This R tutorial describes how to create line plots using R software and ggplot2 package.

In a line graph, observations are ordered by x value and connected.

The functions geom_line(), geom_step(), or geom_path() can be used.

x value (for x axis) can be :

date : for a time series data

texts

discrete numeric values

continuous numeric values

Basic line plots

Data

Data derived from ToothGrowth data sets are used. ToothGrowth describes the effect of Vitamin C on tooth growth in Guinea pigs.

df <- data.frame(dose=c("D0.5", "D1", "D2"),
                len=c(4.2, 10, 29.5))

head(df)
##   dose  len
## 1 D0.5  4.2
## 2   D1 10.0
## 3   D2 29.5

len : Tooth length

dose : Dose in milligrams (0.5, 1, 2)

Create line plots with points

# Basic line plot with points
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line()+
  geom_point()

# Change the line type
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(linetype = "dashed")+
  geom_point()

# Change the color
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(color="red")+
  geom_point()

Read more on line types : ggplot2 line types

You can add an arrow to the line using the grid package :

library(grid)
# Add an arrow
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(arrow = arrow())+
  geom_point()

# Add a closed arrow to the end of the line
myarrow=arrow(angle = 15, ends = "both", type = "closed")
ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_line(arrow=myarrow)+
  geom_point()

Observations can be also connected using the functions geom_step() or geom_path() :

ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_step()+
  geom_point()


ggplot(data=df, aes(x=dose, y=len, group=1)) +
  geom_path()+
  geom_point()

geom_line : Connecting observations, ordered by x value

geom_path() : Observations are connected in original order

geom_step : Connecting observations by stairs

Line plot with multiple groups

Data

Data derived from ToothGrowth data sets are used. ToothGrowth describes the effect of Vitamin C on tooth growth in Guinea pigs. Three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods [orange juice (OJ) or ascorbic acid (VC)] are used :

df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("D0.5", "D1", "D2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))

head(df2)
##   supp dose  len
## 1   VC D0.5  6.8
## 2   VC   D1 15.0
## 3   VC   D2 33.0
## 4   OJ D0.5  4.2
## 5   OJ   D1 10.0
## 6   OJ   D2 29.5

len : Tooth length

dose : Dose in milligrams (0.5, 1, 2)

supp : Supplement type (VC or OJ)

Create line plots

In the graphs below, line types, colors and sizes are the same for the two groups :

# Line plot with multiple groups
ggplot(data=df2, aes(x=dose, y=len, group=supp)) +
  geom_line()+
  geom_point()

# Change line types
ggplot(data=df2, aes(x=dose, y=len, group=supp)) +
  geom_line(linetype="dashed", color="blue", size=1.2)+
  geom_point(color="red", size=3)

Change line types by groups

In the graphs below, line types and point shapes are controlled automatically by the levels of the variable supp :

# Change line types by groups (supp)
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()

# Change line types and point shapes
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point(aes(shape=supp)

It is also possible to change manually the line types using the function scale_linetype_manual().

# Set line types manually
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()+
  scale_linetype_manual(values=c("twodash", "dotted"))

You can read more on line types here : ggplot2 line types

If you want to change also point shapes, read this article : ggplot2 point shapes

Change line colors by groups

Line colors are controlled automatically by the levels of the variable supp :

p<-ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(color=supp))+
  geom_point(aes(color=supp))
p

It is also possible to change manually line colors using the functions :

scale_color_manual() : to use custom colors

scale_color_brewer() : to use color palettes from RColorBrewer package

scale_color_grey() : to use grey color palettes

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))

# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")

# Use grey scale
p + scale_color_grey() + theme_classic()

Read more on ggplot2 colors here : ggplot2 colors

Change the legend position

p <- p + scale_color_brewer(palette="Paired")+
  theme_minimal()

p + theme(legend.position="top")

p + theme(legend.position="bottom")

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

The allowed values for the arguments legend.position are : “left”,”top”, “right”, “bottom”.

