library(ggplot2)
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg))
Bar Graphs
A bar graph is a visualization used to display and compare the frequency, count, or other measure (e.g., mean) for different discrete categories or groups. Properties include:
- Bars: Represent data values.
- Length: Proportional to data value.
- Axis: One axis shows categories, the other a value scale.
- Space: Typically, there’s space between bars.
Types include:
- Vertical (Column) Bar Graph: Standard bar graph.
- Horizontal Bar Graph: Bars run horizontally.
- Stacked Bar Graph: Segments within bars show sub-group proportions.
- Grouped Bar Graph: Bars for different groups are next to each other for easy comparison.
Bar Graph - Layer by Layer
1. Creating data and aesthetics Layer.
2. Adding Geometric layer(geom_bar) with statistical Transformation
# Basic graph using just data
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg,fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean")
3. Adding Scale Layer
# Basic graph with added scale layer
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg,fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean") +
scale_x_discrete(name = "Number of Cylinders") + # Customizing the x-axis
scale_y_continuous(name = "Average Miles per Gallon",limits = c(0, max(mtcars$mpg) + 5), breaks = seq(0, 35, 5)) # Customizing the y-axis
4. Adding Co-ordinate Layer
# Basic graph with added scale layer
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg,fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean") +
scale_x_discrete(name = "Number of Cylinders") + # Customizing the x-axis
scale_y_continuous(name = "Average Miles per Gallon",limits = c(0, max(mtcars$mpg) + 5), breaks = seq(0, 35, 5))+ # Customizing the y-axis
coord_cartesian(xlim = c(1, 3), ylim = c(0, 35)) # Adjust based on your data's range or focus area\
5. Adding Facet layer
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg,fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean") +
scale_x_discrete(name = "Number of Cylinders") + # Customizing the x-axis
scale_y_continuous(name = "Average Miles per Gallon",limits = c(0, max(mtcars$mpg) + 5), breaks = seq(0, 35, 5)) + # Customizing the y-axis
coord_cartesian(xlim = c(1, 3), ylim = c(0, 35)) + # Adjust based on your data's range or focus area\
facet_wrap(~gear)
6. Adding Themes
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg,fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean") +
scale_x_discrete(name = "Number of Cylinders") + # Customizing the x-axis
scale_y_continuous(name = "Average Miles per Gallon",limits = c(0, max(mtcars$mpg) + 5), breaks = seq(0, 35, 5)) + # Customizing the y-axis
coord_cartesian(xlim = c(1, 3), ylim = c(0, 35)) + # Adjust based on your data's range or focus area\
facet_wrap(~gear)+
theme_light()+
theme(plot.title = element_text(hjust = 0.5), # Center the plot title
axis.title.x = element_text(face = "bold", color = "blue"), # Customize x-axis title
axis.title.y = element_text(face = "bold", color = "blue"), # Customize y-axis title
strip.background = element_rect(fill = "lightblue"), # Change facet label background
strip.text = element_text(face = "bold", color = "navy")) # Change facet label text
7. Adding Labels
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
geom_bar(stat = "summary", fun = "mean") +
scale_x_discrete(name = "Number of Cylinders") + # Customizing the x-axis
scale_y_continuous(name = "Average Miles per Gallon",limits = c(0, max(mtcars$mpg) + 5), breaks = seq(0, 35, 5)) + # Customizing the y-axis
scale_fill_manual(values = c("lightcoral", "lightgreen", "lightblue"), name = "Cylinder Count") +
coord_cartesian(xlim = c(1, 3), ylim = c(0, 35)) + # Adjust based on your data's range or focus area\
facet_wrap(~gear)+
theme_light()+
theme(plot.title = element_text(hjust = 0.5), # Center the plot title
axis.title.x = element_text(face = "bold", color = "blue"), # Customize x-axis title
axis.title.y = element_text(face = "bold", color = "blue"), # Customize y-axis title
strip.background = element_rect(fill = "lightblue"), # Change facet label background
strip.text = element_text(face = "bold", color = "navy"))+ # Change facet label text
labs(
title = "Analysis of Car Mileage", # Main title
subtitle = "Grouped by Number of Cylinders and Gears", # Subtitle
caption = "Data source: mtcars dataset", # Caption
#x = "Number of Cylinders", # x-axis label #Already given above.
#y = "Average Miles per Gallon", # y-axis label #Already given above.
)
Vertical (Column) Bar Graph
Properties:
Orientation: Bars are vertical.
Comparison: Easily compare different groups.
Axis: Categories on the x-axis, values on the y-axis.
Length: Bars’ length represents value.
library(ggplot2)
# Data
data(mtcars)
ggplot(mtcars, aes(x=factor(cyl), y=mpg, fill=factor(cyl))) +
geom_bar(stat="identity", position="dodge", linewidth=0.25,width = 0.5) +
scale_fill_brewer(palette="Set1") +
labs(title="Average MPG by Number of Cylinders",
x="Number of Cylinders",
y="Average Miles Per Gallon (MPG)",
fill="Cylinders") +
theme_minimal(base_size = 14) +
theme(legend.position="top",
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1))
Horizontal Bar Graph
Properties:
Orientation: Bars are oriented horizontally instead of vertically.
Comparison: Facilitates comparison of values across categories, especially when category names are long.
Axis: The y-axis typically represents categories, while the x-axis represents values.
Length of Bars: Corresponds to the values they represent, allowing for direct comparison of different categories.
ggplot(mtcars, aes(x=factor(cyl), y=mpg, fill=factor(cyl))) +
geom_bar(stat="identity", position="dodge",width = 0.5) +
coord_flip() +
scale_fill_brewer(palette="Set1") +
labs(title="Miles Per Gallon by Cylinder Count",
x="Number of Cylinders",
y="Miles Per Gallon",
fill="Cylinders") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="top")
Stacked Bar Graph
Properties:
Segmentation: Each bar is divided into segments that represent different sub-categories.
Total Comparison: Allows for the comparison of total amounts across categories.
Sub-category Analysis: Facilitates comparison within categories based on the segments.
Axis: Typically, one axis represents the categories, and the other axis represents a cumulative total value.
ggplot(mtcars, aes(x=factor(cyl), y=mpg, fill=factor(gear))) +
geom_bar(stat="identity", position="stack") +
scale_fill_brewer(palette="Set2") +
labs(title="Stacked Bar Graph of MPG by Cylinder and Gear Type",
x="Cylinder Count",
y="Total Miles Per Gallon") +
theme_minimal() +
theme(legend.position="right")
Grouped Bar Chart
Properties:
Distinct Groups: Categories are divided into subgroups, displayed side-by-side for comparison.
Axes: Typically, the x-axis shows the main categories, while the y-axis represents the measured value.
Colors/Fills: Different colors or patterns distinguish between subgroups within each main category.
Spacing: Bars within a group are placed close to each other but separated from other groups, aiding visual comparison between subgroups.
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
library(dplyr)
# Example dataset (you can replace mtcars with your own dataset)
data <- mtcars %>%
group_by(cyl, gear) %>%
summarise(mpg = mean(mpg), .groups = 'drop')
# Creating a grouped bar chart
ggplot(data, aes(x=factor(cyl), y=mpg, fill=factor(gear))) +
geom_bar(stat="identity", position=position_dodge(), width=0.7) +
scale_fill_brewer(palette="Set2") +
labs(title="Grouped Bar Chart of MPG by Cylinder and Gear",
x="Number of Cylinders",
y="Average Miles Per Gallon",
fill="Number of Gears") +
theme_minimal() +
theme(axis.text.x = element_text(angle=45, hjust=1),
legend.position="top")