R programming

Learning Objectives

• Describe the purpose of the RStudio Script, Console, Environment, and Plots panes.
• Organize files and directories for a set of analyses as an R Project, and understand the purpose of the working directory.
• Use the built-in RStudio help interface to search for more information on R functions.
• Demonstrate how to provide sufficient information for troubleshooting with the R user community.
• Define the following terms as they relate to R: object, assign, call, function, arguments, options.
• Assign values to objects in R.
• Learn how to name objects
• Use comments to inform script.
• Solve simple arithmetic operations in R.
• Call functions and use arguments to change their default options.
• Inspect the content of vectors and manipulate their content.
• Subset and extract values from vectors.
• Analyze vectors with missing data.
• Load external data from a .csv file into a data frame.
• Describe what a data frame is.
• Summarize the contents of a data frame.
• Use indexing to subset specific portions of data frames.
• Describe what a factor is.
• Convert between strings and factors.
• Reorder and rename factors.
• Change how character strings are handled in a data frame.
• Format dates.

Reading

### R programming

• R introduction
• R Programming for Data Science
• R for Data Science
• Efficient R programming

Lecture Notes

• R programming

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Exercises

1. -- R-001 --

   What are the values after each statement in the following?

   mass <- 47.5            # mass?
   age  <- 122             # age?
   mass <- mass * 2.0      # mass?
   age  <- age - 20        # age?
   mass_index <- mass/age  # mass_index?
   

2. -- R-002 --

   We’ve seen that atomic vectors can be of type character, numeric (or double), integer, and logical. But what happens if we try to mix these types in a single vector?

   What will happen in each of these examples? (hint: use class() to check the data type of your objects):

   num_char <- c(1, 2, 3, "a")
   num_logical <- c(1, 2, 3, TRUE)
   char_logical <- c("a", "b", "c", TRUE)
   tricky <- c(1, 2, 3, "4")
   

   Why do you think it happens?

   How many values in combined_logical are “TRUE” (as a character) in the following example:

   num_logical <- c(1, 2, 3, TRUE)
   char_logical <- c("a", "b", "c", TRUE)
   combined_logical <- c(num_logical, char_logical)
   

   You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

3. -- R-003 --

   Using this vector of heights in inches, create a new vector, heights_no_na, with the NAs removed.

   heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)
   

   • Use the function median() to calculate the median of the heights vector.
   • Use R to figure out how many people in the set are taller than 67 inches.