Tools for Analyzing R Code the Tidy Way
Lucy D’Agostino McGowan
Wake Forest UniversitySean Kross
UC San DiegoJeffrey Leek
Johns Hopkins Bloomberg School of Public Health2020-09-10
Abstract
With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode. These packages build on methods created for natural language processing; rather than allowing for the processing of natural language, we focus on R code as the substrate of interest. The matahari package facilitates the logging of everything that is typed in the R console or in an R script in a tidy data frame. The tidycode package contains tools to allow for analyzing R calls in a tidy manner. We demonstrate the utility of these packages as well as walk through two examples.Introduction
With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted (Goecks et al. 2010; Peng 2011; McNutt 2014; Miguel et al. 2014; Ioannidis et al. 2014; Richard 2014; Leek and Peng 2015; Nosek et al. 2015; Sidi and Harel 2018). In order to accurately replicate a result, the exact methods used for data analysis need to be recorded, including the specific analytic steps taken as well as the software utilized (Waltemath and Wolkenhauer 2016). Studies across multiple disciplines have examined the global set of possible data analyses that can be conducted on a specific data set (Silberzhan et al. 2018). While we are able to define this global set, very little is known about the actual variation that exists between researchers. For example, it is possible that the true range of data analysis choices is realistically a much more narrow set than the global sets that are presented. There is a breadth of excellent research and experiments examining how people read visual information (Majumder, Hofmann, and Cook 2013; Loy, Hofmann, and Cook 2017; Wickham, Cook, and Hofmann 2015; Buja et al. 2009; Loy, Follett, and Hofmann 2016), for example the Experiments on Visual Inference detailed here: (http://mamajumder.github.io/html/experiments.html), but not how they actually make analysis choices, specifically analysis coding choices. In addition to not knowing about the “data analysis choice” variability between researchers, we also do not know which portions of the data analysis pipeline result in the most variability in the ultimate research result. We seek to build tools to analyze these two aspects of data analysis:
The between researcher variability in data analysis choices
The aspects within the data analysis pipeline that contribute to the variability in results
Specifically, we have designed a framework to conduct such analyses and created two R packages that allow for the study of data analysis code conducted in R. In addition to answering these crucial questions for broad research fields, we see these tools having additional concrete use cases. These tools will facilitate data science and statistics pedagogy, allowing researchers and instructors to investigate how students are conducting data analyses in the classroom. Alternatively, a researcher could use these tools to examine how collaborators have conducted a data analysis. Finally, these tools could be used in a meta-manner to explore how current software and tools in R are being utilized.
Tidy principles
We specifically employ tidy principles in our proposed packages. Tidy refers to an implementation strategy propagated by Hadley Wickham and implemented by the Tidyverse team at RStudio (Wickham and Grolemund 2016) Here, by tidy we mean our packages adhere to the following principles:
Our functions follow the principles outlined in R packages (Wickham 2015) as well as the tidyverse style guide (Wickham 2019).
Our output data sets are tidy, as in:
- Each variable has its own column.
- Each observation has its own row.
- Each value has its own cell.
By implementing these tidy principles, and thus outputting tidy data frames, we allow for data manipulation and analysis to be conducted using a specific set of tools, such as those included in the tidyverse meta package (Wickham et al. 2019).
Ultimately, we create a mechanism to utilize methods created for natural language processing; here the substrate is code rather than natural language. We model our tools to emulate the tidytext package (Silge and Robinson 2016, 2017); instead of analyzing tokens of text, we are analyzing tokens of code.
We present two packages, matahari, a package for logging everything that is typed in the R console or in an R script, and tidycode, a package with tools to allow for analyzing R calls in a tidy manner. In this paper, we first explain how these packages work. We then demonstrate two examples, one that analyzes data collected from an online experiment, and one that analyzes “old” data via previously created R scripts.
Methods
We have created two R packages, matahari and tidycode. The former is a way to log R code, the latter allows the user to analyze R calls on the function-level in a tidy manner. Figure 1 is a flowchart of the process described in more detail below. This flowchart is adapted from Figure 2.1 in Text Mining with R: A Tidy Approach (Silge and Robinson 2017).
We demonstrate how to create these tidy data frames of R code and then emulate the data analysis workflow similar to that put forth in the tidy text literature.
