Exercise 1: Simple Calculations

• Use R to calculate the following:
  • multiply 31 and 78
  • divide 697 with 41
• Assign the value of 39 to x
• Assign the value of 22 to y

31 * 78

## [1] 2418

697 / 41

## [1] 17

x <- 39
x

## [1] 39

y <- 22
y

## [1] 22

Exercise 2: Working with Vectors

• Create a vector called newvec with 20 elements
• add names to the elements
• retrieve 1st 5 elements
• retrieve any 3 elements by name

myvec <- c(1:20)

names(myvec) <- LETTERS[1:20]

myvec

##  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T 
##  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

myvec[1:5]

## A B C D E 
## 1 2 3 4 5

myvec[c(1,4,6)]

## A D F 
## 1 4 6

Exercise 3: Lists and Data Frames

• Write a R program to create a list of dataframes and access each of those data frames from the list.
• Create a data frame from a matrix of your choice, change the row names so every row says id_i (where i is the row number) and change the column names to variable_i (where i is the column number). I.e., for column 1 it will say variable_1, and for row 2 will say id_2 and so on.

mylist <- list(data.frame("one"=1:10,"two"=11:20,"three"=21:30),
               data.frame("A"=letters[1:5],"B"=letters[6:10],"C"=letters[11:15] )
)

mylist

## [[1]]
##    one two three
## 1    1  11    21
## 2    2  12    22
## 3    3  13    23
## 4    4  14    24
## 5    5  15    25
## 6    6  16    26
## 7    7  17    27
## 8    8  18    28
## 9    9  19    29
## 10  10  20    30
## 
## [[2]]
##   A B C
## 1 a f k
## 2 b g l
## 3 c h m
## 4 d i n
## 5 e j o

mylist[[1]]

mylist[[2]]

mymatrix <- matrix(1:100,nrow = 20, ncol = 5)
mydf <- data.frame(mymatrix)
colnames(mydf) <- paste0("variable_",1:ncol(mydf))
rownames(mydf) <- paste0("id_",rownames(mydf))
mydf

Exercise 4: Reading & Writing

• load iris data. save the dataframe in .csv, .tsv and .xls format

data(iris)

write.csv(iris, file = "irisdata.csv")

write.table(iris, file = "irisdata.tsv", sep = "\t", quote = FALSE, row.names = FALSE)

library(openxlsx)
write.xlsx(iris, file = "irisdata.xlsx", colNames = TRUE, borders = "columns")

Exercise 5 [simple]

Draw a scatterplot of Petal.Length versus Sepal.Length.

Then mark the mean Petal.Length of each species by a dashed vertical line. Give each line its own color.

Hints:

• The means are pre-computed in the “How To Draw a Bar Chart” example. You can extract the Petal.Length means from the respective column of df.
• Use ?par to see the Graphical Parameters help page. Parameter lty sets the line type

## The code of the example:
df <- aggregate(
        iris[,1:4], 
        by = list(iris$Species), 
        FUN = mean)

# Get the means from the example df:
m <- df$Petal.Length

# Draw the scatterplot:
plot(iris$Petal.Length, 
     iris$Sepal.Length, 
     pch=19, col=2)

# Draw the lines:
abline(v=m, 
       col=c("red","green","blue"), 
       lty=2)

Exercise 6 [medium]

Draw a scatterplot of Petal.Length versus Sepal.Length. Color the points by species.

Then mark the point (x=mean(Petal.Length), y=mean(Sepal.Length)) of each species with fat points on the plot. Color each point color according to its species.
Put the names of the species as text labels next to the points. Add a title to the plot.

Hints:

• The means are pre-computed in the “How To Draw a Bar Chart” example. You can extract the Petal.Length means from the respective column of df.
• The col parameter of plot() can take a vector argument, assigning an individual color to each point. To construct this vector from the Species column
  • first define a mapping from species to colors (a vector, where each element is a color and each element name is a species)
  • then index this vector with the Species column
• Use ?par to see the Graphical Parameters help page. Parameter cex changes the point size. Use ?base::plot to see the help page of the plot function. Parameter “main” sets the title.

