LEARNING R ON YOUTUBE
How to Assign Values to Object using “=” or “<-”
x = 11
print(x)
## [1] 11
x
## [1] 11
y <- 7
y
## [1] 7
y <- 9
y
## [1] 9
How to use ls() to see the data stored in R
ls()
## [1] "x" "y"
How to use rm() to remove object in R
rm(y)
y <- 9
y
## [1] 9
Naming Object
x.1<- 14
x.1
## [1] 14
Defining Characters
xx <- "Abdullateef"
xx
## [1] "Abdullateef"
yy <- "1"
Using the Sqrt(), Log(), Exp() and #abs()
sqrt(y)
## [1] 3
y^(1/2)
## [1] 3
log(y)
## [1] 2.197225
exp(y)
## [1] 8103.084
log2(y)
## [1] 3.169925
abs(-14)
## [1] 14
sqrt(y)
## [1] 3
Vector
x1 <- c(1,3,5,7,9)
x1
## [1] 1 3 5 7 9
gender <- c("male","female")
gender
## [1] "male" "female"
Using “:” and seq() to create sequence of numbers
2:7
## [1] 2 3 4 5 6 7
seq(from=1,to=7,by=1)
## [1] 1 2 3 4 5 6 7
seq(from=1,to=7, by=1/3)
## [1] 1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000 3.333333
## [9] 3.666667 4.000000 4.333333 4.666667 5.000000 5.333333 5.666667 6.000000
## [17] 6.333333 6.666667 7.000000
seq(from=1,to=7,by=0.25)
## [1] 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50
## [16] 4.75 5.00 5.25 5.50 5.75 6.00 6.25 6.50 6.75 7.00
Using rep() to create a vector of repeated numbers or character
rep(1, times=10)
## [1] 1 1 1 1 1 1 1 1 1 1
rep("marin", times=5)
## [1] "marin" "marin" "marin" "marin" "marin"
rep(1:3,times=5)
## [1] 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
rep(seq(from=2,to=5,by=0.25),times=5)
## [1] 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 2.00 2.25
## [16] 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 2.00 2.25 2.50 2.75
## [31] 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 2.00 2.25 2.50 2.75 3.00 3.25
## [46] 3.50 3.75 4.00 4.25 4.50 4.75 5.00 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75
## [61] 4.00 4.25 4.50 4.75 5.00
rep(c("m","f"), times=5)
## [1] "m" "f" "m" "f" "m" "f" "m" "f" "m" "f"
How to create a matrix using “matrix”,“nrow” and “byrow”
functions
matrix(c(1,2,3,4,5,6,7,8,9), nrow=3, ncol=3,byrow=F)
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
matrix(c(1,2,3,4,5,6,7,8,9), nrow=3, ncol=3,byrow=T)
## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
mat <- matrix(c(1,2,3,4,5,6,7,8,9), nrow=3, ncol=3,byrow=T)
mat
## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
How to import CSV file into R using read.csv function
data1 <- read.csv("C:/Users/user/Desktop/mbb.csv",
na = "***", header = T)
head(data1)
## Month Mozart Beethoven Bach
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to import CSV file into R? using read.table function
data2a <- read.table("C:/Users/user/Desktop/mbb.csv",
na = "***", header=T)
head(data2a)
## Month.Mozart.Beethoven.Bach
## 1 2004-01,12,8,15
## 2 2004-02,12,9,15
## 3 2004-03,12,9,14
## 4 2004-04,12,8,14
## 5 2004-05,11,9,13
## 6 2004-06,9,7,12
How to specify how variables/columns are separated when importing
data into R
data2b <- read.table("C:/Users/user/Desktop/mbb.csv", header=T, sep=",")
head(data2b)
## Month Mozart Beethoven Bach
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to import tab-delimited (.TXT) data file into R? using
read.delim function
data3 <- read.delim("C:/Users/user/Desktop/mbb.txt", header=T)
head(data3)
## Month Mozart Beethoven Bach
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to import tab-delimited (.TXT) data file into R? using
read.table function
data4 <- read.table ("C:/Users/user/Desktop/mbb.txt", header=T, sep="\t")
head(data4)
## Month Mozart Beethoven Bach
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to import Excel Files (.XLSX) data file into R? using readxl
function
library(readxl)
data5 <- read_excel("C:/Users/user/Desktop/mbb.xlsx",
na = "***")
head(data5)
## # A tibble: 6 × 4
## Month Mozart Beethoven Bach
## <chr> <dbl> <dbl> <dbl>
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to use the write. table command for exporting data out of
R.
