Stat 585 - The Split-Apply-Combine Strategy

Prompt:

The plyr package has by now been replaced by other, even faster packages, but the idea of Split, apply, combine is as relevant as ever.

Read the paper The Split-Apply-Combine Strategy for Data Analysis by Hadley Wickham.

Write a blog post addressing the following questions:

The R code for the split-apply-combine paper is posted with the paper. Pick one of the examples demonstrating plyr functionality (such as dlply or ddply, …) and rewrite the example using functionality from the package dplyr. Make sure that your example works and the results are identical.

library(plyr)
baseball1 <- ddply(baseball, .(id), transform, 
  cyear = year - min(year) + 1)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:plyr':
#> 
#>     arrange, count, desc, failwith, id, mutate, rename, summarise,
#>     summarize
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
baseball2 <- baseball %>%
  group_by(id) %>%
  mutate(cyear = year - min(year) + 1) %>%
  arrange(id) %>%
  as.data.frame()

identical(baseball1, baseball2)
#> [1] TRUE

Which (base R) functions do you know that support the split-apply-combine strategy? In your opinion, are these sufficient? State why or why not?

With a dataframe df with a column col1, you can subset by value val1 of col1 with df[df$col1 == val1, ]. You could then do whatever procedure you wanted on that subsetted dataframe. There’s also the apply functions in base R (or a for loop), which would allow you to apply the same procedure to multiple values and combine the values. However, I don’t believe the base R functions are sufficient. The split-apply-combine strategy is so common in data analysis that it shouldn’t take so many (not super readable) lines of code to accomplish it. The functions in dplyr are much more intuitive to code with and understand, reducing the cognitive load.

The ChatGPT response to the prompt “Describe the split-apply-combine paradigm” is given below. Generally, the chatbot’s answers are decent. ChatGPT does have problems getting details right. Which details would you change in the response?

The “split-apply-combine” paradigm is a data analysis strategy used in statistics and data science to analyze grouped data. It consists of three steps:

Split: The data is first split into groups based on one or more variables of interest.

Apply: A specific operation or function is applied to each group of data, usually for the purpose of aggregating, summarizing, or transforming the data within each group.

Combine: The results of the operation applied to each group are then combined and returned as a single output.

The split-apply-combine paradigm is particularly useful in cases where the data cannot be analyzed as a whole and instead must be analyzed in smaller, more manageable chunks. This allows for more efficient data processing and improved scalability. The paradigm is commonly implemented using tools such as the “groupby” function in the R and Python programming languages, and the “dplyr” library in R.

One important thing that the answer doesn’t mention is that the operations on each group must be assumed to be independent. In addition, the last paragraph has a few misleading details. The data usually can be analyzed as a whole, but analyzing by group gives us different information than analyzing as a whole. In addition, the paradigm is not so much useful in runtime efficiency/scalability but rather in reducing the cognitive load for certain common data analysis tasks (which can increase efficiency in writing/understanding code). The last sentence would also be more accurate as: The paradigm is implemented using tools such as the group_by and groupby functions in the dplyr and pandas libraries in the R and Python programming languages, respectively.

You can write your answers directly the README.Rmd file. Make sure that the file knits (you will need to activate any packages your use in your code chunks with a call to library(xxx), where xxx is the name of the package, such as plyr ). Commit your changes and push to your repo; add any files in the README_files directory to your repository.