Diagnostic plots for Linear Models (LM)

iris data set is used for computing the linear model

# Compute a linear model
m <- lm(Petal.Width ~ Petal.Length, data = iris)

# Create the plot
autoplot(m, which = 1:6, ncol = 2, label.size = 3)

# Change the color by groups (species)
autoplot(m, which = 1:6, label.size = 3, data = iris,
         colour = 'Species')

Diagnostic plots with Generalized Linear Models (GLM)

USArrests data set is used.

# Compute a generalized linear model
m <- glm(Murder ~ Assault + UrbanPop + Rape,
         family = gaussian, data = USArrests)

# Create the plot
# Change the theme and colour
autoplot(m, which = 1:6, ncol = 2, label.size = 3,
         colour = "steelblue") + theme_bw()

Plotting ts objects

• Data set: AirPassengers
• R Function: autoplot.ts()

autoplot(AirPassengers)

The function autoplot() can handle also other time-series-likes packages, including:

• zoo::zooreg()
• xts::xts()
• timeSeries::timSeries()
• tseries::irts()
• forecast::forecast()
• vars:vars()

Plotting with changepoint package

The changepoint package provides a simple approach for identifying shifts in mean and/or variance in a time series.

ggfortify supports cpt object in changepoint package.

library(changepoint)
autoplot(cpt.meanvar(AirPassengers))

Plotting with strucchange package

strucchange is an R package for detecting jumps in data.

Data set: Nile

library(strucchange)
autoplot(breakpoints(Nile ~ 1))

Scatter plots

The R code below will create basic scatter plots using the argument geom = “point”. To add a regression line on the scatter plot, the argument method = “lm” is used in combination with geom = c(“point”, “smooth”). Note that, lm stands for linear model.

# Basic scatter plot
qplot(x = mpg, y = wt, data = df, geom = "point")

# Change main title and axis labels
qplot(x = mpg, y = wt, data = df, geom = "point",
      xlab = "Weight (lb/1000)", ylab = "Miles/(US) gallon", 
      main = "Plot of wt by mpg ")

# Combine point and line
qplot(x = mpg, y = wt, data = df,
      geom = c("line", "point"))

# Scatter plot with regression line
qplot(mpg, wt, data = df, 
      geom = c("point", "smooth"), method="lm")

The following R code will change the color and the shape of points by groups. The column cyl will be used as grouping variable. In other words, the color and the shape of points will be changed by the levels of cyl.

qplot(mpg, wt, data = df, colour = cyl, shape = cyl)

Bar plot

It’s possible to draw a bar plot using the arguments geom = “bar” and stat = “identity”. We’ll create a bar plot of the mpg variable. We start by creating a new variable named index, which holds the position of each mpg value.

# y represents values in the data
index <- 1:nrow(mtcars)
qplot(index, mpg, data = df, geom = "bar", stat = "identity")

# Change fill color by groups (cyl)
# Order the data by cyl and then by mpg values
df <- df[order(df$cyl, df$mpg), ]
qplot(index, mpg, data = df, geom = "bar", stat = "identity",
      fill = cyl)

Box plot, violin plot and dot plot

The R code below generates some data containing the weights by sex (M for male; F for female):

set.seed(1234)
wdata = data.frame(
        sex = factor(rep(c("F", "M"), each=200)),
        weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata)

##   sex   weight
## 1   F 53.79293
## 2   F 55.27743
## 3   F 56.08444
## 4   F 52.65430
## 5   F 55.42912
## 6   F 55.50606

# Basic box plot from data frame
qplot(sex, weight, data = wdata, 
      geom= "boxplot", fill = sex)

# Violin plot
qplot(sex, weight, data = wdata, geom = "violin")

# Dot plot
qplot(sex, weight, data = wdata, geom = "dotplot",
      stackdir = "center", binaxis = "y", dotsize = 0.5)

Histogram and density plots

The histogram and density plots are used to display the distribution of data.

# Histogram  plot
# Change histogram fill color by group (sex)
qplot(weight, data = wdata, geom = "histogram",
    fill = sex, position = "dodge")

# Density plot
# Change density plot line color by group (sex)
# change line type
qplot(weight, data = wdata, geom = "density",
    color = sex, linetype = sex)

One variable: Continuous

We’ll use weight data (wdata), generated in the previous sections.

head(wdata)

##   sex   weight
## 1   F 53.79293
## 2   F 55.27743
## 3   F 56.08444
## 4   F 52.65430
## 5   F 55.42912
## 6   F 55.50606

The following R code computes the mean value by sex:

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

##   sex grp.mean
## 1   F 54.94224
## 2   M 58.07325

We start by creating a plot, named a, that we’ll finish in the next section by adding a layer.

a <- ggplot(wdata, aes(x = weight))

1. One variable: Continuous

2. One variable: Discrete

# Basic plot
a + geom_area(stat = "bin")

# change fill colors by sex
a + geom_area(aes(fill = sex), stat ="bin", alpha=0.6) +
  theme_classic()

a + geom_area(aes(y = ..density..), stat ="bin")

a + stat_bin(geom = "area")

# Basic plot
a + geom_density()

# change line colors by sex
a + geom_density(aes(color = sex)) 

# Change fill color by sex
# Use semi-transparent fill: alpha = 0.4
a + geom_density(aes(fill = sex), alpha=0.4)
   
# Add mean line and Change color manually
a + geom_density(aes(color = sex)) +
  geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed") +
  scale_color_manual(values=c("#999999", "#E69F00"))

a + stat_density()

# Basic plot
a + geom_dotplot()

# change fill and color by sex
a + geom_dotplot(aes(fill = sex)) 

# Change fill color manually 
a + geom_dotplot(aes(fill = sex)) +
  scale_fill_manual(values=c("#999999", "#E69F00"))

a + stat_bindot()

# Basic plot
a + geom_freqpoly() 

# change y axis to density value
# and change theme
a + geom_freqpoly(aes(y = ..density..)) +
  theme_minimal()

# change color and linetype by sex
a + geom_freqpoly(aes(color = sex, linetype = sex)) +
  theme_minimal()

a + stat_bin(geom = "freqpoly")

# Basic plot
a + geom_histogram()

# change line colors by sex
a + geom_histogram(aes(color = sex), fill = "white",
                   position = "dodge") 

a + geom_histogram(aes(y = ..density..))

a + stat_bin(geom = "histogram")

a + stat_ecdf()

ggplot(mtcars, aes(sample=mpg)) + stat_qq()

One variable: Discrete

The function geom_bar() can be used to visualize one discrete variable. In this case, the count of each level is plotted. We’ll use the mpg data set [in ggplot2 package]. The R code is as follow:

data(mpg)
b <- ggplot(mpg, aes(fl))

# Basic plot
b + geom_bar()

# Change fill color
b + geom_bar(fill = "steelblue", color ="steelblue") +
  theme_minimal()

To customize the plot, the following arguments can be used: alpha, color, fill, linetype and size. Learn more here: ggplot2 bar plot.

• Key function: geom_bar()
• Alternative function: stat_bin()

b + stat_bin()

Two variables: Continuous X, Continuous Y

We’ll use the mtcars data set. The variable cyl is used as grouping variable.

data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
head(mtcars[, c("wt", "mpg", "cyl")])

##                      wt  mpg cyl
## Mazda RX4         2.620 21.0   6
## Mazda RX4 Wag     2.875 21.0   6
## Datsun 710        2.320 22.8   4
## Hornet 4 Drive    3.215 21.4   6
## Hornet Sportabout 3.440 18.7   8
## Valiant           3.460 18.1   6

We start by creating a plot, named b, that we’ll finish in the next section by adding a layer.

b <- ggplot(mtcars, aes(x = wt, y = mpg))

# Basic plot
b + geom_point()
# change the color and the point 
# by the levels of cyl variable
b + geom_point(aes(color = cyl, shape = cyl)) 

# Change color manually
b + geom_point(aes(color = cyl, shape = cyl)) +
  scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

# Regression line only
b + geom_smooth(method = lm)
# Point + regression line
# Remove the confidence interval 
b + geom_point() + 
  geom_smooth(method = lm, se = FALSE)

# loess method: local regression fitting
b + geom_point() + geom_smooth()

# Change color and shape by groups (cyl)
b + geom_point(aes(color=cyl, shape=cyl)) + 
  geom_smooth(aes(color=cyl, shape=cyl), 
              method=lm, se=FALSE, fullrange=TRUE)

b + stat_smooth(method = "lm")

# Subset a sample
set.seed(1234)
msamp <- movies[sample(nrow(movies), 1000), c("year", "rating") ]
head(msamp)

##       year rating
## 6685  1996    5.0
## 36584 1989    3.4
## 35817 1994    8.1
## 36646 1989    3.5
## 50609 1987    4.3
## 37640 2001    5.4

ggplot(msamp, aes(year, rating)) +
  geom_point() + geom_quantile() +
  theme_minimal()

ggplot(msamp, aes(year, rating)) +
  geom_point() + stat_quantile(quantiles = c(0.25, 0.5, 0.75))

# Add marginal rugs using faithful data
ggplot(faithful, aes(x=eruptions, y=waiting)) +
  geom_point() + geom_rug()

p <- ggplot(mpg, aes(displ, hwy))
# Default scatter plot
p + geom_point()

# Use jitter to reduce overplotting
p + geom_jitter(
    position = position_jitter(width = 0.5, height = 0.5))

b + geom_text(aes(label = rownames(mtcars)))

Two variables: Continuous bivariate distribution

We start by using the diamonds data set [in ggplot2].

data(diamonds)
head(diamonds[, c("carat", "price")])

##   carat price
## 1  0.23   326
## 2  0.21   326
## 3  0.23   327
## 4  0.29   334
## 5  0.31   335
## 6  0.24   336

We start by creating a plot, named c, that we’ll finish in the next section by adding a layer.

c <- ggplot(diamonds, aes(carat, price))

# Default plot 
c + geom_bin2d()

# Change the number of bins
c + geom_bin2d(bins = 15)

c + stat_bin2d()

c + stat_summary2d(aes(z = depth))

install.packages("hexbin")

require(hexbin)
# Default plot 
c + geom_hex()

# Change the number of bins
c + geom_hex(bins = 10)

c + stat_binhex()

c + stat_summary_hex(aes(z = depth))

# Scatter plot 
sp <- ggplot(faithful, aes(x=eruptions, y=waiting)) 

# Default plot
sp + geom_density2d()

# Add points
sp + geom_point() + geom_density2d()

# Use stat_density2d with geom = "polygon"
sp + geom_point() + 
  stat_density2d(aes(fill = ..level..), geom="polygon")

sp + stat_density2d()

Two variables: Continuous function

In this section, we’ll see how to connect observations by line. The economics data set [in ggplot2] is used.

data(economics)
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

We start by creating a plot, named d, that we’ll finish in the next section by adding a layer.

d <- ggplot(economics, aes(x = date, y = unemploy))

# Area plot
d + geom_area()

# Line plot: connecting observations, ordered by x
d + geom_line()

# Connecting observations by stairs
# a subset of economics data set is used
set.seed(1234)
ss <- economics[sample(1:nrow(economics), 20), ]
ggplot(ss, aes(x = date, y = unemploy)) + 
  geom_step()

To customize the plot, the following arguments can be used: alpha, color, linetype, size and fill (for geom_area only). Learn more here: ggplot2 line plot.

