Raincloud Plot Analysis

Author

G CHAITANYA and HARSHA MV

Problem Statement: Create a raincloud plot (combining boxplot and density plot) using total_bill as the numerical variable and day as the categorical variable to analyze the distribution of total bill across different days.

Step 1: Load the Required Libraries

Load ggplot2 and ggdist libraries.
ggplot2: Needed to create box plots and other visualizations.
ggdist: for density (raincloud effect)

library(ggplot2)
library(ggdist)   # for density (raincloud effect)

Warning: package 'ggdist' was built under R version 4.5.3

Step 2: Load CSV Dataset

Load the CSV dataset.
read.csv() → loads your downloaded dataset Dataset Info:
Contains restaurant billing data Important columns:
total_bill → numeric (bill amount)
day → categorical (Thur, Fri, Sat, Sun)

data <- read.csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv")

Step 3 : Explore the Dataset

Before performing any analysis,we should understand the dataset.

We will check:

Number of rows and columns
Column names
Data types
Summary statistics
First few rows

# Dimensions (rows and columns)
dim(data)

[1] 244   7

# Column names
names(data)

[1] "total_bill" "tip"        "sex"        "smoker"     "day"       
[6] "time"       "size"

#Structure of dataset
str(data)

'data.frame':   244 obs. of  7 variables:
 $ total_bill: num  17 10.3 21 23.7 24.6 ...
 $ tip       : num  1.01 1.66 3.5 3.31 3.61 4.71 2 3.12 1.96 3.23 ...
 $ sex       : chr  "Female" "Male" "Male" "Male" ...
 $ smoker    : chr  "No" "No" "No" "No" ...
 $ day       : chr  "Sun" "Sun" "Sun" "Sun" ...
 $ time      : chr  "Dinner" "Dinner" "Dinner" "Dinner" ...
 $ size      : int  2 3 3 2 4 4 2 4 2 2 ...

#Summary statistics
summary(data)

   total_bill         tip             sex               smoker         
 Min.   : 3.07   Min.   : 1.000   Length:244         Length:244        
 1st Qu.:13.35   1st Qu.: 2.000   Class :character   Class :character  
 Median :17.80   Median : 2.900   Mode  :character   Mode  :character  
 Mean   :19.79   Mean   : 2.998                                        
 3rd Qu.:24.13   3rd Qu.: 3.562                                        
 Max.   :50.81   Max.   :10.000                                        
     day                time                size     
 Length:244         Length:244         Min.   :1.00  
 Class :character   Class :character   1st Qu.:2.00  
 Mode  :character   Mode  :character   Median :2.00  
                                       Mean   :2.57  
                                       3rd Qu.:3.00  
                                       Max.   :6.00

#First six row
head(data)

  total_bill  tip    sex smoker day   time size
1      16.99 1.01 Female     No Sun Dinner    2
2      10.34 1.66   Male     No Sun Dinner    3
3      21.01 3.50   Male     No Sun Dinner    3
4      23.68 3.31   Male     No Sun Dinner    2
5      24.59 3.61 Female     No Sun Dinner    4
6      25.29 4.71   Male     No Sun Dinner    4

Step 4: Initialize Plot

p <- ggplot(data, aes(x = day, y = total_bill))
p

Initializes plot object p Defines:
x = day (categorical variable)
y = total_bill (numeric variable)
No plot appers yet, it just defines the outline of the plot

Step 5: Add Raincloud Components

###what is Raincloud Plot? - It is the combination of Density and boxplot. In simple words, Raincloud Plot = Density + Boxplot.

p <- p +
  stat_halfeye(
    adjust = 0.5,
    width = 0.6,
    justification = -0.3,
    fill = "skyblue",
    alpha = 0.6
  ) +
  geom_boxplot(
    width = 0.2,
    outlier.color = "red",
    outlier.shape = 16,
    fill = "lightgreen",
    alpha = 0.7
  )
p

In this step, we have combined both density plot and boxplot to form a raincloud plot.
The function stat_halfeye() is used to create the density part of the plot.
It shows the distribution (spread) of total bill values for each day.
The adjust parameter controls how smooth the density curve looks.
The justification parameter shifts the density slightly to the side so it does not overlap with the boxplot.
The function geom_boxplot() is used to create the boxplot.
It displays important statistical values such as:
```
  Median (middle line)
```
```
  Interquartile Range (the box)
```
```
  Whiskers (range of data)
```
```
  Outliers (extreme values)
```
The outliers are highlighted using red color and solid circular points.
The alpha parameter is used to control the transparency of the plot.
This step improves the visualization by combining:
```
  Shape of distribution (density)
```
```
  Statistical summary (boxplot)
```

Step 6:Add Labels

Adding descriptive labels to the plot.
Makes plot readable and presentation-ready.
Important in reports and dashboards.

p <- p + labs(
  title = "Total Bill Distribution by Day",
  subtitle = "Raincloud Plot (Boxplot + Density)",
  x = "Day",
  y = "Total Bill"
)
p

Step 7:Add Minimal theme

Applying a minimal theme (clean look).
Removes unnecessary background elements.
Focuses attention on data.

p <- p + theme_minimal()
p