Shortcuts

A few basic keyboard shorcuts to make life simpler in R:

# To insert the "<-" symbol use alt + "-" (alt+minus)
# To insert the pipe operator %>% use Ctrl + Shift + M
# To insert a new code chunk in R Markdown use Ctrl + Alt + I

Comments

# Use the hash tag symbol before a string of text to create comments. Comments help people understand your code.

Basic Math

R can perform basic math calculations

2+2
## [1] 4
1/6
## [1] 0.1666667
2^5
## [1] 32

VARIABLES

To assign a value to a variable use: <- (keyboard shortcut: alt + “-”)

Your variable will be visible in the Environment Tab (upper right) and can be recalled any time you need it.

a <- 2
b <- "Apples"

# You can perform math on numeric variables:
a*2
## [1] 4

VECTORS

The most basic data structure in R. In the previous example, “a” is a vector of length 1. We use the “c” or combine function to create a vector.

vector1 <- c(1,2,4,-7,9,-12,6,-3, 2, 5, 16)
# a numeric vector

vector2 <- c("apples", "oranges", "pears")
# a character vector

vector3 <- c(1,4,"apples", 5)
# vectors can only contain one data type. R will force (coerce) the numbers to strings to create a vector of only one data type.

vector1
##  [1]   1   2   4  -7   9 -12   6  -3   2   5  16
vector2
## [1] "apples"  "oranges" "pears"
vector3
## [1] "1"      "4"      "apples" "5"

Subsetting Vectors

Using square brackets [] we can access items from a vector based on their position. Unlike other languages, R starts at position 1 (not position 0)

vector1[2]
## [1] 2
vector1[2:4]
## [1]  2  4 -7
vector2[1]
## [1] "apples"
# If you choose a position outside the length of the vector you will get "NA" as a response

vector3[10]
## [1] NA
# We can modify vector contents by using subsetting:
vector2[3] <- "bananas"
vector2
## [1] "apples"  "oranges" "bananas"
# Or by using an ifelse function (this returns vector1 and replaces all negative values with 0)
ifelse(vector1 < 0, 0, vector1)
##  [1]  1  2  4  0  9  0  6  0  2  5 16
# We can select vector items using logcal operators
vector1[vector1<0]
## [1]  -7 -12  -3
# Performing math on a vector
vector1 + 4
##  [1]  5  6  8 -3 13 -8 10  1  6  9 20
mean(vector1)
## [1] 2.090909
median(vector1)
## [1] 2
sum(vector1)
## [1] 23
vector1 > 0
##  [1]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
# returns logical TRUE/FALSE values

FUNCTIONS

You can create your own functions in R using the following structure:

function.name <- function(argument1, argument2, …)

{

some functionality

}

# example #1

add.numbers <- function(num1, num2)
{
  new_num = num1+num2
  new_num
}

add.numbers(3,6)
## [1] 9
# example #2

is.even <- function(a.number)
  {
  remainder <- a.number %% 2
  if (remainder==0)
    return(TRUE)
  return(FALSE)
}

# testing
is.even(10)
## [1] TRUE
is.even(7)
## [1] FALSE

MATRICES

A matrix is a rectangular array of values (of any type) arranged in rows and columns.

# This uses the matrix function to create a matrix with all values in a single column
a.matrix <- matrix(c(1,2,3,4,5,6))

a.matrix
##      [,1]
## [1,]    1
## [2,]    2
## [3,]    3
## [4,]    4
## [5,]    5
## [6,]    6
# This uses the matrix function to create a matrix with values in two columns
a.matrix <- matrix(c(1,2,3,4,5,6), ncol=2)

a.matrix
##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
# we could also use the cbind function to combine two vectors to achieve the same result
a2.matrix <- cbind(c(1,2,3), c(4,5,6))
a2.matrix
##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
# Use the rbind function to combine the vectors in the transpose (rows and columns switched)
a3.matrix <- rbind(c(1,2,3),c(4,5,6))
a3.matrix
##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

To perform math on a matrix use functions such as rowSums(), colSums(), rowMeans(), and colMeans()

a2.matrix
##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
colSums(a2.matrix)
## [1]  6 15
rowMeans(a2.matrix)
## [1] 2.5 3.5 4.5

To index the element of a matrix you subscript by specifying the row # and column # (in that order)

a2.matrix[2,1]
## [1] 2
# if you leave one of the spaces empty R will assume you want the whole row or column
a2.matrix[,2]
## [1] 4 5 6

DATA LOADING IN R

# data1 <- read.table("filename.txt", sep = ",", header=TRUE)
# data2 <- read.csv("filename.csv")

# if you get an error message that your file cannot be found, specify the entire file path in between the quotations or alternatively write your code like this:

# data1 <- read.table(file.choose(), sep = ",", header=TRUE)

# We can see the names R has assigned to the columns of the data frame using the names() function. Eg: names(data1)

# We can get the basic structure of the data frame using the str() function. Eg: str(data1)

Example:

create a txt file with the following
flavour,number

pistachio,6

vanilla,12

chocolate,8

strawberry,14

mint chocolate chip,3

rocky road,9

caramel fudge swirl,5

flav <- read.table(file.choose(), sep=",", header=TRUE)

# or you could use read.csv
# flav <-  read.csv(file.choose())

flav
##               flavour number
## 1           pistachio      6
## 2             vanilla     12
## 3           chocolate      8
## 4          strawberry     14
## 5 mint chocolate chip      3
## 6          rocky road      9
## 7 caramel fudge swirl      5
class(flav$flavour)
## [1] "character"

Why does R sometimes label my string variable as “Factor” and not “Character”?

R, by default, automatically interprets a column from the data read from disk as a type factor if it contains characters. We can prevent this behavior manually by adding the stringsAsFactors optional keyword argument to the read.* commands:

# data3 <- read.csv("filename,csv", stringsAsFactors = FALSE)