, 3.29511, 3.38750, 7.98824, 9.00000, 8.69635, 7.5…
$ W_FSTURWT70  <dbl> 2.78196, 9.89489, 10.16250, 2.66275, 3.00000, 8.40156, 7.…
$ W_FSTURWT71  <dbl> 2.73358, 3.29830, 3.38750, 8.31429, 9.00000, 8.76685, 2.5…
$ W_FSTURWT72  <dbl> 8.18690, 9.88533, 10.16250, 2.66275, 9.00000, 8.69635, 2.…
$ W_FSTURWT73  <dbl> 8.20073, 10.33466, 10.16250, 7.68679, 2.82857, 3.05511, 2…
$ W_FSTURWT74  <dbl> 2.67796, 10.36607, 10.16250, 7.68679, 3.00000, 2.92228, 2…
$ W_FSTURWT75  <dbl> 2.68151, 9.89489, 10.16250, 7.98824, 8.48571, 8.69635, 7.…
$ W_FSTURWT76  <dbl> 2.73358, 3.45536, 3.38750, 8.31429, 9.00000, 2.89878, 2.4…
$ W_FSTURWT77  <dbl> 2.63139, 3.45536, 3.38750, 2.66275, 8.48571, 2.58510, 7.5…
$ W_FSTURWT78  <dbl> 7.89416, 9.88533, 10.16250, 2.77143, 8.48571, 8.40156, 2.…
$ W_FSTURWT79  <dbl> 2.62882, 3.29830, 3.38750, 7.68679, 2.82857, 8.76685, 7.3…
$ W_FSTURWT80  <dbl> 8.03388, 3.44489, 3.38750, 2.77143, 8.48571, 2.68850, 7.3…
$ UNIT         <dbl> 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, …
$ WVARSTRR     <dbl> 70, 53, 2, 27, 35, 50, 65, 1, 63, 4, 13, 65, 42, 68, 15, …
$ PV1MATH      <dbl> 639.004, 697.191, 693.710, 427.317, 436.462, 569.982, 771…
$ PV2MATH      <dbl> 601.251, 754.277, 654.450, 410.376, 453.450, 539.609, 672…
$ PV3MATH      <dbl> 621.480, 671.940, 696.938, 423.586, 392.315, 531.648, 653…
$ PV4MATH      <dbl> 631.596, 657.300, 646.187, 388.935, 439.986, 534.368, 734…
$ PV5MATH      <dbl> 579.276, 621.126, 678.119, 330.962, 443.125, 465.815, 727…
$ PV6MATH      <dbl> 591.791, 655.729, 644.019, 379.988, 452.648, 528.509, 729…
$ PV7MATH      <dbl> 600.709, 747.934, 720.531, 398.535, 396.970, 514.326, 597…
$ PV8MATH      <dbl> 587.322, 694.365, 671.425, 422.127, 459.945, 521.029, 772…
$ PV9MATH      <dbl> 618.131, 742.732, 694.085, 375.354, 438.166, 472.382, 694…
$ PV10MATH     <dbl> 581.973, 656.934, 668.304, 453.348, 448.084, 503.387, 725…
$ PV1READ      <dbl> 676.298, 625.585, 620.116, 381.495, 448.199, 469.441, 744…
$ PV2READ      <dbl> 692.247, 686.716, 559.078, 400.815, 560.636, 500.350, 679…
$ PV3READ      <dbl> 690.981, 663.147, 554.767, 374.911, 365.478, 375.703, 635…
$ PV4READ      <dbl> 643.067, 567.435, 587.026, 367.484, 469.970, 377.452, 725…
$ PV5READ      <dbl> 627.908, 614.500, 591.806, 336.009, 503.664, 470.781, 731…
$ PV6READ      <dbl> 684.676, 604.745, 570.547, 324.630, 481.215, 415.448, 684…
$ PV7READ      <dbl> 661.380, 669.375, 599.078, 396.242, 436.800, 448.547, 646…
$ PV8READ      <dbl> 674.070, 623.735, 545.610, 374.723, 531.226, 434.381, 756…
$ PV9READ      <dbl> 666.282, 649.579, 610.466, 314.704, 480.997, 411.703, 653…
$ PV10READ     <dbl> 657.387, 571.261, 590.758, 342.956, 478.578, 410.846, 784…
$ PV1SCIE      <dbl> 710.634, 670.646, 666.095, 340.308, 456.333, 475.158, 693…
$ PV2SCIE      <dbl> 618.739, 748.839, 604.771, 329.889, 453.400, 470.030, 626…
$ PV3SCIE      <dbl> 591.623, 635.443, 704.217, 411.353, 498.937, 461.218, 627…
$ PV4SCIE      <dbl> 659.770, 639.735, 687.659, 327.974, 532.324, 504.199, 676…
$ PV5SCIE      <dbl> 635.892, 608.385, 690.974, 292.183, 508.231, 486.930, 661…
$ PV6SCIE      <dbl> 646.901, 670.662, 617.175, 355.423, 504.461, 493.011, 618…
$ PV7SCIE      <dbl> 603.569, 734.807, 692.886, 400.182, 404.572, 469.950, 602…
$ PV8SCIE      <dbl> 621.352, 639.748, 630.900, 317.518, 549.457, 464.012, 653…
$ PV9SCIE      <dbl> 659.674, 716.768, 656.620, 298.893, 411.062, 440.113, 645…
$ PV10SCIE     <dbl> 649.719, 655.670, 649.087, 362.702, 473.613, 495.410, 662…
$ PV1MCCR      <dbl> 649.392, 636.431, 645.218, 437.613, 474.516, 471.365, 718…
$ PV2MCCR      <dbl> 575.372, 674.370, 680.260, 498.958, 521.987, 546.743, 708…
$ PV3MCCR      <dbl> 603.792, 716.787, 711.792, 402.737, 434.209, 581.279, 707…
$ PV4MCCR      <dbl> 656.162, 616.569, 633.124, 407.151, 432.725, 567.093, 720…
$ PV5MCCR      <dbl> 625.136, 701.626, 711.941, 450.975, 392.901, 498.145, 736…
$ PV6MCCR      <dbl> 582.174, 686.793, 749.576, 487.698, 444.589, 519.416, 670…
$ PV7MCCR      <dbl> 665.104, 665.775, 692.915, 391.175, 382.733, 521.639, 749…
$ PV8MCCR      <dbl> 599.694, 729.194, 648.755, 426.641, 497.778, 513.726, 729…
$ PV9MCCR      <dbl> 616.165, 715.697, 721.097, 366.883, 439.754, 519.125, 696…
$ PV10MCCR     <dbl> 628.181, 688.830, 657.165, 435.455, 486.126, 544.408, 779…
$ PV1MCQN      <dbl> 615.137, 661.706, 569.292, 403.429, 432.794, 526.659, 703…
$ PV2MCQN      <dbl> 538.617, 762.401, 647.507, 419.847, 494.698, 580.938, 707…
$ PV3MCQN      <dbl> 587.580, 712.182, 660.431, 418.996, 429.147, 577.745, 703…
$ PV4MCQN      <dbl> 686.280, 685.954, 569.983, 375.158, 464.070, 622.873, 631…
$ PV5MCQN      <dbl> 589.078, 722.402, 637.599, 433.093, 446.069, 557.871, 673…
$ PV6MCQN      <dbl> 554.608, 677.709, 652.780, 453.028, 441.193, 488.815, 645…
$ PV7MCQN      <dbl> 682.886, 691.586, 636.887, 435.661, 417.452, 602.295, 740…
$ PV8MCQN      <dbl> 574.889, 688.120, 611.227, 441.660, 474.220, 578.260, 757…
$ PV9MCQN      <dbl> 601.470, 704.436, 685.701, 430.417, 443.916, 541.486, 721…
$ PV10MCQN     <dbl> 627.403, 692.040, 604.016, 382.344, 476.528, 572.958, 715…
