Chapter 6 Data Wrangling

This chapter focuses on some of the most frequently used data manipulations and shows how to implement them in R and Python. It is critical to explore the data with descriptive statistics (mean, standard deviation, etc.) and data visualization before analysis. Transform data so that the data structure is in line with the requirements of the model. You also need to summarize the results after analysis.

Load the R packages first:

# install packages from CRAN
p_needed <- c('readr','dplyr','data.table','reshape2','tidyr')
packages <- rownames(installed.packages())
p_to_install <- p_needed[!(p_needed %in% packages)]
if (length(p_to_install) > 0) {
    install.packages(p_to_install)
}

lapply(p_needed, require, character.only = TRUE)