Day 1: Introduction to R

In-class worksheet, solutions

June 29th, 2020

Computational analyses require methods and notes to be recorded the same way you would for wet lab experiments. An excellent way to do this is via R Markdown documents. R Markdown documents are documents that combine text, R code, and R output, including figures. They are a great way to produce self-contained and documented statistical analyses.

In this first worksheet, you will learn how to do some basic markdown editing in addition to the basic use of variables and functions in R. After you have made a change to the document, press “Knit HTML” in R Studio and see what kind of a result you get. Note: You may have to disable pop-ups to get this to work.

1. Basic Markdown

Try out basic R Markdown features, as described here. Write some text that is bold, and some that is in italics. Make a numbered list and a bulleted list. Make a nested list. Try the block-quote feature.

–Try your Markdown syntax here–

2. Embedding R code

R code embedded in R chunks will be executed and the output will be shown.

# R code is embedded into this chunk
x <- 5
y <- 7
z <- x * y
z
## [1] 35

Play around with some basic R code, trying the following:

  1. Assign integers to variables (demonstrated in the above code block).
  2. Assign some strings to variables.
  3. Make a vector of strings containing your top 5 favorite foods.
  4. Make a vector containing 5 random numbers.
  5. Combine the two vectors you created in the previous step into one dataframe.
  6. Call the first column of the dataframe that you create.
# your R code here

3. Built-in functions and datasets

A function is statement internally (i.e., “under the hood”) coded to perform a specific task. For instance, the head() function displays the first several rows of a dataframe or values in a vector.

R comes with many built-in functions and datasets. Type data() in the console to look at a list of all available datasets. Type ?iris in the console for more information about this specific dataset. You can take a glance at the iris dataset using the head() function. Run the code chunk below to test this.

# preview the first few rows a dataframe
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

You can also use the summary() function to see the summary statistics of a dataset at a glance. Try this now with the iris dataset.

# your R code here

You can see the column names of iris from the code output above. Calculate the mean of the Petal.Length column using the mean() function. Calculate the range of the Petal.Width column using the range() function. Hint: call the columns the same way you did in Part 2 of the worksheet.

# your R code here

4. Reading, writing and locating files

There are several ways to upload data into your R environment. We covered one way in Part 1 of the worksheet: manual entry. However, this is clearly not feasible for big datasets–more often, we want to read in a file containing our data. Also, we tend to modify dataframes and save them to a new file.

Try the following:

  1. Download the test dataset mushrooms_small.csv from the “Test dataset” link on the class webpage.
  2. Upload it to the RStudio server.
  3. Use the read_csv() function to read the file, and save it to a dataframe called mushrooms. Important: The filename must be given to the function as a string.
  4. Use the head() function to preview the first 10 rows of the new dataframe. Specify the integer as the second argument of the function.
  5. Save the output of the head() function to a new dataframe called mushrooms_tiny.
  6. Use the write_csv function to write the dataframe mushrooms_tiny to a new .csv file. Important: The filename must be given to the function as a string.

Note: If you are coding on a local installation of R, you will have to specify a path to the location of the file or move the file to the working directory. Local installations of R do now have an “Upload” function. These concepts are covered at the end of this section.

# your R code here

For this class, we are using a computer server where everyone has a preset working directory associated with your unique student ID number. Type getwd() to see the file path to your working directory. On a local installation, the output of this function might look something like C:/Users/Rachael/Documents.

# your R code here

This directory is where R auto-directs when you specify a file to read or write. In real life, we keep all the information we need in folders (aka sub-directories). Perform the following steps to familiarize yourself with file paths and R’s perception of where files are:

  1. Use the “New Folder” option in the window on the bottom right to create a new folder called “day1_data”.
  2. Select mushrooms_tiny.csv by checking the box.
  3. Go to “More” > “Copy To…” and select the new folder.
  4. Run list.files() to see all the files in the current working directory.
  5. Run list.files("day1_data") to see the files in the new sub-directory.
  6. Run #5 again, but this time specify that full.names = TRUE as the second argument in the function.
# your R code here

Clear your global environment (the broom symbol in the top right window). Read the file in the sub-directory “day1_data” using read_csv. The function will need the full path given by the output from the code chunk above.

# your R code here