May 12, 2021 R language tutorial
Data remodeling in the R language is about changing the way data is organized into rows and columns. M ost of the time data processing in the R language is done by using the input data as a data frame. I t is easy to extract data from rows and columns of data frames, but in some cases we need a different format for data frames than the ones we receive for data frames. The R language has many features that split in a data frame, merge and change rows to columns, and vice versa.
We can use the cbind() function to connect multiple vectors to create data frames. In addition, we can use the rbind() function to combine two data frames.
# 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 ") # 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 ") # 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 ") # Print the result. print(all.addresses)
When we execute the code above, it produces the following results -
# # # # 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
We can use the merge() function to combine two data frames. The data frame must have the same column name and be merged on it.
In the following example, we consider the dataset on diabetes in Pima Indian Women in the library name "MASS". W e combine two datasets based on the values of blood pressure ("bp") and body mass index ("bmi"). When you select these two columns for merge, the values of the two variables are combined to form a single data frame by matching records in the two data set.
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 code above, it produces the following results -
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
Status Age V1 V2 V3 V4
P 23646 45190 50333 55166 56271
CC 26174 35535 38227 37911 41184
CC 27723 25691 25712 26144 26398
CC 27193 30949 29693 29754 30772
CC 24370 50542 51966 54341 54273
CC 28359 58591 58803 59435 61292
CC 25136 45801 45389 47197 47126
zz <- read.csv("mr.csv", strip.white = TRUE)
zzz <- cbind(zz[gl(nrow(zz), 1, 4*nrow(zz)), 1:2], stack(zz[, 3:6]))
Status Age values ind
X1 P 23646 45190 V1
X2 CC 26174 35535 V1
X3 CC 27723 25691 V1
X4 CC 27193 30949 V1
X5 CC 24370 50542 V1
X6 CC 28359 58591 V1
X7 CC 25136 45801 V1
X11 P 23646 50333 V2
...
> reshape(zz, idvar="id",timevar="var",
varying=list(c("V1","V2","V3","V4")),direction="long")
Status Age var V1 id
1.1 P 23646 1 45190 1
2.1 CC 26174 1 35535 2
3.1 CC 27723 1 25691 3
4.1 CC 27193 1 30949 4
5.1 CC 24370 1 50542 5
6.1 CC 28359 1 58591 6
7.1 CC 25136 1 45801 7
1.2 P 23646 2 50333 1
2.2 CC 26174 2 38227 2
...