AAQ1
LeeKeatEn U2102836
12/31/2021
(a) Find and get a dataset from the datasets available within R.
Perform exploratory data analysis (EDA) and prepare a codebook on that dataset using a newer method in R.
Loading iris data set
library(datasets)
data(iris)Peek at data
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaIris data set contains around 150 observations on three species of iris flower: setosa, versicolor and virginica. Every observation contains four measurements of flower’s Petal length, Petal width, Sepal length and Sepal width.
Quick data visualization
Histogram
Basic Histogram
hist(iris$Sepal.Length,
col = "lightgreen", #col argument is used to change the foreground colour of symbols.
main = "Sepal Length of Iris" #Add your own title
)
hist(iris$Sepal.Width, col = "darkgreen", main = "Sepal Width of Iris")
hist(iris$Petal.Length, col = "pink", main = "Petal Length of Iris")
hist(iris$Petal.Width, col = "magenta", main = "Petal Width of Iris")
Histogram By Group
Example: Petal Width by Species
# Put graph in 3 rows and 1 column
par(mfrow = c(3,1))
# Histogram for each species using options
hist(iris$Petal.Width [iris$Species == "setosa"],
xlim = c(0, 3),
breaks = 9,
main = "Petal Width for Setosa",
xlab = "",
col = "brown1")
hist(iris$Petal.Width [iris$Species == "versicolor"],
xlim = c(0, 3),
breaks = 9,
main = "Petal Width for Versicolor",
xlab = "",
col = "dodgerblue1")
hist(iris$Petal.Width [iris$Species == "virginica"],
xlim = c(0, 3),
breaks = 9,
main = "Petal Width for Virginica",
xlab = "",
col = "limegreen")
library(DataExplorer)
plot_histogram(iris)
Boxplot
Basic Boxplot
Example r code of Sepal Length by Species
boxplot(iris$Sepal.Length ~ iris$Species,
ylab = "Sepal Length", #y-axis label
xlab = "Species", #x-axis label
main = "Sepal Length by Species")
Scatter plot
Basic Scatter Plot
To create a scatter plot just specify any two variables of the data set in plot() function
plot(iris$Petal.Length, iris$Petal.Width,
pch=16, #pch(plotting character) argument is used to specify symbols to use when plotting points
cex=0.6, #cex(character expansion) argument is used to change the size of the plotted characters
col = "blue",
main = "Iris Flower Data Set",
xlab = "Petal Length",
ylab = "Petal Width",
)
Scatter Plot of Multiple Groups
Following example uses the pch argument to plot each point with a different plotting character, according to the parallel factor “Species”.
# A scatter plot that shows the points in groups according to their "species"
plot(Petal.Width ~ Petal.Length, data=iris,
col=c("brown1","dodgerblue1","limegreen")[as.integer(Species)],
pch=c(1,2,3)[as.integer(Species)])
legend(x="topleft",
legend=c("setosa","versicolor","virginica"),
col=c("brown1","dodgerblue1","limegreen"),
pch=c(1,2,3))
Codebook (Using newer method in R)
class(iris)## [1] "data.frame"sapply(iris, class)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## "numeric" "numeric" "numeric" "numeric" "factor"summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## (b) Demonstrate these FIVE (5) functions of dplyr for data manipulation:
# Set directory path
setwd("C:/Users/kelee/Downloads")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Read csv file
data = read.csv("Student - Sheet1.csv")
data## Name Age Gender heightInCM weightInKG
## 1 Lee Keat En 19 female 150 51
## 2 John Doe 22 male 185 70
## 3 Siti Rokhiah 18 female 155 48
## 4 Amelia 20 female 158 50
## 5 Isabella 19 female 160 61
## 6 Mia 17 female 148 42
## 7 William 18 male 180 68
## 8 James 21 male 183 73
## 9 Lucas 16 male 169 63
## 10 Lucy 18 female 999 300
## 11 Elijah 15 female 153 45
## 12 David 22 male 173 77
## 13 Asher 19 female 161 56
## 14 Bella 23 female 158 55
## 15 Sophie 17 female 155 50
## 16 Aziq 18 male 188 78
## 17 Jackson Chong 16 male 181 76
## 18 Tammy 20 male 178 75
## 19 Eunice 22 male 154 50filter()
Exclude “Lucy” row 10, as her height (999cm) and weight (300kg) is invalid data.