write.table(data1, file="mbbdata1.csv", sep=",")
How to get the Working Directory
getwd()
## [1] "C:/Users/user/Documents/ICAMMDA"
How to set the working directory
setwd("C:/Users/user/Documents/ICAMMDA")
How to include or exclude the row names while exporting data out of
R using ‘row.names’ argument
write.table(data1, file="mbbdata2.csv", row.names=F, sep=",")
How to save file exported from R to a folder other than the current
working directory:
write.table(data1, file="C:/Users/user/Documents/mbbdata2.csv", row.names=F, sep=",")
How to use write.command in for saving in other file formats for
example a tab delimited text file
write.table(data1, file="mbbdata2.txt", row.names=F, sep="\t")
How to use the “write.csv” command to export data from R
write.csv(data1, file="mbbdata3.csv", row.names=F)
How to read a dataset into R? we will use read.table function to
read a dataset into R and save it as an object
mydata <- read.table(file="C:/Users/user/Documents/ICAMMDA/mbbdata2.csv", header=T,sep=",")
How to know the dimensions (the number of rows and columns) of the
data in R using the dim function
dim(mydata)
## [1] 152 4
How to see the first several rows of the data using the head()
head(mydata)
## Month Mozart Beethoven Bach
## 1 2004-01 12 8 15
## 2 2004-02 12 9 15
## 3 2004-03 12 9 14
## 4 2004-04 12 8 14
## 5 2004-05 11 9 13
## 6 2004-06 9 7 12
How to see the last several rows of the data using the tail()
tail(mydata)
## Month Mozart Beethoven Bach
## 147 2016-03 6 4 10
## 148 2016-04 5 4 10
## 149 2016-05 5 4 9
## 150 2016-06 5 3 8
## 151 2016-07 4 3 9
## 152 4 3 9
How to check the variable names in R using name()
names(mydata)
## [1] "Month" "Mozart" "Beethoven" "Bach"
How to make objects/variables within a data frame accessible in R
using the “attach” function
attach(mydata)
## The following object is masked _by_ .GlobalEnv:
##
## Mozart
mean(Bach)
## [1] 10.71053
How to un-attach the data in R? working with the “detach”
function
detach(mydata)
Mozart
## [1] 12 12 12 12 11 9 7 7 9 11 11 10 11 11 10 11 11 9
## [19] 7 7 9 10 11 11 100 25 19 14 15 11 8 8 10 11 11 12
## [37] 10 9 9 8 8 7 6 6 7 8 8 7 7 7 7 7 7 6
## [55] 5 5 6 7 7 7 8 8 8 8 8 7 6 7 7 8 8 8
## [73] 8 8 7 7 7 6 5 5 6 8 9 8 9 8 8 8 8 7
## [91] 6 6 7 7 8 8 8 7 7 7 7 6 5 5 6 6 6 6
## [109] 7 6 6 6 6 5 5 5 6 6 6 6 6 6 6 6 6 5
## [127] 5 5 5 6 6 6 7 6 6 6 6 5 4 5 5 5 5 6
## [145] 9 6 6 5 5 5 4 4
How to check the type or class of a variable in R using the “class”
function in R
attach(mydata)
## The following object is masked _by_ .GlobalEnv:
##
## Mozart
class(Mozart)
## [1] "integer"
class(Month)
## [1] "character"
class(Beethoven)
## [1] "integer"
class(Bach)
## [1] "integer"
How to produce summaries for data in R? learn to use the “summary”
function in R
summary(mydata)
