5 r tutorial data visualization

R ProgrammingSakthi Dasan Sekar

http://shakthydoss.com 1

Data visualization

R Graphics

R has quite powerful packages for data visualization.

R graphics can be viewed on screen and saved in various format like pdf, png, jpg, wmf,ps and etc.

R packages provide full control to customize the graphic needs.


Data visualization

Simple bar chart

A bar graph are plotted either horizontal or vertical bars to show comparisons among categorical data.

Bars represent lengths or frequency or proportion in the categorical data.

barplot(x)


Data visualization

Simple bar chart

counts <- table(mtcars$gear)

barplot(counts)

#horizontal bar chart

barplot(counts, horiz=TRUE)


Data visualization

Simple bar chart

Adding title, legend and color.

counts <- table(mtcars$gear)barplot(counts,

main="Simple Bar Plot",xlab="Improvement",ylab="Frequency",legend=rownames(counts),col=c("red", "yellow", "green")

)


Data visualization

Stacked bar plot

# Stacked Bar Plot with Colors and Legend

counts <- table(mtcars$vs, mtcars$gear)

barplot(counts,

main="Car Distribution by Gears and VS",

xlab="Number of Gears",

col=c("grey","cornflowerblue"),

legend = rownames(counts))


Data visualization

Grouped Bar Plot

# Grouped Bar Plot

counts <- table(mtcars$vs, mtcars$gear)

barplot(counts,

main="Car Distribution by Gears and VS",

xlab="Number of Gears",

col=c("grey","cornflowerblue"),

legend = rownames(counts), beside=TRUE)


Data visualization

Simple Pie Chart

slices <- c(10, 12,4, 16, 8)

lbls <- c("US", "UK", "Australia", "Germany", "France")

pie( slices, labels = lbls, main="Simple Pie Chart")


Data visualization

Simple Pie Chart

slices <- c(10, 12,4, 16, 8)

pct <- round(slices/sum(slices)*100)

lbls <- paste(c("US", "UK", "Australia",

"Germany", "France"), " ", pct, "%", sep="")

pie(slices, labels=lbls2,

col=rainbow(5),main="Pie Chart with Percentages")


Data visualization

Simple pie chart – 3D

library(plotrix)

slices <- c(10, 12,4, 16, 8)

lbls <- paste(

c("US", "UK", "Australia", "Germany", "France"),

" ", pct, "%", sep="")

pie3D(slices, labels=lbls,explode=0.0,

main="3D Pie Chart")


Data visualization

Histograms

Histograms display the distribution of a continuous variable.

It by dividing up the range of scores into bins on the x-axis and displaying the frequency of scores in each bin on the y-axis.

You can create histograms with the function

hist(x)


Data visualization

Histograms

mtcars$mpg #miles per gallon data

hist(mtcars$mpg)

# Colored Histogram with Different Number of Bins

hist(mtcars$mpg, breaks=8, col="lightgreen")


Data visualization

Kernal density ploy

Histograms may not be the efficient way to view distribution always.

Kernal density plots are usually a much more effective way to view the distribution of a variable.

plot(density(x))


Data visualization

Kernal density plot

# kernel Density Plot

density_data <- density(mtcars$mpg)

plot(density_data)

# Filling density Plot with colour

density_data <- density(mtcars$mpg)

plot(density_data, main="Kernel Density of Miles Per Gallon")

polygon(density_data, col="skyblue", border="black")


Data visualization

Line Chart

The line chart is represented by a series of data points connected with a straight line. Line charts are most often used to visualize data that changes over time.

lines(x, y,type=)


Data visualization

Line Chart

weight <- c(2.5, 2.8, 3.2, 4.8, 5.1,

5.9, 6.8, 7.1, 7.8,8.1)

months <- c(0,1,2,3,4,5,6,7,8,9)

plot(months,

weight, type = "b",

main="Baby weight chart")


Data visualization

Box plot

The box plot (a.k.a. whisker diagram) is another standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.


Data visualization

Box Plot

vec <- c(3, 2, 5, 6, 4, 8, 1, 2, 3, 2, 4)summary(vec)boxplot(vec, varwidth = TRUE)

#varwidth=TRUE to make box plot proportionate to width


Data visualization

Heat Map

A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.


Data visualization

Heat Map

data <- read.csv("HEATMAP.csv",header = TRUE)

#convert Data frame into matrix

data <- data.matrix(data[,-1])

heatmap(data,Rowv=NA, Colv=NA,

col = heat.colors(256), scale="column")


Data visualization

Word cloud

A word cloud (a.ka tag cloud) can be an handy tool when you need to highlight the most commonly cited words in a text using a quick visualization.

R packages : wordcloud


Data visualization

Word cloud

install.packages("wordcloud")

library("wordcloud")

data <- read.csv("TEXT.csv",header = TRUE)

head(data)

wordcloud(words = data$word,

freq = data$freq, min.freq = 2,

max.words=100, random.order=FALSE)


Data visualization

Graphic outputs can be redirected to files.

pdf("filename.pdf") #PDF file

win.metafile("filename.wmf") #Windows metafile

png("filename.png") #PBG file

jpeg("filename.jpg") #JPEG file

bmp("filename.bmp") #BMP file

postscript("filename.ps") #PostScript file


Data visualization


Example

jpeg("myplot.jpg")

counts <- table(mtcars$gear)

barplot(counts)

dev.off()


Data visualization


Function dev.off( ) should be used to return the control back to terminal.

Another way saving graphics to file.dev.copy(jpeg, filename="myplot.jpg");counts <- table(mtcars$gear)barplot(counts)dev.off()


Data visualization

Export graphs in RStudio

In Graphic panel of RStuido

Step1 : Select Plots tab Click Explore menu

and chose Save as Image.

Step 2: Save image window will open.

Step3 : Select image format and the

directory to save the file.

Step4 : Click save.


Data visualization

Export graphs in RStudio

To Export as pdf

Step 1: Click Export Menu and

click save as PDF.

Step 2:Select the directory to

save the file.

Step3: Click Save.


Data visualization

Knowledge Check


Data visualization

____________ represent lengths or frequency or proportion in the categorical data.

A. Line charts B. Bot plot C. Bar charts D. Kernal Density plot

Answer C


Data visualization

___________ displays the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.

A. Line charts B. Bot plot C. Bar charts D. Kernal Density plot

Answer B


Data visualization

Histograms display the distribution of a continuous variable.

A. TRUE

B. FALSE

Answer A


Data visualization

Graphic outputs can be redirected to file using _____________ function.

A. save("filename.png")

B. write.table("filename.png")

C. write.file("filename.png")

D. png("filename.png")

Answer D


Data visualization

___________ visualization can be used highlight the most commonly cited words in a text.

A. Word Stemmer B. Word cloud C. Histograms D. Line chats

Answer B


5 r tutorial data visualization

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