To standardize multiple columns not all in data.table object in R, we can follow the below steps −First of all, create a data.table object.Then, subset the columns with single square brackets and use lapply, list and scale function to standardize those columns.ExampleCreate the data.table objectLet’s create a data.table object as shown below −library(data.table) x

If we have a single row for a matrix then creation of a matrix with equal rows can be easily done with the help of rep function and if we do not have the row then we would need to pass the row value inside rep function.Check out the below examples to understand how to create a matrix with equal rows if one row is known.Example 1Consider the below vector −Row_1

To impute missing values by random value for a single column in R, we can use impute function from Hmisc package.For example, if we have a data frame called that contains a column say C which has some missing values then we can use the below given command to fill those missing values randomly −df$C

To create scatterplot using data frame columns, we need to convert the data frame columns into a variable and the value for each column will be read in a new column against each column name. This can be done with the help of melt function in reshape2 package.After that we can use ggplot function to create the scatterplot with new data frame as shown in the below example.ExampleFollowing snippet creates a sample data frame −x1

When we large number of values in each element of a list in R, we might want to have a look at some top values to understand the data characteristics. For this purpose, we can extract first n values from each element in an R list by using lapply function along with head function as shown in the below given examples.Example 1Following snippet creates a list −List1

To multiply each element of a larger vector with a smaller vector, we can perform outer product calculation with the help of %o% operator.For example, if we have two vectors say x and y where x is of shorter length than y then we can multiply each element of y with each element of x by using the command given below −x%o%yCheck out the below examples to understand how it works.Example 1To multiply each element of a larger vector with a smaller vector, use the code given below −x1

If we have categorical data in a data.table object and some values are duplicate then we might want to extract unique rows from that object.To extract unique rows by categorical column of a data.table object, we can use unique function and define the columns with by argument as shown in the below examples. To understand how the extraction is done check out the below examples.Example 1Following snippet creates a data.table object −library(data.table) grp

The diagonal elements of a matrix occur at the position where column and row indices are same hence, we can make use of those indices to extract diagonal elements of a matrix if we do not want to use diag function.For example, if we have a matrix called M then diagonal elements of M can be extracted by using the command given below − M[row(M)==col(M)]Check out the below examples to understand how it works.Example 1Following snippet creates a matrix −M1

To create a data frame column with equally spaced values between 0 and 1, we can use ppoints function. For example, if we want to create a data frame df with hundred equally spaced values between 0 and 1 then we can use the below mentioned command −df

To save the str output as a string in R, we can use capture.output function along with str function.For example, if we have a data frame called df and we want to store the str output of df as a string then we can use the command given below −capture.output(str(df))It would be better if we save it in an object as str_df