# Two independent plots side by side for the same variable

## SamrtEDA function to visualise two independ plots side by side for the same variable

In exploratory data analysis, it is common to want to make two different plots for same variables. For example, a survey data may have a large number of questions like age, gender, region etc. It includes several categorical and numerical features. Suppose a analyst want to see age variable distributions with gender and without gender in side by side view. There is no direct functions available in any of the statistical packages. Here is a way to achieve the same thing using R using ggplot2 customized function ExpTwoPlots

Function definition:

ExpTwoPlots(
data,
plot_type = "numeric",
iv_variables = NULL,
target = NULL,
lp_geom_type = "boxplot",
lp_arg_list = list(),
rp_geom_type = "boxplot",
rp_arg_list = list(),
fname = NULL,
page = NULL,
theme = "Default"
)

## 1. Plot Numerical independent variables - without target variable

Different use cases

target = "gear"
categorical_features <- c("vs", "carb") # we can add as many categorical variables
numeircal_features <- c("mpg", "qsec") # we can add as many numerical variables

#### 1.1 Left side Boxplot and Right side Histogram

num_1 <- ExpTwoPlots(mtcars,
plot_type = "numeric",
iv_variables = numeircal_features,
target = NULL,
lp_arg_list = list(fill="orange"),
lp_geom_type = 'boxplot',
rp_arg_list = list(alpha=0.5, fill="white", color = "red", binwidth=1),
rp_geom_type = 'histogram',
page = c(2,1),
theme = "Default")
num_1

#### 1.3 Left side Density and Right side Boxplot

num_3 <- ExpTwoPlots(mtcars,
plot_type = "numeric",
iv_variables = numeircal_features,
target = NULL,
lp_arg_list = list(color = "red"),
lp_geom_type = 'density',
rp_arg_list = list(fill="orange"),
rp_geom_type = 'boxplot',
page = c(2,1),
theme = "Default")
num_3

## 2. Plot Numerical independent variables - with target variable

#### 2.1 Left side Boxplot and Right side Histogram

num_21 <- ExpTwoPlots(mtcars,
plot_type = "numeric",
iv_variables = numeircal_features,
target = "gear",
lp_arg_list = list(fill="pink"),
lp_geom_type = 'boxplot',
rp_arg_list = list(alpha=0.5, fill = c("grey", "orange", "lightblue"), binwidth=1),
rp_geom_type = 'histogram',
page = c(2,1),
theme = "Default")
num_21

#### 2.3 Left side Density and Right side Boxplot

num_23 <- ExpTwoPlots(mtcars,
plot_type = "numeric",
iv_variables = numeircal_features,
target = "gear",
lp_arg_list = list(fill = "grey"),
lp_geom_type = 'density',
rp_arg_list = list(fill = c("blue", "orange", "pink"), alpha=0.5),
rp_geom_type = 'boxplot',
page = c(2,1),
theme = "Default")
num_23

#### 2.5 Left side Boxplot and Right side qqplot

num_25 <- ExpTwoPlots(mtcars,
plot_type = "numeric",
iv_variables = numeircal_features,
target = "gear",
lp_arg_list = list(fill = "orange"),
lp_geom_type = 'boxplot',
rp_arg_list = list(fill = c("blue", "green", "red"), alpha=0.5),
rp_geom_type = 'qqplot',
page = c(2,1),
theme = "Default")
num_25

#### 3.2 Left side donut chart and Right side Pie chart

cat_2 <- ExpTwoPlots(mtcars,
plot_type = "categorical",
iv_variables = categorical_features,
target = NULL,
lp_arg_list = list(),
lp_geom_type = 'donut',
rp_arg_list = list(),
rp_geom_type = 'pie',
page = c(2,1),
theme = "Default")
cat_2

## 4. Plot categorical independent variables - with target variable

#### 4.1 Left side donut chart and Right side stacked bar chart

cat_41 <- ExpTwoPlots(mtcars,
plot_type = "categorical",
iv_variables = categorical_features,
target = 'gear',
lp_arg_list = list(),
lp_geom_type = 'donut',
rp_arg_list = list(stat = 'identity'),
rp_geom_type = 'bar',
page = c(2,1),
theme = "Default")
cat_41