R Basics

Basic Objects

Vectors (Classes)

Type	Example
Logical	TRUE, FALSE
Numeric	1, 55, 999
Integer	1L, 32L, 0L
Complex	2 + 3i
Character	"great", "23.4"

Creating a vector & print statement example

apple <- c('red','green','yellow')

print(apple)

# Get the class of the vector
print(class(apple))

Output:

"red"  "green"  "yellow"
"character"

Anchors

Anchor	Special Character	Example
Beginning of line	^	^New
End of line	\\$	y$
Beginning of string	\\A	\\AHello
End of string	\\Z or \\z	End\\Z

Lists

Contain many different types of elements inside

# Create a list.
list1 <- list(c(2,5,3),21.3)

# Print the list.
print(list1)

Output:

[[1]]
2 5 3
[[2]]
21.3

Matrices

Two-dimensional rectangular data set

M = matrix( c('a','a','b','b','c','c'), nrow = 2, ncol = 3, byrow = TRUE)
print(M)

Output:

     [,1] [,2] [,3]
[1,] "a"  "a"  "b" 
[2,] "b"  "c"  "c" 

Arrays

Multi-dimensional data set

a <- array(c('a','b'),dim = c(4,4,2))
print(a)

Output:

, , 1
    [,1] [,2] [,3] [,4]
[1,] "a"  "a"  "a"  "a" 
[2,] "b"  "b"  "b"  "b" 
[3,] "a"  "a"  "a"  "a" 
[4,] "b"  "b"  "b"  "b" 

, , 2

     [,1] [,2] [,3] [,4]
[1,] "a"  "a"  "a"  "a" 
[2,] "b"  "b"  "b"  "b" 
[3,] "a"  "a"  "a"  "a" 
[4,] "b"  "b"  "b"  "b" 

Factors

Stores the vector along with the distinct values of the elements in the vector as labels

apple <- c('red','green','yellow')
factor_apple <- factor(apple)
print(factor_apple)

Output:

red    green  yellow
Levels: green red yellow

Data Frames

Tabular data objects

# Create the data frame.
BMI <- 	data.frame(
   gender = c('Male', 'Female','Female'),
   height = c(182, 141.5, 165),
   weight = c(101,95, 88),
   Age = c(35,43,22)
)
print(BMI)

Output:

  gender height weight Age
1   Male  182.0    101  35
2 Female  141.5     95  43
3 Female  165.0     88  22

Loops

Repeat Loop

repeat {
   commands
   if(condition) {
      break
   }
}

While Loop

while (test_expression) {
   statement
}

For Loop

while (test_expression) {
   statement
}

Working with Data

Useful Packages

Tasks	Lists
Load data	utils, openxlsx, foreign, haven
Manipulate data	tidyverse, dplyr,tidyr
Visualize data	ggplot2, lattice, plotly
Modeling	2 + 3i
Character	glmnet, randomForest, caret, survival

Import Data

#CSV
df <- read.csv("c:/data.csv", header = T)

#Excel
df <- read.xlsx("c:/data.xlsx")

For the following examples, use package called datasets

Data Exploration

Use dataset called mtcars from package datasets

• str(data): gives a quick overview of the rows and columns of the dataset.

import Pkg; Pkg.add("RCall")
import Pkg; Pkg.add("RDatasets")
using RCall
using RDatasets
mtcars = dataset("datasets","mtcars");
@rput mtcars

R"str(mtcars)"

Output:

e[?25le[2Ke[?25hUnable to automatically install 'Zlib' from '/home/runner/.julia/packages/Zlib_jll/BGVLi/Artifacts.toml'

• head(data,n) and tail(data,n)

head(): Top n elements

tail(): Bottom n elements

@rput mtcars

R"print(head(mtcars, n = 3))"
R"print(tail(mtcars, n = 3))"

Output:

LoadError: UndefVarError: @rput not defined
in expression starting at none:1

Descriptive Statistics

• summary(data): gives descriptive statistics for each variable

Common Functions

Tasks	Functions
Mean	mean()
Standard deviation	sd()
Variance	var()
Minimum	min()
Maximum	max()
Median	median()
Range of values	range()
Sample quantiles	quantile()
Interquartile range	IQR()

Case of missing values

• na.rm = T

@rput mtcars

R"print(mean(mtcars$MPG, na.rm = T))"

Output:

LoadError: UndefVarError: @rput not defined
in expression starting at none:1

Basic Plots

• plot()

using RCall
using RDatasets
mtcars = dataset("datasets","mtcars");

@rput mtcars

R"
png('plot2.png')
plot(mtcars$Disp, mtcars$DRat)
dev.off()
"

• barplot()

R"
png('barplot2.png')
barplot(mtcars$Cyl,main = 'Number of Cylinders',xlab = 'cyl', col='blue',horiz = FALSE)
dev.off()
"

Output:

• histogram()

R"
png('histogram.png')
hist(mtcars$Disp,main = 'Displacement (cu.in.)',xlab = 'disp', col='red')
dev.off()
"

Output:

• boxplot()

using RCall
using RDatasets
mtcars = dataset("datasets","mtcars");

@rput mtcars

R"
png('boxplot2.png')
boxplot(mtcars$Disp)
dev.off()
"

Output:

• qqplot() or qqnorm(): check whether the data is normally distributed

• qqline(): adds a reference line

using RCall
using RDatasets
mtcars = dataset("datasets","mtcars");

@rput mtcars

R"
png('qqnorm.png')
qqnorm(mtcars$Disp)
qqline(mtcars$Disp)
dev.off()
"

Output:

ArgumentError: Package RCall not found in current path:
- Run `import Pkg; Pkg.add("RCall")` to install the RCall package.

Statistical Analysis

Analysis	Continuous Outcome(Y)	Binary Outcome(Y)
Correlation Analysis
X: Continuous	cor.test()	t.test()
X: Categorical	t.test(), ANOVA()	chisq.test()
Regression Model	lm()	glm()