Read more on ggplot legend : ggplot2 legend

Line plot with a numeric x-axis

If the variable on x-axis is numeric, it can be useful to treat it as a continuous or a factor variable depending on what you want to do :

# Create some data
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("0.5", "1", "2"),2),
               len=c(6.8, 15, 33, 4.2, 10, 29.5))
head(df2)
##   supp dose  len
## 1   VC  0.5  6.8
## 2   VC    1 15.0
## 3   VC    2 33.0
## 4   OJ  0.5  4.2
## 5   OJ    1 10.0
## 6   OJ    2 29.5

# x axis treated as continuous variable
df2$dose <- as.numeric(as.vector(df2$dose))
ggplot(data=df2, aes(x=dose, y=len, group=supp, color=supp)) +
  geom_line() + geom_point()+
  scale_color_brewer(palette="Paired")+
  theme_minimal()

# Axis treated as discrete variable
df2$dose<-as.factor(df2$dose)
ggplot(data=df2, aes(x=dose, y=len, group=supp, color=supp)) +
  geom_line() + geom_point()+
  scale_color_brewer(palette="Paired")+
  theme_minimal()

Line plot with dates on x-axis

economics time series data sets are used :

head(economics)
        date   pce    pop psavert uempmed unemploy
## 1 1967-06-30 507.8 198712     9.8     4.5     2944
## 2 1967-07-31 510.9 198911     9.8     4.7     2945
## 3 1967-08-31 516.7 199113     9.0     4.6     2958
## 4 1967-09-30 513.3 199311     9.8     4.9     3143
## 5 1967-10-31 518.5 199498     9.7     4.7     3066
## 6 1967-11-30 526.2 199657     9.4     4.8     3018

Plots :

# Basic line plot
ggplot(data=economics, aes(x=date, y=pop))+
  geom_line()

# Plot a subset of the data
ggplot(data=subset(economics, date &gt; as.Date("2006-1-1")), 
       aes(x=date, y=pop))+geom_line()

Change line size :

1
2
3

# Change line size
ggplot(data=economics, aes(x=date, y=pop, size=unemploy/pop))+
  geom_line()

Line graph with error bars

The function below will be used to calculate the mean and the standard deviation, for the variable of interest, in each group :

#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

Summarize the data :

df3 <- data_summary(ToothGrowth, varname="len", 
                    groupnames=c("supp", "dose"))
head(df3)
##   supp dose   len       sd
## 1   OJ  0.5 13.23 4.459709
## 2   OJ  1.0 22.70 3.910953
## 3   OJ  2.0 26.06 2.655058
## 4   VC  0.5  7.98 2.746634
## 5   VC  1.0 16.77 2.515309
## 6   VC  2.0 26.14 4.797731

The function geom_errorbar() can be used to produce a line graph with error bars :

# Standard deviation of the mean
ggplot(df3, aes(x=dose, y=len, group=supp, color=supp)) + 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1) +
    geom_line() + geom_point()+
   scale_color_brewer(palette="Paired")+theme_minimal()

# Use position_dodge to move overlapped errorbars horizontally
ggplot(df3, aes(x=dose, y=len, group=supp, color=supp)) + 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line() + geom_point()+
   scale_color_brewer(palette="Paired")+theme_minimal()

Customized line graphs

# Simple line plot
# Change point shapes and line types by groups
ggplot(df3, aes(x=dose, y=len, shape=supp, linetype=supp))+ 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line() +
    geom_point()+
    labs(title="Plot of lengthby dose",x="Dose (mg)", y = "Length")+
    theme_classic()


# Change color by groups
# Add error bars
p <- ggplot(df3, aes(x=dose, y=len,  color=supp))+ 
    geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.1, 
    position=position_dodge(0.05)) +
    geom_line(aes(linetype=supp)) + 
    geom_point(aes(shape=supp))+
    labs(title="Plot of lengthby dose",x="Dose (mg)", y = "Length")+
    theme_classic()

p + theme_classic() + scale_color_manual(values=c(&#39;#999999&#39;,&#39;#E69F00&#39;))

Change colors manually :

p + scale_color_brewer(palette="Paired") + theme_minimal()

# Greens
p + scale_color_brewer(palette="Greens") + theme_minimal()

# Reds
p + scale_color_brewer(palette="Reds") + theme_minimal()

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)

Contribution from ：http://www.sthda.com/english/wiki/ggplot2-line-plot-quick-start-guide-r-software-and-data-visualization

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quick start guide of ggplot2 line plot - R software and data visualization

Basic line plots

Data

Create line plots with points

Line plot with multiple groups

Data

Create line plots

Change line types by groups

Change line colors by groups

Change the legend position

Line plot with a numeric x-axis

Line plot with dates on x-axis

Line graph with error bars

Customized line graphs

Infos