Terminology
In this paper, we refer to R “expressions” or “calls” as well as R “functions” and “arguments”. An R call is a combination of an R function with arguments. For example, the following is an R call (Example 1).
library(tidycode)Example 1. R call, library
Another example of an R call is the following piped chain of functions from the dplyr package (Example 2).
starwars %>%
select(height, mass)Example 2. Piped R call
Specifically, we know something is a call in R if
is.call() is TRUE.
quote(starwars %>%
select(height, mass)) %>%
is.call()#> [1] TRUECalls in R are made up of a function or name of a function, and
arguments. For example, the call library(tidycode) from
Example 1 is comprised of the function library() and the
argument tidycode. Example 2 is a bit more complicated. The
piped code can be rewritten, as seen in Example 3.
`%>%`(starwars, select(height, mass))Example 3. Rewritten piped R call
From this example, it is easier to see that the function for this R
call is %>% with two arguments, starwars
and select(height, mass). Notice that one of these
arguments is an R call itself, select(height, mass).
matahari
matahari is a simple package for logging R code in a tidy manner. It can be installed from CRAN using the following code.
install.packages("matahari")There are three ways to use the matahari package:
Record R code as it is typed and output a tidy data frame of the contents
Input a character string of R code and output a tidy data frame of the contents
Input an R file containing R code and output a tidy data frame of the contents
In the following sections, we will split these into two categories, tidy logging from the R console (1) and tidy logging from an R script (2 and 3).
Tidy logging from the R console
In order to begin logging from the R console, the
dance_start() function is used. Logging is paused using
dance_stop() and the log can be viewed using
dance_tbl(). For example, the following code will result in
the subsequent tidy data frame.
library(matahari)
dance_start()
1 + 2
"here is some text"
sum(1:10)
dance_stop()
dance_tbl()#> # A tibble: 6 x 6
#> expr value path contents selection dt
#> <list> <list> <list> <list> <list> <dttm>
#> 1 <languag... <S3: sessionIn... <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12
#> 2 <languag... <lgl [1]> <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12
#> 3 <languag... <lgl [1]> <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12
#> 4 <chr [1]> <lgl [1]> <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12
#> 5 <languag... <lgl [1]> <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12
#> 6 <languag... <S3: sessionIn... <lgl [1... <lgl [1... <lgl [1]> 2018-09-11 22:22:12Example 4. Logging R code from the R console using matahari
The resulting tidy data frame consists of 6 columns:
expr, the R call that was run, value, the
value that was output, path, if the code was run within
RStudio, this will be the path to the file in focus,
contents, the file contents of the RStudio editor tab in
focus, selection, the text that is highlighted in the
RStudio editor tab in focus, and dt, the date and time the
expression was run. By default, value, path,
contents and selection will not be logged
unless the argument is set to TRUE in the
dance_start() function. For example, if the analyst wanted
the output data frame to include the values computed, they would input
dance_start(value = TRUE).
In this particular data frame, there are 6 rows. The first and final
rows report the R session information at the time when
dance_start() was initiated (row 1) and when
dance_stop() was run (row 6). The second row holds the R
call dance_start(), the first command run in the R console,
was run; the third row holds 1 + 2, the fourth holds
here is some text, and the fifth holds
sum(1:10).
dance_tbl()[["expr"]]#> [[1]]
#> sessionInfo()
#>
#> [[2]]
#> dance_start()
#>
#> [[3]]
#> 1 + 2
#>
#> [[4]]
#> [1] "here is some text"
#>
#> [[5]]
#> sum(1:10)
#>
#> [[6]]
#> sessionInfo()These functions work by saving an invisible data frame called
.dance that is referenced by dance_tbl(). Each
time dance_start() is subsequently run after
dance_stop(), new rows of data are added to this data
frame. This invisible data frame exists in a new environment created by
the matahari
package. We can remove this data frame by running
dance_remove().