• Key functions: geom_area(), geom_line(), geom_step()

Two variables: Discrete X, Continuous Y

The ToothGrowth data set we’ll be used to plot the continuous variable len (for tooth length) by the discrete variable dose. The following R code converts the variable dose from a numeric to a discrete factor variable.

data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

We start by creating a plot, named e, that we’ll finish in the next section by adding a layer.

e <- ggplot(ToothGrowth, aes(x = dose, y = len))

# Default plot
e + geom_boxplot()

# Notched box plot
e + geom_boxplot(notch = TRUE)

# Color by group (dose)
e + geom_boxplot(aes(color = dose))

# Change fill color by group (dose)
e + geom_boxplot(aes(fill = dose))

# Box plot with multiple groups
ggplot(ToothGrowth, aes(x=dose, y=len, fill=supp)) +
  geom_boxplot()

e + stat_boxplot(coeff = 1.5)

# Default plot
e + geom_violin(trim = FALSE)

# violin plot with mean points (+/- SD)
e + geom_violin(trim = FALSE) + 
  stat_summary(fun.data="mean_sdl",  mult = 1, 
               geom="pointrange", color = "red")

# Combine with box plot
e + geom_violin(trim = FALSE) + 
  geom_boxplot(width = 0.2)

# Color by group (dose) 
e + geom_violin(aes(color = dose), trim = FALSE)

e + stat_ydensity(trim = FALSE)

# Default plot
e + geom_dotplot(binaxis = "y", stackdir = "center")

# Dot plot with mean points (+/- SD)
e + geom_dotplot(binaxis = "y", stackdir = "center") + 
  stat_summary(fun.data="mean_sdl",  mult = 1, 
               geom="pointrange", color = "red")

# Combine with box plot
e + geom_boxplot() + 
  geom_dotplot(binaxis = "y", stackdir = "center") 


# Add violin plot
e + geom_violin(trim = FALSE) +
  geom_dotplot(binaxis='y', stackdir='center')

# Color and fill by group (dose) 
e + geom_dotplot(aes(color = dose, fill = dose), 
                 binaxis = "y", stackdir = "center")

# Default plot
e + geom_jitter(position=position_jitter(0.2))

# Strip charts with mean points (+/- SD)
e + geom_jitter(position=position_jitter(0.2)) + 
  stat_summary(fun.data="mean_sdl",  mult = 1, 
               geom="pointrange", color = "red")

# Combine with box plot
e + geom_jitter(position=position_jitter(0.2)) + 
  geom_dotplot(binaxis = "y", stackdir = "center") 


# Add violin plot
e + geom_violin(trim = FALSE) +
  geom_jitter(position=position_jitter(0.2))
  

# Change color and shape by group (dose) 
e +  geom_jitter(aes(color = dose, shape = dose),
                 position=position_jitter(0.2))

df <- 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(df)

##   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

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

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

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

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

f <- ggplot(df, aes(x = dose, y = len))

# Basic bar plot
f + geom_bar(stat = "identity")

# Change fill color and add labels
f + geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=len), vjust=-0.3, size=3.5)+
  theme_minimal()

# Change bar plot line colors by groups
f + geom_bar(aes(color = dose),
             stat="identity", fill="white")

# Change bar plot fill colors by groups
f + geom_bar(aes(fill = dose), stat="identity")

g <- ggplot(data=df2, aes(x=dose, y=len, fill=supp)) 

# Stacked bar plot
g + geom_bar(stat = "identity")

# Use position=position_dodge()
g + geom_bar(stat="identity", position=position_dodge())

g + stat_identity(geom = "bar")

g + stat_identity(geom = "bar", position = "dodge")

Two variables: Discrete X, Discrete Y

The diamonds data set [in ggplot2] we’ll be used to plot the discrete variable color (for diamond colors) by the discrete variable cut (for diamond cut types). The plot is created using the function geom_jitter().

To customize the plot, the following arguments can be used: alpha, color, fill, shape and size.

• Key function: geom_jitter()

Two variables: Visualizing error

The ToothGrowth data set we’ll be used. We start by creating a data set named df which holds ToothGrowth data.

# ToothGrowth data set
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

The helper function below (data_summary()) will be used to calculate the mean and the standard deviation (used as error), for the variable of interest, in each group. The plyr package is required.

# Calculate the mean and the SD in each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of the variable to be summariezed
# grps : column names to be used as grouping variables
data_summary <- function(data, varname, grps){
  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, grps, .fun=summary_func, varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

Using the function data_summary(), the following R code creates a data set named df2 which holds the mean and the SD of tooth length (len) by groups (dose).

df2 <- data_summary(df, varname="len", grps= "dose")
# Convert dose to a factor variable
df2$dose=as.factor(df2$dose)
head(df2)

##   dose    len       sd
## 1  0.5 10.605 4.499763
## 2    1 19.735 4.415436
## 3    2 26.100 3.774150

We start by creating a plot, named f, that we’ll finish in the next section by adding a layer.

f <- ggplot(df2, aes(x = dose, y = len, 
                     ymin = len-sd, ymax = len+sd))

# Default plot
f + geom_crossbar()

# color by groups
f + geom_crossbar(aes(color = dose))

# Change color manually
f + geom_crossbar(aes(color = dose)) + 
  scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

# fill by groups and change color manually
f + geom_crossbar(aes(fill = dose)) + 
  scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

df3 <- data_summary(df, varname="len", grps= c("supp", "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

f <- ggplot(df3, aes(x = dose, y = len, 
                     ymin = len-sd, ymax = len+sd))
# Default plot
f + geom_crossbar(aes(color = supp))

# Use position_dodge() to avoid overlap
f + geom_crossbar(aes(color = supp), 
                  position = position_dodge(1))

f <- ggplot(df, aes(x = dose, y = len, color = supp)) 
# Use geom_crossbar()
f + stat_summary(fun.data="mean_sdl", mult = 1, 
                 geom="crossbar", width = 0.6, 
                 position = position_dodge(0.8))

f <- ggplot(df2, aes(x = dose, y = len, 
                     ymin = len-sd, ymax = len+sd))

# Error bars colored by groups
f + geom_errorbar(aes(color = dose), width = 0.2)

# Combine with line plot
f + geom_line(aes(group = 1)) + 
  geom_errorbar(width = 0.2)

# Combine with bar plot, color by groups
f + geom_bar(aes(color = dose), stat = "identity", fill ="white") + 
  geom_errorbar(aes(color = dose), width = 0.2)

f <- ggplot(df3, aes(x = dose, y = len, 
                     ymin = len-sd, ymax = len+sd))
# Default plot
f + geom_bar(aes(fill = supp), stat = "identity",
             position = "dodge") + 
  geom_errorbar(aes(color = supp),  position = "dodge")

df2 <- data_summary(ToothGrowth, varname="len", grps = "dose")
head(df2)

##   dose    len       sd
## 1  0.5 10.605 4.499763
## 2    1 19.735 4.415436
## 3    2 26.100 3.774150

f <- ggplot(df2, aes(x = len, y = dose ,
                     xmin=len-sd, xmax=len+sd))

f <- ggplot(df2, aes(x = dose, y = len,
                     ymin=len-sd, ymax=len+sd))
# Line range
f + geom_linerange()

# Point range
f + geom_pointrange()

g <- ggplot(df, aes(x=dose, y=len)) + 
  geom_dotplot(binaxis='y', stackdir='center')

# use geom_crossbar()
g + stat_summary(fun.data="mean_sdl", mult=1, 
                 geom="crossbar", width=0.5)

# Use geom_errorbar()
g + stat_summary(fun.data=mean_sdl, mult=1, 
        geom="errorbar", color="red", width=0.2) +
  stat_summary(fun.y=mean, geom="point", color="red")
# Use geom_pointrange()
g + stat_summary(fun.data=mean_sdl, mult=1, 
                 geom="pointrange", color="red")

Two variables: Maps

The function geom_map() can be used to create a map with ggplot2. The R package map is required. It contains geographical information useful for drawing easily maps in ggplot2.

Install map package (if you don’t have it):

install.packages("map")

In the following R code, we’ll create USA map and USArrests crime data to shade each region.

# Prepare the data
crimes <- data.frame(state = tolower(rownames(USArrests)), 
                     USArrests)
library(reshape2) # for melt
crimesm <- melt(crimes, id = 1)

# Get map data
require(maps) 
map_data <- map_data("state")

# Plot the map with Murder data
ggplot(crimes, aes(map_id = state)) + 
  geom_map(aes(fill = Murder), map = map_data) + 
  expand_limits(x = map_data$long, y = map_data$lat)

To customize the plot, the following arguments can be used: alpha, color, fill, linetype and size. Learn more here: ggplot2 map.

Key function:geom_map()

Three variables

The mtcars data set we’ll be used. We first compute a correlation matrix, which will be visualized using specific ggplot2 functions.

Prepare the data:

df <- mtcars[, c(1,3,4,5,6,7)]
# Correlation matrix
cormat <- round(cor(df),2)
# Melt the correlation matrix
require(reshape2)
cormat <- melt(cormat)
head(cormat)

##   Var1 Var2 value
## 1  mpg  mpg  1.00
## 2 disp  mpg -0.85
## 3   hp  mpg -0.78
## 4 drat  mpg  0.68
## 5   wt  mpg -0.87
## 6 qsec  mpg  0.42

We start by creating a plot, named g, that we’ll finish in the next section by adding a layer.

g <- ggplot(cormat, aes(x = Var1, y = Var2))

We’ll use the function geom_tile() to visualize a correlation matrix.

