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Articles by Nizamuddin Siddiqui
Page 102 of 196
How to combine year, month, and day column in an R data frame?
Sometimes date variable is recorded in three different columns representing year, month, and day instead of a single column as date. Therefore, we need to combine these three columns and create a single column. This can be done by using paste function and define the values with as.Date.Consider the below data frame −ExampleYear
Read MoreHow to replace NA values in columns of an R data frame form the mean of that column?
In the whole world, the first step people teach to impute missing values is replacing them with the relevant mean. That means if we have a column which has some missing values then replace it with the mean of the remaining values. In R, we can do this by replacing the column with missing values using mean of that column and passing na.rm = TRUE argument along with the same.Consider the below data frame −Exampleset.seed(121) x
Read MoreHow to create a string vector with numbers at the end in R?
If we want to create a categorical vector with all unique values representing strings with numbers at the end then paste0 function can help us in the same. For example, if we want to create a vector for ID up to 10 as ID1, ID2, ID3, ID4, ID5, ID6, ID7, ID8, ID9, and ID10 then it can be done as paste0("ID",seq(1:10)).Examplex1
Read MoreHow to save an R data frame as txt file?
If we want to use a data frame created in R in the future then it is better to save that data frame as txt file because it is obvious that data creation takes time. This can be done by using write.table function. For example, if we have a data frame df then we can save it as txt file by using the code write.table(df,"df.txt",sep="\t",row.names=FALSE)Consider the below data frame −Exampleset.seed(111) x1
Read MoreHow to find the correlation coefficient between rows of two data frames in R?
It is common the find the correlation coefficient between columns of an R data frame but we might want to find the correlation coefficient between rows of two data frames. This might be needed in situations where we expect that there exists some relationship row of an R data frame with row of another data frame. For example, row of an R data frame showing buying trend of a customer in one year and the same row of the other data frame showing buying trend of the same customer in another year.Consider the below data frame −Examplex1
Read MoreHow to find the sum of anti-diagonal elements in a matrix in R?
The anti-diagonal elements in a matrix are the elements that form straight line from right upper side to right bottom side. For example, if we have a matrix as shown below −1 2 3 4 5 6 7 8 9then the diagonal elements would be 1, 5, 9 and the anti-diagonal elements would be 3, 5, 7.To find the sum of these anti-diagonal elements, we can use apply function.ExampleM1
Read MoreHow to create a subset of matrix in R using greater than or less than a certain value of a column?
Subsetting can be required in many different ways, we can say that there might be infinite number of ways for subsetting as it depends on the objective of the bigger or smaller analysis. One such way is subsetting a matrix based on a certain value of column of the matrix. In R, we can easily do the same with the help of subset function as shown in below example.ExampleM3)Output [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 4 14 24 34 44 54 64 74 84 94 [2,] 5 15 25 35 45 55 65 75 85 95 [3,] 6 16 26 36 46 56 66 76 86 96 [4,] 7 17 27 37 47 57 67 77 87 97 [5,] 8 18 28 38 48 58 68 78 88 98 [6,] 9 19 29 39 49 59 69 79 89 99 [7,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,1]75)Output[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 6 16 26 36 46 56 66 76 86 96 [2,] 7 17 27 37 47 57 67 77 87 97 [3,] 8 18 28 38 48 58 68 78 88 98 [4,] 9 19 29 39 49 59 69 79 89 99 [5,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,9]>81)Output[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 2 12 22 32 42 52 62 72 82 92 [2,] 3 13 23 33 43 53 63 73 83 93 [3,] 4 14 24 34 44 54 64 74 84 94 [4,] 5 15 25 35 45 55 65 75 85 95 [5,] 6 16 26 36 46 56 66 76 86 96 [6,] 7 17 27 37 47 57 67 77 87 97 [7,] 8 18 28 38 48 58 68 78 88 98 [8,] 9 19 29 39 49 59 69 79 89 99 [9,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,9]
Read MoreHow to create a graph in R using ggplot2 with all the four quadrants?
The default graph created by using ggplot2 package shows the axes labels depending on the starting and ending values of the column of the data frame or vector but we might want to visualize it just like we do in paper form of graphs that shows all of the four quadrants. This can be done by using xlim, ylim, geom_hline, and geom_vline functions with ggplot function of ggplot2 package.Consider the below data frame −Examplex
Read MoreHow to find the number of NA's in each column of an R data frame?
Sometimes the data frame is filled with too many missing values/ NA’s and each column of the data frame contains at least one NA. In this case, we might want to find out how many missing values exists in each of the columns. Therefore, we can use colSums function along with is.na in the following manner: colSums(is.na(df)) #here df refers to data frame name.Consider the below data frame −Exampleset.seed(109) x1
Read MoreHow to find contingency table of means from an R data frame using cast function?
The contingency table considers the numerical values for two categorical variables. Often, we require contingency table for counts, especially in non-parametric analysis but it is also possible that we want to use means for our analysis. Hence, we can use cast function from reshape package which solves the problem of creating contingency table easily.Consider the below data frame −Exampleset.seed(99) x1
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