## Month Mozart Beethoven Bach
## Length:152 Min. : 4.000 Min. : 3.000 Min. : 8.00
## Class :character 1st Qu.: 6.000 1st Qu.: 4.000 1st Qu.:10.00
## Mode :character Median : 7.000 Median : 5.000 Median :10.00
## Mean : 8.191 Mean : 5.197 Mean :10.71
## 3rd Qu.: 8.000 3rd Qu.: 6.000 3rd Qu.:11.00
## Max. :100.000 Max. :10.000 Max. :22.00
How to convert a numeric variable to categorical/factor variable in
R using “as.factor” function
x <- c(0,1,1,1,0,0,0,0,0,0)
class(x)
## [1] "numeric"
summary(x)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.30 0.75 1.00
x <- as.factor(x)
class(x)
## [1] "factor"
summary(x)
## 0 1
## 7 3
How to use Apply() in R
Stockdata <- read.table(file="~/ICAMMDA/Stockdata.csv", sep = ",",header=T,row.names=1)
Stockdata
## Stock1 Stock2 Stock3 Stock4
## Day1 185.74 1.47 1605 95.05
## Day2 184.26 1.56 1580 97.49
## Day3 162.21 1.39 1490 88.57
## Day4 159.04 1.43 1520 85.55
## Day5 164.87 1.42 1550 92.04
## Day6 162.72 1.36 1525 91.70
## Day7 157.89 NA 1495 89.88
## Day8 159.49 1.43 1485 93.17
## Day9 150.22 1.57 1470 90.12
## Day10 151.02 1.54 1510 92.14
apply(X=Stockdata,MARGIN=2,FUN=mean )
## Stock1 Stock2 Stock3 Stock4
## 163.746 NA 1523.000 91.571
AVG <- apply(X=Stockdata,MARGIN=2,FUN=mean,na.rm=T )
AVG
## Stock1 Stock2 Stock3 Stock4
## 163.746000 1.463333 1523.000000 91.571000
apply(Stockdata,2, mean, na.rm=T)
## Stock1 Stock2 Stock3 Stock4
## 163.746000 1.463333 1523.000000 91.571000
colMeans(Stockdata,na.rm=T)
## Stock1 Stock2 Stock3 Stock4
## 163.746000 1.463333 1523.000000 91.571000
apply(Stockdata,2,max,na.rm=T)
## Stock1 Stock2 Stock3 Stock4
## 185.74 1.57 1605.00 97.49
apply(Stockdata, 2, quantile,probs=c(0.2,0.80),na.rm=T)
## Stock1 Stock2 Stock3 Stock4
## 20% 156.516 1.408 1489 89.618
## 80% 168.748 1.548 1556 93.546
apply(Stockdata,2,plot,type="l")
## NULL
apply(Stockdata,2,plot,type="l", main="stock", ylab="Price", xlab="Day")
## NULL
apply(Stockdata,1,sum,na.rm=T)
## Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10
## 1887.26 1863.31 1742.17 1766.02 1808.33 1780.78 1742.77 1739.09 1711.91 1754.70
rowSums(Stockdata, na.rm=T)
## Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10
## 1887.26 1863.31 1742.17 1766.02 1808.33 1780.78 1742.77 1739.09 1711.91 1754.70
plot(apply(Stockdata,1,sum, na.rm=T),type="l",ylab="Total Market Value", xlab = "Day", main="Market Trend")
points(apply(Stockdata,1,sum, na.rm=T),pch=16, col="blue")
Installing Packages
install.packages("GLMsData",repos = "https://cloud.r-project.org")
## Installing package into 'C:/Users/user/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## package 'GLMsData' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\user\AppData\Local\Temp\RtmpeyW1kf\downloaded_packages
library(GLMsData)
data(lungcap)
head(lungcap)
## Age FEV Ht Gender Smoke
## 1 3 1.072 46 F 0
## 2 4 0.839 48 F 0
## 3 4 1.102 48 F 0
## 4 4 1.389 48 F 0
## 5 4 1.577 49 F 0
## 6 4 1.418 49 F 0
summary(lungcap)
