The pheatmap function is used to create clustered heatmaps but we can change the aesthetics of the plot by using color argument which is one of the main functionalities of pheatmap function. There are many other arguments that differentiate pheatmap from heatmap function.Examplelibrary(pheatmap) M1

If we want to create a table of sums of a discrete variable for two categorical variables then xtabs function can be used. The output will be a contingency table or cross tabulation table which looks like a matrix. For example, if we have a data frame df with two categorical column x and y and a count column freq then the table of sums for freq can be created by using xtabs(freq~x+y,data=df1).ExampleConsider the below data frame − Live Demox1

To create a rectangle inside boxplot in base R, we can use rect function after creating the boxplot. The rect function has many arguments but for the creation of a rectangle only first four are necessary and these are defined as xleft - a vector (or scalar) of left x positions, ybottom - a vector (or scalar) of bottom y positions, xright - a vector (or scalar) of right x positions and ytop - a vector (or scalar) of top y positions.Example Live Demox

A three-dimensional array can have matrices of different size and they are not necessarily to be square or rectangular. Also, all the elements in an array are of same data type. If we want to find the mean of a three-dimensional array then apply function can be used where we need to refer the columns and rows of the array elements using combination function.Example Live DemoA1 apply(A1,c(1,2),mean) [,1] [,2] [1,] 5 7 [2,] 6 8ExampleA2

A group column in an R data frame have duplicate values and we might want to create a column with the serial number based on the values such as first value of the first group gets 1, the same value gets 2 when occurred second time in the same column and so on. This can be done by using ave function as shown in the below examples.ExampleConsider the below data frame − Live DemoS.No

If we have positive as well as negative values in a matrix then the maximum of the matrix will be a positive number but if we want to ignore the sign then a number represented with negative sign can also be the maximum. If we want to get the maximum with its sign then which.max function can be used in R. Check out the below examples to understand how to do it.Example Live DemoM1

Ranking of a variable has many objectives such as defining order based on hierarchy but in data science, we use it mainly for analyzing non-parametric data. The ranking of a variable in an R data frame can be done by using rank function. For example, if we have a data frame df that contains column x then rank of values in x can be found as rank(df$x).Example Live DemoConsider the below data frame: x1

The default value of Y-axis tick marks using ggplot2 are taken by R using the provided data but we can set it by using scale_y_continuous function of ggplot2 package. For example, if we want to have values starting from 1 to 10 with a gap of 1 then we can use scale_y_continuous(breaks=seq(1,10,by=1)).Example Live DemoConsider the below data frame: x

If we want to match the names of a vector in sequence with string vector values in another vector having same values then pmatch function can be used. The pmatch function means pattern match hence it matches all the corresponding values and returns the index of the values. Check out the below examples to understand how it works.Example Live Demox1

Duplication is also a problem that we face during data analysis. We can find the rows with duplicated values in a particular column of an R data frame by using duplicated function inside the subset function. This will return only the duplicate rows based on the column we choose that means the first unique value will not be in the output.Example Live DemoConsider the below data frame: x1