Reshaping Wide Format to Multi-Column Long Format

Introduction

When working with datasets that have multiple tests measured at different time points, it’s often necessary to reshape the data into a long format. This allows for easier analysis and manipulation of the data. In this article, we’ll explore how to achieve this using the reshape2 and data.table packages in R.

Problem Statement

Given a wide format dataset with multiple tests measured at 3 time points (Fall, Spring, Winter), we want to reshape it into a long format where each test is separated by column, but the measurement times are converted to individual columns.

Wide Format Dataset

The following is an example of a wide format dataset:

ID	Test	Year	Fall	Spring	Winter
1	1	2008	15	16	19
1	1	2009	12	13	27
1	2	2008	22	22	24
1	2	2009	10	14	20
2	1	2008	12	13	25
2	1	2009	16	14	21
2	2	2008	13	11	29
2	2	2009	23	20	26
3	1	2008	11	12	22
3	1	2009	13	11	27
3	2	2008	17	12	23
3	2	2009	14	9	31

Expected Long Format Dataset

The desired long format dataset would have the following structure:

ID	Year	Time	Test1	Test2
1	2008	Fall	15	22
1	2008	Spring	16	22
1	2008	Winter	19	24
1	2009	Fall	12	10
1	2009	Spring	13	14
1	2009	Winter	27	20
2	2008	Fall	12	13
2	2008	Spring	13	11
2	2008	Winter	25	29
2	2009	Fall	16	23
2	2009	Spring	14	20
2	2009	Winter	21	26
3	2008	Fall	11	17
3	2008	Spring	12	12
3	2008	Winter	22	23
3	2009	Fall	13	14
3	2009	Spring	11	9
3	2009	Winter	27	31

Using `reshape2`

One way to achieve this is by using the melt function from the reshape2 package. Here’s an example of how to do it:

# load required libraries
library(reshape2)

# create a sample dataset
df <- data.frame(ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
                 Test = c(1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2),
                 Year = c(2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009),
                 Fall = c(15, 12, 22, 10, 12, 16, 13, 23, 11, 13, 17, 14),
                 Spring = c(16, 13, 22, 14, 13, 14, 12, 20, 12, 11, 12, 9),
                 Winter = c(19, 27, 24, 20, 25, 21, 29, 26, 22, 27, 23, 31))

# melt the dataset
df.m <- melt(df, id.var = c("ID", "Year"), variable.name = "Time")

# transform the Time column to be in the format 'TestX'
df.m$Time <- gsub("(.*)_", "_\\1", df.m$Time)

# cast the melted dataset back into a long format
df.cast <- dcast(df.m, ID + Year + Time ~ Test, value.var = "value")

# print the resulting dataset
print(df.cast)

Using `data.table`

Another way to achieve this is by using the melt and dcast functions from the data.table package. Here’s an example of how to do it:

# load required libraries
library(data.table)

# create a sample dataset
df <- data.frame(ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
                 Test = c(1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2),
                 Year = c(2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009, 2008, 2009),
                 Fall = c(15, 12, 22, 10, 12, 16, 13, 23, 11, 13, 17, 14),
                 Spring = c(16, 13, 22, 14, 13, 14, 12, 20, 12, 11, 12, 9),
                 Winter = c(19, 27, 24, 20, 25, 21, 29, 26, 22, 27, 23, 31))

# convert the dataframe to a data.table
dt <- as.data.table(df)

# melt the dataset
dt.m <- melt(dt, id.var = c("ID", "Year"), variable.name = "Time")

# add the Test column back in as a paste of 'TestX'
dt.m$Test <- paste0("Test", dt.m$Test)

# cast the melted dataset back into a long format
df.cast <- dcast(dt.m, ID + Year + Time ~ Test, value.var = "value")

# print the resulting dataset
print(df.cast)

Benchmarking Performance

To compare the performance of reshape2 and data.table, we can create some larger datasets and measure the time it takes to reshape them.

# generate a larger sample dataset
set.seed(45L)
DT <- data.table(ID = sample(1e2, 1e7, TRUE),
                 Test = sample(1e3, 1e7, TRUE),
                 Year = sample(2008:2014, 1e7,TRUE),
                 Fall = sample(50, 1e7, TRUE),
                 Spring = sample(50, 1e7,TRUE),
                 Winter = sample(50, 1e7, TRUE))

# convert the data.table to a dataframe
DF <- as.data.frame(DT)

# reshape using reshape2
reshape2_melt <- function(df) {
    df.m <- melt(df, id.var = c("ID", "Test", "Year"), variable.name = "Time")
}

# reshape using data.table
DT_melt <- function(dt) {
    dt.m <- melt(dt, id.var = c("ID", "Test", "Year"), variable.name = "Time")
}

# time the reshape functions
system.time(reshape2_melt(DF))
system.time(DT_melt(DT))

# compare the results
cat("reshape2 is approximately", round((1/reshape2_melt(DF))/ (1/DT_melt(DT))), "times faster than data.table")

Conclusion

Reshaping wide format datasets to multi-column long formats can be achieved using reshape2 and data.table. While both packages provide similar functionality, data.table is significantly faster for larger datasets. By understanding the strengths and weaknesses of each package, you can choose the best tool for your specific data transformation needs.

Last modified on 2024-05-08