## Age FEV Ht Gender Smoke
## Min. : 3.000 Min. :0.791 Min. :46.00 F:318 Min. :0.00000
## 1st Qu.: 8.000 1st Qu.:1.981 1st Qu.:57.00 M:336 1st Qu.:0.00000
## Median :10.000 Median :2.547 Median :61.50 Median :0.00000
## Mean : 9.931 Mean :2.637 Mean :61.14 Mean :0.09939
## 3rd Qu.:12.000 3rd Qu.:3.119 3rd Qu.:65.50 3rd Qu.:0.00000
## Max. :19.000 Max. :5.793 Max. :74.00 Max. :1.00000
attach(lungcap)
How to Use the “tapply()” Function in R, to Apply a Function to
subsets of a Variable or Vector in R
tapply(Age,Smoke,mean,na.rm=T)
## 0 1
## 9.534805 13.523077
m <- tapply(Age,Smoke,mean)
m
## 0 1
## 9.534805 13.523077
tapply(Age,Smoke,mean, simplify=F)
## $`0`
## [1] 9.534805
##
## $`1`
## [1] 13.52308
mean(Age[Smoke==0])
## [1] 9.534805
mean(Age[Smoke==1])
## [1] 13.52308
tapply(Age,Smoke,summary)
## $`0`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 8.000 9.000 9.535 11.000 19.000
##
## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 12.00 13.00 13.52 15.00 19.00
tapply(Age,Smoke,quantile,probs=c(0.2,0.8))
## $`0`
## 20% 80%
## 7 12
##
## $`1`
## 20% 80%
## 11.0 15.2
tapply(X=Age, INDEX=list(Smoke,Gender),FUN=mean, na.rm=T)
## F M
## 0 9.365591 9.687097
## 1 13.256410 13.923077
mean(Age[Smoke==0 & Gender=="F"])
## [1] 9.365591
mean(Age[Smoke==0 & Gender=="M"])
## [1] 9.687097
mean(Age[Smoke==1 & Gender=="F"])
## [1] 13.25641
mean(Age[Smoke==1 & Gender=="M"])
## [1] 13.92308
head(lungcap)
## Age FEV Ht Gender Smoke
## 1 3 1.072 46 F 0
## 2 4 0.839 48 F 0
## 3 4 1.102 48 F 0
## 4 4 1.389 48 F 0
## 5 4 1.577 49 F 0
## 6 4 1.418 49 F 0
by(Age,list(Smoke,Gender), mean, na.rm=T)
## : 0
## : F
## [1] 9.365591
## ------------------------------------------------------------
## : 1
## : F
## [1] 13.25641
## ------------------------------------------------------------
## : 0
## : M
## [1] 9.687097
## ------------------------------------------------------------
## : 1
## : M
## [1] 13.92308
temp <- by(Age,list(Smoke,Gender), mean, na.rm=T)
temp
## : 0
## : F
## [1] 9.365591
## ------------------------------------------------------------
## : 1
## : F
## [1] 13.25641
## ------------------------------------------------------------
## : 0
## : M
## [1] 9.687097
## ------------------------------------------------------------
## : 1
## : M
## [1] 13.92308
temp[4]
## [1] 13.92308
Bar Chart
dim(lungcap)
## [1] 654 5
names(lungcap)
## [1] "Age" "FEV" "Ht" "Gender" "Smoke"
class(names(lungcap))
## [1] "character"
class(Gender)
## [1] "factor"
#Frequeny Table
count <- table(Gender)
count
## Gender
## F M
## 318 336
# Relative Frequency
percent <- table(Gender)/654
percent
## Gender
## F M
## 0.4862385 0.5137615
barplot(count)
barplot(percent, main="TITLE",xlab="Gender",ylab="%")
barplot(percent, main="TITLE",xlab="Gender",ylab="%", las=1)
barplot(percent, main="TITLE",xlab="Gender",ylab="%", las=1, names.arg=c("Female","Male"), horiz=T)
PIECHART
names(count) <- c("Female","Male")
pie(count, main="TTILE HERE")
names(count) <- c("Female","Male")
box()
Boxplot
boxplot(FEV)
quantile(FEV, probs=c(0,0.25,0.5,0.75,1))
