Exercises Week 1

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Importing data

Exercise 1. Data import in R is straight forward. We can import tabular files like .csv and .xlsx or specialized files from other statistical softwares (e.g. STATA-, SAS- and SPSS files).

Using read_excel(), how would you import the file cyclingStudy.xlsx into your R session?

Solution Ex. 1

# Install packages, you only need to do this once
# install.packages("readxl")

# Load packages
library(readxl)

# Store the data set as an object in the environment
cyclingStudy <- read_excel("./data/cyclingStudy.xlsx")


# look at the data
cyclingStudy

Exercise 3. Notice that when looking at the cycling data set, the tibble (data frame) says that many variables containing numbers are character variables. Why do you think this is the case here?

Try to change the import to make sure that the values are numeric. Read the help file and think about how you can change it (?read_excel).

Solution Ex. 2

The import function does not treat “NA” as missing values. The read_excel() function has a argument for missing values, setting na = "NA" tells R that all values that are “NA” should be treated as missing values.

# Install packages, you only need to do this once
# install.packages("readxl")

# Load packages
library(readxl)

# Store the data set as an object in the environment
cyclingStudy <- read_excel("./data/cyclingStudy.xlsx", na = "NA")


# look at the data
cyclingStudy

Exercise 3. Download and save the data set that we will call hypertrophy

Solution Ex. 3

# Download and save the file
download.file(url = "https://ndownloader.figstatic.com/files/14702420", destfile = "./data/hypertrophy.csv")

# load package to read csv files
library(readr)

hypertrophy <- read_csv("./data/hypertrophy.csv")

# look at the data 
hypertrophy