R-CMD-check

The goal of learningtower is to provide a user-friendly R package to provide easy access to a subset of variables from PISA data collected from the OECD, for the years 2000 - 2018, collected on a three year basis. This is an excellent dataset for exploring and visualizing data. This dataset can also be used for other analytically purposes as well as statistical computations.

What is PISA?

The Programme for International Student Assessment (PISA) is an international assessment measuring student performance in reading, mathematical and scientific literacy.

PISA assesses the extent to which 15-year-old students have acquired some of the knowledge and skills that are essential for full participation in society, and how well they are prepared for lifelong learning in the areas of reading, mathematical and scientific literacy.

In 2018, PISA involved 79 countries and 600,000+ students worldwide.

Read more about the Programme here.

Installation

You can install the learningtower package from CRAN with:

install.packages("learningtower")

To install the development version of learningtower from GitHub use:

devtools::install_github("kevinwang09/learningtower")

Data Description and Usage

The learningtower gives access to a subset of variables from PISA data originally collected and are available from OECD, for the years 2000 - 2018, collected on a three year basis.

The learningtower package contains mainly three datasets:

  • student
  • school
  • countrycode

This provides us with information about the students scores in mathematics, reading and science. Their school details, and which country they are from. Let’s have a look at how these datasets can be downloaded.

Student Dataset

The student dataset comprises of the scores from the triennial testing of 15-year-olds worldwide. In addition, this dataset contains interesting information on their parents qualifications, family wealth, gender, and possession of computers, internet, cars, books, rooms, desks, and similar other variables.

Keeping in mind the package’s size constraints. The package only includes a portion of student data, called the student subset data, this subset of student data is available for all the years when the PISA experiment took place. The full year wise student data set can be downloaded using the load_student() function included in this package.

As an example, we can load the student data as follows:

  • The student subset dataset can be loaded as follows
library(learningtower)

#loading the student subset data for the year 2018
data(student_subset_2018)


head(student_subset_2018)
#> # A tibble: 6 x 22
#> # Groups:   country [1]
#>   year  country school_id student_id mother_educ father_educ gender computer
#>   <fct> <fct>   <fct>     <fct>      <fct>       <fct>       <fct>  <fct>   
#> 1 2018  ALB     800059    805376     ISCED 3A    ISCED 3A    male   yes     
#> 2 2018  ALB     800084    802061     ISCED 3A    ISCED 3B, C female yes     
#> 3 2018  ALB     800093    800674     ISCED 3A    ISCED 2     male   yes     
#> 4 2018  ALB     800278    803561     ISCED 2     ISCED 2     male   yes     
#> 5 2018  ALB     800055    801356     ISCED 3A    ISCED 2     female <NA>    
#> 6 2018  ALB     800279    804382     ISCED 3A    ISCED 3A    male   yes     
#> # ... with 14 more variables: internet <fct>, math <dbl>, read <dbl>,
#> #   science <dbl>, stu_wgt <dbl>, desk <fct>, room <fct>, dishwasher <fct>,
#> #   television <fct>, computer_n <fct>, car <fct>, book <fct>, wealth <dbl>,
#> #   escs <dbl>
  • The entire student data can be downloaded as follows:
#load the entire student data for a single year
student_data_2018 <- load_student(2018)

#load the entire student data for two/three years (2000, 2012, 2018)
student_data_2012_2018 <- load_student(c(2012, 2018))
student_data_2000_2012_2018 <- load_student(c(2000, 2012, 2018))

#load the entire student data
student_data_all <- load_student("all")

See ?student_subset_2000 for information pertaining to variables captured.

School Dataset

The school dataset comprises school weight and other information such as the funding distribution of the schools, whether the school is private or public, the enrollment of boys and girls, the school size, and similar other characteristics of interest of different schools these 15-year-olds attend throughout the world.

#loading the school data
data(school)

head(school)
#> # A tibble: 6 x 13
#>   year  country school_id fund_gov fund_fees fund_donation enrol_boys
#>   <fct> <fct>   <fct>        <dbl>     <dbl>         <dbl>      <dbl>
#> 1 2000  ALB     01001          100         0             0       1191
#> 2 2000  ALB     01004           98         1             1        334
#> 3 2000  ALB     01005           91         5             2        403
#> 4 2000  ALB     01010          100         0             0        114
#> 5 2000  ALB     01013            0        50            30        250
#> 6 2000  ALB     01017           95         2             3        771
#> # ... with 6 more variables: enrol_girls <dbl>, stratio <dbl>,
#> #   public_private <fct>, staff_shortage <dbl>, sch_wgt <dbl>,
#> #   school_size <dbl>

See ?school for more information on the different variables present in the the school dataset.

Countrycode Dataset

The countrycode dataset contains mapping of the country ISO code to the country name. More information on the participating countries can be found here

#loading the countrycode data
data(countrycode)

head(countrycode)
#> # A tibble: 6 x 2
#>   country country_name
#>   <chr>   <chr>       
#> 1 AZE     Azerbaijan  
#> 2 ARG     Argentina   
#> 3 AUS     Australia   
#> 4 AUT     Austria     
#> 5 BEL     Belgium     
#> 6 BRA     Brazil

See ?countrycode for more detailed information on the countries that participated in the PISA experiment.

Exploring the data

In the gif shown below, shows the weighted mean of mathematics scores of these 15 year old students for a few selected countries, this gif helps us observe changing of the scales over-highlights and the differences in the means between various countries in the different years.

  • Similarly, you can find more code examples and data visualizations for exploring learningtower through our vignettes and articles

  • Further data exploration can be found in our articles exploring temporal trends here.

Citation

To cite the learningtower package, please use:

citation("learningtower")
#> 
#> To cite package 'learningtower' in publications use:
#> 
#>   Kevin Wang, Paul Yacobellis, Erika Siregar, Sarah Romanes, Kim
#>   Fitter, Giulio Valentino Dalla Riva, Dianne Cook, Nick Tierney and
#>   Priya Dingorkar (2021). learningtower: OECD PISA datasets from
#>   2000-2018 in an easy-to-use format.
#>   https://kevinwang09.github.io/learningtower/,
#>   https://github.com/kevinwang09/learningtower.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {learningtower: OECD PISA datasets from 2000-2018 in an easy-to-use format},
#>     author = {Kevin Wang and Paul Yacobellis and Erika Siregar and Sarah Romanes and Kim Fitter and Giulio {Valentino Dalla Riva} and Dianne Cook and Nick Tierney and Priya Dingorkar},
#>     year = {2021},
#>     note = {https://kevinwang09.github.io/learningtower/, https://github.com/kevinwang09/learningtower},
#>   }

Motivation for learningtower

Acknowledgement

The work to make the data available is the effort of several researchers from Australia, New Zealand and Indonesia, conducted as part of the ROpenSci OzUnconf held in Sydney, Dec 11-13, 2019.