Automating Subtraction of Columns in R

Introduction

In this article, we will explore how to automate the subtraction of different columns in R. The goal is to create new columns that represent the result of a specific calculation and divide if possible.

Understanding the Data

First, let’s understand the structure of our data. We have a data frame named df with 4 columns: Sample, HFW01_V2, HFW01_V3, HFW02_V2, HFW02_V3, HFW03_V2, and HFW03_V3. The first two columns are repeated across different samples, while the last four are unique to each sample.

Using dplyr for Automation

One way to automate this process is by using the dplyr library in R. dplyr provides a grammar of data manipulation operations that can be used to efficiently and effectively manipulate datasets.

Step 1: Pivot Long Format

The first step is to pivot our data into a long format. This can be achieved using the pivot_longer() function from the tidyr package.

library(dplyr)
library(tidyr)

df %>% 
  pivot_longer(-Sample, names_pattern = "(.*)_(.*)", names_to = c("hfw", ".value"))

In this code:

We select all columns (-) except for the Sample column.
The names_pattern argument is used to specify a regular expression pattern that matches the column names. In this case, we’re matching any characters between two underscores ((.*)_(.*)).
The names_to argument specifies where we want to assign these matched patterns.

Step 2: Calculate Differences

After pivoting our data into a long format, we can calculate the differences between consecutive values in each column using the mutate() function from dplyr.

df %>% 
  pivot_longer(-Sample, names_pattern = "(.*)_(.*)", names_to = c("hfw", ".value")) %>% 
  mutate(diff = (V3 - V2)/V2)

In this code:

We apply the same pivot as before and add a new column diff with the result of the calculation.

Step 3: Pivot Back to Wide Format

Finally, we need to pivot our data back into its original wide format using the pivot_wider() function from dplyr.

df %>% 
  pivot_longer(-Sample, names_pattern = "(.*)_(.*)", names_to = c("hfw", ".value")) %>% 
  mutate(diff = (V3 - V2)/V2) %>% 
  pivot_wider(id_cols = Sample, names_from = "hfw", values_from = c("V2", "diff"), names_glue = "{hfw}_{.value}")

In this code:

We apply the same calculations as before.
The pivot_wider() function selects columns to keep (id_cols), specifies which columns we want to aggregate from (values_from), and provides a glue for naming these columns (names_glue). In this case, it creates new column names like “HFW01_V2_diff” and “HFW01_V3_diff”.

Data

Below is the R code snippet that represents our data:

library(dplyr)
library(tidyr)

df <- structure(
  list(Sample = c("s001", "s002", "s003", "s004"),
       HFW01_V2 = 5:8,
       HFW01_V3 = 10:13,
       HFW02_V2 = 15:18,
       HFW02_V3 = 20:23,
       HFW03_V2 = 25:28, 
       HFW03_V3 = 28:31),
  class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -4L))

Conclusion

In this article, we have learned how to automate the subtraction of different columns in R using dplyr and tidyr. By following these steps, you can efficiently manipulate your dataset and create new columns that represent meaningful calculations.

The process involves pivoting the data into a long format, applying calculations, and then pivoting it back to its original wide format. This approach allows for flexibility and ease of use when working with datasets in R.

Additional Examples

Let’s consider an additional example where we want to automate the subtraction of columns across different groups:

df %>% 
  group_by(HFW01_V2) %>% 
  mutate(diff = (V3 - V2)/V2)

In this code, we’re grouping by HFW01_V2 and applying the same calculation as before.

Similarly, let’s consider an additional example where we want to automate the subtraction of columns across different samples:

df %>% 
  group_by(Sample) %>% 
  mutate(diff = (V3 - V2)/V2)

In this code, we’re grouping by Sample and applying the same calculation as before.

By using these techniques, you can extend your data manipulation capabilities in R and efficiently process complex datasets.

Last modified on 2023-06-09