This data frame can be manipulated using common R techniques. Below,
we rerun the same code as above, this time saving the values that are
computed in the R console by using the value = TRUE
parameter.
dance_start(value = TRUE)
1 + 2
"here is some text"
sum(1:10)
dance_stop()
tbl <- dance_tbl()As an example of the type of data wrangling that this tidy format allows for, using dplyr and purrr, we can manipulate this to only examine expressions that result in numeric values.
library(dplyr)
library(purrr)
t_numeric <- tbl %>%
mutate(
numeric_output = map_lgl(value, is.numeric)
) %>%
filter(numeric_output)
t_numeric#> # A tibble: 3 x 7
#> expr value path contents selection dt numeric_output
#> <list> <list> <list> <list> <list> <dttm> <lgl>
#> 1 <language> <int [1]> <lgl [1]> <lgl [1]> <lgl [1]> 2019-04-29 22:39:05 TRUE
#> 2 <language> <dbl [1]> <lgl [1]> <lgl [1]> <lgl [1]> 2019-04-29 22:39:05 TRUE
#> 3 <language> <int [1]> <lgl [1]> <lgl [1]> <lgl [1]> 2019-04-29 22:39:05 TRUE Here, three rows are output, since we have filtered to only calls with numeric output:
The
dance_start()call (this defaults to have a numeric value ofThe
1 + 2call, resulting in avalueof3The
sum(1:10), resulting in avalueof55
Tidy logging from an R script
In addition to allowing for the logging of everything typed in the R
console, the matahari
package also allows for the logging of pre-created R scripts. This can
be done using the dance_recital() function, which allows
for either a .R file or a character string of R calls as the input. For
example, if we have a code file called sample_code.R, we
can run dance_recital("sample_code.R") to create a tidy
data frame. Alternatively, we can enter code directly as a string of
text, such as dance_recital("1 + 2") to create the tidy
data frame. Below illustrates this functionality.
code_file <- system.file("test", "sample_code.R", package = "matahari")
dance_recital(code_file)#> # A tibble: 7 x 6
#> expr value error output warnings messages
#> <list> <list> <list> <list> <list> <list>
#> 1 <language> <dbl [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 2 <chr [1]> <chr [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 3 <language> <dbl [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 4 <language> <NULL> <S3: simpleError> <NULL> <NULL> <NULL>
#> 5 <language> <chr [1]> <NULL> <chr [1]> <chr [1]> <chr [0]>
#> 6 <language> <NULL> <NULL> <chr [1]> <chr [0]> <chr [1]>
#> 7 <language> <NULL> <NULL> <chr [1]> <chr [0]> <chr [0]>Example 5. R call, Logging code from a .R file using matahari
code_string <- '
4 + 4
"wow!"
mean(1:10)
stop("Error!")
warning("Warning!")
message("Hello?")
cat("Welcome!")
'
dance_recital(code_string)#> # A tibble: 7 x 6
#> expr value error output warnings messages
#> <list> <list> <list> <list> <list> <list>
#> 1 <language> <dbl [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 2 <chr [1]> <chr [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 3 <language> <dbl [1]> <NULL> <chr [1]> <chr [0]> <chr [0]>
#> 4 <language> <NULL> <S3: simpleError> <NULL> <NULL> <NULL>
#> 5 <language> <chr [1]> <NULL> <chr [1]> <chr [1]> <chr [0]>
#> 6 <language> <NULL> <NULL> <chr [1]> <chr [0]> <chr [1]>
#> 7 <language> <NULL> <NULL> <chr [1]> <chr [0]> <chr [0]>Example 6. Logging code from a character string using matahari
The resulting tidy data frame from dance_recital(), as
seen in Examples 5 and 6, is different from that of
dance_tbl(). This data frame has 6 columns. The first is
the same as the dance_tbl(), expr, the R calls
in the .R script or string of code. The subsequent columns are,
value, the computed result of the R call,
error, which contains the resulting error object from a
poorly formed call, output, the printed output from a call,
warnings, the contents of any warnings that would be
displayed in the console, and messages, the contents of any
generated diagnostic messages. Now that we have a tidy data frame with R
calls obtained either from the R console or from a .R script, we can
analyze them using the tidycode
package.
The development version of the matahari package can be found on GitHub at https://github.com/jhudsl/matahari. Users can submit feature requests, issues, and bug reports here.
tidycode
The goal of tidycode is to allow users to analyze R scripts, calls, and functions in a tidy way. There are two main tasks that can be achieved with this package:
We can “tokenize” R calls
We can classify the functions run into one of nine potential data analysis categories: “Setup”, “Exploratory”, “Data Cleaning”, “Modeling”, “Evaluation”,“Visualization”, “Communication”, “Import”, or “Export”.