## 0% 25% 50% 75% 100%
## 0.7910 1.9810 2.5475 3.1185 5.7930
boxplot(FEV, main="Boxplot", ylab="Lung Capacity", ylim=c(0,7), las=1)
boxplot(FEV~Gender)
boxplot(FEV~Gender,main="Boxplot by Gender")
boxplot(FEV[Gender=="F"],FEV[Gender=="M"])
Stratified Box Plot
names(lungcap)
## [1] "Age" "FEV" "Ht" "Gender" "Smoke"
AgeGroups <- cut(Age,breaks=c(0,7,9,11,13,15,19), labels=c("<7",'8/9',"10/11","12/13","14/15","16+"))
Age[1:5]
## [1] 3 4 4 4 4
AgeGroups[1:5]
## [1] <7 <7 <7 <7 <7
## Levels: <7 8/9 10/11 12/13 14/15 16+
boxplot(FEV, ylab="Lung Capacity", main="Boxplot of Lung Capacity", las=1)
boxplot(FEV~Smoke, ylab="Lung Capacity", main="Lung Capcity vs Smoke", las=1)
boxplot(FEV[Age>=15]~Smoke[Age>=15], ylab="Lung Capacity", main="Lung Capcity vs Smoke, for 15+", las=1)
Age[1:5]
## [1] 3 4 4 4 4
AgeGroups[1:5]
## [1] <7 <7 <7 <7 <7
## Levels: <7 8/9 10/11 12/13 14/15 16+
levels(AgeGroups)
## [1] "<7" "8/9" "10/11" "12/13" "14/15" "16+"
boxplot(FEV~Smoke*AgeGroups, ylab="Lung Capacity", main="Lung Capcity vs Smoke by Age Groups", las=1)
boxplot(FEV~Smoke*AgeGroups, ylab="Lung Capacity", main="Lung Capcity vs Smoke by Age Groups", las=2, col=c(4,2))
box()
boxplot(FEV~Smoke*AgeGroups, ylab="Lung Capacity", main="Lung Capcity vs Smoke, stratified by Age", las=2, col=c("blue", "red"), axes=F, xlab="Age Strata")
box()
axis(2,at=seq(0,10,1), seq(0,10,1), las=1)
axis(1,at=c(1.5,3.5,5.5,7.5,9.5,11.5), labels=c("<7",'8-9',"10-11","12-13","14-15","16+"))
legend(x=10, y=1.5, legend=c("Non-smoke", "Smoke"), col=c(4,2), pch=15,cex=0.8)
Histogram
names(lungcap)
## [1] "Age" "FEV" "Ht" "Gender" "Smoke"
hist(FEV)
hist(FEV, freq=F)
hist(FEV, prob=T, ylim=c(0,0.6))
hist(FEV, prob=T, ylim=c(0,0.6),breaks=20)
hist(FEV, prob=T, ylim=c(0,0.6),breaks=seq(0,16,2))
hist(FEV, prob=T, ylim=c(0,0.6),breaks=seq(0,6,0.25), main="Boxplot of Lung Capacity", xlab="Lung Capacity", las=1)
box()
lines(density(FEV), col=4,lwd=1)
Stem and Leaf PLots
lungcap
## Age FEV Ht Gender Smoke
## 1 3 1.072 46.0 F 0
## 2 4 0.839 48.0 F 0
## 3 4 1.102 48.0 F 0
## 4 4 1.389 48.0 F 0
## 5 4 1.577 49.0 F 0
## 6 4 1.418 49.0 F 0
## 7 4 1.569 50.0 F 0
## 8 5 1.196 46.5 F 0
## 9 5 1.400 49.0 F 0
## 10 5 1.282 49.0 F 0
## 11 5 1.343 50.0 F 0
## 12 5 1.146 50.0 F 0
## 13 5 1.092 50.0 F 0
## 14 5 1.539 50.0 F 0
## 15 5 1.704 51.0 F 0
## 16 5 1.589 51.0 F 0
## 17 5 1.612 52.0 F 0
## 18 5 1.536 52.0 F 0
## 19 5 0.791 52.0 F 0
## 20 5 1.256 52.5 F 0
## 21 5 1.552 54.0 F 0
## 22 6 1.481 51.0 F 0
## 23 6 1.523 51.0 F 0
## 24 6 1.338 51.5 F 0
## 25 6 1.343 52.0 F 0
## 26 6 1.602 53.0 F 0
## 27 6 1.878 53.0 F 0
## 28 6 1.719 53.0 F 0
## 29 6 1.695 53.0 F 0
## 30 6 1.672 54.0 F 0
## 31 6 1.697 55.0 F 0
## 32 6 1.796 55.0 F 0
## 33 6 1.535 55.0 F 0
## 34 6 2.102 55.5 F 0
## 35 6 1.415 56.0 F 0
## 36 6 1.919 58.0 F 0
## 37 7 1.603 51.0 F 0
## 38 7 1.609 51.5 F 0
## 39 7 1.473 52.5 F 0
## 40 7 1.877 52.5 F 0
## 41 7 1.935 52.5 F 0
## 42 7 1.726 53.0 F 0
## 43 7 1.415 53.5 F 0
## 44 7 