This package is a preliminary exploratory data analysis (EDA) tool to make useful feature EDA plots and provide relevant information to simplify an otherwise tedious EDA step of any data science project. Specifically this package allows users to target any two features, whether they are numeric or categorical, and create visualization plots supplemented with useful summary and test statistics.
This package provides a streamlined and easy to use solution for basic EDA tasks that would otherwise require significant amount of coding to achieve.
R_prelim_eda_helper enables user to write quick visualization queries. At the same time, as we understand visually strong effects on graphs are not necessarily statistically meaningful, prelim_eda_helper is designed to combine graphic visualizations with preliminary statistical test results. We aim to create a helper package to really help researchers to get a quick sense of how our data look like, without making charts and doing tests separately in earlier stages of projects. While existing packages for plotting and statistical tests already exist (e.g., ggplot2(https://ggplot2.tidyverse.org/reference/ggplot.html) in tidyverse), we believe the combination of graphical and statistical output is what makes prelim_eda_helper a unique yet handy helper package.
To achive this goal, R_prelim_eda_helper creates charts with the visualization library ggplot2 and conducts statistical tests with ‘scipy’.
Usage
Installation
The development version of the package can be installed with the code below.
devtools::install_github( 'UBC-MDS/R_prelim_eda_helper')
num_dist_by_cat
Creates a pair of plots showing the distribution of the numeric variable when grouped by the categorical variable. Output includes a histogram and boxplot. In addition, basic test statistics will be provided for user reference.
num_dist_by_cat(
num = x, group = classes, data = data,
title_hist = 'Distribution of X', title_boxplot = 'X Seperated by Group',
lab_num = 'X', lab_group = 'Group', num_on_x = TRUE, stat = TRUE)
num_dist_scatter
Creates a scatter plot given two numerical variables. The plot can provide regression trendline and include confidence interval bands. Spearman and Pearson’s correlation will also be returned to aid the user to determining feature relationship.
num_dist_scatter(
num1 = 'num1', num2 = 'num2', data = df, title = 'num1 v num2',
stat = FALSE, trend = FALSE)
cat_dist_heatmap
Creates concatenated charts showing the heatmap of two categorical variables and a bar chart for occurrence of these variables.
cat_dist_heatmap(
cat1 = 'group1', cat2 = 'group2', data = df, title = 'Group1 & Group2 Distribution',
heatmap = TRUE, barchart = TRUE)
num_dist_summary
Creates a distribution plot of the given numeric variable and provides a statistical summary of the feature. In addition, the correlation values of the variable with other numeric features will be provided based on a given threshold.
num_dist_summary(
col_name = num1, data = df, title = 'num_col1', label_x = 'distribution of num1',
thresh_corr = 0.0, stat = TRUE)Contributing
Please check the contributing guidelines if you are interested in contributing. As the project is released with a Code of Conduct, you will agree to follow the terms if you wish to contribute.