A state of mind

EDA is an essential phase of any analysis. Cleaning the data however, sometimes may take some work. Visualizing, transforming, and modeling all may be required to glean meaning from the dataset. Within EDA, there may be variation within a variable and covariation between variables. Beyond that, there may be categorical and continuous variables that need to be counted differently. Visualizing the data may help you determine the differences among them.

library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Diamond data mining