R is one of the most frequently used programming languages in the Machine Learning and Data Science fields and is used extensively in both academia and a variety of industries. Many data scientists now use R as their preferred computing environment since it is simple to learn, open-sourced, and capable of handling statistical computations and complex data.

Excited to learn R? In this article, we will be exploring an overview of R using an easy-to-understand cheat sheet. Use it as a convenient, high-level guide to starting with R immediately.

## R Cheat Sheet

Here, we will explore the various shortcuts and symbols related to R.

### Basic Syntax

## Operator |
## Purpose |

<- orÂ = |
Assignment |

# |
Comment |

/ |
Division |

<<- |
Global Assignment |

%% |
Remainder |

* |
Scalar Multiplication |

%/% |
Integer Division |

%*% |
Matrix Multiplication |

v[1] |
First vector element |

### Accessing Help

## Function Name |
## Purpose |

help.start() |
opens help |

class(df_name) |
Returns class of the given object |

?tidyverse |
Shows tidyverse package documentation |

str(df_name) |
Returns information and structure of the given object |

?function_name |
Shows in-built functionsâ€™ documentation |

??â€ťsome_inputâ€ť |
Shows a given inputâ€™s documentation |

### Dataframe

## Method/Definition |
## Description |

summary(df_name) |
Returns the statistics of data in a descriptive format |

view(df_name) |
Opens the editor |

df_name = data.fÂramÂe(sÂtudentÂID=Â1:5Â, year=cÂ("1960",Â"Â1980","1990Â"Â,"1998",Â"Â2001"),ÂscoÂre=Âc(6,1,Â3,2,2)) |
Dataframe definition |

### Utility

## Method |
## Purpose |

order(index)Â |
To find the index to sort a vector |

apply(data, axis, function_name) |
To apply data to function in the particular axis |

data = read.csv(file.choose()) |
To read data from the file explorer |

dim() |
To find dimensions of matrix/dataframe/vector |

lapply(data, function_name) |
To apply the data to the function |

getwd() |
Gets the working directory |

length() |
To find the vector length |

install.packages(â€śpackage_nameâ€ť) |
Installs the required R package |

names() |
Returns the column names |

setwd(â€śC:/file/pathâ€ť) |
To set the current working directory |

rapply(data, function_name, how) |
Depending on the value of how, the data is applied to the function |

sort() |
To sort a vector |

rm(variable_name) |
To remove the variable |

detach(â€śpackage nameâ€ť) |
Detaches the given package |

ls() |
To list all the variables |

library(â€śpackage nameâ€ť) |
Makes contents of the given package ready to use |

### Vector

## Method |
## Purpose |

range(vec) |
To find the range of a vector |

num = c(3,7,Â2,1Â,8,5) |
Defining a numeric vector |

rep(1:Â8, tÂimes=2) |
Replicates the elements of the vector by the given number of times |

sd(vec) |
To find the standard deviation of a vector |

chr = c("rte",Â"Âqhz Â") |
Defining a character vector |

var(vec) |
To find the variance of a vector |

log = c(FALSEÂ, ÂFALSE, TRUE) |
Logical vector |

whiÂch.mÂaxÂ(vec) |
To find the position of the max value |

which.mÂinÂ(veÂc) |
To find the position of the min value |

mean(vec) |
To find the mean of the values of a vector |

### Matrix and Arrays

## Method |
## Purpose |

rbind(ÂmatÂrix1,matrix2) |
To row bind matrix1 and matrix2 |

cbind(ÂmatrixÂ1,matrix2) |
To column bind matrix1 and matrix2 |

mat = matrixÂ(1:15, nrow=3, ncol=5) |
To define a matrix |

1D = array(Â1:14) |
To define a 1-dimensÂional array |

2D = array(Â1:20, dim = c(1,3)) |
To define a 2-dimensÂional array |

3D = array(Â1:20, dim = c(1,4,5)) |
To define a 3-dimensÂional array |

### Hypothesis

## Method |
## Purpose |

aov() |
To find ANOVA or Analysis of Variance |

wilcox.teÂst(Âdata) |
To find the Wilcox test on the given data |

t.testÂ(data) |
To find 1 sample t-test of the given data |

cor.teÂst(ÂdatÂa1,Âdata2) |
To find the correlÂation test of the given data |

t.testÂ(daÂta1Â,data2) |
To find the 2 sample t-test of the given data |

chisq.tÂesÂt(data) |
To find the Chi-square test of the given data |

t.testÂ(prÂe, pÂostÂ, paÂireÂd=TRUE) |
To find the paired sample t-test of the given data |

shapirÂo.tÂestÂ(data) |
To find the Shapiro test of the given data |

### Statistics and Descriptive Statistics

## Method |
## Purpose |

colSumÂs(dÂata[]) |
To find the column sum of a particular column of the given data |

rowSumÂs(dÂata[]) |
To find the row sum of a particular row of the given data |

summarÂy(lm(y ~ x1 + x2 + x3, data=mÂydata)) |
To find the multiple regression of a given data |

cluster = kmeansÂ(data) |
To find the kmeans cluster analysis of a given data |

summarÂy(glm(y ~ x1 + x2 + x3, familyÂ="", data=mÂydata)) |
To find the classiÂficÂation of a given data |

colMeaÂns(Âdata[]) |
To find the column mean of a particular column of the given data |

rowMeaÂns(Âdata[]) |
To find the row mean of a particular row of the given data |

### Visualization

## Method |
## Purpose |

geom_hist() |
To find the histogram of a given data |

coord_Âflip() |
To flip the x and y coordiÂnates of a given point |

ggplotÂ(data = NULL, mapping = aes(), ...) |
To initiaÂlize a ggplot object of a given data |

geom_dÂensity() |
To produce a density plot of a given data |

facet_Âgrid() |
To lay out panels in a grid of a given data |

geom_point() |
To produce scatter plots of a given data |

qplot(Âdata, line=TÂRUEÂ,...) |
To produce the quantile plot of a given data |

geom_bar() |
To produce a bar graph of a given data |

### Strings

## Method |
## Purpose |

paste (â€¦, sep = " ", collapse = NULL) |
ConcatÂenate the vectors after converting to character |

toÂlower() |
Converts the given text to Âlower case characters |

toupper() |
Converts the given text to Âupper case characters |

toStriÂng(x) |
A helper function to produce a single character string |

substrÂingÂ(chÂr, n, n) |
To replace or retrieve the substring of a given string |

### Probability

## Method |
## Purpose |

rexp(n) |
To find the ExponeÂntial distriÂbution of n |

runif(n, min = 0, max = 1) |
To find the Uniform distriÂbution of n |

rbinom(n, size, prob) |
To find the Binomial distriÂbution of n |

rnorm(Ân, mÂean, sd) |
To find the Normal distriÂbution of n |

rpois(Ân, size) |
To find the Poisson distriÂbution of n |

### Loops

## Statement |
## Purpose |

if(condiÂtion){ block of statements } else { block of statements } |
if-else statements format |

while(condiÂtion){ block of statements } |
while loop format |

for(variable in the sequence){ block of statements } |
for loop format |

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## Want to learn more?

In this article, we covered all the basics of R with all the required shortcuts. To learn more about R and become a machine learning expert, check out Simplilearnâ€™s Data Science with R Certification Course!