Programming Articles - Page 897 of 3363

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

To find the column variance if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to find the column variance if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Group

To find the sum of rows of a column based on multiple columns in R data frame, we can follow the below steps −First of all, create a data frame.Then, use aggregate function to find the sum of rows of a column based on multiple columns.ExampleCreate the data frameLet’s create a data frame as shown below −Grp1

To subset groups that occur less than n times in R data frame, we can use filter function of dplyr package.For Example, if we have a data frame called df that contains a grouping column say Group then we can subset groups that occur less than 4 times by using the below mentioned command −df%%group_by(Group)%%filter(n()=4)Example 1Following snippet creates a sample data frame −Grp