## The code of the example:
df <- aggregate(
        iris[,1:4], 
        by = list(iris$Species), 
        FUN = mean)

# Get the means from the example df:
mPL <- df$Petal.Length
mSL <- df$Sepal.Length

# print them:
mPL

## [1] 1.462 4.260 5.552

mSL

## [1] 5.006 5.936 6.588

# Define the colors:
species_cols <- c("red","green","blue")
# associate species names with colors:
names(species_cols) <- df$Group.1
# print the result
species_cols

##     setosa versicolor  virginica 
##      "red"    "green"     "blue"

# make the per-species color vector:
my_cols <- species_cols[iris$Species]
# print it
my_cols

##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa     setosa     setosa     setosa     setosa     setosa     setosa 
##      "red"      "red"      "red"      "red"      "red"      "red"      "red" 
##     setosa versicolor versicolor versicolor versicolor versicolor versicolor 
##      "red"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor versicolor versicolor versicolor versicolor versicolor 
##    "green"    "green"    "green"    "green"    "green"    "green"    "green" 
## versicolor versicolor  virginica  virginica  virginica  virginica  virginica 
##    "green"    "green"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica  virginica  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"     "blue"     "blue"     "blue"     "blue" 
##  virginica  virginica  virginica 
##     "blue"     "blue"     "blue"

# Draw the scatterplot, using ny_cols as color argument
plot(iris$Petal.Length, 
     iris$Sepal.Length, 
     pch=19, 
     col=my_cols)


# plot the points marking the means:
points(mPL,
       mSL,
       pch=19,
       col=species_cols,
       cex=3
       )

Exercise 7 [complex]

Similar to the “Organs Distributions” example, combine three histograms in one plot, one for each of the 3 species in the iris dataset. Color the 3 histograms differently. Add a title and a legend to the plot. Can you position the legend towards the top middle, such that it does not overlap the histograms?

The complexity of this is in part not a question of plotting, but of programmatically producing the input to the plot!

Hints:

• I suggest to use tapply() to slice the iris dataset into a named list of per-species datasets. See ?tapply. If x is an index column in a data.frame df (like Species in our case), then l <- tapply(df, ~x, data.frame) returns a list named l, with as many elements as there are different x values. Each list element holds a subset of df with a fixed x value. In the tapply call, the third parameter is a function, to be a applied to the subset defined by the current index value. Here, the data.frame() function insists that the subset is to be returned as a data.frame rather than a list.

Can you transfer this to the present case?

• Once you have the list, the scheme for plotting the histograms is as follows
  • Plot the histogram of `l[[1]]. This initializes the plot, which means that you have to set global parameters like the title, the x axis label, and the x range (xlim). Keep in mind that the histograms of all species must fit on the canvas!
  • Loop over the list elements 2 and 3 and plot their histograms.
• For the legend, see the “How To Draw a Histogram” example. However if you just copy the example, then the legend will partly cover the histograms. Use ?legend and check the meaning of the x and y arguments. Try to find an optimal position!

# split the iris data.frame by species (the result is a named list):
iris_by_species <- 
  tapply(iris, 
         ~Species, 
         data.frame)

# The histogram of the first species initiates the plot:
# global parameters must be set here
hist(iris_by_species[[1]]$Petal.Length,
     col = 1,

     ## mind that the final plot will cover the
     ## entire species range!
     xlim=range(iris$Petal.Length), 
     xlab="Petal.Length",
     main = "Petal.Length by Species"
)
# plot the remaining species histograms in a loop:
for(i in 2:length(iris_by_species)) 
    hist(iris_by_species[[i]]$Petal.Length,
         col = i,
     add=TRUE
    )   
# plot the legend at an optimal position:
legend("topright", 
       legend=levels(iris$Species), 
       #fill=c(rgb(1, 0, 0, alpha=0.5), 
       #       rgb(0, 0, 1, alpha=0.5))
       fill=1:3,
       x=2,
       y=13
       )

library(ggplot2)

