In Rkabacoff/qacr: Functions to Facilitate Data Science Projects

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(qacr)
library(knitr)

The purpose of the qacr package is to provide functions for descriptive statistics, data management, and data visualization. As a part of this package, the contents function produces a series of informational tables that allow for users to have a comprehensive understanding of their dataset of choice as well as the each of the quantitative and categorical variables featured in their dataset. Graphical functions, such as barcharts, histograms, and densities provide succinct visualizations of the variables in a data frame.

Example Dataset: Motor Trend Magazine Review of Cars

How can you quickly become familiar with the data in a data frame? We'll use the cars74 data frame as an example. This data frame contains information on the characteristics of 32 cars in 1974.

data(cars74)

Data description with `contents()`

contents(cars74)

As shown above, the contents function produced three tables, one that provides an overall summary of the data and two tables that break down the quantitative and categorical variables of the dataset respectively.

The overall table describes each of the variables in the data frame, listing their column position, variable name, the variable type, number of unique values, the number of missing values, and the corresponding percentage of missing values.

The quantitative table provides information for each of the quantitative variables in the data frame, listing the variable name, the number of non-missing values, and a series of summary statistics including the mean, standard deviation, skewness, minimum, 25%tile, median, 75%tile, and maximum.

The categorical table provides information for each of the categorical variables in the data frame, listing the variable name, the specific levels of each variable, and the number of observations for each level, and percentage distribution of each variable level. By default, up to 10 levels of a categorical variable are displayed (but you can increase this by adding the option maxcat = #, where # is the number of levels to display).

If the dataset only contains quantitative or categorical, then only the overall summary table and the relevant variable table would be printed.