$ PV1MCSS      <dbl> 686.193, 664.374, 655.070, 397.737, 429.063, 466.035, 637…
$ PV2MCSS      <dbl> 656.043, 660.937, 612.146, 336.048, 437.136, 498.416, 712…
$ PV3MCSS      <dbl> 650.782, 692.654, 646.904, 438.551, 451.101, 502.442, 702…
$ PV4MCSS      <dbl> 643.126, 654.111, 590.767, 347.416, 464.708, 517.999, 702…
$ PV5MCSS      <dbl> 675.389, 644.941, 727.077, 420.278, 338.774, 452.092, 663…
$ PV6MCSS      <dbl> 591.001, 699.918, 648.958, 401.609, 453.538, 494.653, 632…
$ PV7MCSS      <dbl> 684.203, 653.602, 623.166, 413.761, 366.873, 463.116, 727…
$ PV8MCSS      <dbl> 617.908, 629.891, 677.449, 299.027, 469.776, 498.289, 719…
$ PV9MCSS      <dbl> 601.076, 696.030, 654.643, 373.784, 438.499, 441.962, 635…
$ PV10MCSS     <dbl> 645.941, 663.634, 649.460, 363.817, 484.175, 411.098, 774…
$ PV1MCUD      <dbl> 597.328, 655.345, 658.932, 393.019, 429.756, 491.327, 650…
$ PV2MCUD      <dbl> 564.341, 732.821, 648.412, 393.366, 491.067, 522.747, 721…
$ PV3MCUD      <dbl> 655.238, 737.658, 672.524, 384.864, 445.708, 486.597, 663…
$ PV4MCUD      <dbl> 638.884, 651.654, 614.591, 379.824, 510.568, 524.480, 680…
$ PV5MCUD      <dbl> 604.706, 690.450, 664.442, 501.245, 403.908, 483.108, 753…
$ PV6MCUD      <dbl> 576.996, 666.422, 687.078, 453.339, 412.346, 505.925, 667…
$ PV7MCUD      <dbl> 672.527, 673.451, 708.829, 425.168, 419.173, 501.566, 746…
$ PV8MCUD      <dbl> 599.424, 728.294, 640.191, 407.016, 576.384, 506.734, 801…
$ PV9MCUD      <dbl> 604.423, 701.038, 732.526, 421.738, 392.309, 499.849, 638…
$ PV10MCUD     <dbl> 664.795, 650.797, 619.894, 421.415, 461.595, 442.838, 734…
$ PV1MPEM      <dbl> 604.382, 705.040, 676.642, 401.548, 437.563, 528.852, 692…
$ PV2MPEM      <dbl> 575.460, 710.217, 705.385, 389.686, 474.378, 604.877, 678…
$ PV3MPEM      <dbl> 534.443, 713.023, 585.184, 390.502, 433.958, 498.130, 711…
$ PV4MPEM      <dbl> 571.301, 679.747, 670.486, 434.343, 442.276, 511.479, 711…
$ PV5MPEM      <dbl> 675.638, 661.754, 645.880, 366.385, 522.369, 555.593, 706…
$ PV6MPEM      <dbl> 566.880, 718.268, 760.958, 401.969, 417.482, 555.839, 749…
$ PV7MPEM      <dbl> 582.805, 613.478, 731.917, 496.875, 444.665, 553.932, 690…
$ PV8MPEM      <dbl> 558.696, 643.541, 676.092, 353.233, 464.823, 571.562, 708…
$ PV9MPEM      <dbl> 662.795, 640.522, 684.182, 476.154, 434.014, 520.155, 719…
$ PV10MPEM     <dbl> 640.998, 655.268, 702.866, 342.948, 458.216, 609.277, 671…
$ PV1MPFS      <dbl> 518.732, 763.661, 690.547, 421.798, 454.383, 493.759, 702…
$ PV2MPFS      <dbl> 557.279, 729.497, 728.787, 467.856, 401.375, 538.097, 662…
$ PV3MPFS      <dbl> 497.254, 714.971, 633.737, 414.444, 453.331, 485.088, 670…
$ PV4MPFS      <dbl> 615.386, 753.899, 703.501, 445.029, 414.743, 465.568, 658…
$ PV5MPFS      <dbl> 615.007, 719.492, 654.486, 389.460, 443.411, 493.251, 653…
$ PV6MPFS      <dbl> 591.702, 715.191, 734.709, 414.555, 391.559, 545.850, 699…
$ PV7MPFS      <dbl> 595.836, 702.035, 737.481, 489.032, 386.903, 504.961, 697…
$ PV8MPFS      <dbl> 540.481, 704.257, 685.489, 363.830, 452.824, 532.093, 692…
$ PV9MPFS      <dbl> 600.664, 664.705, 665.867, 419.104, 478.373, 568.115, 656…
$ PV10MPFS     <dbl> 613.118, 705.987, 727.280, 394.856, 407.059, 485.863, 698…
$ PV1MPIN      <dbl> 602.757, 733.566, 682.130, 407.066, 414.746, 459.876, 691…
$ PV2MPIN      <dbl> 571.184, 744.273, 692.729, 381.339, 399.365, 490.634, 671…
$ PV3MPIN      <dbl> 646.605, 758.913, 647.770, 364.773, 447.814, 386.584, 737…
$ PV4MPIN      <dbl> 679.914, 695.003, 629.600, 406.470, 454.758, 499.082, 731…
$ PV5MPIN      <dbl> 685.582, 714.181, 693.276, 433.901, 442.892, 441.024, 737…
$ PV6MPIN      <dbl> 637.760, 716.221, 660.979, 406.423, 463.269, 544.965, 763…
$ PV7MPIN      <dbl> 645.213, 663.813, 684.474, 489.700, 423.967, 475.647, 782…
$ PV8MPIN      <dbl> 577.579, 662.428, 656.617, 432.277, 464.304, 504.819, 769…
$ PV9MPIN      <dbl> 661.673, 640.743, 687.070, 512.069, 435.375, 459.819, 686…
$ PV10MPIN     <dbl> 670.254, 768.695, 648.410, 374.502, 495.469, 481.600, 708…
$ PV1MPRE      <dbl> 537.068, 706.337, 630.753, 378.730, 364.784, 523.219, 756…
$ PV2MPRE      <dbl> 614.320, 672.767, 694.543, 400.807, 399.972, 536.264, 700…
$ PV3MPRE      <dbl> 583.272, 651.949, 604.546, 407.607, 452.831, 454.319, 687…
$ PV4MPRE      <dbl> 620.093, 620.759, 614.087, 336.451, 394.357, 501.514, 701…
$ PV5MPRE      <dbl> 634.054, 645.072, 603.798, 317.742, 409.755, 498.824, 724…
$ PV6MPRE      <dbl> 602.552, 677.174, 644.046, 349.040, 428.787, 523.705, 680…
$ PV7MPRE      <dbl> 595.217, 634.813, 710.851, 450.198, 375.015, 497.360, 697…
$ PV8MPRE      <dbl> 603.353, 648.907, 656.938, 392.060, 414.975, 508.547, 736…
$ PV9MPRE      <dbl> 611.942, 641.203, 690.323, 447.422, 441.178, 499.691, 734…
$ PV10MPRE     <dbl> 663.352, 644.001, 664.134, 382.088, 421.531, 580.387, 727…
$ SENWT        <dbl> 0.63867, 0.80357, 0.80736, 0.64732, 0.69397, 0.67917, 0.5…
$ VER_DAT      <chr> "01MAY23:14:19:45", "01MAY23:14:19:44", "01MAY23:14:19:45…
$ i            <dbl> 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 21…