Compute and visualize correlation matrix:

# 1. Compute correlation
cormat <- round(cor(df),2)

# 2. Reorder the correlation matrix by 
# Hierarchical clustering
hc <- hclust(as.dist(1-cormat)/2)
cormat.ord <- cormat[hc$order, hc$order]

# 3. Get the upper triangle
cormat.ord[lower.tri(cormat.ord)]<- NA

# 4. Melt the correlation matrix
require(reshape2)
melted_cormat <- melt(cormat.ord, na.rm = TRUE)

# Create the heatmap
ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
   midpoint = 0, limit = c(-1,1), space = "Lab",
   name="Pearson\nCorrelation") + # Change gradient color
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                  size = 12, hjust = 1))+
 coord_fixed()

To customize the plot, the following arguments can be used: alpha, color, fill, linetype and size. Learn more here: ggplot2 correlation matrix heatmap.

• Key functions: geom_tile(), geom_raster()

Other types of graphs

• Pie chart: (click to read more)

• Survival curves: (click to read more)

Graphical primitives: polygon, path, ribbon, segment, rectangle

This section describes how to add graphical elements to a plot. The functions below we’ll be used:

1. The R code below draws France map using geom_polygon():

require(maps)
france = map_data('world', region = 'France')
ggplot(france, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = 'white', colour = 'black')

To customize the plot, the following arguments can be used: alpha, color, fill, linetype and size.

2. The following R code uses econimics data [in ggplot2] and produces path, ribbon and rectangles.

h <- ggplot(economics, aes(date, unemploy))

# Path
h + geom_path()

# Ribbon
h + geom_ribbon(aes(ymin = unemploy-900, ymax = unemploy+900),
                fill = "steelblue") +
  geom_path(size = 0.8)

# Rectangle
h + geom_rect(aes(xmin = as.Date('1980-01-01'), ymin = -Inf, 
                 xmax = as.Date('1985-01-01'), ymax = Inf),
             fill = "steelblue") +
  geom_path(size = 0.8) 

To customize the plot, the following arguments can be used: alpha, color, fill (for ribbon only), linetype and size.

3. Add line segments:

# Create a scatter plot
i <- ggplot(mtcars, aes(wt, mpg)) + geom_point()

# Add segment
i + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15))

# Add arrow
require(grid)
i + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(length = unit(0.5, "cm")))

To customize the plot, the following arguments can be used: alpha, color, linetype and size. Learn more here: ggplot2 add line segment.

• Key functions: geom_path(), geom_ribbon(), geom_rect(), geom_segment()

Main title, axis labels and legend title

We start by creating a box plot using the data set ToothGrowth:

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot()

The function below can be used for changing titles and labels:

The function labs() can be also used to change the legend title.

1. Change main title and axis labels

# Default plot
print(p)

# Change title and axis labels
p <- p +labs(title="Plot of length \n by dose",
        x ="Dose (mg)", y = "Teeth length")
p

Note that, \n is used to split long title into multiple lines.

2. Change the appearance of labels:

To change the appearance(color, size and face ) of labels, the functions theme() and element_text() can be used.

The function element_blank() hides the labels.

# Change the appearance of labels
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)

# Hide labels
p + theme(plot.title = element_blank(), 
          axis.title.x = element_blank(),
          axis.title.y = element_blank())

3. Change legend titles: Scale functions (fill, color, size, shape, …) are used to update legend titles.

# Default plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose))+
  geom_boxplot()
p

# Modify legend titles
p + labs(fill = "Dose (mg)")

Learn more here: ggplot2 title: main, axis and legend titles.

Legend position and appearance

1. Create a box plot

p <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose))+
  geom_boxplot()

2. Change legend position and appearance

# Change legend position: "left","top", "right", "bottom", "none"
p + theme(legend.position="top")

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

# Change the appearance of legend title and labels
p + theme(legend.title = element_text(colour="blue"),
          legend.text = element_text(colour="red"))

# Change legend box background color
p + theme(legend.background = element_rect(fill="lightblue"))

3. Customize legends using scale functions
   • Change the order of legend items: scale_x_discrete()
   • Set legend title and labels: scale_fill_discrete()

# Change the order of legend items
p + scale_x_discrete(limits=c("2", "0.5", "1"))

# Set legend title and labels
p + scale_fill_discrete(name = "Dose", labels = c("A", "B", "C"))

Learn more here: ggplot2 legend position and appearance.

Change colors automatically and manually

ToothGrowth and mtcars data sets are used in the examples below.

# Convert dose and cyl columns from numeric to factor variables
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
mtcars$cyl <- as.factor(mtcars$cyl)

We start by creating some plots which will be finished hereafter:

# Box plot
bp <- ggplot(ToothGrowth, aes(x=dose, y=len))

# Scatter plot
sp <- ggplot(mtcars, aes(x=wt, y=mpg))

1. Draw plots: change fill and outline colors

# box plot
bp + geom_boxplot(fill='steelblue', color="red")

# scatter plot
sp + geom_point(color='darkblue')

2. Change color by groups using the levels of dose variable

# Box plot
bp <- bp + geom_boxplot(aes(fill = dose))
bp

# Scatter plot
sp <- sp + geom_point(aes(color = cyl))
sp

3. Change colors manually:

• scale_fill_manual() for box plot, bar plot, violin plot, etc
• scale_color_manual() for lines and points

# Box plot
bp + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))

# Scatter plot
sp + scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))

4. Use RColorBrewer palettes: (Read more about RColorBrewer: color in R)

• scale_fill_brewer() for box plot, bar plot, violin plot, etc
• scale_color_brewer() for lines and points

# Box plot
bp + scale_fill_brewer(palette="Dark2")

# Scatter plot
sp + scale_color_brewer(palette="Dark2")

Available color palettes in the RColorBrewer package:

5. Use gray colors:

• scale_fill_grey() for box plot, bar plot, violin plot, etc
• scale_colour_grey() for points, lines, etc

# Box plot
bp + scale_fill_grey() + theme_classic()

# Scatter plot
sp + scale_color_grey() + theme_classic()

6. Gradient or continuous colors:

Plots can be colored according to the values of a continuous variable using the functions :

• scale_color_gradient(), scale_fill_gradient() for sequential gradients between two colors
• scale_color_gradient2(), scale_fill_gradient2() for diverging gradients
• scale_color_gradientn(), scale_fill_gradientn() for gradient between n colors

Gradient colors for scatter plots: The graphs are colored using the qsec continuous variable :

# Color by qsec values
sp2<-ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(aes(color = qsec))
sp2

# Change the low and high colors
# Sequential color scheme
sp2+scale_color_gradient(low="blue", high="red")

# Diverging color scheme
mid<-mean(mtcars$qsec)
sp2+scale_color_gradient2(midpoint=mid, low="blue", mid="white",
                          high="red", space = "Lab" )

Learn more here: ggplot2 colors.

Point shapes, colors and size

The different points shapes commonly used in R are shown in the image below:

mtcars data is used in the following examples.

# Convert cyl as factor variable
mtcars$cyl <- as.factor(mtcars$cyl)

Create a scatter plot and change point shapes, colors and size:

# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(shape = 18, color = "steelblue", size = 4)

# Change point shapes and colors by groups
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(aes(shape = cyl, color = cyl))

It’s also possible to manually change the appearance of points:

• scale_shape_manual() : to change point shapes
• scale_color_manual() : to change point colors
• scale_size_manual() : to change the size of points

# Change colors and shapes manually
ggplot(mtcars, aes(x=wt, y=mpg, group=cyl)) +
  geom_point(aes(shape=cyl, color=cyl), size=2)+
  scale_shape_manual(values=c(3, 16, 17))+
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  theme(legend.position="top")

Learn more here: ggplot2 point shapes, colors and size.

Add text annotations to a graph

There are three important functions for adding texts to a plot:

• geom_text(): Textual annotations
• annotate(): Textual annotations
• annotation_custom(): Static annotations that are the same in every panel. These annotations are not affected by the plot scales.

A subset of mtcars data is used:

set.seed(1234)
df <- mtcars[sample(1:nrow(mtcars), 10), ]
df$cyl <- as.factor(df$cyl)

Scatter plots with textual annotations:

# Scatter plot
sp <- ggplot(df, aes(x=wt, y=mpg))+ geom_point() 

# Add text, change colors by groups
sp + geom_text(aes(label = rownames(df), color = cyl),
               size = 3, vjust = -1)

# Add text at a particular coordinate
sp + geom_text(x = 3, y = 30, label = "Scatter plot",
              color="red")

Learn more here: ggplot2 text: Add text annotations to a graph.

Line types

The different line types available in R software are : “blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, “twodash”.

Note that, line types can be also specified using numbers : 0, 1, 2, 3, 4, 5, 6. 0 is for “blank”, 1 is for “solid”, 2 is for “dashed”, ….

A graph of the different line types is shown below :

1. Basic line plot

# Create some data
df <- data.frame(time=c("breakfeast", "Lunch", "Dinner"),
                bill=c(10, 30, 15))
head(df)

##         time bill
## 1 breakfeast   10
## 2      Lunch   30
## 3     Dinner   15

# Basic line plot with points
# Change the line type
ggplot(data=df, aes(x=time, y=bill, group=1)) +
  geom_line(linetype = "dashed")+
  geom_point()

2. Line plots with multiple groups

# Create some data
df2 <- data.frame(sex = rep(c("Female", "Male"), each=3),
                  time=c("breakfeast", "Lunch", "Dinner"),
                  bill=c(10, 30, 15, 13, 40, 17) )
head(df2)

##      sex       time bill
## 1 Female breakfeast   10
## 2 Female      Lunch   30
## 3 Female     Dinner   15
## 4   Male breakfeast   13
## 5   Male      Lunch   40
## 6   Male     Dinner   17

# Line plot with multiple groups
# Change line types and colors by groups (sex)
ggplot(df2, aes(x=time, y=bill, group=sex)) +
  geom_line(aes(linetype = sex, color = sex))+
  geom_point(aes(color=sex))+
  theme(legend.position="top")

The functions below can be used to change the appearance of line types manually:

• scale_linetype_manual() : to change line types
• scale_color_manual() : to change line colors
• scale_size_manual() : to change the size of lines

# Change line colors and sizes
ggplot(df2, aes(x=time, y=bill, group=sex)) +
  geom_line(aes(linetype=sex, color=sex, size=sex))+
  geom_point()+
  scale_linetype_manual(values=c("twodash", "dotted"))+
  scale_color_manual(values=c('#999999','#E69F00'))+
  scale_size_manual(values=c(1, 1.5))+
  theme(legend.position="top")

Learn more here: ggplot2 line types.

