rmarkdown in order to ‘knit’ the HTML output.ctrl/cmd + shift + K in RStudio) to produce a HTML file.rmarkdown (comes with RStudio)tidyverseTeachingDemosYour first task is simply to show that you have been able to install and run R and R Markdown. You don’t have to change this code, just uncomment it. Then the correct output will automatically appear when you ‘knit’ the document.
# UNCOMMENT THE CODE
#R.version
#sessionInfo()Which version of R are you running? On which platform are you running it?
ANSWER:
(your answer here)
Install the package tidyverse. Don’t install it in the code below. Instead, install it through the console. Then write code below to load the package and show the sessionInfo again.
# YOUR CODE HEREWhich version of tidyverse do you have installed?
ANSWER:
(your answer here)
Install the package TeachingDemos. Then uncomment the code below and change "yourLastName" to your lastname. Use all lowercase letters for your lastname.
# UNCOMMENT THE CODE AND CHANGE YOUR LASTNAME
#library(TeachingDemos)
#lastname <- "yourLastName"Roll three six-sided dice by uncommenting the code below.
# UNCOMMENT THE CODE
#char2seed(lastname)
#dice(rolls = 1, ndice = 3, sides = 6, plot.it = TRUE)What values did the dice show?
ANSWER:
(your answer here)
Roll the dice again.
# UNCOMMENT THE CODE
#dice(rolls = 1, ndice = 3, sides = 6, plot.it = TRUE)What values did the dice show this time?
ANSWER:
(your answer here)
Roll the dice again. But first reset the random seed.
#UNCOMMENT THE CODE
#char2seed(lastname)
#dice(rolls = 1, ndice = 3, sides = 6, plot.it = TRUE)What values did the dice show this time? Do you think R generates truly random numbers?
ANSWER:
(your answer here)
The iris data set comes with base R. You can read about this data set by running ?iris in the console. It is a data frame. In this course, we prefer to use tibbles (tidy tables) instead of data frames.
Convert the iris data frame into a tibble using as_tibble(). Put this in a new variable called iris_tibble. Then print the tibble using the print() function.
# YOUR CODE HEREWhich data type is the variable “Species”? How do you know?
ANSWER:
(your answer here)
Starting from the complete iris data set, filter only the flowers with a sepal length at least 4.5cm. Do this by piping (%>%) the iris_tibble to the filter() function. Hint: You can type the pipe quickly in RStudio with the command ctrl/cmd + shift + M.
# YOUR CODE HEREHow many datapoints (i.e. flowers) are left? How do you know?
ANSWER:
(your answer here)
Starting from the complete iris data set, create a new variable called petal_area (the area of a petal = petal width times petal length). Do this by piping iris_tibble to mutate().
# YOUR CODE HEREFind out the mean sepal length for each species. Do this with by piping iris_tibble to group_by() and then to summarise(). For instructions read the help page for summarise().
# YOUR CODE HEREWhat is the mean sepal length for virginica?
ANSWER:
(your answer here)
Starting from the complete iris data set, filter only the flowers that are either ‘versicolor’ or ‘virginica’ and have a petal width between 1.5 and 2.0cm (inclusive). Hint: read the help pages on %in% and between().
# YOUR CODE HEREHow many datapoints (i.e. flowers) are left? How do you know?
ANSWER:
(your answer here)
Using the iris data set, create a scatterplot of sepal width (x axis) against sepal length (y axis) using ggplot(). Show each species in a different colour.
# YOUR CODE HEREWhich species stands out visually? Why?
ANSWER:
(your answer here)
Using the iris data set, create a scatterplot of petal width (x axis) against petal length (y axis). Vary the size of the points depending on the sepal width and the colour depending on the sepal length. Use ggplot().
# YOUR CODE HEREWhat do you notice about the relationship between petal length and sepal length?
ANSWER:
(your answer here)
End of homework sheet