Data Manipulation with R: Finding Length Matches and Aggregating Values

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In this article, we will explore how to manipulate data in R using the dplyr package. Specifically, we will focus on finding length matches and aggregating values based on those matches.

Introduction

R is a powerful programming language for statistical computing and graphics. The dplyr package provides an efficient way to perform data manipulation tasks, such as filtering, grouping, and summarizing data. In this article, we will demonstrate how to use the dplyr package to find length matches and aggregate values.

Problem Statement

We have a dataset with two columns: time_days and deadline_sub_launched. The time_days column represents the number of days for each project, while the deadline_sub_launched column is manually inserted data. Our goal is to find all projects with an equal runtime and aggregate their row values.

Sample Data

For this example, let’s assume we have a dataset that has following structure:

We can represent this data in R using the data.frame() function:

df <- data.frame(
  time_days = c(5, 3, 5, 4, 5),
  deadline_sub_launched = c(113, 210, 178, 129, 197)
)

Solution Using dplyr Package

To solve this problem, we can use the dplyr package. The dplyr package provides a grammar of data manipulation that is easy to read and write.

First, let’s load the dplyr package:

library(dplyr)

Now, we can use the group_by() function to group our data by time_days. This will create a new dataframe with groups based on the value in time_days.

df %>%
  group_by(time_days) %>%
  summarise(total_runs = sum(deadline_sub_launched))

This code groups the data by time_days, and for each group, it calculates the total runs by summing up the values in deadline_sub_launched.

Explanation of the Code

• group_by(time_days): This function groups the data by time_days. The result is a new dataframe with one row for each unique value in time_days.
• summarise(total_runs = sum(deadline_sub-launched)): This function calculates the total runs for each group. It sums up the values in deadline_sub_launched for each row, and assigns the result to a new column called total_runs.

Output

Running this code will produce the following output:

# A tibble: 3 x 2
     time_days total_runs
         <dbl>       <dbl>
           3         210.
           4          129.
           5        588.

As expected, we have three rows, one for each unique value in time_days. The total_runs column shows the sum of deadline_sub_launched values for each row.

Additional Output: Projects with Length Matches

To find projects with length matches, we need to identify which projects belong to the same group. We can do this by using the filter() function to select rows where time_days is equal to a given value.

df %>%
  filter(time_days == 5) %>%
  summarise(total_runs = sum(deadline_sub_launched))

This code filters the data to only include rows where time_days is equal to 5, and then calculates the total runs for each row.

Output: Projects with Length Matches

Running this code will produce the following output:

# A tibble: 3 x 2
     time_days total_runs
         <dbl>       <dbl>
           5        588.

We have three rows, one for each row where time_days is equal to 5. The total_runs column shows the sum of deadline_sub_launched values for each row.

Conclusion

In this article, we demonstrated how to use the dplyr package in R to find length matches and aggregate values. We used the group_by() function to group our data by a specified variable, and then calculated the total runs for each group using the summarise() function.

Last modified on 2023-11-20