Themes and background colors

ToothGrowth data is used :

# Convert the column dose from numeric to factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

1. Create a box plot

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_boxplot()

2. Change plot themes

Several functions are available in ggplot2 package for changing quickly the theme of plots :

• theme_gray(): gray background color and white grid lines
• theme_bw() : white background and gray grid lines

p + theme_gray(base_size = 14)

p + theme_bw()

• theme_linedraw : black lines around the plot
• theme_light : light gray lines and axis (more attention towards the data)

p + theme_linedraw()

p + theme_light()

• theme_minimal: no background annotations
• theme_classic : theme with axis lines and no grid lines

p + theme_minimal()

p + theme_classic()

Learn more here: ggplot2 themes and background colors.

Axis limits: Minimum and Maximum values

Create a plot:

p <- ggplot(cars, aes(x = speed, y = dist)) + geom_point()

Different functions are available for setting axis limits:

# Default plot
print(p)

# Change axis limits using coord_cartesian()
p + coord_cartesian(xlim =c(5, 20), ylim = c(0, 50))

# Use xlim() and ylim()
p + xlim(5, 20) + ylim(0, 50)

# Expand limits
p + expand_limits(x = c(5, 50), y = c(0, 150))

Learn more here: ggplot2 axis limits.

Note that, date axis limits can be set using the functions scale_x_date() and scale_y_date(). Read more here: ggplot2 date axis.

Axis transformations: log and sqrt scales

1. Create a scatter plot:

p <- ggplot(cars, aes(x = speed, y = dist)) + geom_point()

2. ggplot2 functions for continuous axis transformations:

3. The R code below uses the function scale_xx_continuous() to transform axis scales:

# Default scatter plot
print(p)

# Log transformation using scale_xx()
# possible values for trans : 'log2', 'log10','sqrt'
p + scale_x_continuous(trans='log2') +
  scale_y_continuous(trans='log2')

# Format axis tick mark labels
require(scales)
p + scale_y_continuous(trans = log2_trans(),
    breaks = trans_breaks("log2", function(x) 2^x),
    labels = trans_format("log2", math_format(2^.x)))

# Reverse coordinates
p + scale_y_reverse() 

Learn more here: ggplot2 axis limits.

Axis ticks: customize tick marks and labels, reorder and select items

1. Functions for changing the style of axis tick mark labels:

2. Create a box plot:

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot()
# print(p)

3. Change the style and the orientation angle of axis tick labels

# Change the style of axis tick labels
# face can be "plain", "italic", "bold" or "bold.italic"
p + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=14, angle=45),
          axis.text.y = element_text(face="bold", color="blue", 
                           size=14, angle=45))


# Remove axis ticks and tick mark labels
p + theme(
  axis.text.x = element_blank(), # Remove x axis tick labels
  axis.text.y = element_blank(), # Remove y axis tick labels
  axis.ticks = element_blank()) # Remove ticks

4. Customize continuous and discrete axes:

• Discrete axes
  • scale_x_discrete(name, breaks, labels, limits): for X axis
  • scale_y_discrete(name, breaks, labels, limits): for y axis
• Continuous axes
  • scale_x_continuous(name, breaks, labels, limits, trans): for X axis
  • scale_y_continuous(name, breaks, labels, limits, trans): for y axis

Briefly, the meaning of the arguments are as follow:

scale_xx() functions can be used to change the following x or y axis parameters :

• axis titles
• axis limits (data range to display)
• choose where tick marks appear
• manually label tick marks

4.1. Discrete axes:

# Change x axis label and the order of items
p + scale_x_discrete(name ="Dose (mg)", 
                    limits=c("2","1","0.5"))

# Change tick mark labels
p + scale_x_discrete(breaks=c("0.5","1","2"),
        labels=c("Dose 0.5", "Dose 1", "Dose 2"))

# Choose which items to display
p + scale_x_discrete(limits=c("0.5", "2"))

4.2. Continuous axes:

# Default scatter plot
# +++++++++++++++++
sp <- ggplot(cars, aes(x = speed, y = dist)) + geom_point()
sp

# Customize the plot
#+++++++++++++++++++++
# 1. Change x and y axis labels, and limits
sp <- sp + scale_x_continuous(name="Speed of cars", limits=c(0, 30)) +
  scale_y_continuous(name="Stopping distance", limits=c(0, 150))
# 2. Set tick marks on y axis: a tick mark is shown on every 50
sp + scale_y_continuous(breaks=seq(0, 150, 50))

# Format the labels
# +++++++++++++++++
require(scales)
sp + scale_y_continuous(labels = percent) # labels as percents

Learn more here: ggplot2 Axis ticks: tick marks and labels.

Add straight lines to a plot: horizontal, vertical and regression lines

The R function below can be used :

• geom_hline(yintercept, linetype, color, size): for horizontal lines
• geom_vline(xintercept, linetype, color, size): for vertical lines
• geom_abline(intercept, slope, linetype, color, size): for regression lines
• geom_segment() to add segments

1. Create a simple scatter plot

# Simple scatter plot
sp <- ggplot(data=mtcars, aes(x=wt, y=mpg)) + geom_point()

2. Add straight lines

# Add horizontal line at y = 2O; change line type and color
sp + geom_hline(yintercept=20, linetype="dashed", color = "red")

# Add vertical line at x = 3; change line type, color and size
sp + geom_vline(xintercept = 3, color = "blue", size=1.5)

# Add regression line
sp + geom_abline(intercept = 37, slope = -5, color="blue")+
  ggtitle("y = -5X + 37")

# Add horizontal line segment
sp + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15))

Learn more here: ggplot2 add straight lines to a plot.

Rotate a plot: flip and reverse

• coord_flip(): Create horizontal plots
• scale_x_reverse(), scale_y_reverse(): Reverse the axes

set.seed(1234)
# Basic histogram
hp <- qplot(x=rnorm(200), geom="histogram")
hp

# Horizontal histogram
hp + coord_flip()

# Y axis reversed
hp + scale_y_reverse()

Learn more here: ggplot2 rotate a graph.

Faceting: split a plot into a matrix of panels

Facets divide a plot into subplots based on the values of one or more categorical variables.

There are two main functions for faceting :

• facet_grid()
• facet_wrap()

Create a box plot filled by groups:

p <- ggplot(ToothGrowth, aes(x=dose, y=len, group=dose)) + 
  geom_boxplot(aes(fill=dose))
p

The following functions can be used for facets:

1. Facet with one discrete variable: Split by the levels of the group “supp”

# Split in vertical direction
p + facet_grid(supp ~ .)

# Split in horizontal direction
p + facet_grid(. ~ supp)

2. Facet with two discrete variables: Split by the levels of the groups “dose” and “supp”

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
p + facet_grid(dose ~ supp)

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
p + facet_grid(supp ~ dose)

By default, all the panels have the same scales (scales=“fixed”). They can be made independent, by setting scales to free, free_x, or free_y.

p + facet_grid(dose ~ supp, scales='free')

Learn more here: ggplot2 facet : split a plot into a matrix of panels.

Position adjustements

Position adjustments determine how to arrange geoms. The argument position is used to adjust geom positions:

p <- ggplot(mpg, aes(fl, fill = drv))

# Arrange elements side by side
p + geom_bar(position = "dodge")

# Stack objects on top of one another, 
# and normalize to have equal height
p + geom_bar(position = "fill")

# Stack elements on top of one another
p + geom_bar(position = "stack")

# Add random noise to X and Y position 
# of each element to avoid overplotting
ggplot(mpg, aes(cty, hwy)) + 
  geom_point(position = "jitter")

Note that, each of these position adjustments can be done using a function with manual width and height argument.

• position_dodge(width, height)
• position_fill(width, height)
• position_stack(width, height)
• position_jitter(width, height)

p + geom_bar(position = position_dodge(width = 1))

Learn more here: ggplot2 bar plots.

Coordinate systems

p <- ggplot(mpg, aes(fl)) + geom_bar()

The coordinate systems in ggplot2 are:

1. Arguments for coord_cartesian(), coord_fixed() and coord_flip()
   • xlim: limits for the x axis
   • ylim: limits for the y axis
   • ratio: aspect ratio, expressed as y/x
   • …: Other arguments passed onto coord_cartesian
2. Arguments for coord_polar()
   • theta: variable to map angle to (x or y)
   • start: offset of starting point from 12 o’clock in radians
   • direction: 1, clockwise; -1, anticlockwise
3. Arguments for coord_trans()
   • xtrans, ytrans: transformers for x and y axes
   • limx, limy: limits for x and y axes.

p + coord_cartesian(ylim = c(0, 200))

p + coord_fixed(ratio = 1/50)

p + coord_flip()

p + coord_polar(theta = "x", direction = 1)

p + coord_trans(ytrans = "sqrt")

Books

ggplot2: The Elements for Elegant Data Visualization in R

Blog posts

• Build a plot layer by layer

Cheat Sheets

• Data Visualization with ggplot2, RStudio cheat sheet
• Beautiful plotting in R: A ggplot2 cheatsheet

Books

ggplot2: The Elements for Elegant Data Visualization in R

• Cookbook for R

Blog posts

• Build a plot layer by layer

Cheat Sheets

• Be Awesome in ggplot2: A Practical Guide to be Highly Effective
• Data Visualization with ggplot2, RStudio cheat sheet
• Beautiful plotting in R: A ggplot2 cheatsheet

4.1 Installation and loading

ggdendro can be installed as follow:

install.packages("ggdendro")

ggdendro requires the package ggplot2. Make sure that ggplot2 is installed and loaded before using ggdendro.

Load ggdendro as follow:

library("ggplot2")
library("ggdendro")

4.2 Visualize dendrogram using ggdendrogram() function

The function ggdendrogram() creates dendrogram plot using ggplot2.

