Introduction to R Programming
Compiled by Dr. Hum Nath Bhandari, Roger Williams University
2025-09-29
Section 1 Activities
a <- 9
b <- 10
c <- 2
a %% c## [1] 1b - c## [1] 8(a-c) ^ c## [1] 49v <- c(15,20,12,11,32,10,9,43,21,9,12,56,34,4,2)
sort(v)## [1] 2 4 9 9 10 11 12 12 15 20 21 32 34 43 56a <- 9
b <- 10
c <- 2
a %% c## [1] 1b - c## [1] 8(a-c) ^ c## [1] 49v <- c(15,20,12,11,32,10,9,43,21,9,12,56,34,4,2)
a && b## [1] TRUEa > b## [1] FALSEb < c## [1] FALSEb>c## [1] TRUEu <- TRUE
v <- FALSE
(u|v) & (!v)## [1] TRUEd <- c(1,2,3,4)
e <- c(TRUE,TRUE,TRUE,FALSE)
f <- c("z", "y", "x", "w")
g <- 1:20
g## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20h <- c(1:10, 9:1)
h## [1] 1 2 3 4 5 6 7 8 9 10 9 8 7 6 5 4 3 2 1x <- seq(from = 2, to = 30, by = 2)
x## [1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30length(x)## [1] 15x[length(x)]## [1] 30x[x >= 10 & x <= 20]## [1] 10 12 14 16 18 20x[x%% 5 ==0]## [1] 10 20 30seq( 3,38,length.out=10)## [1] 3.000000 6.888889 10.777778 14.666667 18.555556 22.444444 26.333333
## [8] 30.222222 34.111111 38.000000a <- c(1,2,3,4,5)
b <- c(6,7,8,9,10)
a == b## [1] FALSE FALSE FALSE FALSE FALSEa == c(1,2,3,4,5)## [1] TRUE TRUE TRUE TRUE TRUEab_array <- cbind(a,b)
ab_array## a b
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10fn <- c("Lily", "Owen", "Olive", "Caitlin")
ages <-c(19, 17, 3, 19)
by <- c(2006, 2007, 2022, 2006)
fam_array <- rbind(fn, ages, by)
fam_array## [,1] [,2] [,3] [,4]
## fn "Lily" "Owen" "Olive" "Caitlin"
## ages "19" "17" "3" "19"
## by "2006" "2007" "2022" "2006"fn <- c("Lily", "Owen", "Olive", "Caitlin")
ages <-c(19, 17, 3, 19)
by <- c(2006, 2007, 2022, 2006)
M <- rbind(fn, ages, by)
M## [,1] [,2] [,3] [,4]
## fn "Lily" "Owen" "Olive" "Caitlin"
## ages "19" "17" "3" "19"
## by "2006" "2007" "2022" "2006"M[1,]## [1] "Lily" "Owen" "Olive" "Caitlin"M[,4]## fn ages by
## "Caitlin" "19" "2006"rownames(M) <- c("name", "age", "birth year")
N <- matrix(data = c(1,2,3,8,5,6,7,4,9,10,11,12), nrow = 3, ncol = 4)
N## [,1] [,2] [,3] [,4]
## [1,] 1 8 7 10
## [2,] 2 5 4 11
## [3,] 3 6 9 12c <- c(1,2,3,4)
N[3,] == c## [1] FALSE FALSE FALSE FALSEcol_means <- colMeans(N)
print(col_means)## [1] 2.000000 6.333333 6.666667 11.000000View(mtcars)
mtcars[, 1:3]## mpg cyl disp
## Mazda RX4 21.0 6 160.0
## Mazda RX4 Wag 21.0 6 160.0
## Datsun 710 22.8 4 108.0
## Hornet 4 Drive 21.4 6 258.0
## Hornet Sportabout 18.7 8 360.0
## Valiant 18.1 6 225.0
## Duster 360 14.3 8 360.0
## Merc 240D 24.4 4 146.7
## Merc 230 22.8 4 140.8
## Merc 280 19.2 6 167.6
## Merc 280C 17.8 6 167.6
## Merc 450SE 16.4 8 275.8
## Merc 450SL 17.3 8 275.8
## Merc 450SLC 15.2 8 275.8
## Cadillac Fleetwood 10.4 8 472.0
## Lincoln Continental 10.4 8 460.0
## Chrysler Imperial 14.7 8 440.0
## Fiat 128 32.4 4 78.7
## Honda Civic 30.4 4 75.7
## Toyota Corolla 33.9 4 71.1
## Toyota Corona 21.5 4 120.1
## Dodge Challenger 15.5 8 318.0
## AMC Javelin 15.2 8 304.0
## Camaro Z28 13.3 8 350.0
## Pontiac Firebird 19.2 8 400.0
## Fiat X1-9 27.3 4 79.0
## Porsche 914-2 26.0 4 120.3
## Lotus Europa 30.4 4 95.1
## Ford Pantera L 15.8 8 351.0
## Ferrari Dino 19.7 6 145.0
## Maserati Bora 15.0 8 301.0
## Volvo 142E 21.4 4 121.0mean(mtcars$mpg)## [1] 20.09062summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000x<- -2.5
y<- 6.3
z <- -9.2
abs(x)## [1] 2.5floor(y)## [1] 6sqrt(abs(z))## [1] 3.03315signif(pi, 4)## [1] 3.142v <- c(1,2,3,4,5)
w <- c(6,7,8,9,10)
intersect(v,w)## numeric(0)union(v,w)## [1] 1 2 3 4 5 6 7 8 9 10JJ <- JohnsonJohnson
mean(JJ)## [1] 4.799762sd(JJ)## [1] 4.309991var(JJ)## [1] 18.57602range(JJ)## [1] 0.44 16.20quantile(JJ,0.75)## 75%
## 7.1325summary(JJ)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.440 1.248 3.510 4.800 7.133 16.200boxplot(JJ,
main="J&J Quarterly Earnings per J&J share (1960 to 1981)",
xlab="Quarterly Earnings",
horizontal=TRUE)
hist(LakeHuron,
col="red",
xlab="Level (feet)",
main="Levels of Lake Huron (1875 - 1972)")
addition <- function(a,b){
return(a+b)
}
addition(5,3)## [1] 8print_values_between <- function(a,b){
i <- min(a,b)
while(i <= max(a,b)){
print(i)
i<- i+1
}
}
print_values_between(-5,5)## [1] -5
## [1] -4
## [1] -3
## [1] -2
## [1] -1
## [1] 0
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5addBetween <- function(a,b){
sum <- 0
i <- min(a,b)
while(i <= max(a,b)){
sum <- sum+i
i <- i+1
}
print(sum)
}
addBetween(1,100)## [1] 5050nPr <- function(n, r) {
answer <- factorial(n) / factorial(n - r)
return(answer)
}
nPr(5,2)## [1] 20nCr <- function(n,r){
if(r>n){
print(paste("The # of ways to permute this is 0 since", r, "is greater than", n))
}
combination <- factorial(n)/factorial(n-r)/factorial(r)
print(paste("The value of n choose r is", combination))
}
nCr(5,2)## [1] "The value of n choose r is 10"nCr(10,2)## [1] "The value of n choose r is 45"nCr(20,2)## [1] "The value of n choose r is 190"