Prepare the data

This data will be used for the examples below :

set.seed(1234)

df <- data.frame(
  sex=factor(rep(c("F", "M"), each=200)),
  weight=round(c(rnorm(200, mean=55, sd=5),
                 rnorm(200, mean=65, sd=5)))
  )

head(df)

##   sex weight
## 1   F     49
## 2   F     56
## 3   F     60
## 4   F     43
## 5   F     57
## 6   F     58

Basic area plots

library(ggplot2)
p <- ggplot(df, aes(x=weight))

# Basic area plot
p + geom_area(stat = "bin")

# y axis as density value
p + geom_area(aes(y = ..density..), stat = "bin")

# Add mean line
p + geom_area(stat = "bin", fill = "lightblue")+
  geom_vline(aes(xintercept=mean(weight)),
            color="blue", linetype="dashed", size=1)

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Change line types and colors

# Change line color and fill color
p + geom_area(stat ="bin", color="darkblue",
              fill="lightblue")

# Change line type
p + geom_area(stat = "bin", color= "black",
              fill="lightgrey", linetype="dashed")

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Read more on ggplot2 line types : ggplot2 line types

Change colors by groups

library(plyr)
mu <- ddply(df, "sex", summarise, grp.mean=mean(weight))
head(mu)

##   sex grp.mean
## 1   F    54.70
## 2   M    65.36

# Change area plot fill colors by groups
ggplot(df, aes(x=weight, fill=sex)) +
  geom_area(stat ="bin")

# Use semi-transparent fill
p<-ggplot(df, aes(x=weight, fill=sex)) +
  geom_area(stat ="bin", alpha=0.6) +
  theme_classic()
p

# Add mean lines
p+geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")

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

# use brewer color palettes
p+scale_fill_brewer(palette="Dark2") 

# Use grey scale
p + scale_fill_grey()

Change the legend position

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

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

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

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

Read more on ggplot legends : ggplot2 legends

Use facets

Split the plot in multiple panels :

p<-ggplot(df, aes(x=weight))+
  geom_area(stat ="bin")+facet_grid(sex ~ .)
p

# Add mean lines
p+geom_vline(data=mu, aes(xintercept=grp.mean, color="red"),
             linetype="dashed")

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Read more on facets : ggplot2 facets

Contrasting bar plot and area plot

An area plot is the continuous analog of a stacked bar chart. In the following example, we’ll use diamonds data set [in ggplot2 package]:

# Load the data
data("diamonds")
p <- ggplot(diamonds, aes(x = price, fill = cut))
head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

# Bar plot
p + geom_bar()

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# Area plot
p + geom_area(stat = "bin") +
  scale_fill_brewer(palette="Dark2") 

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Coloring under density curve using geom_area

dat <- with(density(df$weight), data.frame(x, y))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
    geom_line()+
    geom_area(mapping = aes(x = ifelse(x>65 & x< 70 , x, 0)), fill = "red") +
    xlim(30, 80)

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Infos

This analysis has been performed using R software (ver. 3.2.1) and ggplot2 (ver. 1.0.1)