Assigning Multiple Text Flags to an Observation

Introduction

In data analysis and quality control (QA/QC), it is not uncommon to encounter observations that require verification or manual checking. Assigning multiple text flags to such observations can help facilitate this process. In this article, we will explore a more elegant way of achieving this using the tidyverse in R.

The Problem

The provided Stack Overflow question presents an inelegant solution for assigning multiple text flags to observations in a data frame. The current approach involves sequentially overwriting the Flag column with new information from each condition, which can lead to messy code and unnecessary cleaning of introduced NAs. We will explore a cleaner alternative using tidyverse functions.

The Solution

We will demonstrate a solution using the tidyverse package, which provides a set of modern, efficient, and consistent tools for data manipulation in R.

Step 1: Load the tidyverse Package

library(tidyverse)

Step 2: Create the Data Frame

Let’s create the same data frame as in the original question:

df <- structure(list(
  time = 1:20,
  temp = c(1, 2, 3, 4, 5,-60, 7, 8,
           9, 10, NA, 12, 13, 14, 15, 160, 17, 18, 19, 20)
),
class = "data.frame",
row.names = c(NA,-20L))

Step 3: Create the dtIdx Column

We will create a new column dtIdx that contains information about changes in the first derivative of the temperature data:

df %>% 
  mutate(
    dtIdx = ifelse(c(abs(diff(temp, lag = 1)) > 10, FALSE), "D10", NA)
  )

Step 4: Create the Flag Column

Next, we will create the Flag column using the case_when function:

df %>% 
  mutate(
    Flag = case_when(is.na(temp) ~ "MISSING",
                     temp > 120 ~ "High",
                     temp < -40 ~ "Low")
  )

Step 5: Unite the Columns

We will unite the dtIdx and Flag columns into a single column called Flag, ignoring NAs:

df %>% 
  unite(
    Flag,
    c(dtIdx, Flag),
    sep = "_",
    remove = TRUE,
    na.rm = TRUE
  )

The Result

After executing the above code, we will obtain the following output:

Conclusion

In this article, we demonstrated a more elegant way of assigning multiple text flags to observations in R using the tidyverse package. By leveraging functions like case_when and unite, we can create a cleaner and more efficient solution for data manipulation tasks.

Last modified on 2023-07-22