November 3rd, 2021
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 code output, and 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; moreover, if the document contains any erroneous code (e.g., typos), you will get an error that prevents the knit. You can bypass this by debugging the code or commenting it out with a #.
Below I have demonstrated some basic R Markdown features, as described here. In your own work, you can use Markdown syntax to organize your coding notebook.
This text is bold.
This text is in italics.
This is a numbered list:
A bulleted list:
A nested list:
Block quote:
“Science is magic that works.” — Kurt Vonnegut
R code embedded in R chunks will be executed and the output will be shown.
# R code is embedded into this chunk
# when we start a line with '#', that tells R not to interpret the line as code
# this is called commenting code, and documents its purpose
# the code below assigns integers to the variables 'x' and 'y'
x <- 7
y <- 1029
# we can perform operations with variables
z <- x * y
z## [1] 7203# this is a string assigned to the variable 'my_name'
my_name <- "Rachael"
# we can create vectors of integers and strings
nums <- c(4, 8, 3, 6, 9)
fruits <- c("strawberries", "bananas", "apples", "peaches", "mangos")
# and combine them into a table using the data.frame() function
grocery_list <- data.frame(fruits, nums)
grocery_list## fruits nums
## 1 strawberries 4
## 2 bananas 8
## 3 apples 3
## 4 peaches 6
## 5 mangos 9# there are a number of ways to extract specific information from a table
# for instance, selecting the first column:
grocery_list[1]## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangosgrocery_list['fruits']## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangosselect(grocery_list, fruits)## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangos# the following code also targets the first column, but extracts the information in a different way
# can you spot the difference?
grocery_list$fruits## [1] strawberries bananas apples peaches mangos
## Levels: apples bananas mangos peaches strawberriesProblem Set #1:
# your R code hereA function is statement internally (i.e., “under the hood”) coded to perform a specific task. For instance, the head() function displays the first several rows of a data frame or values in a vector.
R comes with many built-in functions and data sets. Type data() in the console to look at a list of all available data sets. Type ?iris in the console for more information about this specific data set. Important: You can ask for help with any built-in data set or function by typing ?<function> in the console; for example, ?head or ?summary.
You can take a glance at the iris data set using the head() function. Run the code chunk below to test this.
# preview the first few rows a data frame
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaYou can also use the summary() function to see the summary statistics of a data set at a glance.
# look at summary statistics for the iris data set
summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## You can see the column names of iris from the code output above. We can perform calculations on this data set using a number of functions built into R. See the example below, which calculates the median of the Sepal.Length column.
# calculate median sepal length across all species of iris
median(iris$Sepal.Length)## [1] 5.8Problem Set #2:
Petal.Length column using the mean() function.Petal.Width column using the range() function.# your R code hereThere 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 data sets–more often, we want to read in a file containing our data. Also, we tend to modify data frames and save them to a new file.
Problem Set #3:
mushrooms_small.csv from the “Test data set” link on the class webpage.read_csv() function to read the file, and save it as a data frame called mushrooms. Important: The file name must be given to the function as a string.head() function to preview the first 10 rows of the new data frame. Specify the integer as the second argument of the function.head() function as a new data frame called mushrooms_tiny.write_csv function to write the data frame mushrooms_tiny to a new .csv file. Important: The file name 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 not have an “Upload” function. These concepts are covered at the end of this section.
# your R code hereFor this class, we are using a computer server where everyone has a preset working directory associated with your unique student ID number. Run 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.
# output the file path associated with the current working directory
getwd()## [1] "/stor/home/student13"This is the directory R will default to for reading and writing files. For real life projects, we keep all the information we need organized into folders (aka sub-directories). More often than not, we have to tell R which sub-directory we want to read a file from or write a file to. Perform the following steps to familiarize yourself with file paths and R’s perception of where files are:
mushrooms_tiny.csv by checking the box.list.files() to see all the files in the current working directory.list.files("new_data") to see the files in the new sub-directory.full.names = TRUE as the second argument in the function.# your R code hereClear your global environment (the broom symbol in the top right window). Read the file in the sub-directory “new_data” using read_csv. The function will need the full path given by the output from the code chunk above.
# your R code here