The tidycode package can be installed from CRAN in the following manner.
install.packages("tidycode")library(tidycode)We can first create a tidy data frame using the matahari
package. Alternatively, we can use a function in the tidycode
package that wraps the dance_recital() function called
read_rfiles(). This function allows you to read in multiple
.R files or links to .R files. There are a few example files included in
the tidycode
package. The paths to these files can be accessed via the
tidycode_example() function. For example, running the
following code will give the file path for the
example_analysis.R file.
tidycode_example("example_analysis.R")#> [1] "/Library/Frameworks/R.framework/Versions/3.5/Resources/library/tidycode/extdata/example_analysis.R"Running the function without any file specified will supply a vector of all available file names.
tidycode_example()#> [1] "example_analysis.R" "example_plot.R"We can use these example files in the read_rfiles()
function.
df <- read_rfiles(tidycode_example(c("example_analysis.R", "example_plot.R")))
df#> # A tibble: 9 x 3
#> file expr line
#> <chr> <list> <int>
#> 1 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 1
#> 2 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 2
#> 3 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 3
#> 4 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 4
#> 5 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 5
#> 6 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 6
#> 7 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 7
#> 8 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 1
#> 9 /Library/Frameworks/R.framework/Versions/3.5/Resources/li~ <langua~ 2This will give a tidy data frame with three columns:
file, the path to the file, expr the R call,
and line the line the call was made in the original .R
file.
We can then use the unnest_calls() function to create a
data frame of the calls, splitting each into the individual functions
and arguments. We liken this to the tidytext
unnest_tokens() function. This function has two parameters,
.data, the data frame that contains the R calls, and
input the name of the column that contains the R calls. In
this case, the data frame is m and the input column is
expr.
u <- unnest_calls(df, expr)
u#> # A tibble: 35 x 4
#> file line func args
#> <chr> <int> <chr> <list>
#> 1 /Library/Frameworks/R.framework/Versions/3.5/R~ 1 libra~ <list [1]>
#> 2 /Library/Frameworks/R.framework/Versions/3.5/R~ 2 libra~ <list [1]>
#> 3 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 <- <list [2]>
#> 4 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 %>% <list [2]>
#> 5 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 %>% <list [2]>
#> 6 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 mutate <named lis~
#> 7 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 / <list [2]>
#> 8 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ( <list [1]>
#> 9 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ^ <list [2]>
#> 10 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ( <list [1]>
#> # ... with 25 more rowsThis results is a tidy data frame with two additional columns:
func the name of the function called and args
the arguments of the function called. Because this function takes a data
frame as the first argument, it works nicely with the tidyverse data
manipulation packages. For example, we could get the same data frame as
above by using the following code.
df %>%
unnest_calls(expr)#> # A tibble: 35 x 4
#> file line func args
#> <chr> <int> <chr> <list>
#> 1 /Library/Frameworks/R.framework/Versions/3.5/R~ 1 libra~ <list [1]>
#> 2 /Library/Frameworks/R.framework/Versions/3.5/R~ 2 libra~ <list [1]>
#> 3 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 <- <list [2]>
#> 4 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 %>% <list [2]>
#> 5 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 %>% <list [2]>
#> 6 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 mutate <named lis~
#> 7 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 / <list [2]>
#> 8 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ( <list [1]>
#> 9 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ^ <list [2]>
#> 10 /Library/Frameworks/R.framework/Versions/3.5/R~ 3 ( <list [1]>
#> # ... with 25 more rowsWe can further manipulate this, for example we could select just the
func and args columns using dplyr’s
select() function.
df %>%
unnest_calls(expr) %>%
select(func, args)#> # A tibble: 35 x 2
#> func args
#> <chr> <list>
#> 1 library <list [1]>
#> 2 library <list [1]>
#> 3 <- <list [2]>
#> 4 %>% <list [2]>
#> 5 %>% <list [2]>
#> 6 mutate <named list [1]>
#> 7 / <list [2]>
#> 8 ( <list [1]>
#> 9 ^ <list [2]>
#> 10 ( <list [1]>
#> # ... with 25 more rowsThe get_classifications() function calls a
classification data frame that we curated that classifies the individual
functions into one of nine categories: setup, exploratory, data
cleaning, modeling, evaluation, visualization, communication, import, or
export. This can also be merged into the data frame. For this
classification analysis, we are using an inner_join(),
keeping only the functions that are classified, similar to the workflow
for a sentiment analysis in tidytext (Silge and Robinson 2017). If you did not want
to remove unclassified functions from your dataframe, the
left_join() function would be appropriate.
