Line Plot using ggplot2 in R

Last Updated : 19 May, 2023

In a line graph, we have the horizontal axis value through which the line will be ordered and connected using the vertical axis values. We are going to use the R package ggplot2 which has several layers in it.

First, you need to install the ggplot2 package if it is not previously installed in R Studio.

Function Used:

geom_line connects them in the order of the variable on the horizontal (x) axis.

Syntax:

geom_line(mapping=NULL, data=NULL, stat=”identity”, position=”identity”,…)

geom_path connects the observation in the same order as in data

Syntax:

geom_path(mapping=NULL, data=NULL, stat=”identity”, position=”identity”,…)

Single Line Plot

In this section, we will be dealing with a single-line chart and will also discuss various attributes that help its appearance.

Data Set in Use:

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))
head(val)

Output:

  course  num
1    DSA   77
2    C++   55
3      R   80
4  Python  60

Basic Line Plot

For a simple line chart data is roughly passed to the function with some required attributes.

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Basic Line
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line()+
  geom_point()

Output:

Formatting Line

Line Type

For this, the command line type is used. ggplot2 provides various line types. For example dotted, two dash, dashed, etc. This attribute is passed with a required value.

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Format the line type
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(linetype = "dotted")+
  geom_point()

Output:

Line Color

The command color is used and the desired color is written in double quotes [" "] inside geom_line( ).

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Format the line color
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(color="green")+
  geom_point()

Output:

Line Size

The line size can be changed using the command size and providing the value of the size inside geom_line( ).

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Format the line size
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(color="green",size=1.5)+
  geom_point()

Output:

Adding Chart Title, Axis Title

ggtitle() with the appropriate title can be used to add chart title and labs again with appropriate input can be used to add axes title.

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Adding titles
line<-ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(color="green",size=1.5)+
  geom_point()

line+ggtitle("Courses vs Students Enrolled in GeeksforGeeks")+
  labs(x="Courses",y="Number of Students")

Output:

Changing the Theme

Use theme_theme_name() to add the theme. There are a lot of themes available in R library. For example: dark, classic, etc. Values can be provided as desired.

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Adding titles
line<-ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(color="green",size=1.5)+
  geom_point()

line+ggtitle("Courses vs Students Enrolled in GeeksforGeeks")+
  labs(x="Courses",y="Number of Students")+
  theme_dark()

Output:

Adding arrow

To add an arrow in line use the grid library is used. Then to add arrows use the arrow( ) to add an arrow. It is also possible to change the parameters in an arrow like angle, type, ends.

Example:

library(ggplot2)
library(grid)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Adding an arrow
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(arrow=arrow())+
  geom_point()

# Adding  closed arrow on both ends of the line
arr=arrow(angle = 20, ends = "both", type = "closed")
ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(arrow=arr)+
  geom_point()

Output:

Adding Data labels

Use label to get the values in y-axis and nudge_y to place the data label.

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Adding data label
ggplot(data=val, aes(x=course, y=num, group=1, label=num)) +
  geom_line()+
  geom_point()+
  geom_text(nudge_y = 2)

Output:

Scaling axis :

Use xlim( ) to change the x-axis scale and ylim( ) to change the y-axis scale and pass appropriate values to these.

Syntax:

xlim(min,max)

ylim(min,max)

Example:

library(ggplot2)

# Create data for chart
val <-data.frame(course=c('DSA','C++','R','Python'),
                num=c(77,55,80,60))

# Storing the line plot
ln <-ggplot(data=val, aes(x=course, y=num, group=1)) +
  geom_line(color="green",size=2)+
  geom_point()

# y-axis limits
ln+ylim(0,100)+
theme_dark()

Output:

Plotting Multiple lines

For plotting multiple plots into one, nothing changes except that group attribute has to set to the name of the column on the basis of which different lines will be drawn.

