The factor with duplicate levels represents grouping data but if we want to convert the grouping data into nominal data then duplicate values must be removed or converted into unique values. To make duplicate factor levels unique in an R data frame, we can use make.unique function.Check out the below Examples to understand how it works.Example 1Following snippet creates a sample data frame −Factor

We sometimes need to perform mathematical operations on all values in the data set. One such operation could be dividing each value by 100.For example, if we have a data frame called df then we can divide each value in df by 100 by using the below command −df[,1:ncol(df)]/100ExampleFollowing snippet creates a sample data frame −x1

To find the groupwise common value for a data.table object, we can use Reduce function with intersect function.For example, if we have a data.table object called DT that contains a numerical column say Num and a categorical column say C where C exists at the first position then the groupwise common value can be found by using the command given below −Reduce(intersect,DT[,.(list(unique(Num))),C]$V1)ExampleConsider the below data.table object −Group

To create ggplot2 graph with reversed Y-axis and X-axis on top, we can use scale_y_reverse and scale_x_continuous function of ggplot2 package.For Example, if we have a data frame called df that contains two columns say X and Y and we want to create the scatterplot between X and Y with reversed Y-axis and X-axis on top then we can use the below command −ggplot(df,aes(X,Y))+geom_point()+scale_y_reverse()+scale_x_continuous(position="top")ExampleFollowing snippet creates a sample data frame −x

To display superscript for X-axis title in base R plot, we can use ^ sign inside mtext function before defining the plain text.For example, if we want to display X2 at position 5 on X-axis then it can be done by using the below command −mtext(expression(paste(plain("X")^plain("2"))), side=1, line=2, at=5, cex=1.2)ExampleConsider the following snippet −plot(1:10) OutputIf you execute the above given snippet, it generates the following Output −To display superscript for X-axis title in base R plot, add the following code to the above snippet −Exampleplot(1:10, xlab="")OutputIf you execute all the above given snippets as a single program, it generates the ... Read More

To find the frequency of exclusive group combinations in an R data frame, we can use count function of dplyr package along with ungroup function.For Example, if we have a data frame called df that contains four grouping columns say Grp1, Grp2, Grp3, and Grp4 then we can count the unique group combinations in df by using the below command −count(df,Grp1,Grp2,Grp3,Grp4)%%ungroup()Example 1Following snippet creates a sample data frame −Class1

To change the order of independent variables in regression Output, we can pass the variables in the sequence we want while creating the regression model.For example, if we want to have three independent variables and we want to display first at the last position then it can be done as follows −lm(DP1~ ind_var_3+ ind_var_2+ind_var_1,data=”data_frame_name”)ExampleFollowing snippet creates a sample dataframe −iv1

If we have a vector called V that contains five values and a matrix say M that contains five columns and we want to check whether first value in the vector is present in the first column of each row in the matrix and so on for each value in the vector then we can use the below command −t(t(M)==V)Example 1Consider the below matrix and vector −M1

To find the row means for columns starting with specific string in an R data frame, we can use mutate function of dplyr package along with rowMeans function.For Example, if we have a data frame called df that contains three columns say x1_x2, x1_x3, x1_x2 and we want to find the row means for columns x1_x2 and x1_x3 then, we can use the below command −df%%mutate(X1_Cmbn=select(.,starts_with("x1_")) %% rowMeans())Example 1Following snippet creates a sample data frame −Grp1_x

To find the row mean for selected columns in R data frame, we can use mutate function of dplyr package along with rowMeans function.For Example, if we have a data frame called df that contains three columns say X, Y, and Z then mean of each row for columns X and Y can be found by using the below command −df %% mutate(X_Y_Mean=select(.,c("X","Y")) %% rowMeans())Example 1Following snippet creates a sample data frame −x1