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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$x))Example1Consider the below data frame −Live Demo> x1 y1 df1 df1Output x1 y1 1 NA 2 2 2 5 3 2 10 4 2 2 5 2 4 6 NA 7 7 ... Read More
Suppose we have two frames each having 5 columns that are stored in a list in R and we want to extract the last row from each data frame then we can use the lapply function. For example, if we have a list called LIST that contains the data frames described above then we can extract the last row from each data frame using the command lapply(LIST, tail, 1).ExampleConsider the below list of data frames −Live Demo> x1 x2 df1 y1 y2 df2 z1 z2 df3 List ListOutput[[1]] x1 x2 1 6 5 2 6 5 3 ... Read More
To find the number of unique values in each row of an R data frame, we can use apply function with length and unique function. For example, if we have a data frame called df that contains multiple columns then the number of unique values in each row of df can be found by using the command apply(df, 1, function(x) length(unique(x))).Example1Consider the below data frame −Live Demo> x1 x2 x3 x4 df1 df1Output x1 x2 x3 x4 1 3 1 1 2 2 3 2 0 2 3 3 2 0 1 4 3 0 3 ... Read More
Suppose we created a function that can take two different values at a time then we can apply that function to two columns of an R data frame by using mapply. For example, if we have a manually created function say func that multiply two values then we can apply it to a data frame called df that has two columns x and y by using the below command −mapply(func, df$x, df$y) Manually created function named as func: func mapply(func, df1$x1, df1$x2)Output[1] 24 35 18 5 56 25 4 48 16 28 30 7 24 30 30 25 12 ... Read More
We can set type argument to s in plot function to create a histogram without bins but first we need to create the histogram and store it in an object. For example, if we have a vector say x then the histogram of x can be stored in an object called p then we can use the command plot(c(p$counts,0),type="s") to create the histogram without bins as shown in the below example.Example> x p plot(c(p$counts,0),type="s")Output
To change the order of boxplot by means using ggplot2, we can use reorder function inside aes of ggplot. For example, if we have a data frame called df that contains two columns say x (categorical) and y(count) then the boxplot ordered by means can be created by using the command ggplot(df, aes(x=reorder(x, y, mean), y))+geom_boxplot()ExampleConsider the below data frame −Live Demo> x y df dfOutput x y 1 A 22 2 A 17 3 A 20 4 A 36 5 A 34 6 A 25 7 A 25 8 A 30 9 A 23 10 A 29 11 B ... Read More
To convert a matrix column into list can be done by using the apply function. We will have to read the columns of the matrix as list by using as.list function. For example, if we have a matrix called M then the columns in M can be converted into list by using the command apply(M, 2, as.list).Example1Live Demo> M1 M1Output [, 1] [, 2] [1, ] -1.3256074 -0.07328026 [2, ] 1.1997584 -1.06542989 [3, ] -0.2214659 -1.75903298 [4, ] 1.4446361 -0.12859397 [5, ] -0.1504967 0.97264445Converting M1 columns to a list −> apply(M1, 2, as.list)Output[[1]] ... Read More
Suppose we have a matrix and a vector containing indices of equal size as the matrix then we can extract the vector from matrix using the index vector. For this purpose, we can use cbind function as shown in the below examples.Example1Live Demo> M1 M1Output [,1] [,2] [1,] 4 0 [2,] 1 1 [3,] 1 2 [4,] 2 0 [5,] 3 2 [6,] 2 2 [7,] 1 6 [8,] 1 2 [9,] 3 1 [10,] 1 2 [11,] 2 3 [12,] 2 0 [13,] 3 0 [14,] 0 1 [15,] 2 4 [16,] 1 1 [17,] 3 1 [18,] 0 2 [19,] 2 1 [20,] 2 0Example> Index_M1 Index_M1Output[1] 2 1 2 1 2 2 1 1 2 1 1 2 1 1 1 1 2 2 1 1Example> M1[cbind(seq_along(Index_M1),Index_M1)]Output[1] 0 1 2 2 2 2 1 1 1 1 2 0 3 0 2 1 1 2 2 2Example2Live Demo> M2 M2Output [,1] [,2] [,3] [,4] [1,] 10 9 9 11 [2,] 13 6 16 8 [3,] 11 11 8 10 [4,] 15 11 9 9 [5,] 10 8 9 9 [6,] 7 14 9 15 [7,] 8 6 8 7 [8,] 4 8 9 12 [9,] 7 12 11 10 [10,] 8 8 9 13 [11,] 9 13 11 6 [12,] 12 5 11 8 [13,] 8 6 15 8 [14,] 6 17 12 7 [15,] 8 10 9 8 [16,] 13 7 11 13 [17,] 5 10 7 7 [18,] 10 11 8 8 [19,] 5 9 9 13 [20,] 5 10 7 6Example> Index_M2 Index_M2Output[1] 3 4 3 3 3 1 3 4 4 3 1 4 3 4 4 1 2 1 1 2Example> M2[cbind(seq_along(Index_M2),Index_M2)]Output[1] 9 8 8 9 9 7 8 12 10 9 9 8 15 7 8 13 10 10 5 10
To aggregate matrix columns by row names, we can use colSums with sapply and transpose the output. For example, if we have a matrix called M then the aggregate matrix columns by row names can be done using t(sapply(by(M, rownames(M), colSums), identity)).Example1Consider the below matrix −Live Demo> M1 rownames(M1) M1Output [, 1] [, 2] B 4 6 D 2 1 B 1 5 C 0 0 A 2 3 B 1 0 B 5 3 D 1 3 C 0 1 C ... Read More