Exploratory Data Analysis is one of the critical processes of performing initial investigations on data analysis.

Basic idea is to discover the patterns, anomalies, test hypotheses, and check the assumptions with the help of summary statistics and graphical representations.

The main idea about exploratory data analysis are

1. maximize insight into a data set;
2. uncover underlying structure;
3. extract important variables;
4. detect outliers and anomalies;
5. test underlying assumptions;
6. develop parsimonious models;
7. determine optimal factor settings.

Exploratory Data Analysis

The dataset we are using for exploratory data analysis is from gss_cat.

gss_cat

The datasets contains 21,483 observations and 9 variables.

Tidyverse in R

# A tibble: 21,483 x 9
    year marital      age race  rincome 
   <int> <fct>      <int> <fct> <fct>   
 1  2000 Never mar~    26 White $8000 to~
 2  2000 Divorced      48 White $8000 to~
 3  2000 Widowed       67 White Not appl~
 4  2000 Never mar~    39 White Not appl~
 5  2000 Divorced      25 White Not appl~
 6  2000 Married       25 White $20000 -~
 7  2000 Never mar~    36 White $25000 o~
 8  2000 Divorced      44 White $7000 to~
 9  2000 Married       44 White $25000 o~
10  2000 Married       47 White $25000 o~

Dataset Overview

gss_cat %>% glimpse()

Above function will provide basic idea about variables in the datasets.

Rows: 21,483
Columns: 9
$ year    <int> 2000, 2000, 2000, 2000,~
$ marital <fct> Never married, Divorced~
$ age     <int> 26, 48, 67, 39, 25, 25,~
$ race    <fct> White, White, White, Wh~
$ rincome <fct> $8000 to 9999, $8000 to~
$ partyid <fct> "Ind,near rep", "Not st~
$ relig   <fct> Protestant, Protestant,~
$ denom   <fct> "Southern baptist", "Ba~
$ tvhours <int> 12, NA, 2, 4, 1, NA, 3,~

Introduce

gss_cat  %>% introduce()

Let’s introduce the data set based on introduce function.

Deep Neural Network in R

rows columns discrete_columns continuous_columns all_missing_columns total_missing_values complete_rows total_observations memory_usage
  <int>   <int>            <int>              <int>               <int>                <int>         <int>              <int>        <dbl>
1 21483       9                6                  3                   0                10222         11299             193347       784776

Now you can see the dataset contains 21483 observations with 9 variables all other kind of information’s.

Visualization

Most EDA techniques are graphical in nature with a few quantitative techniques. Here we are going to explain different kind of visualization techniques to identify the pattern and relationships.

gss_cat %>% plot_intro()

66.7% contains Discrete columns, 33.3% contains continuous columns.

52.6% contains complete rows and 5.3% contains missing observations.

All columns with missing values are 0%.

Linear optimization in R

gss_cat %>% plot_missing()

gss_cat %>% profile_missing()

# A tibble: 9 x 3

Cluster Analysis in R

  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 

Density Plot

gss_cat  %>% plot_density()

Linear Discriminant Analysis in R

Histogram

gss_cat  %>% plot_histogram()

Bar Plot

gss_cat  %>% plot_bar()

Correlation Plot

gss_cat  %>% plot_correlation()

Logistic regression in R

gss_cat  %>% plot_correlation(maxcat = 5) 

maxcat can adjust based on your variables and interest. You can make use Data Explorer package for quick checking before proceeding to further analysis.

Conclusion

Exploratory Data Analysis is an approach to identify every nuance from the data at the early encounter and its plays a crucial role in the data analysis field.

KNN Machine Algorithm

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