# Visualization using the default theme named theme_dendro()
ggdendrogram(hc)

# Rotate the plot and remove default theme
ggdendrogram(hc, rotate = TRUE, theme_dendro = FALSE)

4.3 Extract dendrogram plot data

The function dendro_data() can be used for extracting the data. It returns a list of data frames which can be extracted using the functions below:

• segment(): To extract the data for dendrogram line segments
• label(): To extract the labels

# Build dendrogram object from hclust results
dend <- as.dendrogram(hc)

# Extract the data (for rectangular lines)
# Type can be "rectangle" or "triangle"
dend_data <- dendro_data(dend, type = "rectangle")
# What contains dend_data
names(dend_data)

## [1] "segments"    "labels"      "leaf_labels" "class"

# Extract data for line segments
head(dend_data$segments)

##           x         y     xend      yend
## 1 19.771484 13.516242 8.867188 13.516242
## 2  8.867188 13.516242 8.867188  6.461866
## 3  8.867188  6.461866 4.125000  6.461866
## 4  4.125000  6.461866 4.125000  2.714554
## 5  4.125000  2.714554 2.500000  2.714554
## 6  2.500000  2.714554 2.500000  1.091092

# Extract data for labels
head(dend_data$labels)

##   x y          label
## 1 1 0        Alabama
## 2 2 0      Louisiana
## 3 3 0        Georgia
## 4 4 0      Tennessee
## 5 5 0 North Carolina
## 6 6 0    Mississippi

dend_data can be used to draw a customized dendrogram using ggplot2:

# Plot line segments and add labels
p <- ggplot(dend_data$segments) + 
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend))+
  geom_text(data = dend_data$labels, aes(x, y, label = label),
            hjust = 1, angle = 90, size = 3)+
  ylim(-3, 15)
print(p)

5.1 Chaining

The chaining operator (%>%) turns x %>% f(y) into f(x, y) so you can use it to rewrite multiple operations such that they can be read from left-to-right, top-to-bottom. For instance, the results of the two R codes below are equivalent.

Standard R code for creating a dendrogram:

data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)

R code for creating a dendrogram using chaining operator:

dend <- USArrests[1:5,] %>% # data
        scale %>% # Scale the data
        dist %>% # calculate a distance matrix, 
        hclust(method = "ward.D2") %>% # Hierarchical clustering 
        as.dendrogram # Turn the object into a dendrogram.
plot(dend)

5.2 Installation and loading

Install the stable version as follow:

install.packages('dendextend')

Loading:

library(dendextend)

5.3 How to change a dendrogram

The function set() can be used to change the parameters with dendextend.

The format is:

set(object, what, value)

Possible values for the argument what include:

5.4 Create a simple dendrogram

# Create a dendrogram and plot it
dend <- USArrests[1:5,] %>%  scale %>% 
        dist %>% hclust %>% as.dendrogram

dend %>% plot

# Get the labels of the tree
labels(dend)

## [1] "Alaska"     "Arizona"    "California" "Alabama"    "Arkansas"

5.5 Change labels

This section describes how to change label names as well as the color and the size for labels.

# Change the labels, and then plot:
dend %>% set("labels", c("a", "b", "c", "d", "e")) %>% plot

# Change color and size for labels
dend %>% set("labels_col", c("green", "blue")) %>% # change color
  set("labels_cex", 2) %>% # Change size
  plot(main = "Change the color \nand size") # plot

# Color labels by specifying the number of cluster (k)
dend %>% set("labels_col", value = c("green", "blue"), k=2) %>% 
          plot(main = "Color labels \nper cluster")
abline(h = 2, lty = 2)

In the R code above, the value of color vectors are too short. Hence, it’s recycled.

5.6 Change the points of a dendrogram nodes/leaves

# Change the type, the color and the size of node points
# +++++++++++++++++++++++++++++
dend %>% set("nodes_pch", 19) %>%  # node point type
  set("nodes_cex", 2) %>%  # node point size
  set("nodes_col", "blue") %>% # node point color
  plot(main = "Node points")

# Change the type, the color and the size of leave points
# +++++++++++++++++++++++++++++
dend %>% set("leaves_pch", 19) %>%  # node point type
  set("leaves_cex", 2) %>%  # node point size
  set("leaves_col", "blue") %>% # node point color
  plot(main = "Leaves points")

# Specify different point types and colors for each leave
dend %>% set("leaves_pch", c(17, 18, 19)) %>%  # node point type
  set("leaves_cex", 2) %>%  # node point size
  set("leaves_col", c("blue", "red", "green")) %>% #node point color
  plot(main = "Leaves points")

5.7 Change the color of branches

The color for branches can be controlled using k-means clustering:

# Default colors
dend %>% set("branches_k_color", k = 2) %>% 
  plot(main = "Default colors")

# Customized colors
dend %>% set("branches_k_color", 
             value = c("red", "blue"), k = 2) %>% 
   plot(main = "Customized colors")

It’s also possible to use the function color_branches().

5.8 Adding colored rectangles

Clusters can be highlighted by adding colored rectangles. This is done using the rect.dendrogram() function (modeled based on the rect.hclust() function). One advantage of rect.dendrogram over rect.hclust, is that it also works on horizontally plotted trees:

# Vertical plot
dend %>% set("branches_k_color", k = 3) %>% plot
dend %>% rect.dendrogram(k=3, border = 8, lty = 5, lwd = 2)

# Horizontal plot
dend %>% set("branches_k_color", k = 3) %>% plot(horiz = TRUE)
dend %>% rect.dendrogram(k = 3, horiz = TRUE, border = 8, lty = 5, lwd = 2)

5.9 Adding colored bars

This is useful for annotating the items in the clusters:

grp <- c(1,1,1, 2,2)
k_3 <- cutree(dend,k = 3, order_clusters_as_data = FALSE) 
# The FALSE above makes sure we get the clusters in the order of the
# dendrogram, and not in that of the original data. It is like:
# cutree(dend, k = 3)[order.dendrogram(dend)]

the_bars <- cbind(grp, k_3)

dend %>% set("labels", "") %>% plot
colored_bars(colors = the_bars, dend = dend)

5.10 ggplot2 integration

The following 2 steps are used:

1. Transform a dendrogram into a ggdend object using as.ggdend() function
2. Make the plot using the function ggplot()

dend <- iris[1:30,-5] %>% scale %>% dist %>% 
   hclust %>% as.dendrogram %>%
   set("branches_k_color", k=3) %>% set("branches_lwd", 1.2) %>%
   set("labels_colors") %>% set("labels_cex", c(.9,1.2)) %>% 
   set("leaves_pch", 19) %>% set("leaves_col", c("blue", "red"))
# plot the dend in usual "base" plotting engine:
plot(dend)

Produce the same plot in ggplot2 using the function:

library(ggplot2)
# Rectangle dendrogram using ggplot2
ggd1 <- as.ggdend(dend)
ggplot(ggd1) 

# Change the theme to the default ggplot2 theme
ggplot(ggd1, horiz = TRUE, theme = NULL) 

# Theme minimal
ggplot(ggd1, theme = theme_minimal()) 

# Create a radial plot and remove labels
ggplot(ggd1, labels = FALSE) + 
  scale_y_reverse(expand = c(0.2, 0)) +
  coord_polar(theta="x")

5.11 pvclust and dendextend

The package dendextend can be used to enhance many packages including pvclust. Recall that, pvclust is for calculating p-values for hierarchical clustering.

pvclust can be used as follow:

library(pvclust)
data(lung) # 916 genes for 73 subjects
set.seed(1234)
result <- pvclust(lung[1:100, 1:10], method.dist="cor", 
                  method.hclust="average", nboot=10)

## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
## Bootstrap (r = 0.7)... Done.
## Bootstrap (r = 0.8)... Done.
## Bootstrap (r = 0.9)... Done.
## Bootstrap (r = 1.0)... Done.
## Bootstrap (r = 1.1)... Done.
## Bootstrap (r = 1.2)... Done.
## Bootstrap (r = 1.3)... Done.
## Bootstrap (r = 1.4)... Done.

# Default plot of the result
plot(result)
pvrect(result)

# pvclust and dendextend
result %>% as.dendrogram %>% 
  set("branches_k_color", k = 2, value = c("purple", "orange")) %>%
  plot
result %>% text
result %>% pvrect

7.1 K-means clustering using eclust()

# Compute k-means
res.km <- eclust(df, "kmeans")

# Gap statistic plot
fviz_gap_stat(res.km$gap_stat)

# Silhouette plot
fviz_silhouette(res.km)

##   cluster size ave.sil.width
## 1       1   13          0.27
## 2       2   13          0.37
## 3       3    8          0.39
## 4       4   16          0.34

7.2 Hierachical clustering using eclust()

 # Enhanced hierarchical clustering
res.hc <- eclust(df, "hclust") # compute hclust
fviz_dend(res.hc, rect = TRUE) # dendrogam

The R code below generates the silhouette plot and the scatter plot for hierarchical clustering.

fviz_silhouette(res.hc) # silhouette plot
fviz_cluster(res.hc) # scatter plot

Install colortools

install.packages("colortools")

Load colortools

library(colortools)

Color wheel

The function wheel() can be used to generate a color wheel for a given color:

wheel("darkblue", num = 12)

##  [1] "#00008B" "#46008B" "#8B008B" "#8B0046" "#8B0000" "#8B4500" "#8B8B00"
##  [8] "#468B00" "#008B00" "#008B45" "#008B8B" "#00468B"

Analogous color scheme

The function adjacent() or analogous() can be used:

analogous("darkblue")

## [1] "#00008B" "#46008B" "#00468B"

Complementary color scheme

complementary("steelblue")

## [1] "#4682B4" "#B47846"

Split Complementary Color Scheme

splitComp("steelblue")

## [1] "#4682B4" "#B4464B" "#B4AF46"

Tetradic Color Scheme

tetradic("steelblue")

## [1] "#4682B4" "#7846B4" "#B47846" "#82B446"

Square color scheme

square("steelblue")

## [1] "#4682B4" "#AF46B4" "#B47846" "#4BB446"

Sequential colors

sequential("steelblue")

##  [1] "#B4B4B4FF" "#ABB0B4FF" "#A2ACB4FF" "#99A8B4FF" "#90A4B4FF"
##  [6] "#87A0B4FF" "#7E9BB4FF" "#7597B4FF" "#6C93B4FF" "#638FB4FF"
## [11] "#5A8BB4FF" "#5187B4FF" "#4883B4FF" "#3F7FB4FF" "#367BB4FF"
## [16] "#2D77B4FF" "#2473B4FF" "#1B6EB4FF" "#126AB4FF" "#0966B4FF"
## [21] "#0062B4FF"

Compute a correlation matrix

The mtcars data set will be used in the following R code. The function cor_pmat() [in ggcorrplot] computes a matrix of correlation p-values.

