2 Basics
In this chapter, we will learn how to use R as a calculator; learn the different data types and classes in R; learn how to make assignments; and learn how to get help when you are stuck on a particular issue.
2.1 R as a calculator
You can use R like a calculator: the arithmetic operators are +, -, *, /, ^ (exponentiation), and %% (modular arithmetic)–there are more, but these operators are the basic ones (see more by typing ?'+' into your console).
2.2 Data Types and Classes
Classes are the “foreground” and types are the “background” characteristics of data. Classes affect how data look to the user, whereas types are the specific attributes of some data. We can check the class of an object with the class() function and type with the typeof() function.
Additionally, we can test to see if some data are a particular class or type with the is.*() and convert them with as.*(), where * can represent the classes and types that follow.
2.2.1 Classes
There are many classes–some pre-defined in R, while others have been created externally. The three main classes (besides the numeric and character vector class, which are also types) are the matrix, list, and data frame.
| Type | Decsription | Example |
|---|---|---|
| matrix | A 2-dimensional array of elements, where each column is of the same type. | matrix(1:9, 3, 3) |
| list | A collection of elements, where each one can be a different type or class. | list(1, “a”, matrix(1:9, 3, 3) |
| data frame | A 2-dimensional set of elements, where each column can be a different type. | mtcars |
2.3 Assignments
Making assignments in R allows us to save information into an object, which further allows us to refer to a specific value without having to recalculate it each time.
x <- 2
x## [1] 2For a more complex example, we will run a regression model, save it to an object, and pass it to the summary() function to get more information from our model besides just its coefficients–we’ll learn more about regressions in the Linear Modeling chapter.
my_model <- lm(mpg ~ wt + hp + am, mtcars)
summary(my_model)##
## Call:
## lm(formula = mpg ~ wt + hp + am, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4221 -1.7924 -0.3788 1.2249 5.5317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.002875 2.642659 12.867 2.82e-13 ***
## wt -2.878575 0.904971 -3.181 0.003574 **
## hp -0.037479 0.009605 -3.902 0.000546 ***
## am 2.083710 1.376420 1.514 0.141268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.538 on 28 degrees of freedom
## Multiple R-squared: 0.8399, Adjusted R-squared: 0.8227
## F-statistic: 48.96 on 3 and 28 DF, p-value: 2.908e-112.3.1 Adding/Removing Variables
There are two main ways we can add variables to our dataset: (1) the $ (“accessor”/dollar-sign) method and (2) the transform() method.
mydata <- mtcars # copy data
# Let's create a variable called "my_new_var"
mydata$my_new_var <- with(mtcars, mpg/wt)
# Alternatively, the right-hand side
# could be written as mtcars$mpg/mtcars$wt.
# Show a few rows from our dataset
head(mydata)## mpg cyl disp hp drat wt qsec vs am gear carb my_new_var
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 8.015267
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 7.304348
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 9.827586
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 6.656299
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5.436047
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 5.231214Let’s do it again, but with transform().
# transform() method.
mydata2 <- transform(mydata, my_new_var = mpg/wt)
head(mydata2)## mpg cyl disp hp drat wt qsec vs am gear carb my_new_var
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 8.015267
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 7.304348
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 9.827586
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 6.656299
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5.436047
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 5.231214To remove variables, we assign them to be NULL.
mydata$my_new_var <- NULL
head(mydata)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 12.4 Viewing Data
We can view data like an Excel spreadsheet in a separate window in R (or separate tab in RStudio) via the View() function. Try it out in your console!
View(mtcars)2.5 Getting Help
There are two main ways of getting help in R: (1) using the ? operator to access a function’s documentation and (2) Googling your questions OR searching for them on StackOverflow–when you’re beginning R, chances are that the problems you encounter have been solved.
# Accessing the documentation for the mean function.
?mean2.6 Summary
| Functionality | Description | Example |
|---|---|---|
| +,-,*,/,^/%% | Arithmetic operators | 2+2 |
| Types/Classes | Attributes of an object. | str(mtcars); class(mtcars); typeof(mtcars$mpg) |
| Assignments | Storing a value into an object. | x <- 2 |
| Viewing data | How to view your dataset like an Excel spreadsheet. | View(mtcars) |
| Getting help | How to look for help. | ?mean; Google/StackOverflow |