Merging/Scanning in R using like operator

R is a powerful programming language for statistical computing and graphics, widely used in academia and industry. Its data structures, such as data frames, vectors, and matrices, provide a robust foundation for various applications, including data analysis, visualization, and machine learning. This article focuses on merging or scanning two data frames using the like operator.

Background

The problem at hand involves combining two data frames to produce a new one where each firm is linked to its corresponding year of being a winner. The input data frames df1 and df2 are presented in the question, showcasing their contents.

# df1 data frame
df1 <- data.frame(
  Year = c(1991, 1992, 1993, 1994, 1995, 1996, 1997),
  Winner = c("APPLE ", "apple inc.", "APPLE INC.; IBM CO.", "SONATA", 
             "FAMILY BROS", "family, apple, ibm","family co.")
)

# df2 data frame
df2 <- data.frame(
  Firm = c("APPLE ", "IBM", "Sonata Inc.","Family Bros. Co.")
)

The desired output Data3 is illustrated in Figure 1, with each firm matched to its corresponding year of being a winner.

Problem Statement

To create the desired Data3, it’s essential to identify the firms that were winners in different years. The provided solution employs several techniques, including data splitting, grouping, and linking, using R functions such as adist and hclust.

Splitting Data with Regular Expressions

The first step is to split the Winner column in df1 into individual firms using regular expressions.

# Split winner vector by commas or semicolons
sp <- strsplit(df1$Winner, ',|;')

This produces a list of vectors containing the individual firms for each year. The trimws() function removes leading and trailing whitespace from each firm name.

Determining Firm Groupings

Next, we calculate the maximum length of each vector in the list to identify unique firms. This step can be simplified using the lengths() function in combination with max(), as shown below:

# Calculate max length for each vector and create a data frame
sp <- t(sapply(sp, `length<-`, max(lengths(sp)))) |>
  as.data.frame() |>
  cbind(Year = df1$Year)

This step groups the firms by year.

Reshaping Data

We reshape the resulting data frame to produce a format that can be used for further analysis. The reshape() function is employed with the idvar argument set to 4 (the length of each vector) and direction='l' to link each firm to its corresponding year.

# Reshape data using reshape()
sp <- reshape(sp, 1:3, idvar=4, direction='l', sep='')

This reshaping step produces a new data frame where each row represents a unique combination of firm and year.

Adding Firm Labels

To make the output more interpretable, we use hclust() to perform hierarchical clustering on the distances between firms calculated using adist(). The resulting cluster labels are used to assign meaningful names to the firms. We apply these labels to our reshaped data frame.

# Perform hierarchical clustering for firm grouping
sp$Firm <- cutree(hclust(as.dist(adist(gsub('inc|co', '', tolower(sp$V))))), 4) |>
  factor(labels=c('Apple', 'Sonata Inc.', 'Family Bros. Co.', 'IBM'))

This step creates a Data3 data frame with each firm matched to its corresponding year of being a winner.

Selecting Specific Columns

Finally, we select only the Firm and Year columns from our reshaped data frame to produce the final output:

# Create desired output Data3
subset(sp[order(sp$Firm), ], select=c(Firm, Year))

This step yields the following result:

Conclusion

Merging or scanning data frames using the like operator in R requires a combination of data manipulation and grouping techniques, such as regular expressions, hierarchical clustering, and reshaping. By applying these steps to the provided input data frames df1 and df2, we are able to produce a new data frame Data3 that represents each firm matched to its corresponding year of being a winner.

Last modified on 2024-05-31