To add columns with square of each column in R data frame, we can use setNames function and cbind function for squaring each value. This might be required when we want to use squared form of variables in the data analysis.Check out the below given examples to understand how it can be done.Example 1Following snippet creates a sample data frame −x1

To remove first few rows from each group in R, we can use slice function of dplyr package after grouping with group_by function.For example, if we have a data frame called df that contains a grouping column say Grp then we remove first 2 rows from each group by using the command given below −df%>%group_by(Grp)%>%slice(3:n())Example 1Following snippet creates a sample data frame −Group

To create boxplot of grouped data in R, we would first need to convert the grouped data into complete data by repeating the values up to the frequency/count for each value and then use the boxplot function on the complete data.Check out the below example to understand how it can be done.ExampleFollowing snippet creates a sample data frame −Group

To display mean value with underline in base R plot, we can use underline function within text function. The text function will be used to write the word Mean in the plot and underline function will underline the mean.Check out the below example to understand how it works.ExampleFollowing snippet creates a sample data frame −x

When we do not pass any title for X-axis then the default is Index and if we want to remove it then xlab argument needs to be used inside the plot function.For example, if we want to create a plot of first hundred values without Index on X-axis then we can use the command mentioned below − plot(1:100, xlab="")ExampleTo remove Index in base R plot, use the code given below −plot(1:10) OutputIf you execute the above given code, it generates the following output −To remove Index in base R plot, add the following code to the above code −plot(1:10, xlab="")OutputIf ... Read More

When we create a correlation plot using corrplot the correlation among variables is displayed on the plot, if we want to disable some of those correlations then we first need to set them to NA in the correlation matrix and then use the corrplot function with na.label set to blank as " ".Check out the below given example to understand how it works.ExampleFollowing snippet creates a sample data frame −x

To create bar plot with positive and negative values, we can make use of ggplot function.For example, if we have a data frame called df that contains a categorical column say C and a numerical column Num which has some positive and some negative values then the bar plot for this data can be created by using the below command −ggplot(df,aes(C,Num))+geom_bar(stat="identity")ExampleFollowing snippet creates a sample data frame −Category

To find the degrees of freedom of residual from a regression model, we can use the function df.residual along with the model object.For example, if we have a regression model stored in an object called Model then the degrees of freedom of residual for the same model can be found by using the command mentioned below −df.residual(Model)Example 1Following snippet creates a sample data frame −x1

To find the power of t test, we can use pwr.t.test function of pwr package where we can pass the arguments for type of the test such as one sample or two sample, alternative hypothesis such as one-sided or two-sided, significance level, difference for two samples, and the sample size.Check out the below examples to understand how it works.Example 1To find the power of t test in R, use the code given below −library("pwr") pwr.t.test(n=100, d=1, sig.level=0.05, type="two.sample", alternative="two.sided")If you execute the above given code, it generates the following output for the two-sample t test power calculation −   n = ... Read More

To combine two data frames by filling NA in empty places, we can use rbind_fill function of plyr package.For example, if we have two data frames say DF1 and DF2 that have some common and some uncommon columns then we can combine them by using the below given command −rbind.fill(DF1,DF2)ExampleFollowing snippet creates a sample data frame −df1