Automatic Exploratory Data Analysis

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

library(DataExplorer)
library(tidyverse)

gss_cat

## # A tibble: 21,483 × 9
##     year marital         age race  rincome        partyid    relig denom tvhours
##    <int> <fct>         <int> <fct> <fct>          <fct>      <fct> <fct>   <int>
##  1  2000 Never married    26 White $8000 to 9999  Ind,near … Prot… Sout…      12
##  2  2000 Divorced         48 White $8000 to 9999  Not str r… Prot… Bapt…      NA
##  3  2000 Widowed          67 White Not applicable Independe… Prot… No d…       2
##  4  2000 Never married    39 White Not applicable Ind,near … Orth… Not …       4
##  5  2000 Divorced         25 White Not applicable Not str d… None  Not …       1
##  6  2000 Married          25 White $20000 - 24999 Strong de… Prot… Sout…      NA
##  7  2000 Never married    36 White $25000 or more Not str r… Chri… Not …       3
##  8  2000 Divorced         44 White $7000 to 7999  Ind,near … Prot… Luth…      NA
##  9  2000 Married          44 White $25000 or more Not str d… Prot… Other       0
## 10  2000 Married          47 White $25000 or more Strong re… Prot… Sout…       3
## # … with 21,473 more rows

Create the Automatic EDA Report

Data Introduction

gss_cat %>% introduce()

## # A tibble: 1 × 9
##    rows columns discrete_columns conti…¹ all_m…² total…³ compl…⁴ total…⁵ memor…⁶
##   <int>   <int>            <int>   <int>   <int>   <int>   <int>   <int>   <dbl>
## 1 21483       9                6       3       0   10222   11299  193347  784776
## # … with abbreviated variable names ¹​continuous_columns, ²​all_missing_columns,
## #   ³​total_missing_values, ⁴​complete_rows, ⁵​total_observations, ⁶​memory_usage

gss_cat %>% plot_intro()

Missing Values

gss_cat %>% plot_missing()

gss_cat %>% profile_missing()

## # A tibble: 9 × 3
##   feature num_missing pct_missing
##   <fct>         <int>       <dbl>
## 1 year              0     0      
## 2 marital           0     0      
## 3 age              76     0.00354
## 4 race              0     0      
## 5 rincome           0     0      
## 6 partyid           0     0      
## 7 relig             0     0      
## 8 denom             0     0      
## 9 tvhours       10146     0.472

Continuous Features

gss_cat %>% plot_density()

gss_cat %>% plot_histogram()

Categorical Features

gss_cat %>% plot_bar()

Relationships

gss_cat %>% plot_correlation(maxcat = 15)