Programming Articles - Page 897 of 3366

To standardize selected columns in data.table object in R, we can follow the below steps −First of all, create a data.table object.Then, use scale function and cbind function with subsetting to standardize selected columns.ExampleCreate the data.table objectLet’s create a data.table object as shown below −library(data.table) var1

To repeat column values in R matrix by values in another column, we can follow the below steps −First of all, create a matrix.Then, use rep function along with cbind function to repeat column values in the matrix by values in another column.ExampleCreate the matrixLet’s create a matrix as shown below −x

To save a matrix as CSV file using R, we can use write.matrix function of MASS package. For Example, if we have a matrix called M and we want to save it as CSV file then we can use the below mentioned command −write.matrix(M,file="Mat.csv")ExampleFollowing snippet creates a sample matrix −M

To remove multiple columns from matrix in R by using their names, we can follow the below steps −First of all, create a matrix.Then, add names to columns of the matrix.After that, subset the matrix by deselecting the desired columns with negation and single square brackets for subsetting.ExampleCreate the matrixLet’s create a matrix as shown below −M

To find the index of values in R matrix column if they occur once, we can follow the below steps −First of all, create a matrix.Then, use which function along with duplicated function and single square brackets for subsetting to find the index of values in a column if they occur once.Example 1Create the data frameLet’s create a data frame as shown below −M1

To combine two columns by ignoring missing values if exists in one column in R data frame, we can use paste function and is.na function.For Example, if we have a data frame called df that contains two columns say C1 and C2 where C2 contains some missing values then we can use the below mentioned command to combine C1 and C2 by ignoring missing values in C2 −cbind(df,Combined=paste(df[,1],replace(df[,2],is.na(df[,2]),"")))Example 1Following snippet creates a sample data frame −x1

To find the common elements between two columns of an R data frame, we can use intersect function.For Example, if we have a data frame called df that contains two columns say X and Y then we can find the common elements between X and Y by using the below command −intersect(df$X,df$Y)Example 1Following snippet creates a sample data frame −x1

To subset an R data frame by specifying columns that contains NA, we can follow the below steps −First of all, create a data frame with some columns containing NAs.Then, use is.na along with subset function to subset the data frame by specifying columns that contains NA.ExampleCreate the data frameLet’s create a data frame as shown below −x

To find the number of non-missing values in each group of an R data frame, we can convert the data frame to data.table object and then use the sum function with negation of is.na.For Example, if we have a data frame called df that contains a grouping column say Group and a numerical column with few NAs say Num then we can find the number of non-missing values in each Group by using the below given command −setDT(df)[,sum(!is.na(df)),by=.(Group)]Example 1Following snippet creates a sample data frame −Grp

To create stacked bar chart with percentages on Y-axis using ggplot2 in R, we can use fill argument inside geom_bar and put the second categorical variable with position set to fill.For Example, if we have a data frame called with two categorical columns say C1 and C2 then we can create stacked bar chart with percentages on Y-axis using the below mentioned command −ggplot(df,aes(C1))+geom_bar(aes(fill=C2),position="fill")ExampleFollowing snippet creates a sample data frame −f1