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Getting Started with R Cheat Sheet

This cheat sheet will cover an overview of getting started with R. Use it as a handy, high-level reference for a quick start with R. For more detailed R Cheat Sheets, follow the highlighted cheat sheets below.
Jun 2022  · 9 min read

R is one of the most popular programming languages in data science and is widely used across various industries and in academia. Given that it’s open-source, easy to learn, and capable of handling complex data and statistical manipulations, R has become the preferred computing environment for many data scientists today.

This cheat sheet will cover an overview of getting started with R. Use it as a handy, high-level reference for a quick start with R. You can also try this cheat sheet out interactively on DataCamp Workspace, just follow this link to do so! 

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Accessing Help in R

  • ?max: Shows the help documentation for the max function
  • ?tidyverse: Shows the documentation for the tidyverse package
  • ??"max": Returns documentation associated with a given input
  • str(my_df): Returns the structure and information of a given object
  • class(my_df): Returns the class of a given object

Using Packages in R

R packages are collections of functions and tools developed by the R community. They increase the power of R by improving existing base R functionalities, or by adding new ones.

  • install.packages(“tidyverse”): Lets you install new packages (e.g., tidyverse package)
  • library(tidyverse): Lets you load and use packages (e.g., tidyverse package)

The Working Directory

The working directory is a file path that R will use as the starting point for relative file paths. That is, it's the default location for importing and exporting files. An example of a working directory looks like ”C://file/path"

  • getwd(): Returns your current working directory
  • setwd(“C://file/path”): Changes your current working directory to a desired file path

Operators in R

Arithmetic Operators in R

Operator Description
a + b   Sums two variables
a - b Subtracts two variables
a * b  Multiply two variables
a / b  Divide two variables
a ^ b  Exponentiation of a variable
a %% b  The remainder of a variable
a %/% b Integer division of variables

Relational Operators in R

Operator Description
a == b Tests for equality
a != b Tests for inequality
a > b Tests for greater than
a < b Tests for smaller than
a >= b Tests for greater or equal than
a <= b Tests for smaller or equal than

Logical Operators in R

Operator Description
! Logical NOT
& Element-wise Logical AND
&& Logical AND
| Element-wise Logical OR
|| Logical OR

Assignment Operators in R

Operator Description

x <- 1

x = 1

Assigns a variable to x

Other Operators in R

Operator Description
%in% Identifies whether an element belongs to a vector 
$ Allows you to access objects stored within an object
%>% Part of magrittr package, it’s used to pass objects to functions

Getting Started with Vectors in R

Vectors are one-dimension arrays that can hold numeric data, character data, or logical data. In other words, a vector is a simple tool to store data. 

Creating Vectors in R

Input Output Description
c(1,3,5) 1 3 5 Creates a vector using elements separated by commas
1:7 1 2 3 4 5 6 7 Creates a vector of integers between two numbers
seq(2,8,by = 2) 2 4 6 8 Creates a vector between two numbers, with a specified interval between each element.
rep(2,8,times = 4) 2 8 2 8 2 8 2 8 Creates a vector of given elements repeated a number of times.
rep(2,8,each = 3) 2 2 2 8 8 8 Creates a vector of given elements repeating each element a number of times.

Vector Functions in R

  • sort(my_vector): Returns my_vector sorted
  • rev(my_vector): Reverses the order of my_vector
  • table(my_vector): Count the values in a vector
  • unique(my_vector): Distinct elements in a vector

Selecting Vector Elements in R

  • my_vector[6]: Returns the sixth element of my_vector
  • my_vector[-6]: Returns all but the sixth element
  • my_vector[2:6]: Returns elements two to six
  • my_vector[-(2:6)]: Returns all elements except those between the second and the sixth
  • my_vector[c(2,6)]: Returns the second and sixth elements
  • my_vector[x == 5]: Returns elements equal to 5
  • my_vector[x < 5 ]: Returns elements less than 5
  • my_vector[x %in% c(2, 5 ,8 )]: Returns elements in the set {2, 5, 8} 