Exercise 8: Simple Scatter Plot

• Objective: Create a scatter plot of Sepal.Length vs. Sepal.Width from the iris dataset.
• Difficulty: Easy
• Estimated Time: 5-10 minutes (for beginners)

# Load the iris dataset
data(iris)

# Create a scatter plot with ggplot
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + 
  geom_point()

• Your Task: Color the points by Species and add labels to the x and y axes.
• Difficulty: Easy
• Estimated Time: 5-10 minutes

# Solution to Exercise 1
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  xlab("Sepal Length") +
  ylab("Sepal Width")

Exercise 9: Adding Titles and Labels

• Objective: Use the plot from Exercise 1 and add a title, x-axis label, and y-axis label.
• Difficulty: Easyy
• Estimated Time: 5 minutes

# Create scatter plot with labels
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  ggtitle("Scatter plot of Sepal Dimensions") +
  xlab("Sepal Length (cm)") +
  ylab("Sepal Width (cm)")

• Your Task: Modify the plot to include a subtitle and caption.
• Difficulty: Easy
• Estimated Time: 5 minutes

# Solution to Exercise 2
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  ggtitle("Scatter plot of Sepal Dimensions") +
  xlab("Sepal Length (cm)") +
  ylab("Sepal Width (cm)") +
  labs(subtitle = "Iris dataset", caption = "Source: Fisher, 1936")

Exercise 10: Bar Plot of Species Count

• Objective: Create a bar plot showing the count of each species in the iris dataset.
• Difficulty: Easy
• Estimated Time: 5-10 minutes

# Create a bar plot of species count
ggplot(iris, aes(x = Species)) +
  geom_bar()

• Your Task: Modify the plot to use different colors for each species.
• Difficulty: Easy
• Estimated Time: 5-10 minutes

# Solution to Exercise 3
ggplot(iris, aes(x = Species, fill = Species)) +
  geom_bar() +
  labs(title = "Bar Plot of Species Count")

Exercise 11: Boxplot of Petal Length by Species

• Objective: Create a boxplot of Petal.Length for each species in the iris dataset.
• Difficulty: Medium
• Estimated Time: 5-10 minutes

# Create a boxplot of Petal Length by Species
ggplot(iris, aes(x = Species, y = Petal.Length)) +
  geom_boxplot()

• Your Task: Customize the plot by changing the colors of the boxplots and adding a theme.
• Difficulty: Medium
• Estimated Time: 10-15 minutes

# Solution to Exercise 4
ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_boxplot() +
  theme_minimal() +
  labs(title = "Boxplot of Petal Length by Species")

Exercise 12: Histogram of Sepal Length

• Objective: Create a histogram of Sepal.Length in the iris dataset.
• Difficulty: Easy
• Estimated Time: 5-10 minutes

# Create a histogram of Sepal Length
ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(binwidth = 0.3, fill = "lightblue", color = "black")

• Your Task: Change the number of bins and add a title to the histogram.
• Difficulty: Easy
• Estimated Time: 5-10 minutes

# Solution to Exercise 5
ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(binwidth = 0.2, fill = "lightgreen", color = "black") +
  labs(title = "Histogram of Sepal Length")

Exercise 13: Faceting by Species

• Objective: Use faceting to create multiple scatter plots for each species in the iris dataset.
• Difficulty: Medium
• Estimated Time: 10 minutes

# Facet scatter plot by Species
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  facet_wrap(~ Species)

• Your Task: Customize the facet labels and add a theme to the plot.
• Difficulty: Medium
• Estimated Time: 15 minutes

# Solution to Exercise 6
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  facet_wrap(~ Species, 
             labeller = labeller(Species = toupper),             # 1. Uppercase labels
             nrow = 1) +                                         # 2. Set facets in a single row
  theme_minimal() +                                              # 3. Minimal theme
  theme(strip.text = element_text(face = "bold", size = 14),      # 4. Bold and larger facet labels
        strip.background = element_rect(fill = "lightblue")) +    # 5. Change background color of facet labels
  labs(title = "Faceted Scatter Plot with Custom Labels")