Data Reshaping in R is about changing the way data is organized into rows and columns. Most of the time data processing in R is done by taking the input data as a data frame.
It is easy to extract data from the rows and columns of a data frame but there are situations when we need the data frame in a format that is different from format in which we received it.
R has many functions to split, merge and change the rows to columns and vice-versa in a data frame.
Joining Columns and Rows in a Data Frame
We can join multiple vectors to create a data frame using the cbind()function. Also we can merge two data frames using rbind() function.
# Create vector objects. city <- c("Tampa","Seattle","Hartford","Denver") state <- c("FL","WA","CT","CO") zipcode <- c(33602,98104,06161,80294) # Combine above three vectors into one data frame. addresses <- cbind(city,state,zipcode) # Print a header. cat("# # # # The First data frame\n") # Print the data frame. print(addresses) # Create another data frame with similar columns new.address <- data.frame( city = c("Lowry","Charlotte"), state = c("CO","FL"), zipcode = c("80230","33949"), stringsAsFactors = FALSE ) # Print a header. cat("# # # The Second data frame\n") # Print the data frame. print(new.address) # Combine rows form both the data frames. all.addresses <- rbind(addresses,new.address) # Print a header. cat("# # # The combined data frame\n") # Print the result. print(all.addresses)
When we execute the above code, it produces the following result −
# # # # The First data frame
city state zipcode
[1,] "Tampa" "FL" "33602"
[2,] "Seattle" "WA" "98104"
[3,] "Hartford" "CT" "6161"
[4,] "Denver" "CO" "80294"
# # # The Second data frame
city state zipcode
1 Lowry CO 80230
2 Charlotte FL 33949
# # # The combined data frame
city state zipcode
1 Tampa FL 33602
2 Seattle WA 98104
3 Hartford CT 6161
4 Denver CO 80294
5 Lowry CO 80230
6 Charlotte FL 33949
Merging Data Frames
We can merge two data frames by using the merge() function. The data frames must have same column names on which the merging happens.
In the example below, we consider the data sets about Diabetes in Pima Indian Women available in the library names "MASS". we merge the two data sets based on the values of blood pressure("bp") and body mass index("bmi"). On choosing these two columns for merging, the records where values of these two variables match in both data sets are combined together to form a single data frame.
library(MASS) merged.Pima <- merge(x = Pima.te, y = Pima.tr, by.x = c("bp", "bmi"), by.y = c("bp", "bmi") ) print(merged.Pima) nrow(merged.Pima)
When we execute the above code, it produces the following result −
bp bmi npreg.x glu.x skin.x ped.x age.x type.x npreg.y glu.y skin.y ped.y 1 60 33.8 1 117 23 0.466 27 No 2 125 20 0.088 2 64 29.7 2 75 24 0.370 33 No 2 100 23 0.368 3 64 31.2 5 189 33 0.583 29 Yes 3 158 13 0.295 4 64 33.2 4 117 27 0.230 24 No 1 96 27 0.289 5 66 38.1 3 115 39 0.150 28 No 1 114 36 0.289 6 68 38.5 2 100 25 0.324 26 No 7 129 49 0.439 7 70 27.4 1 116 28 0.204 21 No 0 124 20 0.254 8 70 33.1 4 91 32 0.446 22 No 9 123 44 0.374 9 70 35.4 9 124 33 0.282 34 No 6 134 23 0.542 10 72 25.6 1 157 21 0.123 24 No 4 99 17 0.294 11 72 37.7 5 95 33 0.370 27 No 6 103 32 0.324 12 74 25.9 9 134 33 0.460 81 No 8 126 38 0.162 13 74 25.9 1 95 21 0.673 36 No 8 126 38 0.162 14 78 27.6 5 88 30 0.258 37 No 6 125 31 0.565 15 78 27.6 10 122 31 0.512 45 No 6 125 31 0.565 16 78 39.4 2 112 50 0.175 24 No 4 112 40 0.236 17 88 34.5 1 117 24 0.403 40 Yes 4 127 11 0.598 age.y type.y 1 31 No 2 21 No 3 24 No 4 21 No 5 21 No 6 43 Yes 7 36 Yes 8 40 No 9 29 Yes 10 28 No 11 55 No 12 39 No 13 39 No 14 49 Yes 15 49 Yes 16 38 No 17 28 No [1] 17
Melting and Casting
One of the most interesting aspects of R programming is about changing the shape of the data in multiple steps to get a desired shape. The functions used to do this are called melt() and cast().
We consider the dataset called ships present in the library called "MASS".
library(MASS) print(ships)
When we execute the above code, it produces the following result −
type year period service incidents 1 A 60 60 127 0 2 A 60 75 63 0 3 A 65 60 1095 3 4 A 65 75 1095 4 5 A 70 60 1512 6 ............. ............. 8 A 75 75 2244 11 9 B 60 60 44882 39 10 B 60 75 17176 29 11 B 65 60 28609 58 ............ ............ 17 C 60 60 1179 1 18 C 60 75 552 1 19 C 65 60 781 0 ............ ............
Melt the Data
Now we melt the data to organize it, converting all columns other than type and year into multiple rows.
molten.ships <- melt(ships, id = c("type","year")) print(molten.ships)
When we execute the above code, it produces the following result −
type year variable value 1 A 60 period 60 2 A 60 period 75 3 A 65 period 60 4 A 65 period 75 ............ ............ 9 B 60 period 60 10 B 60 period 75 11 B 65 period 60 12 B 65 period 75 13 B 70 period 60 ........... ........... 41 A 60 service 127 42 A 60 service 63 43 A 65 service 1095 ........... ........... 70 D 70 service 1208 71 D 75 service 0 72 D 75 service 2051 73 E 60 service 45 74 E 60 service 0 75 E 65 service 789 ........... ........... 101 C 70 incidents 6 102 C 70 incidents 2 103 C 75 incidents 0 104 C 75 incidents 1 105 D 60 incidents 0 106 D 60 incidents 0 ........... ...........
Cast the Molten Data
We can cast the molten data into a new form where the aggregate of each type of ship for each year is created. It is done using the cast() function.
recasted.ship <- cast(molten.ships, type+year~variable,sum) print(recasted.ship)
When we execute the above code, it produces the following result −
type year period service incidents 1 A 60 135 190 0 2 A 65 135 2190 7 3 A 70 135 4865 24 4 A 75 135 2244 11 5 B 60 135 62058 68 6 B 65 135 48979 111 7 B 70 135 20163 56 8 B 75 135 7117 18 9 C 60 135 1731 2 10 C 65 135 1457 1 11 C 70 135 2731 8 12 C 75 135 274 1 13 D 60 135 356 0 14 D 65 135 480 0 15 D 70 135 1557 13 16 D 75 135 2051 4 17 E 60 135 45 0 18 E 65 135 1226 14 19 E 70 135 3318 17 20 E 75 135 542 1
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