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To find the number 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 to find the number of zeros in each column.Example 1Create the matrixLet’s create a matrix as shown below −M1
To find the number of zeros in each column of a data.table object in R, we can follow the below steps −First of all, create a data.table object.Then, use colSums function to find the number of zeros in each column.Example 1Create the data.table objectLet’s create a data.table as shown below −library(data.table) x1
To separate two values in single column in data.table object in R, we can follow the below steps −First of all, create a data.table object.Then, use separate function from tidyr package to separate the values in single column.ExampleCreate the data.table objectLet’s create a data.table object as shown below −library(data.table) DT
To convert first letter into capital in single column data.table object in R, we can follow the below steps −First of all, create a data.table object with string column.Then, use capitalize function from R.utils package to convert first letter into capital in single columnExampleCreate the data.table objectLet’s create a data.table object as shown below −library(data.table) Students
To convert first letter into capital in data.table object column in R, we can follow the below steps −First of all, create a data.table object with string column.Then, use sub function to convert first letter into capital in string column.ExampleCreate the data.table objectLet’s create a data.table as shown below −library(data.table) Countries
To remove a particular value from a vector, we can use negation of value.For example, if we have a vector called V that contains repetitive values starting from 1 to 10 and if we want to remove all 10s from V then we can use the command given below −V
Dealing with missing values is one of the most critical task in data analysis. If we have a large amount of data then it is better to remove the rows that contains missing values. For the removal of such rows we can use complete.cases function.For example, if we have a data frame called df that contains some missing values then we can remove the rows with missing values using the below given command −df[complete.cases(df),]Example 1Following snippet creates a sample data frame −x1
To highlight a value with underline in base R plot, we can use underline function within text function. The text function will be used to define the value that we need to underline in the plot and underline function will only underline that part.Check out the below given example to understand how it works.ExampleTo highlight a value with underline in base R plot, use the code given below −plot(1:5)OutputIf you execute the above given code, it generates the following output −To highlight a value with underline in base R plot, add the following code to the above code −plot(1:5) text(4, ... Read More
To find the last non-missing value in each column of a data.table object, we can use lapply function along with tail function for NA values.For example, if we have a data.table object called DT then we can find the last non-missing value in each column of DT by using the below given command −DT[,lapply(.SD,function(x) tail(x[!is.na(x)],1))]Example 1Following snippet creates a data.table object −library(data.table) x1
Sometimes numerical values are being read as strings and hence creating a matrix using that vector is not straight forward.For the conversion of text vector into a matrix, we need to use scan function and specify the appropriate number of rows or columns as shown in the below given examples.Example 1Consider the below given vector x1 −x1