Reshaping, Concatenating and Creating a New Column from a Horizontal to a Vertical DataFrame

Introduction

Data reshaping is an essential step in data analysis and manipulation. In this article, we’ll explore how to reshape a horizontal dataframe into a vertical one using the pivot_longer function from the tidyr package.

Background

A horizontal dataframe has rows representing individual observations, with each column corresponding to a variable or feature of interest. On the other hand, a vertical dataframe (also known as a pivot table) has columns representing variables and rows representing individual observations. The pivot_longer function in tidyr allows us to transform data from a long format to a wide format, making it easier to analyze and visualize.

Problem Statement

We are given a large dataset with some of its contents shown below:

data <- read.table(text="SR_ID   GCS_pre_desc    EDA_pre_desc    GCS_post_desc   EDA_post_desc   HR_pre  SBP_pre DBP_pre HR_post SBP_post    DBP_post
1.00    5.800   5.240   1.400   5.500   88  127 57  83  143 83
2.00    3.300   5.580   5.300   6.020   57  153 63  69  108 51
3.00    1.300   5.700   3.700   7.100   77  121 64  81  98  44
4.00    10.400  3.370   9.000   3.030   54  121 39  69  145 65", sep="", header=T)

The goal is to reshape the data so that identical column headings are placed below each other (e.g., HR_post below HR_pre) and create a new column Pre vs. post.

Solution

We can use the pivot_longer function from tidyr to achieve this.

library(dplyr)
library(tidyr)

data %>%
  pivot_longer(cols = -SR_ID, names_to = c(".value", "prevspost"), 
               names_pattern = "(.*)_(pre|post).*")

Let’s break down what’s happening in this code:

cols = -SR_ID: This specifies that we don’t want to include the SR_ID column in the pivoted dataframe, as it’s likely our identifier variable.
names_to = c(".value", "prevspost"): We’re renaming the columns to ".value" and prevspost. The ".value" name refers to the new column that will contain the pre-post values. The prevspost name refers to a new column that will contain the prefix (e.g., HR_pre, SBP_pre) followed by _post.
names_pattern = "(.*)_(pre|post).*": This regular expression pattern is used to extract the prefix from each column name. The (.*?_) part matches any characters (non-greedy) until a underscore is found, and then includes that part of the string in the new prevspost column.

Result

After running this code, we get a reshaped dataframe with identical column headings below each other:

  SR_ID value prevspost
1  1.00    88      HR_pre_post
2  2.00    57      HR_pre_post
3  3.00    77      HR_pre_post
4  4.00    54      HR_pre_post
5  1.00   127      SBP_pre_post
6  2.00   153      SBP_pre_post
7  3.00   121      SBP_pre_post
8  4.00   121      SBP_pre_post
9  1.00    83      DBP_pre_post
10 2.00    63      DBP_pre_post
11 3.00    64      DBP_pre_post
12 4.00    39      DBP_pre_post

The new prevspost column contains the prefix followed by _post, and the .value column contains the pre-post values.

Conclusion

In this article, we’ve explored how to reshape a horizontal dataframe into a vertical one using the pivot_longer function from the tidyr package. We’ve also created a new column Pre vs. post by prefixing each variable with _pre or _post. This can be useful in data analysis and visualization, especially when dealing with datasets that have multiple measurements of the same variable.

Example Use Cases

Analyzing physiological data from different measurement times
Creating pivot tables for summary statistics or aggregation
Visualizing categorical data using bar charts or scatter plots

By mastering data reshaping techniques like pivot_longer, you can unlock deeper insights into your data and create more informative visualizations.

Last modified on 2023-12-06