Math Functions in R

  • log(x): Returns the logarithm of a variable
  • exp(x): Returns exponential of a variable
  • max(x): Returns the maximum value of a vector
  • min(x): Returns the minimum value of a vector
  • mean(x): Returns the mean of a vector
  • sum(x): Returns the sum of a vector
  • median(x): Returns the median of a vector
  • quantile(x): Percentage quantiles of a vector
  • round(x, n): Round to n decimal places
  • rank(x): Rank of elements in a vector
  • signif(x, n): Round off n significant figures
  • var(x): Variance of a vector
  • cor(x, y): Correlation between two vectors
  • sd(x): Standard deviation of a vector

Getting Started with Strings in R

The “stringr” package makes it easier to work with strings in R - you should install and load this package to use the following functions.

Find matches

#Detects the presence of a pattern match in a string
str_detect(string, pattern, negate = FALSE) 
#Detects the presence of a pattern match at the beginning of a string
str_starts(string, pattern, negate = FALSE) 
#Finds the index of strings that contain pattern match
str_which(string, pattern, negate = FALSE) 
#Locates the positions of pattern matches in a string
str_locate(string, pattern)
#Counts the number of pattern matches in a string
str_count(string, pattern)

Subset

#Extracts substrings from a character vector
str_sub(string, start = 1L, end = -1L)
#Returns strings that contain a pattern match
str_subset(string, pattern, negate = FALSE) 
#Returns first pattern match in each string as a vector
str_extract(string, pattern) 
#Returns first pattern match in each string as a matrix with a column for each group in the pattern
str_match(string, pattern)

Mutate

#Replaces substrings by identifying the substrings with str_sub() and assigning them to the results. 
str_sub() <- value 
#Replaces the first matched pattern in each string.
str_replace(string, pattern, replacement)  
#Replaces all matched patterns in each string
str_replace_all(string, pattern, replacement) 
#Converts strings to lowercase 
str_to_lower(string) 
#Converts strings to uppercase 
str_to_upper(string) 
#Converts strings to title case 
str_to_title(string) 

Join and split

#Repeats strings n times
str_dup(string, n)
#Splits a vector of strings into a matrix of substrings
str_split_fixed(string, pattern, n) 

Getting Started with Data Frames in R

A data frame has the variables of a data set as columns and the observations as rows.

#This creates the data frame df, seen below
df <- data.frame(x = 1:3, y = c(“h”, “i”, “j”), z = 12:14)
x y z
1 h 12
2 i 13
3 j 14
#This selects all columns of the third row
df[ ,3]
x y z
3 j 14
#This selects the column z
df$z
z
12
13
14
#This selects all rows of the second column
df[ ,2]
y
h
i
j
#This selects the third column of the second row
df[2,3]

13

Manipulating Data Frames in R

#Takes a sequence of vector, matrix or data-frame arguments and combines them by columns
bind_cols(df1, df2)
#Takes a sequence of vector, matrix or data frame arguments and combines them by rows
bind_rows(df1, df2)
#Extracts rows that meet logical criteria
filter(df, x == 2)
#Removes rows with duplicate values
distinct(df, z)
#Selects rows by position
slice(df, 10:15)
#Selects rows with the highest values
slice_max(df, z, prop =  0.25)
#Extracts column values as a vector, by name or index
pull(df, y)
#Extracts columns as a table
select(df, x, y)
#Moves columns to a new position
relocate(df, x, .after = last_col())
#Renames columns
rename(df, “age” = z)
#Orders rows by values of a column from high to low
arrange(df, desc(x))
#Computes table of summaries
summarise(df, total = sum(x))
#Computes table of summaries.
summarise(df, total = sum(x))
#Use group_by() to create a "grouped" copy of a table grouped by columns (similarly to a pivot table in spreadsheets). dplyr functions will then manipulate each "group" separately and combine the results

df %>% 
    group_by(z) %>% 
    summarise(total = sum(x))
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