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It is common to read/hear comments saying that the data is in format wide or long.
Which means a table is in format wide? And long?
Which packages/functions in R can be used to transform a table wide in long and vice versa?
There is a that question, but it is restricted to one of the cases and only its operationalization, without entering into conceptual questions.
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In broad format (wide format), the responses of the same individuals will be in a single row and each response will be in a separate column.
For example, in the following dataset, each county was measured at four time points, once every 10 years, starting in 1970. The result variable is Jobs and indicates the number of jobs in each county. There are three predictive variables: Area of the Earth, Natural Amenity (4 = no and 3 = Yes) and the proportion of the county’s population that year College.
As the area of land and the presence of a natural amenity do not change from decade to decade, these predictors have only one variable per municipality. But both our result, Jobs, and a predictor, College, have different values in each year, so require a different variable (column) for each year.
In long format (long format), Each line is one time point per subject. Therefore, each subject (county) will have data in several lines. All variables that do not change over time will have the same value on all lines.
You can see the same data from the five counties below in long format. Each county has four rows of data - one for each year.
All the same information is there; we just configure the data differently.
We don’t need more than four columns to Jobs or College. Instead, all four values of Jobs for each county are stacked. They are all in the column Jobs. The same goes for the four values of College.
But to track which observation occurred in which year, we need to add a variable Year.
You’ll notice that the variables that haven’t changed from year to year - Land Area and Natural Amenity - have the same value on each of the four lines in each county. It sounds strange, but it’s okay to have it that way and as long as you analyze the data using the right procedures, you’ll take into account that they’re redundant.
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What does it mean that a table is in wide format? And long?
The following figure exemplifies this:
Note that in the format wide, x and y are the variable names. When you convert the database to the format long, the values of these variables will be stacked with the respective name of the variable next to it (i.e., in another column), indicating that these values correspond to it.
Which packages/functions in R can be used to turn a wide table into long and vice versa?
In the r utils you can use the function stack (stack) to convert a data.frame of wide for long:
dataset<-data.frame(matrix(runif(6*5,1,10),ncol=6)) # criação do data.frame
dataset
X1 X2 X3 X4 X5 X6
1 7.349284 6.028351 2.688078 6.125223 1.221548 9.612955
2 4.069976 2.874686 6.672611 7.392773 8.788791 1.947049
3 5.601091 5.088117 6.642646 1.919682 5.083521 2.890271
4 8.972191 5.440744 1.900963 2.321034 4.617486 3.135706
5 6.863326 3.664501 8.406267 2.357013 7.787931 5.592315
empilhar<-stack(dataset,select=(1:6)) # converte para o formato long
empilhar
values ind
1 7.349284 X1
2 4.069976 X1
3 5.601091 X1
4 8.972191 X1
5 6.863326 X1
6 6.028351 X2
7 2.874686 X2
8 5.088117 X2
9 5.440744 X2
10 3.664501 X2
11 2.688078 X3
12 6.672611 X3
13 6.642646 X3
14 1.900963 X3
15 8.406267 X3
16 6.125223 X4
17 7.392773 X4
18 1.919682 X4
19 2.321034 X4
20 2.357013 X4
21 1.221548 X5
22 8.788791 X5
23 5.083521 X5
24 4.617486 X5
25 7.787931 X5
26 9.612955 X6
27 1.947049 X6
28 2.890271 X6
29 3.135706 X6
30 5.592315 X6
The function unstack performs the reverse process. That is, converts from long for wide:
desempilhar<-unstack(empilhar) # converte para o formato wide
X1 X2 X3 X4 X5 X6
1 7.349284 6.028351 2.688078 6.125223 1.221548 9.612955
2 4.069976 2.874686 6.672611 7.392773 8.788791 1.947049
3 5.601091 5.088117 6.642646 1.919682 5.083521 2.890271
4 8.972191 5.440744 1.900963 2.321034 4.617486 3.135706
5 6.863326 3.664501 8.406267 2.357013 7.787931 5.592315
In the tidyverse, the package tidyr It is also useful to do what you want.
To stack, you can use the function gather. Considering the same data set:
library(tidyr)
res1<-gather(dataset, key='factor', value = 'my', 1:6)
head(res1,10)
factor my
1 X1 5.938725
2 X1 4.367486
3 X1 3.220609
4 X1 3.357561
5 X1 9.275956
6 X2 2.260197
7 X2 5.880264
8 X2 2.891555
9 X2 7.641574
10 X2 9.611466
key is the name of the vector that will bear the names of the stacked variables;
value represents the variables to be stacked.The reverse process of the function gather is given by the function spread (similar to unstack). More details on documentation.
There are other Packages who do the same (as reshape2).
Main data utilities in format long:
Automatically creates groups for variables (useful in some analyses, such as ANOVA). Moreover, in some functions of tidyverse, as ggplot::facet_wrap, the data must be in this format.
Prevents the execution of loops, when using the group variable as if they were columns of a data.frame.
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