Introduction to R and Data Manipulation

R is a popular programming language for statistical computing and data visualization. It has a vast array of libraries and packages that make it an ideal choice for data analysis, machine learning, and data science tasks. In this blog post, we will explore one of the fundamental concepts in R: data manipulation.

Data manipulation involves changing the structure or format of existing data to extract insights or achieve specific goals. This can include filtering, sorting, grouping, joining, and merging datasets. In this case, we are interested in creating a new column that contains the names of columns with a value of 1 in a given dataset.

Problem Statement

The problem at hand is to create a new column called suspected diagnosis or susp_dx that contains the names of columns (diagnosis, susp_inflamm, susp_bacteria, and susp_virus) with a value of 1 in the dataset. The original code attempts to use the dplyr library but encounters issues with the starts_with function.

Solution Overview

We will break down the solution into several steps, using both base R and the dplyr library. We’ll explore different approaches and their trade-offs to achieve our goal.

Base R Approach

One way to solve this problem is by using a simple for loop in base R:

practice$quux <- ""
for (i in 2:5) {
  if (practice$susp_inflamm[i] == 1 | practice$susp_bacteria[i] == 1 |
      practice$susp_virus[i] == 1 | practice$susp_fungus[i] == 1) {
    quux <- paste(names(practice)[i], collapse = ", ")
    break
  }
}

However, this approach is cumbersome and prone to errors.

Dplyr Approach

We can also use the dplyr library to achieve our goal. Here’s an example code snippet that uses the mutate function:

library(dplyr)
practice |>
  mutate(quux = ifelse(where(is.double) & (starts_with("susp")) == 1), names(.), 0))

Unfortunately, this approach also has issues with the starts_with function.

Pivot Longest Approach

A better approach is to use the pivot_longer function from the tidyr package:

library(dplyr)
library(tidyr)

practice |>
  pivot_longer(-record_id) |>
  filter(value > 0) |>
  summarize(.cols = quux := fun(.), .name = "quux")

In this approach, we first create a new column called quux using the fun function defined earlier. Then, we use the summarize function to assign values to the quux column.

Data.Table Approach

Another way to solve this problem is by using the data.table package:

library(data.table)
practice[, quux := fun(.SD), .SDcols = patterns("^susp_")]

This approach uses a similar fun function as before, but in a more concise and efficient manner.

Comparison of Approaches

Conclusion

Data manipulation is a fundamental concept in R, and creating new columns with specific criteria is a common task. In this blog post, we explored several approaches to solve the problem of creating a new column that contains the names of columns with a value of 1. We compared base R, dplyr, pivot longest, and data.table approaches, highlighting their strengths and weaknesses. Ultimately, the choice of approach depends on personal preference, project requirements, and familiarity with the corresponding libraries.

Last modified on 2024-10-18