sfu_qqq_SG[duplicated(sfu_qqq_SG),]

# A tibble: 0 × 1,279
# ℹ 1,279 variables: CNT <chr>, CNTRYID <dbl>, CNTSCHID <dbl>, CNTSTUID <dbl>,
#   CYC <chr>, NatCen <chr>, STRATUM <chr>, SUBNATIO <chr>, REGION <dbl>,
#   OECD <dbl>, ADMINMODE <dbl>, LANGTEST_QQQ <dbl>, LANGTEST_COG <dbl>,
#   LANGTEST_PAQ <dbl>, Option_CT <dbl>, Option_FL <dbl>, Option_ICTQ <dbl>,
#   Option_WBQ <dbl>, Option_PQ <dbl>, Option_TQ <dbl>, Option_UH <dbl>,
#   BOOKID <dbl>, ST001D01T <dbl>, ST003D02T <dbl>, ST003D03T <dbl>,
#   ST004D01T <dbl>, ST250Q01JA <dbl>, ST250Q02JA <dbl>, ST250Q03JA <dbl>, …

sum(is.na(sfu_qqq_SG))

[1] 4168500

“The dataset comprises 6,066 records of Singapore students across 1,279 variables. Initial inspection suggests a high-dimensional data structure.”
“Given the comprehensive nature of the questionnaire responses, many of the over one thousand variables might be extraneous for our analysis. We will therefore focus on extracting and analyzing only those variables that are relevant.”
“A significant number of variables contain ‘NA’ values, indicating missing data. These will be further assessed and addressed appropriately after isolating the variables critical for our analysis.”
“An initial review confirms that the dataset is free of duplicate entries.”

2.4 Extraction of relevant variables

Below code chunks are used to extract the variables that are required for further analysis to meet the task objectives. Variables required are:

StudentID, denoted by CNTSTUID
Estimate measure for student’s performance
- Plausible values (PVs) for each of the three domains, i.e. MATHS, SCIENCE and READING, denoted by PV{N}{DOMAIN} where N is the nth plausible value for the DOMAIN.
School
- SchoolID, denoted by CNTSCHID
- Whether school is public or independent, denoted by STRATUM.
Gender
- Female or Male, denoted by ST004D01T.
Measure for socioeconomic status
- Denoted by ESCS, which is an index that measures economic, social and cultural status.

req_cols <- c("CNTSTUID", 
              "PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH", "PV6MATH", "PV7MATH", "PV8MATH", "PV9MATH", "PV10MATH", 
              "PV1READ", "PV2READ", "PV3READ", "PV4READ", "PV5READ", "PV6READ", "PV7READ", "PV8READ", "PV9READ", "PV10READ",
              "PV1SCIE", "PV2SCIE", "PV3SCIE", "PV4SCIE", "PV5SCIE", "PV6SCIE", "PV7SCIE", "PV8SCIE", "PV9SCIE", "PV10SCIE",
              "CNTSCHID",
              "STRATUM",
              "ST004D01T", 
              "ESCS", 
              "HOMEPOS", 
              "ICTRES",
              "HISEI",
              "PAREDINT")
sfu_qqq_SG_req <- sfu_qqq_SG[req_cols] %>% 
  mutate(ST004D01T = as.factor(ST004D01T),
         CNTSTUID = as.character(CNTSTUID),
         CNTSCHID = as.character(CNTSCHID))

datatable(head(sfu_qqq_SG_req, 8),
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap")

There exists extracted variables with values of “NA” within the dataset.

names(which(colSums(is.na(sfu_qqq_SG_req))>0))

[1] "ESCS"     "HOMEPOS"  "ICTRES"   "HISEI"    "PAREDINT"

Below code is used to find out the number of “NA” values within each variable, with the most “NA” (at approx. 5% of records) being observed for the variable “HISEI” which is an index representing the highest parental occupational status.

(colSums(is.na(sfu_qqq_SG_req)))

 CNTSTUID   PV1MATH   PV2MATH   PV3MATH   PV4MATH   PV5MATH   PV6MATH   PV7MATH 
        0         0         0         0         0         0         0         0 
  PV8MATH   PV9MATH  PV10MATH   PV1READ   PV2READ   PV3READ   PV4READ   PV5READ 
        0         0         0         0         0         0         0         0 
  PV6READ   PV7READ   PV8READ   PV9READ  PV10READ   PV1SCIE   PV2SCIE   PV3SCIE 
        0         0         0         0         0         0         0         0 
  PV4SCIE   PV5SCIE   PV6SCIE   PV7SCIE   PV8SCIE   PV9SCIE  PV10SCIE  CNTSCHID 
        0         0         0         0         0         0         0         0 
  STRATUM ST004D01T      ESCS   HOMEPOS    ICTRES     HISEI  PAREDINT 
        0         0        47        40        40       310        57

3 Data Wrangling

3.1 Compute mean PV values for each domain

The PISA statistics provides 10 plausible values (PVs) for each student in each of the three domains. The plausible values provides more accurate and reliable estimates of students’ abilities and overall performance of educational systems. The mean value of 10 PVs is computed to represent the average of the 10 estimates, which is then used as an estimation of the particular students’ PV. Below code chunk is used to perform the computation.

# computation for Math PVs
sfu_qqq_SG_req["PVMATH_final"] <- sfu_qqq_SG_req %>%
  select("PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH", "PV6MATH", "PV7MATH", "PV8MATH", "PV9MATH", "PV10MATH") %>% 
  apply(, MARGIN=1, FUN=mean)

# computation for READING PVs
sfu_qqq_SG_req["PVREAD_final"] <- sfu_qqq_SG_req %>%
  select("PV1READ", "PV2READ", "PV3READ", "PV4READ", "PV5READ", "PV6READ", "PV7READ", "PV8READ", "PV9READ", "PV10READ") %>% 
  apply(, MARGIN=1, FUN=mean)

# computation for SCIENCE PVs
sfu_qqq_SG_req["PVSCIE_final"] <- sfu_qqq_SG_req %>%
  select("PV1SCIE", "PV2SCIE", "PV3SCIE", "PV4SCIE", "PV5SCIE", "PV6SCIE", "PV7SCIE", "PV8SCIE", "PV9SCIE", "PV10SCIE") %>% 
  apply(, MARGIN=1, FUN=mean)

datatable(head(sfu_qqq_SG_req[c("CNTSTUID", "PVMATH_final", "PVREAD_final", "PVSCIE_final")], 8),
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap",
          caption = "Table showing computed mean values")

3.2 Mapping PV Values into Performance Levels

Using the performance band definitions of each domain provided in PISA 2022 Technical Report (Chapter 17), the mean PV values were mapped into the respective performance level using the mean of the PV estimate. The performance band definitions for the domains are shown below.

