This R script is to automate the processes of medal projection for World Athletics results collected after running World-Athletics-data-collection/script.py. 2 CSVs will be outputed (1 for the results of Shapiro Wilk's normality tests, 1 for compiling the datasets to be used for simulation)

Open up R console. Change into current directory and run R install.packages() to install the required packages using the following command.

install.packages(c("tidyverse", "ggplot2", "readxl", "chron", "lubridate"))

Ensure that R is installed and added to environment path before proceeding. In your terminal or your preferred CLI, Change into directory of normality_test.R script and run the following command.

Rscript normality_test.R -f INPUT_TARGET_EXCEL_FILE_NAME_HERE

If target excel file name is not specified, the first excel file (.xlsx) that script can find will be used. Hence if there is only one excel file in the directory, this following command with no argument will be sufficient:

Rscript normality_test.R

Run the following command to see help and arguments for the script.

Rscript normality_test.R -h

The script retrieves the data from the different worksheets (Competitors 2019-2023, Competitors 2022-2023 & Competitors 2023) in an excecl file (.xlsx) created by scrapping python script. This target excel file can be defined by the user. If not specified, script will use the first excel file it encounters in the directory.

Using dplyr library, shapiro wilk test will be conducted on the each unique athletes from the data. 2 CSVs will be outputed after running of script. One of the CSVs contains the results of the statistical test while the other contains the particular dataset (by year) that was used to test for the athletes. The reason for the dataset used will be explained in the [remarks] column.

For example, if 2019-2023 dataset used to test for Athlete A, remarks will specify that data points of Athlete A is insufficient for 2022-2023 (datapts < 5).

Do note that if shapiro wilk is significant, the next dataset will be used.

For example, normality test 2022-2023 dataset is significant (p-value <= 0.05, displaying non-normality). Then, 2019-2023 dataset will be used to test for Athlete A. Remarks will specify that p-value of Athlete A is significant for 2022-2023.