Introduction to Summing Rows Based on Exact Conditions in Multiple Columns

In this article, we’ll explore how to sum rows based on exact conditions in multiple columns and save edited rows in the original dataset. This problem involves identifying identical values across three columns (b, c, d) for adjacent rows and applying a specific operation.

The Problem Statement

Given a dataset with time information and various attributes such as ‘a’, ‘b’, ‘c’, ’d’ and an ‘id’ column, we need to:

1. Identify rows where the values in columns ‘b’, ‘c’, and ’d’ are identical.
2. Sum up the corresponding values of ‘b’, ‘c’, and ’d’ for these pairs of adjacent rows.
3. Update the first row of each pair by replacing its ‘b’, ‘c’, or ’d’ value with the summed result.
4. Delete the second row from each pair.

Using dplyr Functions Along with data.table::rleid

To solve this problem, we can use the dplyr package along with data.table::rleid. Here’s a step-by-step guide to achieve our goal:

1. Create a new column called ’temp_col’ that combines the values of columns ‘b’, ‘c’, and ’d’. This is done using the paste function, which concatenates these three columns into one string.

2. Use rleid from data.table to create groups based on identical ’temp_col’ values for adjacent rows. The group_by function then categorizes our data by these groups.

3. Within each group, we use the sum function to calculate the total value of columns ‘b’, ‘c’, and ’d’.

4. We select only the first row from each group using the slice(1L) function. This is because we want to update the original first row with our calculated sum.

5. Finally, we remove the temporary ’temp_col’ column and the grouping information using the ungroup and select(-temp_col, -group) functions.

Example Code

Here’s an example code snippet demonstrating how this problem can be solved:

library(dplyr)
library(data.table)

# Sample dataset
df <- structure(
  list(
    Time = c("2014/10/11", "2014/10/12", "2014/10/13", "2014/10/14", 
            "2014/10/15", "2014/10/16", "2014/10/17", "2014/10/18", 
            "2014/10/19"),
    a = c(1, 1, 2, 4, 4, 2, 2, 1, 3),
    b = c(40, 40, 9, 16, 1, 20, 20, 11, 31),
    c = c(20, 20, 10, 5, 6, 7, 7, 9, 20),
    d = c(10, 10, 9, 12, 5, 8, 8, 5, 23),
    id = 1:9
  ),
  class = "data.frame",
  row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9")
)

# Apply transformation using dplyr and data.table::rleid
df %>% 
  mutate(temp_col = paste(b, c, d, sep = "-")) %>% 
  group_by(group = data.table::rleid(temp_col)) %>% 
  mutate_at(vars(b, c, d), sum) %>% 
  slice(1L) %>% 
  ungroup %>% 
  select(-temp_col, -group)

Conclusion

This problem involves identifying and updating rows based on exact conditions across multiple columns. By using the dplyr package along with data.table::rleid, we can efficiently group, calculate sums, and apply updates to our data.

Last modified on 2025-01-29