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

To find the location of a numerical value in an R data frame we use which function and if the value is string then the same function will be used but we need to pass the value appropriately. For example, if we have a data frame called df that contains a value say tutor then we can find the location of tutor by using the command which(df=="tutor", arr.ind=TRUE).Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output     x1   x2   x3 1  2018 2020 2018 2  2020 2020 2015 3  2018 2020 2015 4  2018 2015 ... Read More

To find the correlation for data frame having numeric and non-numeric columns, we can use cor function with sapply and use complete.obs for pearson method. For example, if we have a data frame called then we can use the below command to find the correlation coefficient −cor(df[, sapply(df, is.numeric)], use="complete.obs", method="pearson")Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output   x1 x2 x3 1   C 11  2 2   A  3  1 3   C  4  0 4   D 10  2 5   A  1  0 6   A  4  1 7   D  4  0 ... Read More

A binary matrix contains values in form of twos such as 0/1, 1/2, Yes/No etc. If we have a matrix that has some values and we expect that there are only two values in the whole matrix then we can check whether only those two values exist in the matrix or not. For example, if we have a matrix called M then we can check whether it contains only 0/1 in the matrix by using the command all(M %in% 0:1).Example1Live Demo> M1 M1Output      [, 1] [, 2] [, 3] [, 4]  [1, ]    0    0   ... Read More

If a matrix has multiple columns and values in each row are different then there will be number of maximums equal to the number of columns. Suppose, we want to extract the index of two maximums in each row in a matrix called M then we can use the below command −t(apply(M, 1, order, decreasing=TRUE)[1:2, ]) Example1Live Demo> M1 M1Output      [, 1] [, 2] [, 3] [, 4]  [1, ]    7    4    4   10  [2, ]    7    1    4    3  [3, ]    9    6    6    4  [4, ... Read More