Implementing descriptive analysis

Descriptive statistical analysis helps you to understand your data properly. Although R provides some functions by default to perform basic statistics, we will use two better alternatives, the DataExplorer and fBasics packages.

Follow these simple steps:

1. As the number of variables in the dataset is high, we will create a list with the variable names to use in our descriptive functions:

Class<-as.data.frame(sapply(train, class))
 colnames(Class)<-"variable_class"
 Class$variable_name<-colnames(train)

numeric_vars<-Class[Class$variable_class=="numeric","variable_name"]

2. A list of 1,492 variables is created. Pass this list to the basicStats function included in the fBasics package:

library(fBasics)
 descriptives_num<-             as.data.frame(t(basicStats(train[,numeric_vars])))
 head(descriptives_num)

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