EDA Workflow

Abstract

A complete EDA Workflow

Setting up R Packages

Install packages using install.packages() in your Console. Load up your libraries in a setup chunk:

library(tidyverse)
library(mosaic)
library(palmerpenguins)
library(ggformula)
library(ggridges)
library(skimr)
##
library(GGally)
library(corrplot)
library(corrgram)
# Set graph theme
# Choose a good graph theme
ggplot2::theme_set(new = theme_classic(
  base_size = 14,
  base_family = "sans"
))

Read Data

• Use readr::read_csv()

Examine Data

• Use dplyr::glimpse()
• Use mosaic::inspect() or skimr::skim()
• Use dplyr::summarise() and crosstable::crosstable()
• Format your tables with knitr::kable()
• Highlight any interesting summary stats or data imbalances

Data Dictionary and Experiment Description

• A table containing the variable names, their interpretation, and their nature(Qual/Quant/Ord…)
• If there are wrongly coded variables in the original data, state them in their correct form, so you can munge the in the next step
• Declare what might be target and predictor variables, based on available information of the experiment, or a description of the data

Data Munging

• Convert variables to factors as needed
• Reformat / Rename other variables as needed
• Clean badly formatted columns (e.g. text + numbers) using tidyr::separate_**_**()
• Save the data as a modified file
• Do not mess up the original data file

Form Hypotheses

Question-1

• State the Question or Hypothesis
• (Temporarily) Drop variables using dplyr::select()
• Create new variables if needed with dplyr::mutate()
• Filter the data set using dplyr::filter()
• Reformat data if needed with tidyr::pivot_longer() or tidyr::pivot_wider()
• Answer the Question with a Table, a Chart, a Test, using an appropriate Model for Statistical Inference
• Use title, subtitle, legend and scales appropriately in your chart
• Prefer ggformula unless you are using a chart that is not yet supported therein (eg. ggbump() or plot_likert())

Inference-1

. . . .

Question-n

Inference-n

One Most Interesting Graph

Conclusion

Describe what the graph shows and why it so interesting. What could be done next?