knitr::opts_chunk$set( echo = TRUE, collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, error = FALSE, outwidth = "100%", fig.width = 6, fig.height = 4, fig.align = "center")
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. Version r utils::packageVersion("learningtower")
of this package provides the data for the years r learningtower:::data_range()
. The survey data is published every three years. This is an excellent real world dataset for data exploring, data visualizing and statistical computations.
This vignette documents how to access the data, and shows a few ways of integrating the data.
student
and school
dataThe size of the full student
is too big to fit inside the package. Hence, in our package, we provide a random subset of the student data, stored as student_subset_yyyy
data objects (where yyyy
denotes the specific year of the study). These subset data can be used to understanding the data structure before using the full dataset which is available for download.
In the student_subset_2018
and school
data, there are three common columns, school_id
, country
and year
. It should be noted that school_id
is only meaningful within a country within a specific year; meaning that when we join the two data, we need to use the keys c("school_id", "country", "year")
.
library(dplyr) library(ggplot2) library(forcats) library(learningtower) #loading the student subset data data(student_subset_2018) #loading the school data data(school) #loading the country data data(countrycode) selected_countries = c("AUS", "FIN", "JPN", "USA", "NZL", "ESP") #joining the student, school dataset school_student_subset_2018 <- left_join( student_subset_2018, school, by = c("school_id", "country", "year")) #check the count of public and private schools in the a few randomly selected countries school_student_subset_2018 |> dplyr::filter(country %in% selected_countries) |> group_by(country, public_private) |> tally() |> dplyr::mutate(percent = n/sum(n)) |> dplyr::ungroup() |> left_join(countrycode, by = "country") |> ggplot(aes(x = percent, y = country_name, fill = public_private)) + geom_col(position = position_stack()) + scale_x_continuous(labels = scales::percent) + scale_fill_manual(values = c("#FF7F0EFF", "#1F77B4FF")) + labs(title = "Distribution of public and private schools in the year 2018", y = "", x = "Percentage of schools", fill = "")
The graph assists us in understanding the distribution of public and private schools in few countries based on the datasets. Taking a closer look at the above plot, we can infer that most countries have more public schools than private schools. Interestingly, Spain had a nearly equal mix of public and private schools in the year 2018.
Similarly, we may derive additional intriguing patterns and analysis by considering the other variables in the school dataset.
student_data_2018 <- load_student("2018") data(school) data(countrycode) school_student_2018 <- left_join( student_data_2018, school, by = c("school_id", "country", "year")) school_student_2018 |> dplyr::filter(country %in% selected_countries) |> group_by(country) |> summarise(avg_fund_gov = mean(fund_gov, na.rm = TRUE)) |> arrange(avg_fund_gov) |> mutate(country = fct_reorder(country, avg_fund_gov)) |> left_join(countrycode, by = "country") |> mutate(country_name = fct_reorder(country_name, avg_fund_gov)) |> ggplot(aes(x=country_name, y=avg_fund_gov)) + geom_segment(aes(xend=country_name, yend=0)) + geom_point(size=4, color="orange") + coord_flip() + theme_bw() + labs(x = "", y = "Average percentage of government funding", title = "Funding for schools in the year 2018 from government")
The above figure shows the average percentage of overall financing in various schools for a random sample of countries. We conclude that countries such as Finland and the United States received the most funding from their governments, whilst Qatar received the least funding.
In addition, to perform a detail analysis on the school and entire student data it can be downloaded for the desired years using the load_student
function available in this package.
Similarly, you may import student data for any chosen year and experiment with PISA scores growth or additional analysis of these datasets with their other elements that assist contributor comprehend the data. Refer to our articles here for additional interesting analyses and plots.
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