Example:

library(ggplot2)

# Inserting data
vacc <- data.frame(type=rep(c("Covishield", "Covaxin"), each=2),
                  dose=rep(c("D1", "D2"),2),
                  slots=c(33, 45, 66, 50))

# Plotting line with multiple groups
ggplot(data=vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(linetype="longdash", color="green", size=1.5)+
  geom_point(color="red", size=5)+
  theme_dark()

Output:

You can also add title, axes title, data labels in the above line plot as discussed in the previous section.

Formatting the plot :

Using separate line types based on groups

To differentiate the lines by changing the type of line provide the line type in geom_line() and shape for the legend in geom_point().

Example:

library(ggplot)

# Inserting data
vacc <- data.frame(type=rep(c("Covishield", "Covaxin"), each=2),
                  dose=rep(c("D1", "D2"),2),
                  slots=c(33, 45, 66, 50))

# Changing the line type on the basis of groups
ggplot(vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(aes(linetype=type))+
  geom_point()+
  theme_classic()

# Changing the line type on the basis of groups and also the shape of points
ggplot(vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(aes(linetype=type))+
  geom_point(aes(shape=type))+
  theme_classic()

Output:

Assigning different line colors on the basis of groups

The following code automatically controls color using the level of the variable "type". It will assign separate colors to each line.

Example:

library(ggplot2)

# Inserting data
vacc <- data.frame(type=rep(c("Covishield", "Covaxin"), each=2),
                  dose=rep(c("D1", "D2"),2),
                  slots=c(33, 45, 66, 50))

# Change line color by group type of vaccine
ln <-ggplot(vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(aes(color=type))+
  geom_point(aes(color=type))+
  theme_classic()
ln

Output:

To enter color manually you can use :

scale_color_brewer( ) : It uses different color palettes from the RColorBrewer package. It has various color palettes.
scale_color_manual( ) : It is used to manually add discrete colors.

Example:

library(ggplot2)

# Inserting data
vacc <- data.frame(type=rep(c("Covishield", "Covaxin"), each=2),
                  dose=rep(c("D1", "D2"),2),
                  slots=c(33, 45, 66, 50))

# Change line color by group type of vaccine
ln <-ggplot(vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(aes(color=type))+
  geom_point(aes(color=type))+
  theme_classic()

# Adding line colors using brewer color palette
ln+scale_color_brewer(palette="Set2")

# Adding line colors using color manual
ln+scale_color_manual(values=c("green", "blue"))

Output:

Multiple line plotGeeksforgeeks — Multiple line plot

Changing the position of legends

For changing the legend position legen.position attribute of the theme function is passed with the required value.

Syntax:

theme(legend.position="pos")

pos It can be top, right, bottom, left or none

Example:

library(ggplot2)

# Inserting data
vacc <- data.frame(type=rep(c("Covishield", "Covaxin"), each=2),
                  dose=rep(c("D1", "D2"),2),
                  slots=c(33, 45, 66, 50))

# Change line color by group type of vaccine
ln <-ggplot(vacc, aes(x=dose, y=slots, group=type)) +
  geom_line(aes(color=type))+
  geom_point(aes(color=type))+
  theme_classic()

ln <- ln + scale_color_brewer(palette="Dark2")+
  theme_classic()

# Legend at top
ln + theme(legend.position="top")

# Legend at left
ln + theme(legend.position="left")

# Remove legend
ln + theme(legend.position="none")

Output:

R - Bar Charts

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Article Tags :

Line Plot using ggplot2 in R

Function Used:

Single Line Plot

Formatting Line

Adding Chart Title, Axis Title

Changing the Theme

Adding arrow

Adding Data labels

Scaling axis :

Plotting Multiple lines

Formatting the plot :

Similar Reads

Introduction to ggplot2

Working with External Data

Basic Plotting with ggplot2

Common Geometric Objects (Geoms)

Advanced Data Visualization Techniques

Adding labels, titles, and legends in r

Customizing Visual Appearance

Handling Data Subsets: Faceting

Grouping Data: Dodge and Position Adjustments

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