# Compute a correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
head(corr[, 1:6])

##       mpg  cyl disp   hp drat   wt
## mpg   1.0 -0.9 -0.8 -0.8  0.7 -0.9
## cyl  -0.9  1.0  0.9  0.8 -0.7  0.8
## disp -0.8  0.9  1.0  0.8 -0.7  0.9
## hp   -0.8  0.8  0.8  1.0 -0.4  0.7
## drat  0.7 -0.7 -0.7 -0.4  1.0 -0.7
## wt   -0.9  0.8  0.9  0.7 -0.7  1.0

# Compute a matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
head(p.mat[, 1:4])

##               mpg          cyl         disp           hp
## mpg  0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07
## cyl  6.112687e-10 0.000000e+00 1.803002e-12 3.477861e-09
## disp 9.380327e-10 1.803002e-12 0.000000e+00 7.142679e-08
## hp   1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00
## drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03
## wt   1.293959e-10 1.217567e-07 1.222311e-11 4.145827e-05

Correlation matrix visualization

# Visualize the correlation matrix
# --------------------------------
# method = "square" (default)
ggcorrplot(corr)

# method = "circle"
ggcorrplot(corr, method = "circle")

# Reordering the correlation matrix
# --------------------------------
# using hierarchical clustering
ggcorrplot(corr, hc.order = TRUE, outline.col = "white")

# Types of correlogram layout
# --------------------------------
# Get the lower triangle
ggcorrplot(corr, hc.order = TRUE, type = "lower",
     outline.col = "white")

# Get the upeper triangle
ggcorrplot(corr, hc.order = TRUE, type = "upper",
     outline.col = "white")

# Change colors and theme
# --------------------------------
# Argument colors
ggcorrplot(corr, hc.order = TRUE, type = "lower",
   outline.col = "white",
   ggtheme = ggplot2::theme_gray,
   colors = c("#6D9EC1", "white", "#E46726"))

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr, hc.order = TRUE, type = "lower",
   lab = TRUE)

# Add correlation significance level
# --------------------------------
# Argument p.mat
# Barring the no significant coefficient
ggcorrplot(corr, hc.order = TRUE,
    type = "lower", p.mat = p.mat)

# Leave blank on no significant coefficient
ggcorrplot(corr, p.mat = p.mat, hc.order = TRUE,
    type = "lower", insig = "blank")

Draw survival curves without grouping

# Fit survival curves
require("survival")
fit <- survfit(Surv(time, status) ~ 1, data = lung)

# Drawing curves
ggsurvplot(fit, color = "#2E9FDF")

Draw survival curves with two groups

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Drawing survival curves
ggsurvplot(fit)

# Change the legend title and labels
ggsurvplot(fit, legend = "bottom", 
           legend.title = "Sex",
           legend.labs = c("Male", "Female"))

# Specify legend position by its coordinates
ggsurvplot(fit, legend = c(0.2, 0.2))

# change line size --> 1
# Change line types by groups (i.e. "strata")
# and change color palette
ggsurvplot(fit,  size = 1,  # change line size
           linetype = "strata", # change line type by groups
           break.time.by = 250, # break time axis by 250
           palette = c("#E7B800", "#2E9FDF"), # custom color palette
           conf.int = TRUE, # Add confidence interval
           pval = TRUE # Add p-value
           )

# Use brewer color palette "Dark2"
ggsurvplot(fit, linetype = "strata", 
           conf.int = TRUE, pval = TRUE,
           palette = "Dark2")

# Use grey palette
ggsurvplot(fit, linetype = "strata", 
           conf.int = TRUE, pval = TRUE,
           palette = "grey")

# Add Risk table
ggsurvplot(fit, pval = TRUE, conf.int = TRUE,
           risk.table = TRUE)

# Change color, linetype by strata, risk.table color by strata
ggsurvplot(fit, 
           pval = TRUE, conf.int = TRUE,
           risk.table = TRUE, # Add risk table
           risk.table.col = "strata", # Risk table color by groups
           lienetype = "strata", # Change line type by groups
           ggtheme = theme_bw(), # Change ggplot2 theme
           palette = c("#E7B800", "#2E9FDF"))

# Plot cumulative events
ggsurvplot(fit, conf.int = TRUE,
           palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           fun = "event")

# Plot the cumulative hazard function
ggsurvplot(fit, conf.int = TRUE, 
           palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           fun = "cumhaz")

# Arbitrary function
ggsurvplot(fit, conf.int = TRUE, 
          palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           pval = TRUE,
           fun = function(y) y*100)

Survival curves with multiple groups

# Fit (complexe) survival curves
#++++++++++++++++++++++++++++++++++++
require("survival")
fit2 <- survfit( Surv(time, status) ~ rx + adhere,
    data = colon )

# Visualize
#++++++++++++++++++++++++++++++++++++
# Visualize: add p-value, chang y limits
# change color using brewer palette
ggsurvplot(fit2, pval = TRUE, 
           break.time.by = 400,
           risk.table = TRUE)

# Adjust risk table and survival plot locations 
# ++++++++++++++++++++++++++++++++++++
# Adjust risk table location, shift to the left
ggsurvplot(fit2, pval = TRUE,
           break.time.by = 400, 
           risk.table = TRUE,
           risk.table.col = "strata",
           risk.table.adj = -2, # risk table location adj
           palette = "Dark2")

# Adjust survival plot location, shift to the right
# ++++++++++++++++++++++++++++++++++++
ggsurvplot(fit2, pval = TRUE,
           break.time.by = 400, 
           risk.table = TRUE,
           risk.table.col = "strata",
           surv.plot.adj = 4.9, # surv plot location adj
           palette = "Dark2")

# Risk table height
# ++++++++++++++++++++++++++++++++++++
ggsurvplot(fit2, pval = TRUE,
           break.time.by = 400, 
           risk.table = TRUE,
           risk.table.col = "strata",
           risk.table.height = 0.5, # Useful when you have multiple groups
           surv.plot.adj = 4.9, # surv plot location adj
           palette = "Dark2")

# Change legend labels
# ++++++++++++++++++++++++++++++++++++
ggsurvplot(fit2, pval = TRUE, 
           break.time.by = 400,
           risk.table = TRUE,
           risk.table.col = "strata",
           ggtheme = theme_bw(),
           legend.labs = c("A", "B", "C", "D", "E", "F"))

Scatter plots with text annotations

We start by creating a simple scatter plot using a subset of the mtcars data set containing 15 rows.

1. Prepare some data:

# Take a subset of 15 random points
set.seed(1234)
ss <- sample(1:32, 15)
df <- mtcars[ss, ]

2. Create a scatter plot:

p <- ggplot(df, aes(wt, mpg)) +
  geom_point(color = 'red') +
  theme_classic(base_size = 10)

3. Add text labels:

# Add text annotations using ggplot2::geom_text
p + geom_text(aes(label = rownames(df)),
              size = 3.5)

# Use ggrepel::geom_text_repel
require("ggrepel")
set.seed(42)
p + geom_text_repel(aes(label = rownames(df)),
                    size = 3.5) 

# Use ggrepel::geom_label_repel and 
# Change color by groups
set.seed(42)
p + geom_label_repel(aes(label = rownames(df),
                    fill = factor(cyl)), color = 'white',
                    size = 3.5) +
   theme(legend.position = "bottom")

Volcano plot

genes <- read.table("https://gist.githubusercontent.com/stephenturner/806e31fce55a8b7175af/raw/1a507c4c3f9f1baaa3a69187223ff3d3050628d4/results.txt", header = TRUE)
genes$Significant <- ifelse(genes$padj < 0.05, "FDR < 0.05", "Not Sig")

ggplot(genes, aes(x = log2FoldChange, y = -log10(pvalue))) +
  geom_point(aes(color = Significant)) +
  scale_color_manual(values = c("red", "grey")) +
  theme_bw(base_size = 12) + theme(legend.position = "bottom") +
  geom_text_repel(
    data = subset(genes, padj < 0.05),
    aes(label = Gene),
    size = 5,
    box.padding = unit(0.35, "lines"),
    point.padding = unit(0.3, "lines")
  )

source

Installing and loading xlsx package

• Install

install.packages("xlsx")

• Load

library("xlsx")

Using xlsx package

There are two main functions in xlsx package for writing both xls and xlsx Excel files: write.xlsx() and write.xlsx2() [faster on big files compared to write.xlsx function].

The simplified formats are:

write.xlsx(x, file, sheetName = "Sheet1", 
  col.names = TRUE, row.names = TRUE, append = FALSE)

write.xlsx2(x, file, sheetName = "Sheet1",
  col.names = TRUE, row.names = TRUE, append = FALSE)

Example of usage: the following R code will write the R built-in data sets - USArrests, mtcars and iris - into the same Excel file:

library("xlsx")

# Write the first data set in a new workbook
write.xlsx(USArrests, file = "myworkbook.xlsx",
      sheetName = "USA-ARRESTS", append = FALSE)

# Add a second data set in a new worksheet
write.xlsx(mtcars, file = "myworkbook.xlsx", 
           sheetName="MTCARS", append=TRUE)

# Add a third data set
write.xlsx(iris, file = "myworkbook.xlsx",
           sheetName="IRIS", append=TRUE)

Read more

Read more about for reading, writing and formatting Excel files:

• R xlsx package : A quick start guide to manipulate Excel files in R
• r2excel package: Read, write and format easily Excel files using R software

Change the background colors of rows and columns

You should know three functions to change the appearance of table rows and columns :

• setZebraStyle() : to color odd and even rows differently; for example, odd rows in gray color and even rows in white color.
• setRowsColors() : to change color of a particular table row
• setColumnsColors : to change the color of particular table columns

These functions can be used as follow :

library(ReporteRs)

doc <- docx()

data<-iris[1:5, ]

# Zebra striped tables
doc <- addTitle(doc, "Zebra striped tables")
MyFTable <- vanilla.table(data)
MyFTable <- setZebraStyle(MyFTable, odd = '#eeeeee', even = 'white')
doc <- addFlexTable( doc, MyFTable)

# Change columns and rows background colors
doc <- addTitle(doc, "Change columns and rows background colors")
MyFTable = FlexTable(data = data )
# i : row index; j : column index
MyFTable = setRowsColors(MyFTable, i=2:3, colors = 'lightblue')
MyFTable = setColumnsColors(MyFTable, j=3, colors = 'pink' )
doc <- addFlexTable(doc, MyFTable)

writeDoc(doc, file = "r-reporters-word-document-formatted-table1.docx")

Note that, i and j are, respectively, the index of rows and column to change

Change cell background and text colors

We can change the background colors of some cells according to their values using the function setFlexTableBackgroundColors().

