Subseting Data Frame Based on Multiple Criteria for Deletion of Rows

In this article, we’ll explore how to subset a data frame based on multiple criteria for the deletion of rows. We’ll use R’s dplyr package to achieve this.

Introduction

Data frames are an essential concept in R and are used extensively in data analysis and visualization. However, when working with large datasets, it can be challenging to filter out specific rows based on multiple conditions. In this article, we’ll show you how to subset a data frame using the dplyr package by creating new columns that help you filter on.

Problem Description

Consider the following data frame consisting of column names “id” and “x”, where each id is repeated four times:

df <- data.frame("id" = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4),
                 "x" = c(2, 2, 1, 1, 2, 3, 3, 3, 1, 2, 2, 3, 2, 2, 3, 3))

The question is about how to subset the data frame by the following criteria:

• Keep all entries of each id if its corresponding values in column “x” do not contain 3 or it has 3 as the last number.
• For a given id with multiple 3s in column “x”, keep all the numbers up to the first 3 and delete the remaining 3s.

Solution

Here’s one solution that uses dplyr to subset the data frame:

library(dplyr)

df <- data.frame("id" = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4),
                 "x" = c(2, 2, 1, 1, 2, 3, 3, 3, 1, 2, 2, 3, 2, 2, 3, 3))

df %>%
  group_by(id) %>%
  mutate(num_threes = sum(x == 3), # count number of 3s
         flag = ifelse(unique(num_threes) > 0, # if there is a 3
                        min(row_number()[x == 3]), # keep the row of the first 3
                        0)) %>%
  filter(num_threes == 0 | row_number() <= flag) %>% # keep ids with no 3s or up to first 3
  ungroup() %>% # remove grouping variable
  select(-num_threes, -flag) # remove helpful columns

This solution works by:

• Creating a new column num_threes that counts the number of 3s in each id.
• Creating another new column flag that indicates whether there is more than one 3 for each id (if so, it stores the row number of the first 3).
• Filtering out rows where num_threes > 0, which means the id has multiple 3s. For these ids, we keep only the rows up to the first 3 by using row_number() <= flag.
• Removing the helpful columns num_threes and flag.

The resulting data frame will have all the entries of each id if its corresponding values in column “x” do not contain 3 or it has 3 as the last number, and for a given id with multiple 3s in column “x”, keep all the numbers up to the first 3 and delete the remaining 3s.

Alternative Solution

Another approach is to use grepl function from the base R package:

df %>%
  group_by(id) %>%
  filter(!grepl("3$", x)) | 
  group_by(id) %>%
  filter(grepl("3$", x) & row_number() == min(row_number()[grepl("3$", x)]))

This solution works by:

• Keeping rows where “x” does not contain the digit 3 using !grepl("3$", x).
• For ids with multiple 3s in column “x”, keeping only the first occurrence by filtering for row_number() == min(row_number()[grepl("3$", x)]).

Both solutions achieve the same result but use different approaches.

Conclusion

Subsetting a data frame based on multiple criteria can be achieved using various methods, including creating new columns and using regular expressions. The approach you choose depends on the specific requirements of your problem and your personal preference. In this article, we showed two alternative solutions to subset a data frame using dplyr package.

Last modified on 2024-10-16