5 Basic `R`

5.1 Using `R` as a calculator

The simplest thing you could do with R is do arithmetic:

1 + 2

## [1] 3

If you type in an incomplete command, R will wait for you to complete it. If the symbol to the left of the bottom line in the console is > then R is ready to accept a new command, while a + means it is waiting for you to finsh the previous command.

If you want to cancel a command you can simply hit Esc and RStudio will give you back the “>” prompt (or Ctrl+C for a Mac). This is a useful tip to remember if you ever get stuck in a + loop in the console!

When using R as a calculator, the order of operations is the same as you would have learnt back in school.

From highest to lowest precedence:

Parentheses: (, )
Exponents: ^ or **
Divide: /
Multiply: *
Add: +
Subtract: -

3 + 5 * 2

## [1] 13

Use parentheses to group operations in order to force the order of evaluation if it differs from the default, or to make clear what you intend.

(3 + 5) * 2

## [1] 16

This can get unwieldy when not needed, but clarifies your intentions. Remember that others may later read your code.

(3 + (5 * (2 ^ 2))) # hard to read
3 + 5 * 2 ^ 2       # clear, if you remember the rules
3 + 5 * (2 ^ 2)     # if you forget some rules, this might help

The text after each line of code is called a “comment”. Anything that follows after the hash (or octothorpe) symbol # is ignored by R when it executes code.

Really small or large numbers get a scientific notation:

2/10000

## [1] 2e-04

Which is shorthand for “multiplied by 10^XX”. So 2e-4 is shorthand for 2 * 10^(-4).

You can write numbers in scientific notation too:

5e3  # Note the lack of minus here

## [1] 5000

5.2 Mathematical functions

R has many built in mathematical functions. To call a function, we simply type its name, followed by open and closing parentheses. Anything we type inside the parentheses is called the function’s arguments:

sin(1)  # trigonometry functions

## [1] 0.841471

log(1)  # natural logarithm

## [1] 0

log10(10) # base-10 logarithm

## [1] 1

exp(0.5) # e^(1/2)

## [1] 1.648721

Don’t worry about trying to remember every function in R. You can simply look them up on google, or if you can remember the start of the function’s name, use the tab completion in RStudio.

This is one advantage that RStudio has over R on its own, it has autocompletion abilities that allow you to more easily look up functions, their arguments, and the values that they take.

Typing a ? before the name of a function will open the help page for that function. As well as providing a detailed description of the function and how it works, scrolling to the bottom of the help page will usually show a collection of code examples which illustrate how to use it. We’ll go through an example later.

5.3 Comparing things

We can also do comparison in R:

1 == 1  # equality (note two equals signs, read as "is equal to")

## [1] TRUE

1 != 2  # inequality (read as "is not equal to")

## [1] TRUE

1 <  2  # less than

## [1] TRUE

1 <= 1  # less than or equal to

## [1] TRUE

1 > 0  # greater than

## [1] TRUE

1 >= -9 # greater than or equal to

## [1] TRUE

A word of warning about comparing numbers: you should never use == to compare two numbers unless they are integers (a data type which can specifically represent only whole numbers).

Computers may only repr esent decimal numbers with a certain degree of precision, so two numbers which look the same when printed out by R, may actually have different underlying representations and therefore be different by a small margin of error (called Machine numeric tolerance).

Instead you should use the all.equal() function.

5.4 Variables and assignment

We can store values in variables using the assignment operator <-, like this:

x <- 1

Notice that assignment does not print a value to the console. Instead, we stored it for later in something called a variable. x now contains the value 1:

## [1] 1

Look for the Environment tab in one of the panes of RStudio, and you will see that x and its value have appeared. Our variable x can be used in place of a number in any calculation that expects a number:

log(x)

## [1] 0

Notice also that variables can be reassigned:

x <- 100

x used to contain the value 1 and and now it has the value 100.

Assignment values can contain the variable being assigned to:

x <- x + 1 # notice how RStudio updates its description of x on the top right tab

The right hand side of the assignment can be any valid R expression. The right hand side is fully evaluated before the assignment occurs.

Variable names can contain letters, numbers, underscores and periods. They cannot start with a number nor contain spaces at all.

5.5 Vectorisation

One final thing to be aware of is that R is vectorised, meaning that variables and functions can have vectors as values. For example:

1:5

## [1] 1 2 3 4 5

2^(1:5)

## [1]  2  4  8 16 32

x <- 1:5
2^x

## [1]  2  4  8 16 32

5.6 Managing your environment

There are a few useful commands you can use to interact with the R session.

ls() will list all of the variables and functions stored in the global environment (your working R session):

ls()

##  [1] "ab_vector"      "another_list"   "concat_example" "list_example"  
##  [5] "my_vec"         "name_vec"       "vec1"           "vec2"          
##  [9] "vec3"           "x"

You can use rm() to delete objects you no longer need:

rm(x)

If you have lots of things in your environment and want to delete all of them, you can pass the results of ls() to the rm() function:

rm(list = ls())

In this case we’ve combined the two. Just like the order of operations, anything inside the innermost parentheses is evaluated first, and so on.

In this case we’ve specified that the results of ls() should be used for the list argument in rm(). When assigning values to arguments by name, you use the = operator!!

If instead we use <-, there will be unintended side effects, or you may just get an error message:

rm(list <- ls())

## Error in rm(list <- ls()): ... must contain names or character strings

Pay attention when R does something unexpected! Errors, like above, are thrown when R cannot proceed with a calculation. Warnings on the other hand usually mean that the function has run, but it probably hasn’t worked as expected.

In both cases, the message that R prints out usually give you clues how to fix a problem.

5.7 R Packages

It is possible to add functions to R by writing a package, or by obtaining a package written by someone else. As of this writing, there are over 7,000 packages available on CRAN (the comprehensive R archive network). R and RStudio have functionality for managing packages:

You can see what packages are installed by typing installed.packages()
You can install packages by typing install.packages("packagename"), where packagename is the package name, in quotes.
You can update installed packages by typing update.packages()
You can remove a package with remove.packages("packagename")
You can make a package available for use with library(packagename)

###################
### Challenge 3 ###
###################

# Which of the following are valid variable names?

# min_height
# max.height
# _age
# .mass
# MaxLength
# min-length
# 2widths
# celsius2kelvin

###################
### Challenge 4 ###
###################

# What will be the value of each  variable  after each statement in the following program?

# mass <- 47.5
# age <- 122
# mass <- mass * 2.3
# age <- age - 20

###################
### Challenge 5 ###
###################

# Run the code from the previous challenge, and write a command to compare mass to age. Is mass larger than age?

###################
### Challenge 6 ###
###################

# Clean up your working environment by deleting the mass and age variables.

###################
### Challenge 7 ###
###################

# Install the following package: `ggplot2`

5 Basic R

5.1 Using R as a calculator