Ggplot Bar Chart Color by Continuous Variable

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

ggplot2 barplots : Quick start guide - R software and data visualization

• Basic barplots
  • Data
  • Create barplots
  • Bar plot with labels
  • Barplot of counts
• Change barplot colors by groups
  • Change outline colors
  • Change fill colors
• Change the legend position
• Change the order of items in the legend
• Barplot with multiple groups
  • Data
  • Create barplots
  • Add labels
• Barplot with a numeric x-axis
• Barplot with error bars
• Customized barplots
• Infos

This R tutorial describes how to create a barplot using R software and ggplot2 package.

The function geom_bar() can be used.

Basic barplots

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 barplots

                    library(ggplot2) # Basic barplot p<-ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity") p     # Horizontal bar plot p + coord_flip()                  

Change the width and the color of bars :

                    # Change the width of bars ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity", width=0.5) # Change colors ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity", color="blue", fill="white") # Minimal theme + blue fill color p<-ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity", fill="steelblue")+   theme_minimal() p                  

Choose which items to display :

                    p + scale_x_discrete(limits=c("D0.5", "D2"))                  

Bar plot with labels

                    # Outside bars ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=len), vjust=-0.3, size=3.5)+   theme_minimal() # Inside bars ggplot(data=df, aes(x=dose, y=len)) +   geom_bar(stat="identity", fill="steelblue")+   geom_text(aes(label=len), vjust=1.6, color="white", size=3.5)+   theme_minimal()                  

Barplot of counts

In the R code above, we used the argument stat = "identity" to make barplots. Note that, the default value of the argument stat is "bin". In this case, the height of the bar represents the count of cases in each category.

To make a barplot of counts, we will use the mtcars data sets :

                    head(mtcars)                  

                    ##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb ## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 ## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 ## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 ## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 ## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 ## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1                  

                    # Don't map a variable to y ggplot(mtcars, aes(x=factor(cyl)))+   geom_bar(stat="bin", width=0.7, fill="steelblue")+   theme_minimal()                  

Change barplot colors by groups

Change outline colors

Barplot outline colors can be automatically controlled by the levels of the variable dose :

                    # Change barplot line colors by groups p<-ggplot(df, aes(x=dose, y=len, color=dose)) +   geom_bar(stat="identity", fill="white") p                  

It is also possible to change manually barplot 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 fill colors

In the R code below, barplot fill colors are automatically controlled by the levels of dose :

                    # Change barplot fill colors by groups p<-ggplot(df, aes(x=dose, y=len, fill=dose)) +   geom_bar(stat="identity")+theme_minimal() p                  

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

• scale_fill_manual() : to use custom colors
• scale_fill_brewer() : to use color palettes from RColorBrewer package
• scale_fill_grey() : to use grey color palettes

                    # Use custom color palettes p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) # use brewer color palettes p+scale_fill_brewer(palette="Dark2") # Use grey scale p + scale_fill_grey()                  

Use black outline color :

                    ggplot(df, aes(x=dose, y=len, fill=dose))+ geom_bar(stat="identity", color="black")+ scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+   theme_minimal()                  

Read more on ggplot2 colors here : ggplot2 colors

Change the legend position

                  # Change bar fill colors to blues p <- p+scale_fill_brewer(palette="Blues") 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

Change the order of items in the legend

The function scale_x_discrete can be used to change the order of items to "2", "0.5", "1" :

                  p + scale_x_discrete(limits=c("D2", "D0.5", "D1"))                

Barplot 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 barplots

A stacked barplot is created by default. You can use the function position_dodge() to change this. The barplot fill color is controlled by the levels of dose :

                    # Stacked barplot with multiple groups ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +   geom_bar(stat="identity") # Use position=position_dodge() ggplot(data=df2, aes(x=dose, y=len, fill=supp)) + geom_bar(stat="identity", position=position_dodge())                  

Change the color manually :

                    # Change the colors manually p <- ggplot(data=df2, aes(x=dose, y=len, fill=supp)) + geom_bar(stat="identity", color="black", position=position_dodge())+   theme_minimal() # Use custom colors p + scale_fill_manual(values=c('#999999','#E69F00')) # Use brewer color palettes p + scale_fill_brewer(palette="Blues")                  

Add labels

Add labels to a dodged barplot :

                    ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +   geom_bar(stat="identity", position=position_dodge())+   geom_text(aes(label=len), vjust=1.6, color="white",             position = position_dodge(0.9), size=3.5)+   scale_fill_brewer(palette="Paired")+   theme_minimal()                  

Add labels to a stacked barplot : 3 steps are required

1. Sort the data by dose and supp : the package plyr is used
2. Calculate the cumulative sum of the variable len for each dose
3. Create the plot

                    library(plyr) # Sort by dose and supp df_sorted <- arrange(df2, dose, supp)  head(df_sorted)                  

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

                    # Calculate the cumulative sum of len for each dose df_cumsum <- ddply(df_sorted, "dose",                    transform, label_ypos=cumsum(len)) head(df_cumsum)                  

                    ##   supp dose  len label_ypos ## 1   OJ D0.5  4.2        4.2 ## 2   VC D0.5  6.8       11.0 ## 3   OJ   D1 10.0       10.0 ## 4   VC   D1 15.0       25.0 ## 5   OJ   D2 29.5       29.5 ## 6   VC   D2 33.0       62.5                  

                    # Create the barplot ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) +   geom_bar(stat="identity")+   geom_text(aes(y=label_ypos, label=len), vjust=1.6,              color="white", size=3.5)+   scale_fill_brewer(palette="Paired")+   theme_minimal()                  

If you want to place the labels at the middle of bars, you have to modify the cumulative sum as follow :

                    df_cumsum <- ddply(df_sorted, "dose",                    transform,                     label_ypos=cumsum(len) - 0.5*len) # Create the barplot ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) +   geom_bar(stat="identity")+   geom_text(aes(y=label_ypos, label=len), vjust=1.6,              color="white", size=3.5)+   scale_fill_brewer(palette="Paired")+   theme_minimal()                  

Barplot 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, fill=supp)) +   geom_bar(stat="identity", position=position_dodge())+   scale_fill_brewer(palette="Paired")+   theme_minimal() # Axis treated as discrete variable df2$dose<-as.factor(df2$dose) ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +   geom_bar(stat="identity", position=position_dodge())+   scale_fill_brewer(palette="Paired")+   theme_minimal()                

Barplot with error bars

The helper 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")) # Convert dose to a factor variable df3$dose=as.factor(df3$dose) head(df3)                

                  ##   supp dose   len       sd ## 1   OJ  0.5 13.23 4.459709 ## 2   OJ    1 22.70 3.910953 ## 3   OJ    2 26.06 2.655058 ## 4   VC  0.5  7.98 2.746634 ## 5   VC    1 16.77 2.515309 ## 6   VC    2 26.14 4.797731                

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

                  # Standard deviation of the mean as error bar p <- ggplot(df3, aes(x=dose, y=len, fill=supp)) +     geom_bar(stat="identity", position=position_dodge()) +   geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.2,                  position=position_dodge(.9))    p + scale_fill_brewer(palette="Paired") + theme_minimal()                

Customized barplots

                  # Change color by groups # Add error bars p + labs(title="Plot of length  per dose",           x="Dose (mg)", y = "Length")+    scale_fill_manual(values=c('black','lightgray'))+    theme_classic()                

Change fill colors manually :

                  # Greens p + scale_fill_brewer(palette="Greens") + theme_minimal() # Reds p + scale_fill_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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