Description
Salary Analysis by the Swiss Federal Office for Gender Equality.
Description
Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis. This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.
README.md
Logib
Introduction
The package logib
is an R
implementation of the Swiss Confederation's salary analysis tool for assessing equal pay practices (https://www.logib.admin.ch)
Installation
GitHub
In order to install the package from GitHub, use the devtools
package:
install.packages("devtools")
devtools::install_github("JLDC/logib")
CRAN
In order to install the latest release from CRAN, use:
install.packages("logib")
Examples
library(logib)
# ------------------------------------------------------------------------------
# Variant 1: Using an official datalist or exportfile
# Indicate path to the pre-filled datalist or exportfile
# (see 'Further Resources' below)
path_to_my_data <- "path/to/my/datalist_or_exportfile.xlsx"
# Read the data from an official datalist to R
my_data <- read_data(data_path = path_to_my_data)
# ------------------------------------------------------------------------------
# Variant 2: Using a pre-loaded dataframe called 'my_dataframe'
# Read the data from the pre-loaded dataframe
my_data <- read_data(custom_data = my_dataframe)
# ==============================================================================
# Run the analysis and store the results
results <- analysis(data = my_data, reference_month = 8, reference_year = 2020)
# Display the results of the analysis
summary(results)