Math:

Below code chunks map the mean PVs of the three domains into their respective performance levels:

# mapping MATH PV
sfu_qqq_SG_req["MATH_level"] <- cut(sfu_qqq_SG_req$PVMATH_final, 
                                    breaks = c(0, 233.17, 295.46999, 357.76999, 420.06999, 482.37999, 544.67999, 606.98999, 669.29999, 2000),
                                    labels = c("Below 1c", "Level 1c", "Level 1b", "Level 1a", "Level 2", "Level 3", "Level 4", "Level 5", "Level 6"))

# mapping READING PV
sfu_qqq_SG_req["READ_level"] <- cut(sfu_qqq_SG_req$PVREAD_final, 
                                    breaks = c(0, 189.32999, 262.03999, 334.74999, 407.46999, 480.17999, 552.88999, 625.60999, 698.31999, 2000),
                                    labels = c("Below 1c", "Level 1c", "Level 1b", "Level 1a", "Level 2", "Level 3", "Level 4", "Level 5", "Level 6"))

# mapping SCIENCE PV
sfu_qqq_SG_req["SCIE_level"] <- cut(sfu_qqq_SG_req$PVSCIE_final, 
                                    breaks = c(0, 260.53999, 334.93999, 409.53999, 484.13999, 558.72999, 633.32999, 707.92999, 2000),
                                    labels = c("Below 1b", "Level 1b", "Level 1a", "Level 2", "Level 3", "Level 4", "Level 5", "Level 6"))

datatable(head(sfu_qqq_SG_req[c("CNTSTUID", "MATH_level", "READ_level", "SCIE_level")], 8),
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap",
          caption = "Table showing mapped performance levels for three domains")

3.3 Recoding School Category and Gender

The values of the variables representing school category (i.e. public or private) and gender (i,e, male or female) are recoded for easier visual interpretation. These values are represented using numbers or non-intuitive tags in the dataset. The code chunks below are used to recode the values.

sfu_qqq_SG_req <- sfu_qqq_SG_req %>% 
# gender
  mutate(ST004D01T = case_when(ST004D01T == 1 ~ "Female",
                               ST004D01T == 2 ~ "Male",
                               TRUE ~ "Not mentioned")) %>% 
# school category
  mutate(STRATUM = case_when(STRATUM == "SGP01" ~ "Public Sch",
                             STRATUM == "SGP02" ~ "Public Sch",
                             STRATUM == "SGP03" ~ "Private Sch",
                             STRATUM == "SGP97" ~ "Undisclosed",
                             TRUE ~ "Unknown code"))

datatable(head(sfu_qqq_SG_req[c("CNTSTUID", "ST004D01T", "STRATUM")], 8),
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap",
          caption = "Table showing recoded values")

Visualising distributions of Gender:

gender_bar <- ggplot(data = sfu_qqq_SG_req,
       aes(x=ST004D01T)) +
  geom_bar(fill = "lightblue") + 
  labs(x = NULL, 
       y = "Num of students") +
  theme_pander() +
  ggtitle("Distribution of students by gender") + 
  theme(plot.title = element_text(size = 12)) 

gender_box_math <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = ST004D01T, y = PVMATH_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Math") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

gender_box_read <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = ST004D01T, y = PVREAD_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Reading") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

gender_box_scie <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = ST004D01T, y = PVSCIE_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Science") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

gender_bar | (gender_box_math / gender_box_read / gender_box_scie)

The recoded gender data, using “ST004D01T”, shows a balanced distribution between male and female students. Initial boxplot analysis suggests females outperform males in Reading, and males outperform females in Math and Science. Given the overlap in boxplots, further exploratory data analysis is required to uncover deeper insights.

Visualising distributions of School Category:

school_bar <- ggplot(data = sfu_qqq_SG_req,
       aes(x=STRATUM)) +
  geom_bar(fill = "lightblue") + 
  labs(x = NULL, 
       y = "Num of students") +
  theme_pander() +
  ggtitle("Distribution of students by school") + 
  theme(plot.title = element_text(size = 12)) 

school_box_math <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = STRATUM, y = PVMATH_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Math") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

school_box_read <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = STRATUM, y = PVREAD_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Reading") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

school_box_scie <- ggplot(data = sfu_qqq_SG_req,
                     aes(x = STRATUM, y = PVSCIE_final)) +
  geom_boxplot() +
  scale_y_continuous(limits = c(0,1000)) +
  theme_pander() +
  labs (x = NULL,
        y = NULL) +
  ggtitle("Science") +
  theme(plot.title = element_text(size = 10)) +
  coord_flip()

school_bar | (school_box_math / school_box_read / school_box_scie)

Post-recoding the school category with “STRATUM”, an uneven distribution between public and private school students was noted, aligning with the greater prevalence of public schools in Singapore.

For further exploratory data analysis, it’s essential to consider this imbalance. A probability density approach is suggested to more accurately compare performance distributions across school types.

4 Exploratory Data Analysis

4.1 Distribution of Singapore students’ performance in Math, Reading, and Science

In this section, the raincloud plot is used to provide a visual overview of students’ performances in the three domains. The raincloud plot is a data visualisation technique that provides a half density distribution diagram, and thus is able to provide additional insights, such as the existence of multiple modalities, which could suggests the possibility of groups. Using the raincloud plot, we were able to come up with insights and hypothesis, which could be examined further using CDA techniques.

Below are the code chunks for this data visualisation:

# Combine score data into one data frame with domain as a factor
all_scores <- sfu_qqq_SG_req %>%
  select(PVMATH_final, PVREAD_final, PVSCIE_final) %>%
  pivot_longer(cols = everything(), names_to = "Domain", values_to = "Score") %>%
  mutate(Domain = factor(Domain, levels = c("PVMATH_final", "PVREAD_final", "PVSCIE_final"), 
                         labels = c("MATH", "READING", "SCIENCE")))

# Plot the distribution of student performance by domain
ggplot(all_scores, aes(x = Domain, y = Score)) +
  stat_halfeye(adjust = 0.5,
               justification = -0.4,
               .width = 0,
               point_colour = NA,
               scale = 0.2,
               alpha = 0.4,
               fill = "blue") +
  geom_boxplot(width = 0.1, outlier.shape = NA) +
  stat_dots(side = "left",
            justification = 1.5,
            binwidth = unit(0.0022, "npc"),
            dotsize = 1,
            scale = 0.01,
            position = "dodge",
            alpha = 0.4,
            color = "lightblue") +
  coord_flip(xlim = c(0.6, 2.8)) +
  theme_minimal() +
  labs(title = "Distribution of Singapore Students' Performance by Domain")

The visualizations reveal distinct aspects of Singapore students’ performance across domains:

The density peaks for Math and Science are positioned at higher scores than Reading, indicating stronger performance in these areas. This is corroborated by the boxplots, which show a lower median score for Reading.
The inverted dot plots demonstrate comparable distribution shapes across domains, but with a denser concentration of high-scoring dots in Math and Science, suggesting more students excelling in these subjects.
Outliers are present only on the lower score range for all domains, but their scarcity suggests a minor impact on the overall data.
The dot plots hint at potential multimodality in the Reading and Science score distributions, an insight that could guide further analysis.

