A pie chart is a circular representation of data which is created for either nominal data or ordinal data. The slices in the pie chart depends on the magnitude of the data values. If we want to create a pie chart in base R with then pie function can be used along with labels argument.Check out the Examples given below to understand how it can be done.ExampleTo create a pie chart in base R with labels, use the following command −x

To convert a numeric column to binary factor based on a condition in R data frame, we can use factor function along with ifelse function.For Example, if we have a data frame called df that contains a numerical column say Num and we want to convert it to a binary factor if Num is less than 100 then it will be Minor otherwise Major then we can use the below given command −df$Num_Factor

To standardize data.table object column by group, we can use scale function and provide the grouping column with by function.For Example, if we have a data.table object called DT that contains two columns say G and Num where G is a grouping column and Num is a numerical column then we can standardize Num by column G by using the below given command −DT[,"Num":=as.vector(scale(Num)),by=G]Example 1Consider the below data.table object −library(data.table) Grp

Suppose we have three columns say X, Y, and Z in an R data frame called df and we want to replace values in columns X and Y with the same value if the values are greater than values in Z and if they are less than the values in Z then we can replace with Z values.Check out the below Examples to understand how it can be done.Example 1Following snippet creates a sample data frame −x1

The random replacement values in an R data frame column can be done with the help of sample function along with nrow function and single square subsetting.For Example, if we have a data frame called df that contains a columns say X and we want to randomly replace 5 values in X with 1.5 then we can use the below given command −df$X[sample(nrow(df),5)]

To multiply corresponding values from two data.table objects in R, we can follow the below steps −First of all, create two data.table objects.Then, use mapply function to multiply corresponding values from those two data.table objects.ExampleCreate the first data.table objectLet’s create a data.table object as shown below −library(data.table) x1

To find the percentage of zeros in each column of a matrix in R, we can follow the below steps −First of all, create a matrix.Then, use colSums function along with nrow function to find the percentage of zeros in each column.Example 1Create the matrixLet’s create a matrix as shown below −M1

To round each value in columns if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to round each value in columns if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Level

To change the color of a particular bar using geom_bar in R, we can provide the count corresponding to the value for which we want to change the color inside aes function.For Example, if we have a data frame called df that contains two columns say V and F where V is categorical and F is for frequency and we want to change the color of frequency 10 in bar plot then we can use the below mentioned command −ggplot(df, aes(V, F))+geom_bar(aes(fill=..F..==10), stat="identity")ExampleFollowing snippet creates a sample data frame −xRead More

To find the log2 of each value if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to find the log2 if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Level