Appendix A — Rcode

Here is a list of the R code we use in this class.

A.1 R Code : Creating Variables in R

Code Description

Code	Description
`variable <- c(#, #, #, #, etc.)` `tired <- c(1,2,3,4)`	variable = an object that you will define in R <- = “assign”; tells R to save whatever comes on the right to whatever object is on the left. c = combine : tells R to combine whatever happens in the parentheses () = parentheses to group related terms # = what you store in the variable; each item should be separated by a comma and space.
`hist(dat$variable)`	For continuous variables : draws a histogram.

variable <- c(#, #, #, #, etc.)

tired <- c(1,2,3,4)

variable = an object that you will define in R

<- = “assign”; tells R to save whatever comes on the right to whatever object is on the left.

c = combine : tells R to combine whatever happens in the parentheses

() = parentheses to group related terms

# = what you store in the variable; each item should be separated by a comma and space.

hist(dat$variable) For continuous variables : draws a histogram.

`variable <- c(“name1”, “name2”, “name1”, etc.)` `emotion <- c(“sad”, “happy”, “sad”)`	variable = an object that you will define in R <- = “assign”; tells R to save whatever comes on the right to whatever object is on the left. c = combine : tells R to combine whatever happens in the parentheses () = parentheses to group related terms # = what you store in the variable; each item should be separated by a comma and space.
`as.factor(variable)` `as.factor(emotion)`	as.factor() # converts a string variable into a categorical factor
`variable <- as.factor(variable)`	# “saves” this conversion as the original variable
`plot(dat$variable)`	For categorical variables : draws a barplot. For continuous variables : illustrates values of the variable (y-axis) as a function of their index (x-axis).

R Command	What it Does
`dat <- read.csv(“path/file.csv”, stringsAsFactors = T)`	loads the data file into R (or use the “point & click method”); sets string variables to be categorical factor variables.
`head(dat)`	looks at the first 6 rows of the data file
`tail(dat)`	looks at the last 6 rows of the data file
`nrow(dat)`	displays the number of rows (each row = an individual)
`ncol(dat)`	displays the number of columns (each column = a variable)
`names(dat)`	displays the names of the object (column names = names of variables)
`dat$variable`	displays the variable from a dataset
`dat$variable[i]`	displays the individual row [i] from the variable
`dat[i, j]`	displays an individual row [i] and column [j] from the dataset

R Command	What it Does
`summary(dat)`	Reports descriptive statistics for all variables in the dataset.
`summary(dat$variable)`	Reports descriptive statistics for a categorical variable (frequency / number of individuals in each level) or continuous variable (mean, range, etc.)
`as.numeric(dat$variable)`	Makes the variable numeric (for continuous graphs)
`as.factor(dat$variable)`	Makes the variable a categorical factor (for categorical graphs)
`dat$variable <- as.factor(dat$variable)`	Assigns the as.factor output to the original variable. (In other words, this saves your new categorical factor variable by overwriting the old one.)
`plot(dat$variable)`	For categorical variables : draws a barplot. For continuous variables : illustrates values of the variable (y-axis) as a function of their index (x-axis).
`hist(dat$variable)`	For continuous variables : draws a histogram.
`par(mfrow = c(i, j))`	Splits your graphics window into i rows and j columns.

Below is a list of code we’ll use to calculate descriptive statistics in R.

R Command	What It Does
`summary(dat)`	Reports descriptive statistics for all variables in the dataset.
`summary(dat$variable)`	Reports descriptive statistics for a continuous variable. Reports frequency for a categorical variable.
`mean(dat$variable, na.rm = T)`	Reports the mean (average) of a variable; you must include the na.rm = T argument if there is missing data (otherwise R will return NA as the result).
`median(dat$variable, na.rm = T)`	Reports the median (middle point) of a variable.
`range(dat$variable, na.rm = T)`	Reports the lower limit and upper limit of the variable.
`sd(dat$variable, na.rm = T)`	Reports the standard deviation of the variable.
`hist(dat$variable)` `abline(v =mean(dat$variable))`	Draws a line on a plot or histogram at specified values (e.g., this draws a vertical line at the mean of dat$variable. You can replace v with h to draw a horizontal line. We will use abline() later in the semester in a different way.
`par(mfrow = c(i, j))`	Splits your graphics window into i rows and j columns (replace i and j with numbers)