Filtering Rows After Pattern Matched with grepl in Certain Column

In this post, we will explore a common problem in data analysis: filtering rows after a pattern is matched in certain column. We will use the dplyr library in R to achieve this and provide examples using real-world datasets.

Introduction

When working with large datasets, it’s essential to efficiently filter out irrelevant data points that don’t match specific criteria. In this case, we’re interested in filtering rows where a URL contains a certain pattern, but also want to include the row that follows it in the filtered results.

Understanding grepl and Pattern Matching

To approach this problem, let’s first understand what grepl is and how it works. grepl is a function in R that performs a regular expression search for a pattern in a string. The general syntax is:

data <- grepl(pattern, data, ignore.case = T)

In our example, we’re using grepl('\\bgoogle.com/search\\b', desktop.url, ignore.case = T) to match URLs that contain the substring “google.com/search”.

Filtering Rows with dplyr Library

To filter rows after a pattern is matched in certain column, we’ll use the filter function from the dplyr library. Here’s an example using the built-in desktop dataset:

library(dplyr)

data_google <- desktop %>%
  filter(grepl('\\bgoogle.com/search\\b', url, ignore.case = T))

However, this approach only filters out rows that match the pattern and does not include the row that follows it.

Using lag to Filter Rows After Pattern Match

To achieve our goal, we need to find a way to identify the row that comes after the one we’re interested in. We can use the lag function from the dplyr library to do this.

Here’s an example using the built-in iris dataset:

library(dplyr)

vec1 <- which(grepl("set", iris$Species))
vec2 <- vec1+1
vec3 <- unique(c(vec1, vec2))

iris[vec3, ]

In this example, we first find the indices of rows where the species name starts with “set” using grepl. We then add 1 to these indices to get the row that follows each one. Finally, we use the resulting indices to subset the original dataset.

However, this approach assumes that the data is sorted chronologically, which may not always be the case.

Grouping and Filtering with lag

Another approach is to group the data by a specific column (in our example, it’s the cut level) and find where the color contains the pattern “E”. We can then use the lag function on a flag variable to get the row after it. Here’s an example using the built-in diamonds dataset:

library(dplyr)

diamonds2 <- diamonds %>%
  arrange(cut) %>% 
  group_by(cut) %>% 
  mutate(
    fl = ifelse(color %in% which(grepl("E", color)), 1, 0 ),
    fl2 = lag(fl)) %>% 
  filter(fl == 1 | fl2 == 1)

In this example, we first group the data by cut level and calculate a flag variable fl that indicates whether the color contains “E”. We then use the lag function on this flag variable to get the row after each one. Finally, we filter out rows where the flag variable is not equal to 1.

Using rownames to Filter Rows

Another approach is to use the rownames function to subset the data. Here’s an example using the built-in desktop dataset:

library(dplyr)

data_google <- desktop %>%
  filter(grepl('\\bgoogle.com/search\\b', url, ignore.case = T)) %>%
  arrange(rowname) %>% 
  head(2)

In this example, we first filter out rows where the URL contains “google.com/search”. We then sort the data by row names and take the first two rows using head.

Conclusion

Filtering rows after a pattern is matched in certain column can be achieved using various techniques such as grouping, using lag, or calculating flag variables. Each approach has its own strengths and weaknesses, and the choice of method depends on the specific requirements of the project.

In this post, we’ve explored several approaches to achieve our goal, including using grepl with filtering, lag with grouping, and rownames with sorting. We hope that this helps you in your data analysis journey!

Last modified on 2023-10-02