Chapter 2 R and Calculations

  • invent good folder structures for this class
  • do not save all of this on desktop / downloads
1 + 3
## [1] 4
2 * 8
## [1] 16

2.1 R is a calculator

  • click in a row of code to run it, or highlight chunks of code to run it
  • ctrl + enter to run a block
  • R does not care about white space
1+         4
## [1] 5
  • We suggest using a readable style for your code
  • I suggest STYLR

2.2 Functions

  • for most things in R, we will use functions
  • functions need inputs (aka arguments), and output something
  • outputs can vary drastically, and may include graphs, datasets, animations, etc.
  • every function has a name, e.g. c
  • for example, the c function stands for concatenate a vector (a list of things)
  • use parenthesis after the function name to list arguments/inputs
c(1,2,3)
## [1] 1 2 3
c(1,   2,  3)
## [1] 1 2 3
  • Code looks best with oxford commas (space after each item)

2.3 Store objects for future use in environment

  • the vectors above were shown in the console pane, but not saved anywhere

  • make a vector and and store it as vector1 object

vector1 = c(1, 2, 3)

  • notice that vector1 was added to the environment pane

  • Access the object you stored

vector1
## [1] 1 2 3

  • the preferred way to assign a new object is <-

  • This avoids the mathematical meaning of =

vector1 <- c(1, 2, 3)

2.4 Mean function for averages

  • google or ChatGPT: “how do I take an average in R?”

  • Internet is great for finding function names / packages

  • Use ? to find help on something, e.g. ?mean

  • Help opens in the help pane

?mean
  • In a function, the equals sign temporarily assigns inputs to arguments for the purposes of the function
mean(x = vector1     )
## [1] 2

  • you do not always need to label arguments

  • labeling arguments can make code more readable

  • without labels, R assumes arguments are provided in predetermined order (as shown in help)

  • Some parameters may have default values as shown in help, e.g.
    mean(x, trim = 0, na.rm = FALSE, ...)

  • story of defaults in a logistic regression function that caused problems in research

  • trim would involve removing a proportion of values; na.rm would involve NA missing values

mean(vector1)
## [1] 2

  • make a vector with missing values

  • NA is shown in color, having special meaning

missing_vector <- c(5, 9, 14, NA)
missing_vector
## [1]  5  9 14 NA
  • suggests creating descriptive object names
mean(x = missing_vector)
## [1] NA

  • the answer is not available! We cannot include missing values in math

  • try using the option to remove missing values

  • TRUE is a special word for the logical value “true”

mean(x= missing_vector, na.rm = TRUE)
## [1] 9.333333
  • aha, now the mean is calculated based on the three known values because we added a parameter to tell the mean function to ignore missing data