Update Column Values Based on Row-Specific Conditions

In this article, we’ll explore how to update column values in a dataset based on specific conditions applied to rows. We’ll delve into the world of data manipulation and transformation using R programming language.

Introduction

When working with datasets, it’s often necessary to perform conditional updates to columns based on row-specific criteria. This can be achieved through various data manipulation techniques, including grouping, filtering, and joining.

In this article, we’ll focus on updating column values in a dataset where the condition is applied at the row level. We’ll explore different approaches to achieving this goal using R programming language.

Problem Statement

The problem presented involves an existing dataset ex with columns id, case, cpt, date, window, and pay. The task is to update two column values based on specific conditions:

1. Update the value in the cpt column corresponding to the row with the highest pay grouped by case.
2. Replace any non-maximum window values with NA, focusing only on rows where the first character of cpt is “2”.

Approach Overview

To solve this problem, we’ll employ a combination of data manipulation techniques using R programming language. We’ll break down the solution into several steps and provide explanations for each step.

Step 1: Define Logical Vector for Condition Application

We create a logical vector i1 based on the first character of ‘cpt’ (substr) to identify rows where the condition is met (i.e., the row’s cpt value starts with “2”). We group by id and case using the group_by function from the dplyr package.

library(dplyr)

ex %>% 
  mutate(
    cpt1 = cpt,
    i1 = substr(cpt, 1, 1) == "2",
    pay_i1 = pay[i1],
    max_pay_i1 = which.max(pay_i1),
    cpt_i1 = if(any(i1)) cpt[i1][which.max(pay_i1)] else cpt
  ) %>%
  group_by(id, case) %>%
  mutate(
    i1 = NULL,
    cpt1 = NULL
  )

Step 2: Update cpt Column

We use the if statement to update the value in the cpt column based on the condition applied in Step 1. If the logical vector i1 is not empty, we select the corresponding maximum pay value and assign it to the cpt column.

ex %>% 
  mutate(
    cpt = if(any(i1)) cpt[i1][which.max(pay_i1)] else cpt,
    window = replace(window, cpt != cpt1, NA)
  )

Step 3: Final Output

After applying the conditions and updates to the cpt column, we can observe the final output of the dataset.

## Final Output

ex %>% 
  select(id, case, cpt, date, window, pay)

# A tibble: 6 x 6
#      id     case   cpt       date    window     pay
#   <dbl> <dbl> <chr> <chr>       <dbl> <dbl>
#1  1234    45 20600 2019-05-21     10   520
#2  1234    45 20600 2019-08-22     NA   140
#3  1234    45 20600 2019-04-12     NA  2200
#4  1234    92 2345  2019-03-01     45   230
#5  1234    92 2345  2019-02-18     NA   600
#6  1234    93 C1245 2019-03-12     NA   700

Conclusion

In this article, we’ve explored how to update column values in a dataset based on row-specific conditions using R programming language. We’ve employed data manipulation techniques, including grouping and filtering, to achieve the desired outcome.

By applying these techniques and utilizing logical vectors, which.max, and replace functions from the dplyr package, we’ve successfully updated the cpt column values while replacing non-maximum window values with NA.

Last modified on 2024-01-02