# R Programming Basics

*visibility*140

*event*2020-09-23

*access_time*2 months ago

*language*English

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This article provides a basic introduction about programming with R incl. atomic vector, variable, operations, branching, loops and functions.

*info*All examples can run RStudio or R Tools for Visual Studio on Windows. About these two IDEs, refer to R Introduction.

### Hello, World!

We always start with a 'Hello World!' example when learning a new programming language.

The following is the content of an example R script file named ** R01.HelloWorld.R**.

# Set working directory setwd("F:/Projects/RTutorialSamples") # This is a quick demo of R print ('Hello, World!') myVar <- "Hello, World!" print(myVar)

Output:

> # Set working directory > setwd("F:/Projects/RTutorialSamples") > > # This is a quick demo of R > print ('Hello, World!') [1] "Hello, World!" > > myVar <- "Hello, World!" > > print(myVar) [1] "Hello, World!" >

#### Functions

There are two functions used in the above script, **print **and **setwd**.

Function **setwd **sets the current working directory to the path provided. Once the directory is setup, working space related files like __ .RData__ or

*.Rhistory*can be saved in this directory. Relative paths will be resolved using working directory path.

Function **print **simply prints out the input string or string variable to the output stream/console.

#### Variable

As other programming languages, you can define variables in R. The above script defines a variable named ** myVar **and assigns a string literal

*'Hello, World!"*to it.

#### Comments

To add comments into your R script, add character '#' to the comment line as the above script example shows.

#### Run R scripts using command line

R scripts can be invoked using **Rscript.exe** executable:

Rscript.exe R01.HelloWorld.R

### Atomic vector

R is created for statistical computing and graphics thus there is a core difference compared with other programming languages about vectors. A vector is a sequence of data elements of the same basic type. Members in a vector are called components.

In R, the following atomic vectors are available:

- Logical
- Numeric
- Integer
- Complex
- Character
- Raw

All other objects in R are created with these basic vectors.

#### Examples

The following script *R02.AtomicVectors.R* shows some examples of vectors in R:

# Example R02 Basic Vectors # logical v <- TRUE print(v) print(class(v)) # numeric v <- 99.99 print(v) print(class(v)) # integer v <- 100L print(v) print(class(v)) # complex v <- 10 -3i print(v) print(class(v)) # character v <- 'i am a character' print(v) print(class(v))

### Variables

As mentioned in the *'Hello World!' *example, variables are used to store values. Variable value can be access through the identifier (variable name). Variable naming in R needs to following some standards:

- Consists of letters, numbers, . , or _ characters
- Starts with a letter or the dot not followed by a number

To assign values to variables, the following operators can be used:

- <-
- ->
- <<-

To list all the variables in the current environment:

ls() ls(all.names = TRUE)

To find variables with specific patterns:

ls(pattern="pat")

**rm**function:

rm rm(list=ls())

#### Examples

The following code script *R03.Variables.R* shows some examples of using variables:

atomicV<-"Jan" print(v) print(class(v)) print(length(v)) c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec") -> v print(v) print(class(v)) print(length(v)) v2 <- 2 .myv <- TRUE ls() ls(all.names = TRUE) ls(pattern = "v") ls(pattern = "v", all.names = TRUE) ls(pattern = "^v", all.names = TRUE) ls(pattern = "v$", all.names = TRUE) rm(v) rm(list=ls())

##### Sample output

> atomicV<-"Jan" > print(v) [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" > print(class(v)) [1] "character" > print(length(v)) [1] 12 > > c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec") -> v > print(v) [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" > print(class(v)) [1] "character" > print(length(v)) [1] 12 > > v2 <- 2 > .myv <- TRUE > > ls() [1] "atomicV" "cdt" "colors" "data" [5] "floor" "GCtorture" "myVar" "output.forest" [9] "output.tree" "result" "result1" "result2" [13] "sales" "sales.timeseries" "sales1" "sales2" [17] "sales3" "sf" "v" "v2" [21] "X" > > ls(all.names = TRUE) [1] ".myv" ".Random.seed" "atomicV" "cdt" [5] "colors" "data" "floor" "GCtorture" [9] "myVar" "output.forest" "output.tree" "result" [13] "result1" "result2" "sales" "sales.timeseries" [17] "sales1" "sales2" "sales3" "sf" [21] "v" "v2" "X" > > ls(pattern = "v") [1] "v" "v2" > > ls(pattern = "v", all.names = TRUE) [1] ".myv" "v" "v2" > > ls(pattern = "^v", all.names = TRUE) [1] "v" "v2" > > ls(pattern = "v$", all.names = TRUE) [1] ".myv" "v" > > rm(v) > > rm(list=ls())

### Operator

A R operator is a symbol that instructs the compiler to perform specific mathematical or logical manipulations. The following types of operators are available in R:

- Arithmetic
- Relational
- Logical
- Assignment
- Others

The following are some typical operators for each category. Refer to R official manual for the meaning of each of them.

