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When we have two factor columns and one numeric column then we can create a contingency table for the total count of numeric values based on the factor columns. This can be done with the help of xtabs function in base R. For example, if we have a data frame called df that contains two factor columns say f1 and f2, and one numeric column say Y then the contingency table for df can be created by using the command xtabs(Y~f1+f1, df).Example1Consider the below data frame −Live Demo> x1 x2 y1 df1 df1Output x1 x2 y1 1 B a 5 ... Read More
The grouping of values can be done in many ways and one such way is if we have duplicate values or unique values then the group can be set based on that. If all the values are unique then there is no sense for grouping but if we have varying values then the grouping can be done. For this purpose, we can use rleid function as shown in the below examples.Example1Consider the below data frame −Live Demo> x df1 df1Output x 1 2 2 1 3 2 4 2 5 1 6 ... Read More
There is no in-built function to find the mode in R, hence we need to create one and then apply it to the rows of the matrix. The function for mode is created as follows −mode M1 M1Output [,1] [,2] [,3] [,4] [,5] [1,] 2 2 1 2 2 [2,] 2 2 2 2 1 [3,] 2 2 1 1 1 [4,] 2 1 1 1 1 [5,] 2 1 1 2 2> apply(M1,1,mode)Output[1] 2 2 1 1 2Example2Live Demo> M2 M2Output [,1] [,2] [,3] [,4] [,5] [1,] 1 1 2 2 1 [2,] 2 1 1 2 1 [3,] 2 2 1 1 1 [4,] 2 1 1 2 2 [5,] 2 1 1 2 2 [6,] 1 2 1 1 2 [7,] 1 1 2 1 2 [8,] 2 2 1 2 1 [9,] 2 1 1 2 2 [10,] 1 1 2 2 2 [11,] 1 1 2 1 2 [12,] 1 2 2 2 1 [13,] 2 2 2 2 1 [14,] 2 1 2 2 1 [15,] 1 2 1 1 2 [16,] 2 2 1 2 1 [17,] 2 2 1 1 1 [18,] 2 1 1 2 1 [19,] 1 1 1 2 1 [20,] 2 1 1 2 2> apply(M2,1,mode)Output[1] 1 1 1 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 1 2Example3Live Demo> M3 M3Output [,1] [,2] [,3] [,4] [,5] [1,] 1 3 3 2 1 [2,] 2 3 1 2 2 [3,] 2 2 3 3 1 [4,] 1 3 1 3 2 [5,] 3 1 2 1 2 [6,] 2 3 1 1 1 [7,] 2 2 2 3 1 [8,] 1 2 2 2 2 [9,] 2 1 2 1 2 [10,] 1 3 1 2 1 [11,] 2 1 3 1 1 [12,] 1 1 3 2 2 [13,] 2 1 1 1 2 [14,] 2 1 3 3 2 [15,] 1 2 3 1 2 [16,] 1 2 1 2 1 [17,] 3 1 1 3 2 [18,] 3 3 3 3 1 [19,] 3 2 3 1 1 [20,] 3 3 2 2 1> apply(M3,1,mode)Output[1] 1 2 2 1 1 1 2 2 2 1 1 1 1 2 1 1 1 3 1 2Example4Live Demo> M4 M4Output [,1] [,2] [,3] [,4] [,5] [1,] 10 10 9 10 9 [2,] 9 9 10 9 9 [3,] 9 9 9 10 10 [4,] 10 9 9 10 10 [5,] 10 10 9 10 9 [6,] 10 10 9 10 10 [7,] 9 9 9 10 9 [8,] 9 10 9 10 9 [9,] 9 9 9 9 9 [10,] 9 10 9 10 9 [11,] 10 10 9 9 9 [12,] 9 9 9 9 9 [13,] 10 10 10 9 10 [14,] 10 9 10 10 10 [15,] 9 10 9 10 9 [16,] 9 10 9 10 9 [17,] 9 10 10 9 10 [18,] 9 9 9 9 10 [19,] 10 9 9 10 9 [20,] 10 9 9 10 9> apply(M4,1,mode)Output[1] 10 9 9 10 10 10 9 9 9 9 9 9 10 10 9 9 10 9 9 9
If we have factor columns in an R data frame then we want to find the frequency of each factor level for all the factor columns. This can be done with the help of sapply function with table function. For example, if we have a data frame called df that contains some factor columns then the frequency table for factor columns can be created by using the command sapply(df, table).Example1Consider the below data frame −Live Demo> x1 x2 df1 df1Output x1 x2 1 D a 2 D b 3 D c 4 D b 5 D c 6 C a ... Read More
If we have a vector where alternate values may create a tabular form then we might want to convert the vector into a data frame. For this purpose, we first need to convert the vector into a matrix with appropriate number of columns/rows and then read it as a data frame using as.data.frame function. Check out the below examples to understand how it works.Example1Live Demo> x1 x1Output[1] "1" "male" "1" "male" "1" "male" "1" "male" [9] "1" "male" "1" "male" "1" "male" "1" "male" [17] "1" "male" "1" "male" "2" "female" "2" "female" [25] "2" "female" "2" "female" "2" "female" ... Read More
If we have a character column in the data frame that contains string as well as numeric values and the first digit of the numeric values has some meaning that can help in data analysis then we can extract those first digits. For this purpose, we can use stri_extract_first function from stringi package.Example1Consider the below data frame −Live Demo> x1 y1 df1 df1Output x1 y1 1 1 HT14L 2 2 HT14L 3 3 HT23L 4 4 HT14L 5 5 HT32L 6 6 HT32L 7 ... Read More
Most of the times we need to deal with missing values in data science projects and these missing values can be occurred at any position. We might want to change the position of these missing values and send them to the end of the columns in the data frame. This can be done with the help of lapply function as shown in the below examples.Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output x1 x2 x3 1 0 0 2 2 1 1 NA 3 1 NA 0 4 0 NA 2 5 1 NA 2 6 ... Read More
To convert an old data frame to a new data frame, we can simply set the new name. For example, if we have a data frame called df and want to convert it to a new one let’s say df_new then it can be done as df_new x1 x2 df1 df1Output x1 x2 1 8 6 2 4 9 3 3 2 4 3 5 5 7 4 6 4 8 7 8 6 8 12 12 9 8 6 10 ... Read More
To remove rows from the data frame that duplicate values greater than a certain number of times, we can create a subset for rows having duplicate values less than the certain number of times. For this purpose, we first need to extract the rows and then subset the data frame with the particular column as shown in the below examples.Example1Consider the below data frame −Live Demo> x1 x2 df1 df1Output x1 x2 1 0 0 2 0 0 3 1 0 4 0 1 5 0 0 6 1 1 7 0 1 8 1 1 9 1 2 10 ... Read More
We know that a data frame can contain any type of columns such as numerical, character, logical, factor, etc. And if a data frame contains multiple type of columns then we might want to find the number of columns for each type or of one type say numerical. For this purpose, we can use select_if function of dplyr package along with the length function as shown in the below examples.Example1Consider the below data frame −Live Demo> x1 x2 x3 x4 df1 df1Output x1 x2 x3 x4 1 a -0.18404831 0.1082741 2 2 b ... Read More