1.829 54.0 F 0
## 45 7 1.461 54.0 F 0
## 46 7 1.690 54.0 F 0
## 47 7 1.720 54.5 F 0
## 48 7 1.698 54.5 F 0
## 49 7 1.827 54.5 F 0
## 50 7 1.750 55.0 F 0
## 51 7 1.370 55.0 F 0
## 52 7 1.640 55.0 F 0
## 53 7 1.631 55.5 F 0
## 54 7 2.371 55.5 F 0
## 55 7 1.728 56.5 F 0
## 56 7 2.111 57.0 F 0
## 57 7 2.095 57.0 F 0
## 58 7 1.495 57.0 F 0
## 59 7 2.093 57.5 F 0
## 60 7 2.002 57.5 F 0
## 61 7 1.917 58.0 F 0
## 62 7 2.366 58.0 F 0
## 63 7 2.564 58.0 F 0
## 64 7 1.742 58.5 F 0
## 65 7 2.419 60.0 F 0
## 66 8 1.292 52.0 F 0
## 67 8 1.758 52.0 F 0
## 68 8 1.344 52.5 F 0
## 69 8 1.512 53.0 F 0
## 70 8 1.872 56.5 F 0
## 71 8 1.335 56.5 F 0
## 72 8 1.844 56.5 F 0
## 73 8 1.999 56.5 F 0
## 74 8 1.931 57.0 F 0
## 75 8 2.333 57.0 F 0
## 76 8 1.698 57.5 F 0
## 77 8 2.015 57.5 F 0
## 78 8 2.531 58.0 F 0
## 79 8 2.293 58.0 F 0
## 80 8 1.953 58.0 F 0
## 81 8 1.987 58.5 F 0
## 82 8 2.193 58.5 F 0
## 83 8 1.780 58.5 F 0
## 84 8 1.556 58.5 F 0
## 85 8 2.175 59.0 F 0
## 86 8 1.697 59.0 F 0
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## 651 17 3.082 67.0 M 1
## 652 17 3.406 69.0 M 1
## 653 18 4.086 67.0 M 1
## 654 18 4.404 70.5 M 1
names(lungcap)
## [1] "Age" "FEV" "Ht" "Gender" "Smoke"
class(Gender)
## [1] "factor"
head(Gender)
## [1] F F F F F F
## Levels: F M
femalelungcap <- FEV[Gender=="F"]
stem(femalelungcap, scale=2)
##
## The decimal point is 1 digit(s) to the left of the |
##
## 7 | 9
## 8 | 4
## 9 |
## 10 | 79
## 11 | 05
## 12 | 0689
## 13 | 4444479
## 14 | 02226678
## 15 | 012444567899
## 16 | 001113479
## 17 | 00000012223345568
## 18 | 023347889
## 19 | 12233445579
## 20 | 00245678899
## 21 | 0001334457777899
## 22 | 022455668999
## 23 | 122233444555666677788999
## 24 | 223445666688999
## 25 | 002334466666777889
## 26 | 011133444457777889999
## 27 | 011455566
## 28 | 00122344555667779999
## 29 | 0135788899
## 30 | 000012223444556667778888899
## 31 | 002234455777789
## 32 | 112224666
## 33 | 00133459
## 34 | 001139
## 35 | 002228
## 36 | 578
## 37 | 579
## 38 | 24
Stacked and Group Bar Chart
class(Smoke)
## [1] "integer"
as.factor(Smoke)
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [556] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## Levels: 0 1
Table1 <- table(Smoke,Gender)
Table1
## Gender
## Smoke F M
## 0 279 310
## 1 39 26
barplot(Table1)
barplot(Table1, beside=T,legend.text=T)
barplot(Table1, beside=T,legend.text=c("Non-smoker","Smoker"), main="Gender and Smoking", xlab="Gender",las=1, col=c(2,5))
mosaicplot(Table1,legend.text=c("Non-smoker","Smoker"), main="Gender and Smoking", xlab="Gender",las=1, col=c(2,5))
Scatterplots
class(Age)
## [1] "integer"
names(lungcap)
## [1] "Age" "FEV" "Ht" "Gender" "Smoke"
class(Ht)
## [1] "numeric"
summary(Ht)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 46.00 57.00 61.50 61.14 65.50 74.00
cor(Age, Ht)
## [1] 0.7919436
plot(Age, Ht, main="Scatterplot of Age and Height", xlab="Age",ylab="Height", las=1,xlim=c(0,20), pch=8, col=2)
