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6. ggplot2 line plot : Quick start guide - R software and data visualization

ggplot2 line plot : Quick start guide - 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

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

                    library(ggplot2) # 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 > as.Date("2006-1-1")),         aes(x=date, y=pop))+geom_line()                

Change line size :

                  # 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, group = supp, 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, group = supp, 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('#999999','#E69F00'))                

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)

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