To display the data frame summary in vertical order, we can use lapply and cbind function along with the summary function. For example, if we have a data frame called df then the summary of df in vertical order can be found by using the below command −lapply(df, function(x) cbind(summary(x)))Example1Consider the below data frame −Live Demo> x1 x2 x3 x4 x5 df1 df1Output   x1 x2 x3 x4 x5 1   4  0  2  2  6 2   7  2  4  1  7 3   7  2  3  3  6 4   4  0  4  5  2 5   5  2 ... Read More

To select rows of an R data frame that are non-Na, we can use complete.cases function with single square brackets. For example, if we have a data frame called that contains some missing values (NA) then the selection of rows that are non-NA can be done by using the command df[complete.cases(df), ].Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output   x1 x2 x3 1   1 NA NA 2  NA  5  3 3   1  5 NA 4   1 NA NA 5  NA  5 NA 6  NA  5  3 7  NA  5 NA 8   1 ... Read More

To create stacked plot with density using ggplot2, we can use geom_density function of ggplot2 package and position="stack". For example, if we have a data frame called df that contains two columns say x and y, where x is categorical and y is numerical then the stacked plot with density can be created by using the command −ggplot(df, aes(y, y=..density..))+geom_density(aes(fill=x), position="stack")ExampleConsider the below data frame −Live Demo> x y df dfOutput   x y 1  C 3 2  C 5 3  B 4 4  A 7 5  B 1 6  A 6 7  D 4 8  C 3 9  C 7 ... Read More

To create a plot using ggplot2 by excluding values greater than a certain value, we can use subsetting with single square brackets and which function. For example, if we have a data frame called df that contains two columns say x and y, then the point chart by including values of x that are greater than 0 can be created by using the command −ggplot(df[which(df$x>0), ], aes(x, y))+geom_point()ExampleConsider the below data frame −Live Demo> x y df dfOutput             x           y 1  -0.62160328  0.38477515 2   0.68287365 -1.56169067 3   0.75259774 ... Read More

To make a plot title partially bold using ggplot2, we can use bquote function inside labs function and then changing the default font to bold using bold function as shown in the below examples. While using these functions we need to make sure that the title that we want to bold should be inside circular brackets appropriately.ExampleConsider the below data frame −Live Demo> x y df dfOutput             x           y 1  -0.62160328  0.38477515 2   0.68287365 -1.56169067 3   0.75259774  1.28849990 4   0.56688920 -0.17014225 5   1.22351113 -0.32446764 6  -1.54210099 ... Read More

To calculate monthly average for time series object, we can use tapply function with mean. For example, if we have a time series object called TimeData then the monthly average for this series can be found by using the command tapply(TimeData, cycle(TimeData), mean).Example1Consider the below time series object −Live Demo> Data1 Data1Output   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1  988 695 867 211 915 348 729 518 592 447 448 880 2  551 410 427 134 133 572 637 800 630 878 642 940 3  603 335 638 639 595 512 671 863 752 ... Read More

To find the column that has the largest sum, we can use sort function for sorting in decreasing order with colSums and accessing the first element of the output which will be the largest sum. For example, if we have a data frame called df that contains multiple columns then the column that has the largest sum can be found by using the command −str(sort(colSums(df[, 1:length(df)]), decreasing=TRUE)[1])Example1Consider the below data frame −Live Demo> x1 x2 x3 x4 df1 df1Output   x1 x2 x3 x4 1   3  4  4  5 2   6 10  3  3 3   6  5  2 ... Read More

To convert a column with missing values to binary with 0 for missing values, we can use as.integer function with complete.cases for the data frame column. For example, if we have a data frame called df that contains a column x which has some missing values then the column x can be converted to binary with 0 for missing values by using the command −as.integer(complete.cases(df$x))Example1Consider the below data frame −Live Demo> x1 y1 df1 df1Output   x1 y1 1  NA  2 2   2  5 3   2 10 4   2  2 5   2  4 6  NA  7 7 ... Read More

Suppose we have two frames each having 5 columns that are stored in a list in R and we want to extract the last row from each data frame then we can use the lapply function. For example, if we have a list called LIST that contains the data frames described above then we can extract the last row from each data frame using the command lapply(LIST, tail, 1).ExampleConsider the below list of data frames −Live Demo> x1 x2 df1 y1 y2 df2 z1 z2 df3 List ListOutput[[1]]    x1 x2 1   6  5 2   6  5 3 ... Read More

To find the number of unique values in each row of an R data frame, we can use apply function with length and unique function. For example, if we have a data frame called df that contains multiple columns then the number of unique values in each row of df can be found by using the command apply(df, 1, function(x) length(unique(x))).Example1Consider the below data frame −Live Demo> x1 x2 x3 x4 df1 df1Output   x1 x2 x3 x4 1   3  1  1  2 2   3  2  0  2 3   3  2  0  1 4   3  0  3 ... Read More