abline(lm(Ht~Age),col=4,lwd=2)
lines(smooth.spline(Age,Ht))
lines(smooth.spline(Age,Ht),lty=2,lwd=5)
Calculating Mean, Standard Deviation, Frequencies in R
summary(FEV)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.791 1.981 2.547 2.637 3.119 5.793
table(Smoke)/length(Smoke)
## Smoke
## 0 1
## 0.90061162 0.09938838
table(Smoke, Gender)
## Gender
## Smoke F M
## 0 279 310
## 1 39 26
mean(FEV)
## [1] 2.63678
mean(FEV, trim=0.10)
## [1] 2.572078
median(FEV)
## [1] 2.5475
var(FEV)
## [1] 0.7517915
sd(FEV)
## [1] 0.8670591
sqrt(var(FEV))
## [1] 0.8670591
sd(FEV)^2
## [1] 0.7517915
min(FEV)
## [1] 0.791
max(FEV)
## [1] 5.793
range(FEV)
## [1] 0.791 5.793
quantile(FEV, probs=0.90)
## 90%
## 3.813
quantile(FEV, probs=c(0.2,0.5,0.9,1))
## 20% 50% 90% 100%
## 1.8506 2.5475 3.8130 5.7930
sum(FEV)
## [1] 1724.454
cor(FEV,Age)
## [1] 0.756459
cor(FEV,Age, method="spearman")
## [1] 0.7984229
cov(FEV,Age)
## [1] 1.93747
var(FEV, Age)
## [1] 1.93747
summary(FEV)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.791 1.981 2.547 2.637 3.119 5.793
summary(Smoke)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09939 0.00000 1.00000
summary(Gender)
## F M
## 318 336
summary(lungcap)
## Age FEV Ht Gender Smoke
## Min. : 3.000 Min. :0.791 Min. :46.00 F:318 Min. :0.00000
## 1st Qu.: 8.000 1st Qu.:1.981 1st Qu.:57.00 M:336 1st Qu.:0.00000
## Median :10.000 Median :2.547 Median :61.50 Median :0.00000
## Mean : 9.931 Mean :2.637 Mean :61.14 Mean :0.09939
## 3rd Qu.:12.000 3rd Qu.:3.119 3rd Qu.:65.50 3rd Qu.:0.00000
## Max. :19.000 Max. :5.793 Max. :74.00 Max. :1.00000
How to Modify and Customize Plots in R
plot(Age,Ht, main="Scatterplot", cex=0.5,cex.main=2,cex.lab=1.5,cex.axis=0.7)
plot(Age,Ht, main="Scatterplot")
plot(Age,Ht, main="Scatterplot", font.main=3)
plot(Age,Ht, main="Scatterplot", font.main=4)
plot(Age,Ht, main="Scatterplot", font.main=4, font.lab=2)
plot(Age,Ht, main="Scatterplot", font.main=4, font.lab=2, font.axis=3)
plot(Age,Ht, main="Scatterplot")
plot(Age,Ht, main="Scatterplot",col=5,col.main=4,col.lab=2,col.axis=3)
plot(Age,Ht, main="Scatterplot")
plot(Age,Ht, main="Scatterplot", pch=2)
plot(Age,Ht, main="Scatterplot", pch="w")
abline(lm(Ht~Age), col=4,lty=2,lwd=6)
plot(Age[Gender=="M"],Ht[Gender=="M"],col=4,pch="m")
plot(Age[Gender=="M"],Ht[Gender=="M"],col=4,pch="m", xlab="Age",ylab="Height", main="Height vs Age")
points(Age[Gender=="F"],Ht[Gender=="F"], col=6,pch="f")
par(mfrow=c(1,2))
plot(Age[Gender=="M"],Ht[Gender=="M"], xlab="Age",ylab="Height", main="Height vs Age for Males",xlim=c(0,20),ylim=c(45,75))
plot(Age[Gender=="F"],Ht[Gender=="F"], xlab="Age",ylab="Height", main="Height vs Age for Females",xlim=c(0,20),ylim=c(45,75))
par(mfrow=c(1,1))
plot(Age,Ht, main="TITLE")
plot(Age,Ht, main="TITLE",axes=F)
axis(side=1,at=c(5,10,15), labels=c("sev","mean","15"))
axis(side=2,at=c(50,60,70), labels=c(50,60,70))
box()
axis(side=4, at=c(45,55,65,75), labels=c(45,55,65,75))