data1=filter(data, !grepl("Lucy", Name))
data1## Name Age Gender heightInCM weightInKG
## 1 Lee Keat En 19 female 150 51
## 2 John Doe 22 male 185 70
## 3 Siti Rokhiah 18 female 155 48
## 4 Amelia 20 female 158 50
## 5 Isabella 19 female 160 61
## 6 Mia 17 female 148 42
## 7 William 18 male 180 68
## 8 James 21 male 183 73
## 9 Lucas 16 male 169 63
## 10 Elijah 15 female 153 45
## 11 David 22 male 173 77
## 12 Asher 19 female 161 56
## 13 Bella 23 female 158 55
## 14 Sophie 17 female 155 50
## 15 Aziq 18 male 188 78
## 16 Jackson Chong 16 male 181 76
## 17 Tammy 20 male 178 75
## 18 Eunice 22 male 154 50arange()
Arange the age of students in ascending order
data2=arrange(data1, Age)
data2## Name Age Gender heightInCM weightInKG
## 1 Elijah 15 female 153 45
## 2 Lucas 16 male 169 63
## 3 Jackson Chong 16 male 181 76
## 4 Mia 17 female 148 42
## 5 Sophie 17 female 155 50
## 6 Siti Rokhiah 18 female 155 48
## 7 William 18 male 180 68
## 8 Aziq 18 male 188 78
## 9 Lee Keat En 19 female 150 51
## 10 Isabella 19 female 160 61
## 11 Asher 19 female 161 56
## 12 Amelia 20 female 158 50
## 13 Tammy 20 male 178 75
## 14 James 21 male 183 73
## 15 John Doe 22 male 185 70
## 16 David 22 male 173 77
## 17 Eunice 22 male 154 50
## 18 Bella 23 female 158 55mutate()
##This function will add a new variable which is “BMI” to the dataset
heightWeight = select(data2, heightInCM, weightInKG)
data3 <- heightWeight%>%
mutate(BMI = weightInKG / ((heightInCM / 100) ^ 2))
data3## heightInCM weightInKG BMI
## 1 153 45 19.22338
## 2 169 63 22.05805
## 3 181 76 23.19832
## 4 148 42 19.17458
## 5 155 50 20.81165
## 6 155 48 19.97919
## 7 180 68 20.98765
## 8 188 78 22.06881
## 9 150 51 22.66667
## 10 160 61 23.82812
## 11 161 56 21.60410
## 12 158 50 20.02884
## 13 178 75 23.67125
## 14 183 73 21.79820
## 15 185 70 20.45289
## 16 173 77 25.72756
## 17 154 50 21.08281
## 18 158 55 22.03173select()
##This function will only select and show the values of column ‘Name’ and ‘Age’ oand ‘Gender’
data4 <- select(data2, c(Name, Age, Gender))
data4## Name Age Gender
## 1 Elijah 15 female
## 2 Lucas 16 male
## 3 Jackson Chong 16 male
## 4 Mia 17 female
## 5 Sophie 17 female
## 6 Siti Rokhiah 18 female
## 7 William 18 male
## 8 Aziq 18 male
## 9 Lee Keat En 19 female
## 10 Isabella 19 female
## 11 Asher 19 female
## 12 Amelia 20 female
## 13 Tammy 20 male
## 14 James 21 male
## 15 John Doe 22 male
## 16 David 22 male
## 17 Eunice 22 male
## 18 Bella 23 female#Combine previous data
dataFinal <- cbind(data4, data3)
dataFinal## Name Age Gender heightInCM weightInKG BMI
## 1 Elijah 15 female 153 45 19.22338
## 2 Lucas 16 male 169 63 22.05805
## 3 Jackson Chong 16 male 181 76 23.19832
## 4 Mia 17 female 148 42 19.17458
## 5 Sophie 17 female 155 50 20.81165
## 6 Siti Rokhiah 18 female 155 48 19.97919
## 7 William 18 male 180 68 20.98765
## 8 Aziq 18 male 188 78 22.06881
## 9 Lee Keat En 19 female 150 51 22.66667
## 10 Isabella 19 female 160 61 23.82812
## 11 Asher 19 female 161 56 21.60410
## 12 Amelia 20 female 158 50 20.02884
## 13 Tammy 20 male 178 75 23.67125
## 14 James 21 male 183 73 21.79820
## 15 John Doe 22 male 185 70 20.45289
## 16 David 22 male 173 77 25.72756
## 17 Eunice 22 male 154 50 21.08281
## 18 Bella 23 female 158 55 22.03173summary()
summary(dataFinal)## Name Age Gender heightInCM
## Length:18 Min. :15.00 Length:18 Min. :148.0
## Class :character 1st Qu.:17.25 Class :character 1st Qu.:155.0
## Mode :character Median :19.00 Mode :character Median :160.5
## Mean :19.00 Mean :166.1
## 3rd Qu.:20.75 3rd Qu.:179.5
## Max. :23.00 Max. :188.0
## weightInKG BMI
## Min. :42.00 Min. :19.17
## 1st Qu.:50.00 1st Qu.:20.54
## Median :58.50 Median :21.70
## Mean :60.44 Mean :21.69
## 3rd Qu.:72.25 3rd Qu.:22.52
## Max. :78.00 Max. :25.73