u %>%
inner_join(get_classifications()) %>%
select(func, classification, lexicon, score)#> # A tibble: 322 x 4
#> func classification lexicon score
#> <chr> <chr> <chr> <dbl>
#> 1 library setup crowdsource 0.687
#> 2 library import crowdsource 0.213
#> 3 library visualization crowdsource 0.0339
#> 4 library data cleaning crowdsource 0.0278
#> 5 library modeling crowdsource 0.0134
#> 6 library exploratory crowdsource 0.0128
#> 7 library communication crowdsource 0.00835
#> 8 library evaluation crowdsource 0.00278
#> 9 library export crowdsource 0.00111
#> 10 library setup leeklab 0.994
#> # ... with 312 more rowsThere are two lexicons for classification, crowdsource
and leeklab. The former was created by volunteers who
classified R code using the classify shiny
application. The latter was curated by Jeff Leek’s Lab. To select a particular
lexicon, you can specify the lexicon parameter. For
example, the following code will merge in the crowdsource
lexicon only.
u %>%
inner_join(get_classifications("crowdsource")) %>%
select(func, classification, score)#> # A tibble: 271 x 3
#> func classification score
#> <chr> <chr> <dbl>
#> 1 library setup 0.687
#> 2 library import 0.213
#> 3 library visualization 0.0339
#> 4 library data cleaning 0.0278
#> 5 library modeling 0.0134
#> 6 library exploratory 0.0128
#> 7 library communication 0.00835
#> 8 library evaluation 0.00278
#> 9 library export 0.00111
#> 10 library setup 0.687
#> # ... with 261 more rowsIt is possible for a function to belong to multiple classes. This
will result in multiple lines (and multiple classifications) for a given
function. By default, these multiple classifications are included along
with the prevalence of each, indicated by the score column.
To merge in only the most prevalent classification, set the
include_duplicates option to FALSE.
u %>%
inner_join(get_classifications("crowdsource", include_duplicates = FALSE)) %>%
select(func, classification)#> # A tibble: 33 x 2
#> func classification
#> <chr> <chr>
#> 1 library setup
#> 2 library setup
#> 3 <- data cleaning
#> 4 %>% data cleaning
#> 5 %>% data cleaning
#> 6 mutate data cleaning
#> 7 / data cleaning
#> 8 ( data cleaning
#> 9 ^ modeling
#> 10 ( data cleaning
#> # ... with 23 more rowsIn text analysis, there is the concept of “stopwords”. These are
often small common filler words you want to remove before completing an
analysis, such as “a” or “the”. In a tidy code analysis, we can
use a similar concept to remove some functions. For example we may want
to remove the assignment operator, <-, before completing
an analysis. We have compiled a list of common stop functions in the
get_stopfuncs() function to anti join from the data
frame.
u %>%
inner_join(get_classifications("crowdsource", include_duplicates = FALSE)) %>%
anti_join(get_stopfuncs()) %>%
select(func, classification)#> # A tibble: 15 x 2
#> func classification
#> <chr> <chr>
#> 1 library setup
#> 2 library setup
#> 3 mutate data cleaning
#> 4 select data cleaning
#> 5 options setup
#> 6 summary exploratory
#> 7 plot visualization
#> 8 library setup
#> 9 select data cleaning
#> 10 filter data cleaning
#> 11 is.na data cleaning
#> 12 is.na data cleaning
#> 13 ggplot visualization
#> 14 aes visualization
#> 15 geom_point visualizationThe development version of the tidycode package can be found on GitHub at https://github.com/jhudsl/tidycode. Users can submit feature requests, issues, and bug reports here.
