Joining Data with Weighted Averages and Multiple Weights in R

Introduction

In this article, we will explore how to join two datasets in R while calculating weighted averages based on different counts. The problem becomes more complex when there are multiple sets of columns that need to use different weights. We will cover the steps involved in solving this issue using popular R libraries such as dplyr and tidyr.

Prerequisites

Before we dive into the solution, let’s make sure you have the necessary libraries installed:

• dplyr
• tidyr

You can install these libraries by running the following command in your R console:

install.packages(c("dplyr", "tidyr"))

Problem Statement

We are given two datasets, RMS1 and RMS2, which contain the same columns but with different weights for certain columns. We want to join these datasets based on the id column and calculate the weighted average for each row.

RMS1:
id,freq1,sev1,count1,freq2,sev2,count2
111 0    2    50     1     2    25
222 1    3    75     2     4    50

RMS2:               
id,freq1,sev1,count1,freq2,sev2,count2
222 2    4    25     6     6    200
333 4    5    60     3     2    20

Joined:                         
id  freq1   sev1    freq2   sev2        
111 0       2       1       2       
222 1.25*   3.25*   5**     5.5**       
333 4       5       3       2

In this example, the * and ** values represent weighted averages based on count1 and count2, respectively.

Solution

To solve this problem, we will use the dplyr library to perform the necessary operations. Here’s a step-by-step guide:

Step 1: Load Libraries

library(dplyr)
library(tidyr)

# Create datasets
RMS1 <- read.table(text = "id freq1 sev1 count1 freq2 sev2 count2
111 0    2    50     1     2    25
222 1    3    75     2     4    50", header = TRUE)
RMS2 <- read.table(text = "id freq1 sev1 count1 freq2 sev2 count2
222 2    4    25     6     6    200
333 4    5    60     3     2    20", header = TRUE)

Step 2: Join Data

# Join dataframes
Joined <- bind_rows(RMS1, RMS2) %>% 
  group_by(id) %>% 
  summarise_at(vars(-count1), funs(weighted.mean(., count1))) %>% 
  as.data.frame()

However, this approach only works if all columns have the same weight. If you need to use different weights for different columns, you’ll need to modify the solution.

Step 3: Modify Solution

# Create a function to calculate weighted averages
calculate_weighted_averages <- function(x) {
  # Split data into numeric columns and non-numeric columns
  numeric_cols <- c("freq1", "sev1", "count1")
  non_numeric_cols <- setdiff(names(x), numeric_cols)
  
  # Calculate weighted averages for numeric columns
  x[numeric_cols] %>% 
    map_dbl(~ weighted.mean(., .x = ..col..))
}

# Apply function to data
Joined <- bind_rows(RMS1, RMS2) %>% 
  group_by(id) %>% 
  calculate_weighted_averages() %>% 
  ungroup() %>% 
  as.data.frame()

In this modified solution, we define a function calculate_weighted_averages that calculates the weighted average for each numeric column. We then apply this function to the data using the map_dbl function from the purrr package.

Conclusion

Joining data with weighted averages and multiple weights in R requires careful consideration of how to handle different weights for different columns. By using the dplyr library and defining a custom function to calculate weighted averages, we can achieve this goal.

Last modified on 2023-07-11