4.2 Student gender seems to make a difference in performance

Here, we use the ridge plot to plot out the density distributions of the performance proxy, i.e. the average Plausible Value for each domain, and compare the density distributions across the domains by gender, so as to see if any hypothesis or insights on possible relationship between gender and performance can be derived.

Below are the code chunks for the data visualisation.

DATA VIZ

math_ridge <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVMATH_final,
           y = ST004D01T,
           fill = ST004D01T,
           color = ST004D01T))+
  geom_density_ridges(bandwidth = 15,
                      alpha = .5) +
  labs(x = NULL,
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=830,
           y=3,
           label = "Male students recorded peak\nfrequency at higher Math score",
           size = 2) +
  annotate(geom = "label",
           x=120,
           y=3,
           label = "Math",
           size = 4) +
  theme_minimal() +
  theme(legend.position="none",
        axis.text.x = element_blank())

read_ridge <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVREAD_final,
           y = ST004D01T,
           fill = ST004D01T,
           color = ST004D01T))+
  geom_density_ridges(bandwidth = 15,
                      alpha = .5) +
  labs(x = NULL,
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=800,
           y=3.3,
           label = "Female and Male students recorded peak\nfrequency at similar Reading score",
           size = 2) +
  annotate(geom = "label",
           x=120,
           y=3,
           label = "Reading",
           size = 4) +
  theme_minimal() +
  theme(legend.position="none",
        axis.text.x = element_blank())

scie_ridge <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVSCIE_final,
           y = ST004D01T,
           fill = ST004D01T,
           color = ST004D01T))+
  geom_density_ridges(bandwidth = 15,
                      alpha = .5) +
  labs(x = "Score for each domain",
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=820,
           y=3,
           label = "Male students recorded peak\nfrequency at higher Science score",
           size = 2) +
  annotate(geom = "label",
           x=120,
           y=3,
           label = "Science",
           size = 4) +
  theme_minimal() +
  theme(legend.position="none",
        axis.title = element_text(size = 8, color = "grey20"))

fig2 <- math_ridge / read_ridge / scie_ridge

fig2 +
  plot_annotation(title = "Male students performed better in Math and Science")

NORMALITY TEST

Below code chunks are used to plot the performances for the 3 domains by gender on Q-Q plots.

For the normality test at 95% confidence interval, we hypothesize that:

H0 = The performance of students in the 3 domains, grouped by gender, are normally distributed
H1 = The underlying distributions are not normally distributed

Visually, the points can be observed to be deviating from the normal line, hence the implication is that the underlying distributions might not be normal.

qq_gender_math <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVMATH_final,
                    fill = ST004D01T)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~ST004D01T,
             nrow = 1) +
  labs(title = "Q-Q plot for Math \nby gender") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_gender_read <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVREAD_final,
                    fill = ST004D01T)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~ST004D01T,
             nrow = 1) +
  labs(title = "Q-Q plot for Reading \nby gender") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_gender_scie <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVSCIE_final,
                    fill = ST004D01T)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~ST004D01T,
             nrow = 1) +
  labs(title = "Q-Q plot for Science \nby gender") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_gender_math | qq_gender_read | qq_gender_scie

The Shapiro-Wilk test for normality was used to test and confirm the visual observations above. The code chunks below details outcome of th

math_sw <- sfu_qqq_SG_req %>% 
  group_by(ST004D01T) %>% 
  shapiro_test(PVMATH_final)

read_sw <- sfu_qqq_SG_req %>% 
  group_by(ST004D01T) %>% 
  shapiro_test(PVREAD_final)

scie_sw <- sfu_qqq_SG_req %>% 
  group_by(ST004D01T) %>% 
  shapiro_test(PVSCIE_final)

combined_sw <- rbind(math_sw, read_sw, scie_sw)

datatable(data = combined_sw,
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap",
          caption = "Table showing Shapiro-Wilk normality test results")

Since the p-value for all the distributions tested are lower than the significance level of 0.05 (taking a 95% confidence level), we reject the null hypothesis (H0) that the underlying distributions are normally distributed. This means that the performance of students (grouped by gender) in all three domains do not follow a normal distribution. This implication is that a non-parametric test will be used in subsequent analysis to test if there is any significant statistical difference in the student’s performance in the three domains according to their gender.

2 SAMPLE-TEST (NON-PARAMETRIC)

Here, we use the non-parametric Wilcoxon rank-sum test with the following pairs of hypotheses using 95% confidence level. The ggbetweenstats function of ggstatsplot package is used for the test, as well as showing the outcome in a visual plot.

Hypothesis 1:

H0 = There is no difference in the median Math performance of Singapore students by gender.
H1 = There is a difference in the median Math performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = ST004D01T,
               y = PVMATH_final,
               type = "nonparametric",
               conf.level = 0.95,
               ylab = "Math score",
               xlab = "Gender",
               title = "2 sample non-parametric test on Math score by gender") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample non-parametric test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Math performance between genders. Specifically, Male students had a higher median than Female students, suggesting that Male students had performed better in Math.

Hypothesis 2:

H0 = There is no difference in the median Reading performance of Singapore students by gender.
H1 = There is a difference in the median Reading performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = ST004D01T,
               y = PVREAD_final,
               type = "nonparametric",
               conf.level = 0.95,
               ylab = "Reading score",
               xlab = "Gender",
               title = "2 sample non-parametric test on Reading score by gender") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample non-parametric test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Reading performance between genders. Specifically, Female students had a higher median than Male students, suggesting that Female students had performed better in Reading.

Hypothesis 3:

H0 = There is no difference in the median Science performance of Singapore students by gender.
H1 = There is a difference in the median Science performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = ST004D01T,
               y = PVSCIE_final,
               type = "nonparametric",
               conf.level = 0.95,
               ylab = "Science score",
               xlab = "Gender",
               title = "2 sample non-parametric test on Science score by gender") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample non-parrametric test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Science performance between genders. Specifically, Male students had a higher median than Female students, suggesting that Male students had performed better in Science.

DISCUSSIONS & INSIGHTS
Using the above plots, differences in the distribution across both gender groups can be observed. The insights are: The range in performances between the two genders are similar when compared within the same domain. However, across the domains, both genders had density distribution with longer upper tails for Math, while having longer lower tails for Reading. This suggests that both genders likely performed better in Math than Reading. In terms of the peak frequency in the density distribution, Male students recorded peak frequencies at higher scores for both Math and Science, while being at a similar score compared to Females for Reading. This suggests that a higher proportion of Male students performed better for Math and Science, though this needs to be confirmed by CDA techniques as the ridges for the Male students are skewed left. The follow-on non-parametric 2-sample tests confirmed the rejection of the null hypothesis, and suggests that there are differences in performance between the 2 genders.

Using the above plots, differences in the distribution across both gender groups can be observed. The insights are:

The range in performances between the two genders are similar when compared within the same domain. However, across the domains, both genders had density distribution with longer upper tails for Math, while having longer lower tails for Reading. This suggests that both genders likely performed better in Math than Reading.
In terms of the peak frequency in the density distribution, Male students recorded peak frequencies at higher scores for both Math and Science, while being at a similar score compared to Females for Reading. This suggests that a higher proportion of Male students performed better for Math and Science, though this needs to be confirmed by CDA techniques as the ridges for the Male students are skewed left.
The follow-on non-parametric 2-sample tests confirmed the rejection of the null hypothesis, and suggests that there are differences in performance between the 2 genders.