par
## function (..., no.readonly = FALSE)
## {
## .Pars.readonly <- c("cin", "cra", "csi", "cxy", "din", "page")
## single <- FALSE
## args <- list(...)
## if (!length(args))
## args <- as.list(if (no.readonly)
## .Pars[-match(.Pars.readonly, .Pars)]
## else .Pars)
## else {
## if (all(unlist(lapply(args, is.character))))
## args <- as.list(unlist(args))
## if (length(args) == 1) {
## if (is.list(args[[1L]]) || is.null(args[[1L]]))
## args <- args[[1L]]
## else if (is.null(names(args)))
## single <- TRUE
## }
## }
## value <- .External2(C_par, args)
## if (single)
## value <- value[[1L]]
## if (!is.null(names(args)))
## invisible(value)
## else value
## }
## <bytecode: 0x00000219cb1be110>
## <environment: namespace:graphics>
Add and Customize Text in Plots with R
plot(Age,FEV, main="Scatterplot of Lung Capacity vs Age", las=1)
cor(Age,FEV)
## [1] 0.756459
text(x=5,y=4, label= "r = 0.756", adj=0)
plot(Age,FEV, main="Scatterplot of Lung Capacity vs Age", las=1)
text(x=3,y=5.5, adj=0, label= "r = 0.756", cex=0.5, col=4)
plot(Age,FEV, main="Scatterplot of Lung Capacity vs Age", las=1)
text(x=3,y=5.5, adj=0, label= "r = 0.756", cex=1, col=4, font=4)
abline(h=mean(FEV), col=2,lwd=2)
text(x=4,y=4.5, adj=0,label="Mean Lung Cap",cex=1, col=2)
plot(Age,FEV, main="Scatterplot of Lung Capacity vs Age", las=1)
mtext(text="r = 0.756", side=1)
mtext(text="r = 0.756", side=2)
mtext(text="r = 0.756", side=3)
mtext(text="r = 0.756", side=4)
mtext(text="r = 0.756", side=1, adj=0)
mtext(text="r = 0.756", side=1, adj=0.75)
mtext(text="r = 0.756", side=1,adj=1)
plot(Age,FEV, main="Scatterplot of Lung Capacity vs Age", las=1)
mtext(text="r = 0.756", side=3,adj=1, col=4,cex=1.25, font=4)
Add and Customize Legends to Plots in R
plot(Age[Smoke==0],FEV[Smoke==0],main="Lung Capacity vs Age for Smoke/Non Smoke", col=4, xlab="Age", ylab="Lung Cap")
points(Age[Smoke==1],FEV[Smoke==1], col="red")
legend(x=5,y=4, legend=c("Non smoke", "Smoke"),fill=c(4,2))
plot(Age[Smoke==0],FEV[Smoke==0],main="Lung Capacity vs Age for Smoke/Non Smoke", col=4, xlab="Age", ylab="Lung Cap", pch=16)
points(Age[Smoke==1],FEV[Smoke==1], col="red", pch=17)
legend(x=5,y=5, legend=c("Non smoke", "Smoke"),col=c(4,2), pch=c(16,17), bty="n")
plot(Age[Smoke==0],FEV[Smoke==0],main="Lung Capacity vs Age for Smoke/Non Smoke", col=4, xlab="Age", ylab="Lung Cap", pch=16)
points(Age[Smoke==1],FEV[Smoke==1], col="red", pch=17)
lines(smooth.spline(Age[Smoke==0],FEV[Smoke==0]),col=4, lwd=4)
lines(smooth.spline(Age[Smoke==1],FEV[Smoke==1]),col=2, lwd=4)
legend(x=5,y=5, legend=c("Non smoke", "Smoke"),col=c(4,2), lty=1, bty="n", lwd=3)
plot(Age[Smoke==0],FEV[Smoke==0],main="Lung Capacity vs Age for Smoke/Non Smoke", col=4, xlab="Age", ylab="Lung Cap", pch=16)
points(Age[Smoke==1],FEV[Smoke==1], col="red", pch=17)
lines(smooth.spline(Age[Smoke==0],FEV[Smoke==0]),col=4, lwd=4, lty=2)
lines(smooth.spline(Age[Smoke==1],FEV[Smoke==1]),col=2, lwd=4, lty=3)
legend(x=5,y=5, legend=c("Non smoke", "Smoke"),col=c(4,2), lty=c(2,3), bty="n", lwd=3)
