In r2rahul/expdata: Exploratory Data Analysis using data.table

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

The exploratory data analysis is the first step in any data analytic task [@Donoho2015]. The goal of the package is to provide quick first glance into the data profile. The package assumes that data is in tidy format [@JSSv059i10]. Also, package will not convert variable type conversion, instead the data profile can be used to find variables, which might not be correct data type. Here we illustrate the basic functionality of the package using nycflights13 data set. We will use weather and flights data to explore the expdata package.

library(nycflights13)
library(expdata)

First, let us generate the complete data summary of the weather data set using method complete_summary.

sum_out <- complete_summary(weather)
knitr::kable(sum_out)

Please, note that if particular data type is missing then function will give a warning message. For example, there are no factor and logical variable in the data. So, the function gave a warning message.

Now let us explore the flights data to explore each variable method one by one.

• Numeric Type

num_out <- num_summary(flights)
knitr::kable(num_out)

• Character type

char_out <- char_summary(flights)
knitr::kable(char_out)

• Date Type

date_out <- date_summary(flights)
knitr::kable(date_out)

• Logical type

log_out <- log_summary(flights)
knitr::kable(log_out)

Since, there are no logical variable function returned the warning message and stopped.

• Factor Type

fac_out <- factor_summary(flights)
knitr::kable(fac_out)

Since, there are no factor variable function returned the warning message and stopped.

References

r2rahul/expdata documentation built on May 18, 2019, 2:36 a.m.