Merging Complex Data from Multiple Sources into a Single DataFrame

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As data analysis becomes increasingly complex, it’s not uncommon for multiple data sources to be involved in a single project. In this article, we’ll explore how to merge complex data from one dataframe into another, focusing on the nuances of handling unstructured text and separating orders with varying patterns.

Introduction

The challenge at hand is to combine two dataframes, DD1.df and DD2.df, which contain order information. The goal is to create a new column in DD1.df called “Delivered Count,” which calculates the number of orders that were delivered based on the second dataframe.

Background

To tackle this problem, we’ll employ several R packages and techniques:

stringr: for text processing and pattern matching
tidyr: for data manipulation and separation of rows
dplyr: for data manipulation and summarization

Setting Up the Data

Let’s set up our dataframes using sample data:

# Create sample dataframes
df1 <- data.frame(
  Country = c("France", "England", "India", "America", "England"),
  City = c("Paris", "London", "Mumbai", "Los Angeles", "London"),
  Order_Desc = c("No order was placed", "ORD-34212 was the order placed",
                 "ORD-12252 and ORD-78564 was the order placed",
                 "The orders placed before 2017 was ORD-56438, ORD-13198
                 and ORD-12258", "The order was ORD-34567"),
  stringsAsFactors = FALSE
)

df2 <- data.frame(
  OrderNo = c("ORD-34212", "ORD-12252", "ORD-78564", "ORD-56438",
              "ORD-13198", "ORD-12258", "ORD-34567"),
  Status = c("Delivered", "Not delivered", "Not delivered",
             "Delivered", "Not delivered", "Delivered", "Delivered"),
  stringsAsFactors = FALSE
)

Extracting Order Information from Unstructured Text

The Order_Desc column in df1.df contains unstructured text that might include the order number. We’ll use str_extract_all from the stringr package to extract all orders defined by the string “ORD” followed by 5 digits.

# Load necessary libraries and create a new dataframe
library(stringr)
library(dplyr)
library(tidyr)

df1g <- df1 %>%
  group_by(Country, City) %>%
  mutate(
    orders = paste(str_extract_all(Order_Desc, "ORD-\\d{5}", simplify = TRUE),
                   collapse = "|")
  ) %>%
  distinct(Country, City, orders) %>%
  separate_rows(orders, sep = "[|]") %>%
  left_join(df2, by = c("orders" = "OrderNo"))

Calculating Delivered Orders

After extracting the order information, we’ll calculate the total and delivered orders for each country and city combination.

# Calculate total and delivered orders
df1s <- df1g %>%
  group_by(Country, City) %>%
  summarise(
    total_orders = sum(!is.na(Status)),
    delivered_orders = sum(Status == "Delivered")
  )

Resulting DataFrame

The resulting dataframe df1s will contain the merged data with the new “Delivered Count” column.

# Print the final dataframe
print(df1s)

This example demonstrates how to merge complex data from one dataframe into another, handling unstructured text and separating orders with varying patterns. By employing techniques from R packages like stringr, tidyr, and dplyr, you can tackle more intricate data analysis tasks in your own projects.

Conclusion

Data analysis is constantly evolving, and dealing with complex datasets requires a combination of technical skills and domain knowledge. This article has covered the basics of merging data from multiple sources into a single dataframe, focusing on the nuances of handling unstructured text and separating orders with varying patterns.

If you have any questions or need further clarification on specific steps, please don’t hesitate to ask.

Last modified on 2024-07-27