library(plyr)
baseball <- baseball
baseball1 <- ddply(baseball, .(id), transform, cyear = year - min(year) + 1)
head(baseball1, 10)
#> id year stint team lg g ab r h X2b X3b hr rbi sb cs bb so ibb
#> 1 aaronha01 1954 1 ML1 NL 122 468 58 131 27 6 13 69 2 2 28 39 NA
#> 2 aaronha01 1955 1 ML1 NL 153 602 105 189 37 9 27 106 3 1 49 61 5
#> 3 aaronha01 1956 1 ML1 NL 153 609 106 200 34 14 26 92 2 4 37 54 6
#> 4 aaronha01 1957 1 ML1 NL 151 615 118 198 27 6 44 132 1 1 57 58 15
#> 5 aaronha01 1958 1 ML1 NL 153 601 109 196 34 4 30 95 4 1 59 49 16
#> 6 aaronha01 1959 1 ML1 NL 154 629 116 223 46 7 39 123 8 0 51 54 17
#> 7 aaronha01 1960 1 ML1 NL 153 590 102 172 20 11 40 126 16 7 60 63 13
#> 8 aaronha01 1961 1 ML1 NL 155 603 115 197 39 10 34 120 21 9 56 64 20
#> 9 aaronha01 1962 1 ML1 NL 156 592 127 191 28 6 45 128 15 7 66 73 14
#> 10 aaronha01 1963 1 ML1 NL 161 631 121 201 29 4 44 130 31 5 78 94 18
#> hbp sh sf gidp cyear
#> 1 3 6 4 13 1
#> 2 3 7 4 20 2
#> 3 2 5 7 21 3
#> 4 0 0 3 13 4
#> 5 1 0 3 21 5
#> 6 4 0 9 19 6
#> 7 2 0 12 8 7
#> 8 2 1 9 16 8
#> 9 3 0 6 14 9
#> 10 0 0 5 11 10Prompt:
The plyr package has by now been replaced by other, even faster packages, but the idea of Split, apply, combine is as relevant as ever.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
plyrfunctionality (such asdlplyorddply, …) and rewrite the example using functionality from the packagedplyr. Make sure that your example works and the results are identical.
options(dplyr.width = Inf)
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
basketball2 <- baseball %>% group_by(id) %>% mutate(cyear = year-min(year)+1) %>% arrange(id)
head(basketball2, 10)
#> # A tibble: 10 × 23
#> # Groups: id [1]
#> id year stint team lg g ab r h X2b X3b hr
#> <chr> <int> <int> <chr> <chr> <int> <int> <int> <int> <int> <int> <int>
#> 1 aaronha01 1954 1 ML1 NL 122 468 58 131 27 6 13
#> 2 aaronha01 1955 1 ML1 NL 153 602 105 189 37 9 27
#> 3 aaronha01 1956 1 ML1 NL 153 609 106 200 34 14 26
#> 4 aaronha01 1957 1 ML1 NL 151 615 118 198 27 6 44
#> 5 aaronha01 1958 1 ML1 NL 153 601 109 196 34 4 30
#> 6 aaronha01 1959 1 ML1 NL 154 629 116 223 46 7 39
#> 7 aaronha01 1960 1 ML1 NL 153 590 102 172 20 11 40
#> 8 aaronha01 1961 1 ML1 NL 155 603 115 197 39 10 34
#> 9 aaronha01 1962 1 ML1 NL 156 592 127 191 28 6 45
#> 10 aaronha01 1963 1 ML1 NL 161 631 121 201 29 4 44
#> rbi sb cs bb so ibb hbp sh sf gidp cyear
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <dbl>
#> 1 69 2 2 28 39 NA 3 6 4 13 1
#> 2 106 3 1 49 61 5 3 7 4 20 2
#> 3 92 2 4 37 54 6 2 5 7 21 3
#> 4 132 1 1 57 58 15 0 0 3 13 4
#> 5 95 4 1 59 49 16 1 0 3 21 5
#> 6 123 8 0 51 54 17 4 0 9 19 6
#> 7 126 16 7 60 63 13 2 0 12 8 7
#> 8 120 21 9 56 64 20 2 1 9 16 8
#> 9 128 15 7 66 73 14 3 0 6 14 9
#> 10 130 31 5 78 94 18 0 0 5 11 10Which (base R) functions do you know that support the split-apply-combine strategy? In your opinion, are these sufficient? State why or why not?
The first function came into my mind isapply, because I can always write a function to fulfill the requirement. In terms of writing functions to complete tasks,applyis sufficient, but it’s time consuming and low efficient. It has to apply the function many times, store the result before writing it into the dataframe. Besides, users have to write function on their own, which also increases the time cost and risk of raising bugs.Operations in dplyr have different ways of completing tasks in terms of different objects (array, vector, list ect.). These nicely designed data structure save both time and space, which makes the split-apply-combine strategy efficiently implemented.
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.
I think chatGPD got most of the part right except groupby is a function in Python does the grouping work, group_by is the function in R does the same work.
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.