Advanced plot part 1

Code

library(ggdist)
library(ggbeeswarm)
library(patchwork)
library(PupillometryR)
library(janitor)
library(ggthemes)
library(tidyquant)
library(tidylog)
library(flextable)
library(tidyverse)

Description of iris data set

iris is a data frame with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.

Code

data("iris")
iris %>% head(5) %>% flextable::flextable()

Table 1:
Iris data set preview
Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa

0.1 Exploring Sepal Length distribution

Key plot for Data distribution

Different ways to communicate the normality, skewness and kurtosis of continuous variables by a grouping/ cluster/ treatment variable.

Usually, before perform any hypothesis test its recommend to Explore the nature of the outcome variable to acertain the above issues revolving around normality.

Code

#   Histogram
theme_set(theme_light())
iris %>% ggplot(aes(x = Sepal.Length,fill = Species, color = Species)) +
  geom_histogram(alpha = 0.5,position = position_stack()) +
  theme(legend.position = c(.90, .85))
  

#  Density plot
theme_set(theme_pander())
iris %>% ggplot(aes(x = Sepal.Length,fill = Species, color = Species)) +
  geom_density(alpha = 0.5) + theme(legend.position = c(.90, .85))

#  Box plot
theme_set(theme_bw())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_boxplot(alpha = 0.5,width = 0.3) +
  theme(legend.position = "none")

#  Violin plot
theme_set(theme_minimal())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_violin(alpha = 0.5,width = 0.5) +
  theme(legend.position = "none")

#  Dot plot
theme_set(theme_minimal())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_dots(alpha = 0.5,dotsize = 0.5) +
  theme(legend.position = "none")


#  Beeswarm plot
theme_set(theme_minimal())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_beeswarm(alpha = 0.5) +
  theme(legend.position = "none")

#  Jitter/strip plot
theme_set(theme_classic())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_jitter(alpha = 0.6,position = position_jitter(width = 0.12)) +
  theme(legend.position = "none")


#  Eye plot
theme_set(theme_light())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  stat_eye(alpha = 0.3) +
  theme(legend.position = "none")

#  Half eye plot
theme_set(theme_pander())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  stat_halfeye(alpha = 0.3, justification = -0.1) +
  theme(legend.position = "none")

#  Rain drop plot
theme_set(theme_bw())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  stat_halfeye(alpha = 0.3, justification = -0.1,adjust = 0.4) +
  geom_boxplot(width = 0.1, alpha = 0.4) +
  stat_dots(side = "left", justification = 1.1,dotsize = 0.5,alpha = 0.5,adjust = 0.4) +
  coord_flip() +
  theme(legend.position = "none")

# Flat violin
theme_set(theme_minimal())
iris %>% ggplot(aes(x = Species,y = Sepal.Length,fill = Species, color = Species)) +
  geom_flat_violin(alpha = 0.5,adjust = 0.5) + 
  stat_dots(side = "left", justification = 1.1,dotsize = 0.5,alpha = 0.5,adjust = 0.4) +
  coord_flip() +
  theme(legend.position = "none")

# Fill Density plot
theme_set(theme_pander())
iris %>% ggplot(aes(x = Sepal.Length,fill = Species, color = Species)) +
  geom_density(alpha = 0.5,position = "fill") + theme(legend.position = c(.90, .85))