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Lecture13 EDA

The document provides an overview of exploratory data analysis (EDA) techniques using the iris and mtcars datasets in R. It covers visualizing the distribution of quantitative variables, relationships between variables, and categorical data using various plotting methods, including histograms, boxplots, and ggplot2. Additionally, it emphasizes the importance of distinguishing between species in the iris dataset through color and shape in visualizations.

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ilias ahmed
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15 views2 pages

Lecture13 EDA

The document provides an overview of exploratory data analysis (EDA) techniques using the iris and mtcars datasets in R. It covers visualizing the distribution of quantitative variables, relationships between variables, and categorical data using various plotting methods, including histograms, boxplots, and ggplot2. Additionally, it emphasizes the importance of distinguishing between species in the iris dataset through color and shape in visualizations.

Uploaded by

ilias ahmed
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as TXT, PDF, TXT or read online on Scribd
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###############################################################

#################### EDA ####################


##### (1)Can explore the distribution of 1 variable
#### (2) Can explore the relationship between 2 variables

data(iris)
names(iris)
summary(iris)

##examine data distribution of a quantitative variable

hist(iris$Sepal.Length) # distribution of a varaibles

boxplot(iris$Sepal.Length,main="Summary of iris",xlab="Sepal Length")


#viz of descreptive statstics

#relation bw 2 quantitative variables


plot(iris$Sepal.Length,iris$Sepal.Width)

#PLOTTING CATEGORICAL or COUNT VARIABLES

data(mtcars)
names(mtcars)
str(mtcars)
counts <- table(mtcars$gear)
counts
barplot(counts, main="Cars", xlab="Number of Gears")

barplot(counts, main="Cars", xlab="Number of Gears",horiz=TRUE)

barplot(counts, main="Cars", xlab="Number of Gears",horiz=TRUE,col="red")

## Improved data viz

library(ggplot2)

# relation bw Sepal length and width of 3 different species


qplot(Sepal.Length, Petal.Length, data = iris,color = Species)

# We see that Iris setosa flowers have the narrowest petals.


qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)

##Add labels to the plot

qplot(Sepal.Length, Petal.Length, data = iris, color = Species,


xlab = "Sepal Length", ylab = "Petal Length",
main = "Sepal vs. Petal Length in Iris data")

qplot(Sepal.Length, Petal.Length, data = iris, geom = "line", color = Species)


#line plot

########################GGPLOT#####################

##use ggplot for viz


#Format: ggplot(data = , aes(x =, y =, ...)) + geom_xxx()
#aes-> we specify x,y
#geom-> Plot type: wether its a histogram, boxplot
ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
#distinguish between species using colour scheme
ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) + geom_point(aes(colour =
(Species)))
#or
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, shape = Species)) +
geom_point()

## (1) we can only specify colour and shapes on factor variables


##(2) Factors:numbers representing categorical values
##(3)function "factor" turns any number into a qualitative representation

str(mtcars)

#use mtcars as a factor in visualization


ggplot(mtcars, aes(x = mpg, y = wt, colour = factor(gear))) + geom_point()

#histogram
ggplot(iris, aes(x = Sepal.Length)) + geom_histogram()

ggplot(iris, aes(x = Sepal.Length, fill = Species)) + geom_histogram()

#boxplot
ggplot(iris, aes(x = Species, y = Sepal.Length)) +geom_boxplot()

#visualize relationship bw the different variables for the 3 species


ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + facet_grid(. ~
Species) + geom_smooth(method = "lm")

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