Operator category | Operators |

Arithmetic | +, _, *,/, %%, %/%, ^ |

Relational | >, <, ==, <=, >=, != |

Logical | & (element wise), | (element wise), !, &&, || |

Assignment | <-, ->, =, <<- (global variable), ->> |

Others | :, %in%, %*% (transpose) |

#### Examples

The following code script *R04.Operators.R *provides some examples of using these operators:

### Arithmetic operators a <- 15 b <- 2 a+b a-b a*b a/b #Mod a%%b #Divide (quotient) a%/%b a^b # vector with more than one element x<- c(1,2,3) y<- c(4,5,6) x+y ### Relational operators x>=y x>y x<y x!=y x==y ### Logical operators x&y x|y !x !y # Single output x&&y x||y ### Assignment operators c('a',1,TRUE,5+5i) ->> v2 ### Other operators v <- 1:100 print(v) 1 %in% v 101 %in% v # multiply a matrix with its transpose mx = matrix(1:10,nrow=2, ncol=5) View(mx) tmx = t(mx) View(tmx) mx %*% tmx

##### Sample output

> ### Arithmetic operators > a <- 15 > b <- 2 > a+b [1] 17 > a-b [1] 13 > a*b [1] 30 > a/b [1] 7.5 > #Mod > a%%b [1] 1 > #Divide (quotient) > a%/%b [1] 7 > a^b [1] 225 > > # vector with more than one element > x<- c(1,2,3) > y<- c(4,5,6) > x+y [1] 5 7 9 > > ### Relational operators > x>=y [1] FALSE FALSE FALSE > x>y [1] FALSE FALSE FALSE > x<y [1] TRUE TRUE TRUE > x!=y [1] TRUE TRUE TRUE > x==y [1] FALSE FALSE FALSE > > ### Logical operators > x&y [1] TRUE TRUE TRUE > x|y [1] TRUE TRUE TRUE > !x [1] FALSE FALSE FALSE > !y [1] FALSE FALSE FALSE > # Single output > x&&y [1] TRUE > x||y [1] TRUE > > ### Assignment operators > c('a',1,TRUE,5+5i) ->> v2 > > ### Other operators > v <- 1:100 > print(v) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 [21] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 [41] 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 [61] 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 [81] 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 > > 1 %in% v [1] TRUE > 101 %in% v [1] FALSE > > # multiply a matrix with its transpose > mx = matrix(1:10,nrow=2, ncol=5) > View(mx) > tmx = t(mx) > View(tmx) > > mx %*% tmx [,1] [,2] [1,] 165 190 [2,] 190 220

For the matrix and transposed matrix, the output looks like the following:

mx:

tmx:

### Branching

Branching are commonly used in programming and in R the following are the commonly used ones:

- if
- if .. else
- switch

The following flow chart shows in high-level how it works:

#### Examples

The following code script *R05.Branching**.R *provides some examples of using these branching features:

```
rm(list=ls())
# Branching if else switch
a <- 2
b <- 3
if(a>b)
{
print("a is greater than b")
}else
{
print("a is not greater than b")
}
# switch
switch(
5,
"Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"
)
```

##### Sample output

[1] "a is not greater than b" > > > # switch > > > switch( + 5, + "Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec" + ) [1] "May"

### Loops and loop control

R supported loops include: repeat, while, for. The following flow chart shows how that works in high-level:

Loop control language features include **break **and **next**. Break will **break **the current entire loop while **next **will exit and go to next iteration only.

#### Examples

The following code script *R06.Loops.R *provides some examples of using loops and breaks:

```
# Loops
i <- 1
repeat
{
print('I am repeating')
i = i+1
if(i>10)
break
}
j<-1
while(j<=10)
{
print('I am repeating')
j = j+1
}
v<-1:10
for(i in v)
{
print('I am repeating')
}
# only print every two loops
v<-1:10
for(i in v)
{
print(i)
if(i%%2 != 0)
next
print('I am repeating')
}
```

##### Sample output

> # Loops > i <- 1 > repeat + { + print('I am repeating') + i = i+1 + if(i>10) + break + } [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" > > > j<-1 > while(j<=10) + { + print('I am repeating') + j = j+1 + } [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" > > v<-1:10 > for(i in v) + { + print('I am repeating') + } [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" [1] "I am repeating" > > # only print every two loops > > v<-1:10 > for(i in v) + { + print(i) + if(i%%2 != 0) + next + + print('I am repeating') + } [1] 1 [1] 2 [1] "I am repeating" [1] 3 [1] 4 [1] "I am repeating" [1] 5 [1] 6 [1] "I am repeating" [1] 7 [1] 8 [1] "I am repeating" [1] 9 [1] 10 [1] "I am repeating"

### Functions

In the above examples, we used functions several times. A function in R is a set of statements to perform certain task. A function is also one object in R.

Function are created using these components:

- Name
- Arguments
- Body
- Return Value (optional)

Function_Name <- function(arg1, arg2, ...) { Function body Return value }

#### Examples

The following code script *R07.Functions.R *provides some examples of using functions:

RTutorial.plus <- function(a,b) { result <- a+b print(paste('result is ', result)) return(result) } RTutorial.plus(10,11) RTutorial.plus <- function(a,b) { result <- a+b print(paste('result is ', result)) } result<-RTutorial.plus(10,11) print(result) RTutorial.plus <- function(a,b=11) { result <- a+b print(paste('result is ', result)) return(result) } RTutorial.plus(10)It shows example functions with/without return values. The last function also has an optional parameter named 'b' with default value '11'.

##### Sample output

> RTutorial.plus <- function(a,b) + { + result <- a+b + print(paste('result is ', result)) + return(result) + } > > RTutorial.plus(10,11) [1] "result is 21" [1] 21 > > RTutorial.plus <- function(a,b) + { + result <- a+b + print(paste('result is ', result)) + } > > result<-RTutorial.plus(10,11) [1] "result is 21" > > print(result) [1] "result is 21" > > RTutorial.plus <- function(a,b=11) + { + result <- a+b + print(paste('result is ', result)) + return(result) + } > > RTutorial.plus(10) [1] "result is 21" [1] 21

### Summary

Hopefully you now have a good understanding about the basics of R programming. In the other articles of this series, I will show more advanced topics about R programming.

*info*Last modified by Raymond 2 months ago

*copyright*The content on this page is licensed under CC-BY-SA-4.0.

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