Reshaping Data in R with Time Values in Column Names

Reshaping data in R can be a complex task, especially when dealing with data structures that are not conducive to traditional data manipulation techniques. In this article, we will explore how to reshape data from wide format to long format using the melt function in R, and how to handle time values in column names.

Overview of Wide and Long Format Data Structures

Before we dive into the details of reshaping data, it’s essential to understand the difference between wide and long format data structures. In a wide format data structure, each row represents a single observation, and each column represents a variable or predictor. In contrast, a long format data structure has all variables in one column, and observations are represented by multiple rows.

Creating Fake Data

To illustrate the concept of reshaping data, we’ll create some fake data with a similar structure to the original data provided. We’ll use the replicate function to generate random values for each variable, and then create a data frame using the data.frame function.

set.seed(194)
dat = data.frame(replicate(27*8, cumsum(rnorm(24*3))))

names(dat) = paste0(rep(paste0("data",1:27), each=8), ".", rep(paste0("time",1:8), 27))

dat$group = rep(LETTERS[1:3], each=24)

Removing Unnecessary Columns

In the original data, there are some columns that we don’t need for our analysis. Let’s remove those columns using the [,] syntax.

dat = dat[, -c(2,4,9,43,56,78,100:103,115:116,134:136,202,205)]

Reshaping from Wide to Long Format

Now that we have our data in a suitable format, let’s use the melt function to reshape it from wide to long format. The melt function takes three arguments: the data frame to be melted, the variable name for the id variable, and the variable name for the new variable.

datl = melt(dat, id.var="group")

Splitting Data Source and Time Point into Separate Columns

In our long format data structure, we need to split the data source and time point columns into separate variables. We can do this using the gsub function with regular expressions.

datl$source = gsub("(.*)\\..*", "\\1", datl$variable)
datl$time = as.numeric(gsub(".*time(.*)","\\1", datl$variable))

Ordering Data Frame Names by Number

To ensure that our data frame names are in the correct order, we need to set the levels of the source variable. We can do this using the factor function.

datl$source = factor(datl$source, levels=paste0("data",1:length(unique(datl$source))))

Plotting the Data using ggplot2

Now that we have our data in a long format structure, let’s use ggplot2 to plot it. We’ll create a line plot with a mean value and an error bar for the standard deviation.

pd = position_dodge(0.7)

ggplot(datl, aes(time, value, group=group, color=group)) + 
  stat_summary(fun.y=mean, geom="line", position=pd) +
  stat_summary(fun.data=sdFnc, geom="errorbar", width=0.4, position=pd) +
  stat_summary(fun.y=mean, geom="point", position=pd) +
  facet_wrap(~source, ncol=3) +
  theme_bw()

Original (Unnecessary) Reshaping Code

As a point of comparison, let’s look at the original reshaping code. While it looks similar to what we’ve done, it has some key differences.

# Convert data source from wide to long
datl = data.frame()
for (i in seq(1,27*8,8)) {

  tmp.dat = dat[, c(i:(i+7),grep("group",names(dat))]
  tmp.dat$source = gsub("(.*)\\..*", "\\1", names(tmp.dat)[1])
  names(tmp.dat)[1:8] = 1:8

  #datl = rbind(datl, tmp.dat)
  datl = bind_rows(datl, tmp.dat)  # Updated based on comment
}

datl$source = factor(datl$source, levels=paste0("data",1:27))

# Convert time from wide to long
datl = melt(datl, id.var = c("source","group"), variable.name="time")

Conclusion

Reshaping data in R can be a complex task, especially when dealing with data structures that are not conducive to traditional data manipulation techniques. In this article, we explored how to reshape data from wide format to long format using the melt function in R, and how to handle time values in column names. We also looked at an alternative reshaping approach and highlighted its limitations.

Tips and Variations

• When working with large datasets, consider using the dplyr package for data manipulation.
• To avoid duplicate rows when merging data frames, use the bind_rows function instead of rbind.
• For plotting data, consider using ggplot2’s built-in functions like stat_summary and position_dodge.

Last modified on 2024-03-02