To create a large vector of repetitive elements of varying size we can use the rep function along with the logical vector as an index. The logical vector that contains TRUE or FALSE will define the selection or omission of the values in the vector created with the help of rep function as shown in the below examples. If the vector created by using rep is larger than the logical vector then the logical vector will be recycled.Example1 Live Demox1

Suppose we have a list that contain two elements and we get a new value for both of these elements then the problem of adding those values to the original list arises. This can be done with the help of mapply function. We can append the new values in the original easily but a vector of the new values needs to be created first.Example1 Live DemoList1

To create the combinations of 0 and 1 data frame, we can use expand.grid function along with the rep function. If we want to create combination of 0 and 1 with fixed number of 1’s in each row then rowSums functions can be used with the appropriate sum value. For example, to have rows containing less than three 1’s, the rowSums will be extracted from grid for the same.Example1 Live DemoFirst

Sometimes we want to convert a column of an R data frame to binary column using 0 and 1, it is especially done in situations where we have some NAs in the column of the data frame and the other values can be converted to 1 due to some characteristics. To replace NA with 0 and other values to 1, we can use ifelse function.Example1 Live DemoConsider the below data frame −x1

A sparse matrix is a type of matrix that has most of the elements equal to zero but there is no restriction for the number of zero elements. As a general criterion the number of non-zero elements are expected to be equal to the number of rows or number of columns. To convert a sparse matrix into a matrix R, we can use as.matrix function with the sparse matrix object name.Example1 Live Demolibrary(Matrix) i

A sparse matrix is a type of matrix that has most of the elements equal to zero but there is no restriction for the number of zero elements. As a general criterion the number of non−zero elements are expected to be equal to the number of rows or number of columns. To create a sparse matrix in R, we can use sparseMatrix function of Matrix package.Example1 Live DemoLoading Matrix package and creating a sparse matrix −library(Matrix) i

If we have two plots generated by using ggplot2 and arranged in a list then we can create them using ggarrange function. For example, if we have two objects p1 and p2 that are stored in the list called named as LIST then those plots can be created in the plot window by using the command ggarrange(plotlist=LIST,widths=c(2,1),labels=c("Scatter","Hist"))ExampleConsider the below data frame − Live Demoset.seed(21) x

The set.seed helps to create the replicate of the random generation. If the name of the object changes that does not mean the replication will be changed but if we change the position then it will. Here, in the below example x4 in the first random generation and the x_4 in the second random generation with the same set.seed are same but x4 and x4 in both are different.Example Live Demoset.seed(101) x1

To find the least number of arguments a function expects while using in R can be done with the help of the syntax: length(formals(“function_name”)). For example, to find the number of arguments expected by mutate function of dplyr package can be calculated by using the command length(formals(mutate)) but we need to make sure that the package is loaded.Examples Live Demolibrary(ggplot2) length(formals(ggplot))Output[1] 4 length(formals(boxplot)) [1] 2 length(formals(qnorm)) [1] 5 length(formals(rnorm)) [1] 3 length(formals(rpois)) [1] 2 length(formals(runif)) [1] 3 length(formals(punif)) [1] 5 length(formals(plot)) [1] 3 length(formals(pbinom)) [1] 5 length(formals(qbinom)) [1] 5 length(formals(hist)) [1] 2 length(formals(data)) [1] 7 length(formals(matrix)) [1] 5 length(formals(list)) [1] 0 ... Read More

To find the extremes of a data frame column that is numeric can be done with the help of min and max function but if we want to get the same in single line code then range function can be used. If there are some infinity values in the column then range.default function will be used as shown in the below example.Example Live DemoConsider the below data frame −set.seed(214) x1