4.3 Public schools seem to exhibit higher disparity in performance

Here, we use the ridge plot to plot out the density distributions of the performance proxy, i.e. the average Plausible Value for each domain, and compare the density distributions across the domains by school category, so as to see if any hypothesis or insights on possible relationship between school category and performance can be derived.

Together with the density ridge plot, a histogram for the average scores of schools for each of the domains was also plotted, so as to see how the public and private schools compared to each other in terms of average student performance.

Below are the code chunks for the data visualisation.

DATA VIZ:

math_ridge_2 <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVMATH_final,
           y = STRATUM,
           fill = STRATUM,
           color = STRATUM))+
  geom_density_ridges(bandwidth = 20,
                      alpha = .5) +
  labs(x = NULL,
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=275,
           y=2.7,
           label = "Public school students \nrecorded peak frequency at \nhigher Math score",
           size = 2) +
  annotate(geom = "label",
           x=840,
           y=3,
           label = "Math",
           size = 3) +
  theme_minimal() +
  theme(legend.position="none",
        axis.text = element_blank())

read_ridge_2 <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVREAD_final,
           y = STRATUM,
           fill = STRATUM,
           color = STRATUM))+
  geom_density_ridges(bandwidth = 20,
                      alpha = .5) +
  labs(x = NULL,
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=275,
           y=2.8,
           label = "Private school students \nrecorded peak frequency at \nhigher Reading score",
           size = 2) +
  annotate(geom = "label",
           x=820,
           y=3,
           label = "Reading",
           size = 3) +
  theme_minimal() +
  theme(legend.position="none",
        axis.text = element_blank())

scie_ridge_2 <- ggplot(sfu_qqq_SG_req, 
       aes(x = PVSCIE_final,
           y = STRATUM,
           fill = STRATUM,
           color = STRATUM))+
  geom_density_ridges(bandwidth = 20,
                      alpha = .5) +
  labs(x = "Score for each domain",
       y = NULL) +
  scale_x_continuous(limits=c(100,900)) +
  annotate(geom = "text",
           x=275,
           y=2.8,
           label = "Public and Private school \nstudents recorded peak \nfrequency at similar \nScience score",
           size = 2) +
  annotate(geom = "label",
           x=820,
           y=3,
           label = "Science",
           size = 3) +
  theme_minimal() +
  theme(legend.position="none",
        axis.text.y = element_blank(),
        axis.title = element_text(size = 8, color = "grey20"))

# code producing histograms
new_df <- sfu_qqq_SG_req %>% 
  select(c("CNTSCHID", "PVMATH_final", "PVREAD_final", "PVSCIE_final", "STRATUM")) %>% 
  group_by(sfu_qqq_SG_req$CNTSCHID, STRATUM) %>% 
  summarise_at(vars(PVMATH_final, PVREAD_final, PVSCIE_final), mean)

math_hist_1 <- ggplot(data = new_df,
       aes(x = PVMATH_final,
           fill = STRATUM)) +
  geom_histogram(bins = 50, alpha = 0.5) +
  labs(y = NULL,
       x = NULL) +
  scale_x_continuous(limits = c(300,800)) +
  scale_y_continuous(limits = c(0,20)) +
  theme_light() +
  theme(legend.position = "none")

read_hist_1 <- ggplot(data = new_df,
       aes(x = PVREAD_final,
           fill = STRATUM)) +
  geom_histogram(bins = 50, alpha = 0.5) +
  labs(y = "Count of schools",
       x = NULL) +
  scale_x_continuous(limits = c(300,800)) +
  scale_y_continuous(limits = c(0,20)) +
  theme_light() +
  theme(legend.position = "none",
        axis.title = element_text(size = 8, color = "grey20"))

scie_hist_1 <- ggplot(data = new_df,
       aes(x = PVSCIE_final,
           fill = STRATUM)) +
  geom_histogram(bins = 50, alpha = 0.5) +
  labs(y = NULL,
       x = "Ave score of individual school") +
  scale_x_continuous(limits = c(300,800)) +
  scale_y_continuous(limits = c(0,20)) +
  theme_light() +
  theme(legend.title=element_blank(),
        legend.text = element_text(size = 6),
        legend.position = "left",
        axis.title = element_text(size = 8, color = "grey20"))

fig3 <- (math_ridge_2 / read_ridge_2 / scie_ridge_2) | (math_hist_1 / read_hist_1 / scie_hist_1)

fig3 +
  plot_annotation(title = "Do all schools perform equally well?", subtitle = "Private schools appear to have an edge over Public schools")

Warning: Removed 4 rows containing missing values (`geom_bar()`).
Removed 4 rows containing missing values (`geom_bar()`).

Warning: Removed 5 rows containing missing values (`geom_bar()`).

NORMALITY TEST:

Below code chunks are used to plot the performances for the 3 domains by school category on Q-Q plots.

For the normality test at 95% confidence interval, we hypothesize that:

H0 = The performance of students in the 3 domains, grouped by school category, are normally distributed
H1 = The underlying distributions are not normally distributed

Visually, the points can be observed to be deviating from the normal line, hence the implication is that the underlying distributions might not be normal.

qq_school_math <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVMATH_final,
                    fill = STRATUM)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~STRATUM,
             nrow = 1) +
  labs(title = "Q-Q plot for Math \nby school category") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_school_read <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVREAD_final,
                    fill = STRATUM)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~STRATUM,
             nrow = 1) +
  labs(title = "Q-Q plot for Reading \nby school category") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_school_scie <- ggplot(data = sfu_qqq_SG_req,
      mapping = aes(sample = PVSCIE_final,
                    fill = STRATUM)) +
  stat_qq_band(alpha=0.15) +
  stat_qq_line(alpha=0.15) +
  stat_qq_point(size=0.5) +
  facet_wrap(~STRATUM,
             nrow = 1) +
  labs(title = "Q-Q plot for Science \nby school category") +
  theme_linedraw() +
  theme(legend.position = "none",
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect(),
        panel.grid = element_blank())

qq_school_math | qq_school_read | qq_school_scie

The Shapiro-Wilk test for normality was used to test and confirm the visual observations above. However, as the number of samples for “Public Sch” is larger than 5000 (more than 6000), we will conduct the Shapiro-Wilk test for the “Private Sch” distribution, while assuming that that for the “Public Sch” are approximately normal (since sample size is large). Consequently, the choice of the subsequent 2-sample test will be dependent on whether the distribution for “Private Sch” students are normally distributed.

The code chunks below details outcome of the tests.

math_sw_sch <- sfu_qqq_SG_req %>% 
  group_by(STRATUM) %>%
  filter(STRATUM == "Private Sch") %>% 
  shapiro_test(PVMATH_final)

read_sw_sch <- sfu_qqq_SG_req %>% 
  group_by(STRATUM) %>%
  filter(STRATUM == "Private Sch") %>% 
  shapiro_test(PVREAD_final)

scie_sw_sch <- sfu_qqq_SG_req %>% 
  group_by(STRATUM) %>%
  filter(STRATUM == "Private Sch") %>% 
  shapiro_test(PVSCIE_final)

combined_sw_sch <- rbind(math_sw_sch, read_sw_sch, scie_sw_sch)

datatable(data = combined_sw_sch,
          rownames = FALSE,
          options = list(dom = c("t")),
          style = "bootstrap",
          caption = "Table showing Shapiro-Wilk normality test results")

Since the p-value for the distributions of Reading and Science performance are lower than the significance level of 0.05 (taking a 95% confidence level), we reject the null hypothesis (H0) that the underlying distributions are normally distributed, while we fail to reject the hypothesis that the distribution for Math performance of Private Sch students are normal as the p-value obtained is 0.202 (greater than significance level of 0.05).