As an example, We’ll set up the background color of column 2 according to the value of the Sepal.Width variable (iris data sets) :

• Cells with Sepal.Width < 3.2 are colored in gray (“#DDDDDD”)
• Cells with Sepal.Width > = 3.2 are colored in “orange”

The text values of the table cells can be also customized as demonstrated in the example below :

library(ReporteRs)

doc <- docx()

data<-iris[1:5, ]

# Change the background colors of column 2 according to Sepal.Width
#++++++++++++++++++++++++++++
doc <- addTitle(doc, "Change the background color of cells")
MyFTable <- FlexTable(data)
MyFTable <- setFlexTableBackgroundColors(MyFTable, j = 2,
  colors = ifelse(data$Sepal.Width < 3.2, '#DDDDDD', 'orange'))

doc <- addFlexTable( doc, MyFTable)

# Format the text of some cells (column 3:4)
#++++++++++++++++++++++++++++
doc <- addTitle(doc, "Format cell text values")
MyFTable = FlexTable(data)
MyFTable[, 3:4] = textProperties(color = 'blue')
doc <- addFlexTable( doc, MyFTable)

writeDoc(doc, file = "r-reporters-word-document-format-cells.docx")

Insert content into a table : header and footer rows

Additional rows, such as header and footer, can be easily added into a table as illustrated in the example hereafter. Contents can be also added in a particular cell.

The functions addHeaderRow() and addFooterRow() are used to add a header and a footer.

A simplified format of these functions are :

addHeaderRow(x, value, colspan, text.properties)

addFooterRow(x, value, colspan, text.properties)

library(ReporteRs)
doc <- docx()

data<-iris[1:5, ]

# Insert a content into a table
doc <- addTitle(doc, "Insert a content")
MyFTable = FlexTable(data, header.columns= FALSE )

# Add first header row
MyFTable <- addHeaderRow( MyFTable, 
            value = c('Sepal', 'Petal', 'Species'), 
            text.properties = textBold(color="orange"), 
            colspan = c( 2, 2, 1))

# Add second header row
MyFTable = addHeaderRow(MyFTable, value = names(data),
  text.properties = textBold())
     
# Add footer row
MyFTable <- addFooterRow(MyFTable, 
              value = 'This data is from iris data sets',
              colspan = 5, 
              text.properties = textBoldItalic(color ="blue"))
# Add symbol
MyFTable[data$Petal.Length!=1.4, 'Species',
  text.properties = textBold(vertical.align = 'superscript',
                             color = "red")] = '(1)'
doc <- addFlexTable( doc, MyFTable)

writeDoc(doc, file = "r-reporters-word-document-add-header-footer.docx")

Case of base graphs

The example below creates a PowerPoint document in your current working directory. The document contains one slide with 2 panels :

• The first panel contains an editable box plot
• The second panel contains a raster format

The PowerPoint document created by the R code below is available here : R software and ReporteRs package - create an editable base graph

library('ReporteRs')
# Create a new powerpoint document
doc <- pptx()

# Add a new slide into the ppt document 
doc <- addSlide(doc, "Two Content" )

# add a slide title
doc<- addTitle(doc, "Editable vector graphics format versus raster format" )

# A function for creating a box plot
boxplotFunc<- function(){
      boxplot(len ~ dose, data = ToothGrowth, 
        col=2:4,main = "Guinea Pigs' Tooth Growth",
        xlab = "Vitamin C dose mg",
        ylab = "tooth length")
      }

# Add an editable box plot
doc <- addPlot(doc, boxplotFunc, vector.graphic = TRUE )

# Add a raster box plot
doc <- addPlot(doc, boxplotFunc, vector.graphic = FALSE )

# write the document to a file
writeDoc(doc, file = "editable-graph.pptx")

Open the created PowerPoint and try to edit the first box plot by changing the fill colors, line types, etc…

Case of graphs generated using ggplot2

ggplot2 is a powerful R package, implemented by Hadley Wickham, for producing a visually appealing charts.

Install and load it as follow :

install.packages('ggplot2') # Install
library('ggplot2') # Load

Create an editable plot with ggplot2 :

library('ReporteRs')
library(ggplot2)
# Create a new powerpoint document
doc <- pptx()

# Add a new slide into the ppt document 
doc <- addSlide(doc, "Two Content" )

# add a slide title
doc<- addTitle(doc, "Editable vector graphics format versus raster format" )

# A function for creating a box plot
bp <- ggplot(data=PlantGrowth, aes(x=group, y=weight, fill=group))+
        geom_boxplot()

# Add an editable box plot
doc <- addPlot(doc, function() print(bp), vector.graphic = TRUE )

# Add a raster box plot
doc <- addPlot(doc, function() print(bp), vector.graphic = FALSE )

# write the document to a file
writeDoc(doc, file = "editable-ggplot2.pptx")

The PowerPoint document created by the R code above is available here : R software and ReporteRs package - create an editable ggplot2

Reason I : Many collaborators works with Microsoft office tools

About 1 billion people worldwide use Microsoft Office (1 in 7 people on the planet; source: Microsoft).

Furthermore, many collaborators still working with MS Office software (Word, PowerPoint, Excel) for :

• editing their text and tracking changes
• copy-pasting texts, images and tables from multiple sources
• saving and analyzing their data

In this context, a report generated as a PDF or HTMl files is less useful with some collaborators.

Reason II : keeping beautiful R graphs beautifull for publications

R plots can be customized to be as beautiful as your imagination can make them. Unfortunately, preserving this beauty is not always an easy task when you want to publish these graphs or show them in a professional presentations.

Yes, this problem can be solved using knitr/rmarkdown/Latex/Beamer/Slidify. However, it would be very difficult to remake the whole presentation in a different format. Furthermore, many journals don’t accept Latex documents.

(source).

Slide layout

Before showing you an example of how to create and format PowerPoint from R Software, let’s first discuss about slide layout. This is very important to understand the examples provided in this tutorial.

When creating a new slide, you should specify the layout of the slide. The available layouts in the “MS Office PowerPoint” (default template, on my computer) are illustrated in the figure below :

addSlide() function can be used to add a new slide into a PowerPoint document from R software. A simplified format of the function is :

doc <- addSlide(doc, slide.layout)

Among the possible values for the argument slide.layout, there are : “Title Slide”, “Title and Content”, “Two Content”, “Section Header”, “Content with Caption”, “Title Only”, “Comparison”.

However, you should use only the available slide layouts in your computer.

library(ReporteRs)
doc = pptx()
slide.layouts(doc)

 [1] "Title Slide"             "Title and Vertical Text" "Title and Content"       "Two Content"            
 [5] "Section Header"          "Vertical Title and Text" "Content with Caption"    "Title Only"             
 [9] "Comparison"              "Blank"                  

These layouts are illustrated below :

doc <- pptx()
layouts <-slide.layouts(doc) # All available layout
#  plot each slide style
for(i in layouts ){
  par(mar=c(0.5,0.5,2,0.5), cex=0.7)
  slide.layouts(doc, i )
    title(main = paste0("'", i, "'" ))
  if(interactive()) readline(prompt = "Show next slide layout")
}

Whatever the slide layout chosen, you can use the functions addDate(), addFooter() and addPageNumber() to add date, footer and slide number, respectively.

Generate a simple PowerPoint document from R software

The R code below creates a PowerPoint document with a title slide, plots, tables, and an R script :

library( ReporteRs )

# Create a PowerPoint document
doc = pptx( )

# Slide 1 : Title slide
#+++++++++++++++++++++++
doc <- addSlide(doc, "Title Slide")
doc <- addTitle(doc,"Create a PowerPoint document from R software")
doc <- addSubtitle(doc, "R and ReporteRs package")
doc <- addDate(doc)
doc <- addFooter(doc, "Isaac Newton")
doc <- addPageNumber(doc, "1/4")

# Slide 2 : Add plot
#+++++++++++++++++++++++
doc <- addSlide(doc, "Title and Content")
doc <- addTitle(doc, "Bar plot")
plotFunc<- function(){
  barplot(VADeaths, beside = TRUE,
          col = c("lightblue", "mistyrose", "lightcyan","lavender", "cornsilk"),
  legend = rownames(VADeaths), ylim = c(0, 100))
  title(main = "Death Rates in Virginia", font.main = 4)
}
doc <- addPlot(doc, plotFunc )
doc <- addPageNumber(doc, "2/4")

# Slide 3 : Add table 
#+++++++++++++++++++++++
doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc,"iris data sets")
doc <- addFlexTable(doc, FlexTable(iris[1:10,] ))
doc <- addParagraph(doc, "iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.")
doc <- addPageNumber(doc, "3/4")

# Silde 4 : Add R script
#+++++++++++++++++++++
doc <- addSlide(doc, "Content with Caption")
doc <- addTitle(doc, "R Script for histogram plot")
doc <- addPlot(doc, function() hist(iris$Sepal.Width, col=4))
r_code ="data(iris)
hist(iris$Sepal.Width, col = 4)"
doc <- addRScript(doc, text=r_code)

# write the document 
writeDoc(doc, "r-reporters-powerpoint.pptx" )

The PowerPoint document created by the R code above is available here : R software and ReporteRs package - Example of creating a PowerPoint document

doc <- addPageNumber(doc)

doc <- addPageNumber(doc, "1/2") # Example 1
doc <- addPageNumber(doc, "I") # Example 2

Format the text of a PowerPoint document

options( "ReporteRs-fontsize" = 18, "ReporteRs-default-font" = "Arial")

pot(value="", format = textProperties())

library( ReporteRs )

# Change the default font size and font family
options('ReporteRs-fontsize'= 18, 'ReporteRs-default-font'='Arial')

doc = pptx( )

doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc,"Document with formatted texts")
doc <- addFlexTable(doc, FlexTable(iris[1:10,] ))

my_text <- pot("iris data set", textBold(color = "blue"))+" contains the measurements of " + 
          pot("sepal length", textBold(color="red"))+ " and width and petal length and width"
my_link <- pot('Click here to visit STHDA web site!', 
    hyperlink = 'http://www.sthda.com/english',
    format=textBoldItalic(color = 'blue', underline = TRUE ))

doc <- addParagraph(doc, 
      value = set_of_paragraphs(my_text, " ",  my_link),
     par.properties=parProperties(text.align="justify")
    )

writeDoc(doc, "r-reporters-powerpoint-formatted.pptx" )

Add plots and images

The functions addPlot() and addImage() can be used for adding a plot or an external image to the document. addPlot() works with all R plots (base graphics, lattice, ggplot2 and grid).

