 ## Data visualization with R: Histogram, Boxplot, Piechart, Mosiacplot, Correlation

Alternatively this dataset is also available at

We rename the data as dataset in R

`# Assigning it to a new dataframe named "dataset"dataset <- predicting_response_to_telephone_calls `

Data visualization

Histogram

`# The simplest way to visualize the data is to plot the histogram`

`hist(dataset\$age)`

`# Better visualization - add labels to`

`hist(dataset\$age, main="Histogram for age",`
`xlab="age")`

`# where, main is the title of the histogram`## Let us color this histogram``hist(dataset\$age, main="Histogram for age", ``     xlab="age", ``     border="blue", ``     col="green",``     xlim=c(0,100),``     las=1, ``     breaks=10)``
`# Where, xlab is the name of the Axis,# Border = color of the border of the graph# Col = color of the graph# Xlim = is the minimum and maximum value you want the graph to take# las – A numeric value indicating the orientation of the tick mark labels #and any other text added to a plot after its initialization. #The options are as follows: always parallel to the axis (the default, 0), # always horizontal (1), always perpendicular to the axis (2), and always# Breaks - a single number giving the number of cells for the histogram`# Multiple colors``hist(dataset\$age, ``     main="Histogram for age", ``     xlab="age", ``     border="blue", ``     col=c("red", "yellow", "green", "violet", "orange", "blue", "pink", "cyan"),``     xlim=c(0,100),``     las=1, ``     breaks=5)``

Plotting Histogram with mean and standard deviation value

`# Histogram with n = sample size, m= mean, sd = standard deviation`
`score <- rnorm (n=1309, m=29.8831, sd= 14.41)`
`hist(score)`
`## Let us color this histogram`
`hist(score, main="Histogram for age",`
`xlab="age",`
`border="blue",`
`col="green",`
`xlim=c(0,100),`
`las=1,`
`breaks=10)`

Boxplot

`# Box Plot shows 5 statistically significant numbers- the minimum, the 25th percentile, the median,`
`# the 75th percentile and the maximum. It is thus useful for visualizing the spread of the data is`
`# and deriving inferences accordingly.`

`boxplot(dataset\$age)`
`boxplot(dataset\$age, col = "Green")`

`# Bivariate box plot - left of (~) symbol = y axis, right of (~) symbol = x axis`

`boxplot(dataset\$age~dataset\$y) #Creating Box Plot between two variable`

`boxplot(dataset\$age~dataset\$y, col = c("Blue", "Green"))`

`boxplot(dataset\$duration,col="red")`
`boxplot(dataset\$duration~dataset\$y,col="red")`

Piechart

`pie(table(train\$Gender))`

`pie(table(dataset\$housing))`

Mosiac Plot

`#A mosaic plot can be used for plotting categorical data very effectively with the area of the data`
`#showing the relative proportions.`

`mosaicplot(dataset\$ed)`

Visualizing correlation

`## Visualizing correlation`

`install.packages("corrplot")`
`library("corrplot")`
`dataset_subset <-data.frame(dataset\$age, dataset\$balance, dataset\$campaign)`

`M <- cor(dataset_subset)`

`corrplot(M, method = "circle")`
`corrplot(M, method = "square")`
`corrplot(M, method = "ellipse")`
`corrplot(M, method = "number") # Display the correlation coefficient`
`corrplot(M, method = "shade")`
`corrplot(M, method = "color")`
`corrplot(M, method = "pie")`

`## Layout`
`corrplot(M, type = "upper")`
`corrplot(M, type = "lower")`
`corrplot(M, method = "number", type = "lower")`

`## order`

`corrplot(M, order = "AOE")`
`corrplot(M, order = "hclust")`
`corrplot(M, order = "FPC")`
`corrplot(M, order = "alphabet")`

`##`
`res1 <- cor.mtest(mtcars, conf.level = .95)`
`res2 <- cor.mtest(mtcars, conf.level = .99)`

`## specialized the insignificant value according to the significant level`
`corrplot(M, p.mat = res1\$p, sig.level = .2)`
`corrplot(M, p.mat = res1\$p, sig.level = .05)`
`corrplot(M, p.mat = res1\$p, sig.level = .01)`

`## leave blank on no significant coefficient`
`corrplot(M, p.mat = res1\$p, insig = "blank")`