The implication is that a non-parametric test will be used in subsequent analysis for Reading and Science, while a parametric test will be used for Math, to test if there is any significant statistical difference in the student’s performance in the three domains according to their school category.

2 SAMPLE-TEST (NON-PARAMETRIC):

Here, we use either the parametric Welch t-test or the non-parametric Wilcoxon rank-sum test with the following pairs of hypotheses using 95% confidence level. The ggbetweenstats function of ggstatsplot package is used for the test, as well as showing the outcome in a visual plot.

Hypothesis 1:

H0 = There is no difference in the mean Math performance of Singapore students in different category.
H1 = There is a difference in the mean Math performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = STRATUM,
               y = PVMATH_final,
               type = "parametric",
               conf.level = 0.95,
               ylab = "Math score",
               xlab = "School Category",
               title = "2 sample parametric test on Math score by school category") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample t-test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Math performance between school categories. Specifically, private school students had a higher mean than public school students, suggesting that private school students had performed better in Math.

Hypothesis 2:

H0 = There is no difference in the median Reading performance of Singapore students by school category.
H1 = There is a difference in the median Reading performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = STRATUM,
               y = PVREAD_final,
               type = "nonparametric",
               conf.level = 0.95,
               ylab = "Reading score",
               xlab = "School Category",
               title = "2 sample non-parametric test on Reading score by school category") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample non-parametric test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Reading performance between school categories. Specifically, private school students had a higher median than public school students, suggesting that private school students had performed better in Reading.

Hypothesis 3:

H0 = There is no difference in the median Science performance of Singapore students in different school category.
H1 = There is a difference in the median Science performance of Singapore students.

ggbetweenstats(data = sfu_qqq_SG_req,
               x = STRATUM,
               y = PVSCIE_final,
               type = "nonparametric",
               conf.level = 0.95,
               ylab = "Science score",
               xlab = "School Category",
               title = "2 sample non-parametric test on Reading score by school category") +
  scale_color_manual(values = c("palegreen", "pink", "black")) +
  theme_bw() +
  theme(legend.position = "none")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

The p-value obtained from the 2-sample non-parametric test is lower than significance level of 0.05, thus rejecting the null hypothesis that there is no difference in Science performance between school categories. Specifically, private school students had a higher median than public school students, suggesting that private school students had performed better in Science.

DISCUSSIONS & INSIGHTS
Using the above plots, differences in the distributions across school categories were analysed. The insights are: The density ridges for private schools generally has steeper slopes as compared to public schools. From this, we can infer that the differences in performance of public schools varies more, i.e. public schools’ performance are less “equal” than that of private schools. In terms of the peak frequency in the density distribution, private school students recorded peak frequencies at lower score for Math, at higher score for Reading and at similar score for Science. Subsequent CDA techniques was used to confirm the observations. From the histogram plots, it was also observed that the range of school average scores is smaller in private schools than in public schools, suggesting that performance among private schools could be more consistent. The follow-on 2-sample tests confirmed the rejection of the null hypothesis, and suggests that private school students had better performance in all three domains, contrary to what EDA had initially suggested.

Using the above plots, differences in the distributions across school categories were analysed. The insights are:

The density ridges for private schools generally has steeper slopes as compared to public schools. From this, we can infer that the differences in performance of public schools varies more, i.e. public schools’ performance are less “equal” than that of private schools.
In terms of the peak frequency in the density distribution, private school students recorded peak frequencies at lower score for Math, at higher score for Reading and at similar score for Science. Subsequent CDA techniques was used to confirm the observations.
From the histogram plots, it was also observed that the range of school average scores is smaller in private schools than in public schools, suggesting that performance among private schools could be more consistent.
The follow-on 2-sample tests confirmed the rejection of the null hypothesis, and suggests that private school students had better performance in all three domains, contrary to what EDA had initially suggested.

4.4 Positive correlation between performance and socioeconomic status

Next, we use scatter plots to visualise correlations between the performance proxy, i.e. the average Plausible Value for each domain, and the socioeconomic status (measured by ESCS index by OECD PISA), so as to see if any hypothesis or insights on possible relationship between socioeconomic status and performance can be derived.

Below are the code chunks for the data visualisation.

library(ggplot2)
library(patchwork)

# Function to compute correlation and return a formatted annotation string
get_correlation_annotation <- function(score, escs) {
  corr_test <- cor.test(score, escs, method = "pearson")
  paste("r =", round(corr_test$estimate, 3),
        "\np-value =", format.pval(corr_test$p.value, digits = 3))
}

# Function to create scatter plot
create_scatter_plot <- function(data, score_col, score_name, color, annotation) {
  ggplot(data, aes_string(x = score_col, y = "ESCS")) +
    geom_point(size = 0.8, alpha = 0.3, color = color) +
    geom_smooth(method = "lm", linewidth = 0.5, formula = y ~ x) +
    scale_x_continuous(limits = c(100, 900)) +
    labs(x = score_name, y = "Socioeconomic index (ESCS)") +
    annotate("label", x = 500, y = 3.25, label = annotation, size = 2, color = "brown", fill = "lightyellow") +
    theme_light()
}

# Compute correlation annotations
math_annotation <- get_correlation_annotation(sfu_qqq_SG_req$PVMATH_final, sfu_qqq_SG_req$ESCS)
read_annotation <- get_correlation_annotation(sfu_qqq_SG_req$PVREAD_final, sfu_qqq_SG_req$ESCS)
scie_annotation <- get_correlation_annotation(sfu_qqq_SG_req$PVSCIE_final, sfu_qqq_SG_req$ESCS)

# Create scatter plots
math_scatter <- create_scatter_plot(sfu_qqq_SG_req, "PVMATH_final", "Math score", "violet", math_annotation)

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

read_scatter <- create_scatter_plot(sfu_qqq_SG_req, "PVREAD_final", "Reading score", "salmon", read_annotation)
scie_scatter <- create_scatter_plot(sfu_qqq_SG_req, "PVSCIE_final", "Science score", "palegreen", scie_annotation)

# Combine the plots
fig4 <- math_scatter + read_scatter + scie_scatter +
  plot_layout(guides = "collect") & theme(legend.position = "bottom")

# Add titles to the combined plot
fig4 + 
  plot_annotation(
    title = "Student Performance and Socioeconomic Status Correlation",
    subtitle = "There is a positive correlation across all domains, suggesting socioeconomic status influences student performance.",
    theme = theme(plot.title = element_text(hjust = 0.5))
  )

Warning: Removed 47 rows containing non-finite values (`stat_smooth()`).

Warning: Removed 47 rows containing missing values (`geom_point()`).

Warning: Removed 47 rows containing non-finite values (`stat_smooth()`).