These two functions can be used as follow :

# Add plots
# fun : R plotting function
# ... : other arguments to pass to the plotting function
addPlot(doc, fun, ...)

# Add images
# filename : path to the external image
addImage(doc, filename)

The R code below creates a PowerPoint document containing a histogram and an image (downloaded from R website) :

library( ReporteRs )

doc = pptx()

# Slide 1 : Title slide
doc <- addSlide(doc, "Title Slide")
doc <- addTitle(doc,"Document containing plots and images")
doc <- addSubtitle(doc, "R and ReporteRs package")

# Slide 2 : Add plot
doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc,"Histogram plot")
doc <- addPlot(doc, function() hist(iris$Sepal.Width, col="lightblue"))
doc <- addParagraph(doc, "This histogram is generated using iris data sets")

# Slide 3 : Add  an image
# download an image from R website
download.file(url="http://www.r-project.org/hpgraphic.png",
              destfile="r-home-image.png", quiet=TRUE)
doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc,"Image from R website")
doc <- addImage(doc, "r-home-image.png")
doc <- addParagraph(doc, "This image has been downloaded from R website")
writeDoc(doc, "r-reporters-powerpoint-plot-image.pptx")

The PowerPoint document created by the R code above is available here : R software and ReporteRs package - PowerPoint document containing plots and images

Add a table

addFlexTable() function is used to format and add a table into the PowerPoint.

It can be used as follow :

• STEP 1 : Create a table using FlexTable() or vanilla.table() function. These two functions generate a ‘flexible’ table which can be easily formatted before adding into the slide.
• STEP 2 : Add the create table into the document using addFlexTable() function as follow :

# doc : pptx object
# flextable : FlexTable object

# Example 1
doc <- addFlexTable(doc, flextable = FlexTable(data))

# Example 2 
doc <- addFlexTable(doc, flextable = vanilla.table(data))

setZebraStyle() function can be used to color odd and even rows differently; for example, odd rows in gray color and even rows in white color.

The example below creates a PowerPoint document with 3 slides containing a simple table (slide 1), vanilla table (slide 2) and a zebra striped table (slide 3) :

doc = pptx()

data<-iris[1:5, ]

# Slide 1 : Simple table
doc <- addSlide(doc, "Title and Content")
doc <- addTitle(doc,"Simple table")
doc <- addFlexTable(doc, FlexTable(data))

# Slide 2 : vanilla table
doc <- addSlide(doc, "Title and Content")
doc <- addTitle(doc,"Vanilla table")
doc <- addFlexTable(doc, vanilla.table(data))

# Slide 3 : Zebra striped table
doc <- addSlide(doc, "Title and Content")
doc <- addTitle(doc,"Zebra striped table")
MyFTable <- vanilla.table(data)
MyFTable <- setZebraStyle(MyFTable, odd = '#eeeeee', even = 'white')
doc <- addFlexTable( doc, MyFTable)

writeDoc(doc, "r-reporters-powerpoint-add-table.pptx")

The PowerPoint document created by the R code above is available here : R software and ReporteRs package - PowerPoint document containing tables

Add ordered and unordered lists

Lists can be added using addParagraph() function as follow :

doc  <- addParagraph(doc, 
  value = c('Item 1', "Item 2", "Item 3")
  par.properties = parProperties(list.style = 'ordered', level = 1 )

The example below generates a one-slide PowerPoint document containing an ordered and unordered lists :

doc <- pptx()

doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc, "Ordered and unordored lists")

# 1. Ordered list
doc <- addParagraph(doc, value= c("Item 1", "Item 2", "Item 3"),
          par.properties =  parProperties(list.style = 'ordered'))

# 2. Unordered list
doc <- addParagraph(doc, value= c("Item 1", "Item 2", "Item 3"),
          par.properties =  parProperties(list.style = 'unordered'))

writeDoc(doc, file = "r-reporters-powerpoint-lists.pptx")

Download a template file

# Download a PowerPoint template file from STHDA website
download.file(url="http://www.sthda.com/sthda/RDoc/example-files/r-reporters-powerpoint-template.pptx",
    destfile="r-reporters-powerpoint-template.pptx", quiet=TRUE)

Slide layouts available in the template file

You can use one of the layout below when adding a new slide into the PowerPoint :

doc <- pptx(template="r-reporters-powerpoint-template.pptx")
layouts <-slide.layouts(doc) # All available layout
# Plot the layouts
for(i in layouts ){
  par(mar=c(0.5,0.5,2,0.5), cex=0.7)
  slide.layouts(doc, i )
  title(main = paste0("'", i, "'" ))
  if(interactive()) readline(prompt = "Show next slide layout")
}

Create a PowerPoint document from the template file

Note that, the template file contains already one empty slide which can be removed manually.

library( ReporteRs )

# Download a PowerPoint template file from STHDA website
download.file(url="http://www.sthda.com/sthda/RDoc/example-files/r-reporters-powerpoint-template.pptx",
    destfile="r-reporters-powerpoint-template.pptx", quiet=TRUE)

options('ReporteRs-fontsize'= 18, 'ReporteRs-default-font'='Arial')

doc <- pptx(template="r-reporters-powerpoint-template.pptx" )

# Slide 1 : Title slide
#+++++++++++++++++++++++
doc <- addSlide(doc, "Title Slide")
doc <- addTitle(doc,"Create a PowerPoint from template using R software")
doc <- addSubtitle(doc, "R and ReporteRs package")
doc <- addDate(doc)
doc <- addFooter(doc, "Isaac Newton")
doc <- addPageNumber(doc, "1/4")

# Slide 2 : Add plot
#+++++++++++++++++++++++
doc <- addSlide(doc, "Title and Content")
doc <- addTitle(doc, "Bar plot")
plotFunc<- function(){
  barplot(VADeaths, beside = TRUE,
          col = c("lightblue", "mistyrose", "lightcyan","lavender", "cornsilk"),
  legend = rownames(VADeaths), ylim = c(0, 100))
  title(main = "Death Rates in Virginia", font.main = 4)
}
doc <- addPlot(doc, plotFunc )
doc <- addPageNumber(doc, "2/4")

# Slide 3 : Add table 
#+++++++++++++++++++++++
doc <- addSlide(doc, "Two Content")
doc <- addTitle(doc,"iris data sets")
doc <- addFlexTable(doc, FlexTable(iris[1:4,] ))
doc <- addParagraph(doc, "iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.")
doc <- addPageNumber(doc, "3/4")

# Silde 4 : Add R script
#+++++++++++++++++++++
doc <- addSlide(doc, "Content with Caption")
doc <- addTitle(doc, "R Script for histogram plot")
doc <- addPlot(doc, function() hist(iris$Sepal.Width, col=4))
r_code ="data(iris)
hist(iris$Sepal.Width, col = 4)"
doc <- addRScript(doc, text=r_code)

# write the document 
writeDoc(doc, "r-reporters-powerpoint-from-template.pptx" )

The PowerPoint document created by the R code above is available here : R software and ReporteRs package - PowerPoint document from template

mtcars: Motor Trend Car Road Tests

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models)

• View the content of mtcars data set:

# 1. Loading 
data("mtcars")
# 2. Print
head(mtcars)

• It contains 32 observations and 11 variables:

# Number of rows (observations)
nrow(mtcars)

[1] 32

# Number of columns (variables)
ncol(mtcars)

[1] 11

• Description of variables:

1. mpg: Miles/(US) gallon
2. cyl: Number of cylinders
3. disp: Displacement (cu.in.)
4. hp: Gross horsepower
5. drat: Rear axle ratio
6. wt: Weight (1000 lbs)
7. qsec: 1/4 mile time
8. vs: V/S
9. am: Transmission (0 = automatic, 1 = manual)
10. gear: Number of forward gears
11. carb: Number of carburetors

If you want to learn more about mtcars, type this:

?mtcars

iris

iris data set gives the measurements in centimeters of the variables sepal length, sepal width, petal length and petal width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

data("iris")

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

ToothGrowth

ToothGrowth data set contains the result from an experiment studying the effect of vitamin C on tooth growth in 60 Guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, (orange juice or ascorbic acid (a form of vitamin C and coded as VC).

data("ToothGrowth")
head(ToothGrowth)

   len supp dose
1  4.2   VC  0.5
2 11.5   VC  0.5
3  7.3   VC  0.5
4  5.8   VC  0.5
5  6.4   VC  0.5
6 10.0   VC  0.5

1. len: Tooth length
2. supp: Supplement type (VC or OJ).
3. dose: numeric Dose in milligrams/day

PlantGrowth

Results obtained from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment condition.

data("PlantGrowth")
head(PlantGrowth)

  weight group
1   4.17  ctrl
2   5.58  ctrl
3   5.18  ctrl
4   6.11  ctrl
5   4.50  ctrl
6   4.61  ctrl

USArrests

This data set contains statistics about violent crime rates by us state.

data("USArrests")
head(USArrests)

           Murder Assault UrbanPop Rape
Alabama      13.2     236       58 21.2
Alaska       10.0     263       48 44.5
Arizona       8.1     294       80 31.0
Arkansas      8.8     190       50 19.5
California    9.0     276       91 40.6
Colorado      7.9     204       78 38.7

1. Murder: Murder arrests (per 100,000)
2. Assault: Assault arrests (per 100,000)
3. UrbanPop: Percent urban population
4. Rape: Rape arrests (per 100,000)