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To extract the last element in an R matrix, we can use the length function along with single square brackets that are used for subsetting. For example, if we have a matrix called M as shown below −M1 2 3 4 5 6 7 8 9then we can extract last value of M by using the command M[length(M)].ExampleConsider the below matrix −M1
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 −> 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
When we have a factor column in an R data frame that has two levels and a numerical column then we can apply paired-test on this data frame but the data must be collected for same subjects, otherwise it will not be a paired data. The t.test application on the data discussed here can be done by using the command t.test(y1~x1,data=df), where y1 is the numerical column, x1 is the factor column, and both these columns are stored in data frame called df.ExampleConsider the below data frame −x1
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.Example1> 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 2Example2> 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 find the sum of every n values in R data frame columns, we can use rowsum function along with rep function that will repeat the sum for rows. For example, if we have a data frame called df that contains 4 columns each containing twenty values then we can find the column sums for every 5 rows by using the command rowsum(df,rep(1:5,each=4)).ExampleConsider the below data frame −x1
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).Example1> 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]] [[1]][[1]] ... Read More
The combination of values with repetition is the combination where the values can be repeated when creating the combination. For example, if we have three values say 1 and 2 then the combination of these values with repetition will be as follows −1 1 2 1 1 2 2 2For this purpose, we can use expand.grid function as shown in the below examples.Example 1expand.grid(rep(list(1:2),2))Output Var1 Var2 1 1 1 2 2 1 3 1 2 4 2 2Example2expand.grid(rep(list(1:2),3))Output Var1 Var2 Var3 1 1 1 1 2 2 1 1 3 1 2 1 4 2 2 1 5 1 1 2 6 2 1 2 7 1 2 2 8 2 2 2Example3expand.grid(rep(list(1:2),4))Output Var1 Var2 Var3 Var4 1 1 1 1 1 2 2 1 1 1 3 1 2 1 1 4 2 2 1 1 5 1 1 2 1 6 2 1 2 1 7 1 2 2 1 8 2 2 2 1 9 1 1 1 2 10 2 1 1 2 11 1 2 1 2 12 2 2 1 2 13 1 1 2 2 14 2 1 2 2 15 1 2 2 2 16 2 2 2 2Example4expand.grid(rep(list(1:2),5))Output Var1 Var2 Var3 Var4 Var5 1 1 1 1 1 1 2 2 1 1 1 1 3 1 2 1 1 1 4 2 2 1 1 1 5 1 1 2 1 1 6 2 1 2 1 1 7 1 2 2 1 1 8 2 2 2 1 1 9 1 1 1 2 1 10 2 1 1 2 1 11 1 2 1 2 1 12 2 2 1 2 1 13 1 1 2 2 1 14 2 1 2 2 1 15 1 2 2 2 1 16 2 2 2 2 1 17 1 1 1 1 2 18 2 1 1 1 2 19 1 2 1 1 2 20 2 2 1 1 2 21 1 1 2 1 2 22 2 1 2 1 2 23 1 2 2 1 2 24 2 2 2 1 2 25 1 1 1 2 2 26 2 1 1 2 2 27 1 2 1 2 2 28 2 2 1 2 2 29 1 1 2 2 2 30 2 1 2 2 2 31 1 2 2 2 2 32 2 2 2 2 2
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 −> 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 8 ... Read More
To create a blank column with randomization in an R data frame, we can use sample function and pass the blanks with single space. For example, if we want to create a vector say x that will be added in the data frame can be created by using the command −x
To display p-value in stargazer output for linear regression model, we can use the report argument. For example, if we have a model called RegressionModel then to display the p-value with coefficients can be done by using the below command −stargazer(RegressionModel,type="text",report=("vc*p"))ExampleConsider the below data frame −x1