An Analyzer of International Large Scale Assessments in Education.

Rrepest

R package to run estimations with weighted replicate samples and plausible values

Description

Rrepest estimates statistics using replicate weights (balanced repeated replication/brr weights, jackknife replicate weights,...), thus accounting for complex survey designs in the estimation of sampling variances. It is especially designed for use with the international education datasets produced by the OECD and the IEA, and also allows for analyses with multiply imputed variables (plausible values); where plausible values are used, the average estimator across plausible values is reported and the imputation error is added to the variance estimator.

Cheat Sheet

A "cheat sheet" including an overview of the syntax and uses of Rrepest is available here

Installation

Using CRAN

Run the following code:

install.packages("Rrepest")

Using tar.gz file

Download Rrepest, then run

Run the following code replacing "You_R_Name" with your username:

install.packages("C:/Users/You_R_Name/Downloads/Rrepest.tar.gz",
repos = NULL,
type ="source")

Run:

library(Rrepest)

Using a GitLab token

Run the following code replacing "MY_TOKEN" with your gitlab token:

remotes::install_gitlab("edu_data/rrepest", host = "https://algobank.oecd.org:4430", upgrade = "never", auth_token = "MY_TOKEN")

Note: It will take a few minutes to install

Run:

library(Rrepest)

Note: Ensure you have the package data.table installed. For a complete list of the dependencies used, consult the Description file.

Usage

The current version supports uni-variate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, linear regression and covariance.

Statistics

# PISA 2018 Data
# df.qqq <- readRDS("//oecdmain/asgenedu/EDUCATION_DATALAKE/sources/PISA/PISA 2018/R/STU/CY07_MSU_STU_QQQ.rds")

Rrepest::Rrepest(data = df.qqq,
        svy = "PISA2015",
        est = est(c("mean","var","quant",0.5,"iqr",c(.9,.1)),"CNTSCHID"),
        by = c("cnt"))

Frequencies

# TALIS 2018 Data
# df.t <- readRDS(file = "V:/TALIS/BACKUP/DATA/TALIS2018/R/PUF/TTGINTT3_demo.rds")

Rrepest::Rrepest(data = df.t,
                 svy = "TALISTCH",
                 est = est("freq","TT3G06I2"),
                 over = c("TT3G06A2","TT3G52J"),
                 by = "cntry",
                 test = T,
                 isced = 2)

Linear Regression

# TALIS 2018 Data
# df.t <- readRDS(file = "V:/TALIS/BACKUP/DATA/TALIS2018/R/PUF/TTGINTT3_demo.rds")

df.t <- df.t %>% 
  mutate(TT3G01_rec = case_when(TT3G01 == 2 ~ 1,
                                TT3G01 == 1 ~ 0))

Rrepest::Rrepest(data = df.t,
        svy = "TALISTCH",
        est = est("lm","TT3G01_rec",'TT3G39C'),
        by = "cntry")

Correlation

# PISA 2018 Data
# df.qqq <- readRDS("//oecdmain/asgenedu/EDUCATION_DATALAKE/sources/PISA/PISA 2018/R/STU/CY07_MSU_STU_QQQ.rds")

Rrepest::Rrepest(data = df.qqq,
        svy = "PISA2015",
        est = est("corr",c("pv@math","pv@read")),
        by = c("CNT"))

Further examples can be found in the Examples.R file

General Analysis

To incorporate analyses that are not pre-programmed into Rrepest, you can utilize the 'gen' option within the est() function of Rrepest. More about it is presented in the following wiki.

Authors

Francesco Avvisati, Rodolfo Ilizaliturri and François Keslair.

Contact us if you want to join!

Contributing

Do you have suggestions or comments? Please open an issue

Project status

First public release (30 June 2023).