Warning: Removed 47 rows containing missing values (`geom_point()`).

Warning: Removed 47 rows containing non-finite values (`stat_smooth()`).

Warning: Removed 47 rows containing missing values (`geom_point()`).

The analysis of the data suggests that students with higher socioeconomic status tend to have higher performance scores across all three academic domains: Math, Reading, and Science. The consistency of the correlation across different domains implies that socioeconomic factors may have a pervasive influence on student performance. Given the high level of statistical significance, it is unlikely that these findings are due to random variation in the data. This implies that socioeconomic status is a strong predictor of academic performance among Singapore students in this dataset. Further analysis, perhaps including multivariate regression or more sophisticated modeling, could provide additional insights into the nature of these relationships.

4.5 A look into the details in relationship between performance and components of socioeconomic status

Finally, we use multiple trellis box plots to investigate into the possible relations between the performance levels, obtained by mapping the average Plausible Value for each domain to performance scales defined by PISA, and the individual components breakdown (i.e. occupation, education and home possessions of parents) of the ESCS index, so as to see if there could be any dominating components that could explain for the observations in 4.4.

Below are the code chunks for the data visualization.

#HISEI index
math_level_h <- ggplot(data=sfu_qqq_SG_req,
       aes(x=MATH_level,
           y=HISEI)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "blue",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = "Math \nperformance",
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.title.y = element_text(size = 9, color = "grey20"))

Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
ℹ Please use the `fun` argument instead.

read_level_h <- ggplot(data=sfu_qqq_SG_req,
       aes(x=READ_level,
           y=HISEI)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "blue",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = "Reading \nperformance",
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.title.y = element_text(size = 9, color = "grey20"))

scie_level_h <- ggplot(data=sfu_qqq_SG_req,
       aes(x=SCIE_level,
           y=HISEI)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "blue",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = "Science \nperformance",
       y = "Parent \noccupation index") +
  theme_light() + 
  coord_flip() +
  theme(axis.title.x = element_text(size = 9, color = "grey20"),
        axis.title.y = element_text(size = 9, color = "grey20"))

# PAREDINT index
math_level_p <- ggplot(data=sfu_qqq_SG_req,
       aes(x=MATH_level,
           y=PAREDINT)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "black",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.text.y = element_blank())

read_level_p <- ggplot(data=sfu_qqq_SG_req,
       aes(x=READ_level,
           y=PAREDINT)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "black",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.text.y = element_blank())

scie_level_p <- ggplot(data=sfu_qqq_SG_req,
       aes(x=SCIE_level,
           y=PAREDINT)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "black",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = "Highest parent \neducation in years") +
  theme_light() + 
  coord_flip() +
  theme(axis.text.y = element_blank(),
        axis.title.x = element_text(size = 9, color = "grey20"))

# HOMEPOS index
math_level_home <- ggplot(data=sfu_qqq_SG_req,
       aes(x=MATH_level,
           y=HOMEPOS)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "red",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.text.y = element_blank())

read_level_home <- ggplot(data=sfu_qqq_SG_req,
       aes(x=READ_level,
           y=HOMEPOS)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "red",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = NULL) +
  theme_light() + 
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.text.y = element_blank())

scie_level_home <- ggplot(data=sfu_qqq_SG_req,
       aes(x=SCIE_level,
           y=HOMEPOS)) + 
  geom_boxplot(outlier.size = 0.8, 
               outlier.alpha = 0.6, 
               outlier.color = "red",
               fill = "lightblue") + 
  stat_summary(geom = "point",
               fun.y = "mean",
               color = "salmon",
               size = 1) +
  labs(x = NULL,
       y = "Home possessions") +
  theme_light() + 
  coord_flip() +
  theme(axis.text.y = element_blank(),
        axis.title.x = element_text(size = 9, color = "grey20"))

fig5 <- (math_level_h / read_level_h / scie_level_h) | (math_level_p / read_level_p / scie_level_p) | (math_level_home / read_level_home / scie_level_home)

fig5 + 
  plot_annotation(title = "Breakdown by Socioeconomic Components", subtitle = "Plots reveal detailed insights: Parents' occupation and home possessions likely the key contributors \nbehind the correlation between performance and socioeconomic status")

Warning: Removed 310 rows containing non-finite values (`stat_boxplot()`).

Warning: Removed 310 rows containing non-finite values (`stat_summary()`).

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DISCUSSIONS & INSIGHTS
Using the above plots, possible relationships between performance and the components of ESCS index were analysed. The insights are: Parent occupation index: The means and medians generally increases as performance level increases, with an outlier observed for Science, whereby there was a low performer with high parent occupation index. Despite so, as this is an isolated data point, this could be a rare exception. Parent education (in years): Though the means generally increase slightly with increase in performance, the relationship might not be so clear in this case as the boxplots are mostly overlapping. Home possessions: The means and medians generally increases as performance level increase. However, the existence of many outliers makes this observation less certain. Putting the above observations together, it can be inferred that the more likely contributors behind the correlation identified in 4.4 to be due to parents’ occupation and home possessions, and less by parents’ education.

Using the above plots, possible relationships between performance and the components of ESCS index were analysed. The insights are:

Parent occupation index:
- The means and medians generally increases as performance level increases, with an outlier observed for Science, whereby there was a low performer with high parent occupation index. Despite so, as this is an isolated data point, this could be a rare exception.
Parent education (in years):
- Though the means generally increase slightly with increase in performance, the relationship might not be so clear in this case as the boxplots are mostly overlapping.
Home possessions:
- The means and medians generally increases as performance level increase. However, the existence of many outliers makes this observation less certain.

Putting the above observations together, it can be inferred that the more likely contributors behind the correlation identified in 4.4 to be due to parents’ occupation and home possessions, and less by parents’ education.

5 Conclusion

Exploratory Data Analysis (EDA) methods and ggplot2 data visualisation functions were used to analyse the PISA dataset for Singapore students:

the distribution of Singapore students’ performance in mathematics, reading, and science, and
the relationship between these performances with schools, gender and socioeconomic status of the students.

From the exploratory analysis done, key insights obtained are as follows:

There is likelihood that performance differs between genders, where Male students likely perform better in Maths and Science,
Not all schools have performed equally well, with the likelihood that private schools having performed better than public schools,
Likelihood of correlation between socioeconomic status and performance, with parents’ occupation and home possessions as possible key contributors to the correlation.

Further exploratory data analysis and confirmatory data analysis techniques can be performed to further analysis and confirm the insights obtained.

Take-home Exercise 2: DataVis Makeover A Look into Singapore Students’ Performance Using PISA 2022——FU WEIMING

1 Overview

1.1 Setting the scene

1.2 Our task

1.3 The Data

2 Data Preparation

2.1 Loading R packages

2.2 Importing PISA data

2.3 Summary statistics of PISA data

2.4 Extraction of relevant variables

3 Data Wrangling

3.1 Compute mean PV values for each domain

3.2 Mapping PV Values into Performance Levels

3.3 Recoding School Category and Gender

4 Exploratory Data Analysis

4.1 Distribution of Singapore students’ performance in Math, Reading, and Science

4.2 Student gender seems to make a difference in performance

4.3 Public schools seem to exhibit higher disparity in performance

4.4 Positive correlation between performance and socioeconomic status

4.5 A look into the details in relationship between performance and components of socioeconomic status

5 Conclusion

6 References