Examples
Online experiment: P-hack-athon
This first example demonstrates how to use the matahari
and tidycode
packages to analyze data from a prospective study, using the “recording”
capabilities of the matahari
package to capture the code as participants run it. Recently, we
launched a “p-hack-athon” where we encouraged users to analyze a dataset
with the goal of producing the smallest p-value (IRB # IRB00008885, Not
Human Subjects Research Classification, Johns Hopkins Bloomberg School
of Public Health IRB). We captured the code the participants ran using
the dance_start() and dance_stop() functions
from the matahari
package. This resulted in a tidy data frame of R calls for each
participant. We use the tidycode
package to analyze these matahari data frames.
Setup
library(tidyverse)
library(tidycode)
## load the dataset, called df
load("data/df_phackathon.Rda")The data from the “p-hack-a-thon” is saved as a data frame called
df. This includes data from 29 participants. We have bound
the expr column from the matahari
data frame for each participant. Using the unnest_calls()
function, we unnest each of these R calls into a function and it’s
arguments.
tbl <- df %>%
unnest_calls(expr)We can then remove the “stop functions” by doing an anti join with
the get_stopfuncs() function and merge in the crowd-sourced
classifications with the get_classifications()
function.
classification_tbl <- tbl %>%
anti_join(get_stopfuncs()) %>%
inner_join(get_classifications("crowdsource", include_duplicates = FALSE))Classifications
We can use common data manipulation functions from dplyr. For example, on average, “data cleaning” functions made up 36.4% of the functions run by participants (Table 1).
classification_tbl %>%
group_by(id, classification) %>%
summarise(n = n()) %>%
mutate(pct = n / sum(n)) %>%
group_by(classification) %>%
summarise(`Average percent` = mean(pct) * 100) %>%
arrange(-`Average percent`)| classification | Average percent |
|---|---|
| data cleaning | 36.40 |
| visualization | 23.17 |
| exploratory | 21.32 |
| setup | 18.87 |
| modeling | 17.69 |
| mport | 8.58 |
| communication | 5.14 |
| evaluation | 3.62 |
| export | 0.82 |
We can also examine the most common functions in each classification.
func_counts <- classification_tbl %>%
count(func, classification, sort = TRUE) %>%
ungroup()
func_counts#> # A tibble: 152 x 3
#> func classification n
#> <chr> <chr> <int>
#> 1 summary exploratory 361
#> 2 lm modeling 277
#> 3 factor data cleaning 141
#> 4 select data cleaning 138
#> 5 library setup 128
#> 6 as.factor data cleaning 116
#> 7 filter data cleaning 107
#> 8 aes visualization 89
#> 9 ggplot visualization 82
#> 10 lmer modeling 80
#> # ... with 142 more rowsfunc_counts %>%
filter(classification %in% c("data cleaning", "exploratory", "modeling", "visualization")) %>%
group_by(classification) %>%
top_n(5) %>%
ungroup() %>%
mutate(func = reorder(func, n)) %>%
ggplot(aes(func, n, fill = classification)) +
theme_bw() +
geom_col(show.legend = FALSE) +
facet_wrap(~classification, scales = "free_y") +
scale_x_discrete(element_blank()) +
scale_y_continuous("Number of function calls in each classification") +
coord_flip()
We could then examine a word cloud of the functions used, colored by the classification. We can do this using the wordcloud library.
library(wordcloud)
classification_tbl %>%
count(func, classification) %>%
with(
wordcloud(func, n,
colors = brewer.pal(9, "Set1")[factor(.$classification)],
random.order = FALSE,
ordered.colors = TRUE
)
)
Additionally, we could examine the variability in the types of functions used between groups. For example, we asked participants whether they perform analyses as part of their job. 82.76% of participants (n = 24) answered “Yes”.
classification_tbl %>%
group_by(id, analysis_job, classification) %>%
summarise(n = n()) %>%
mutate(pct = n / sum(n)) %>%
group_by(analysis_job, classification) %>%
summarise(n = n()) %>%
mutate(avg_pct = n / sum(n)) %>%
ggplot(aes(x = analysis_job, y = avg_pct, fill = classification)) +
geom_bar(stat = "identity") +
scale_y_continuous("Average percent", labels = scales::percent) +
scale_x_discrete("Participant conducts analyses as part of their job")
Figure 4 demonstrates the variability in the types of functions users ran, split by whether they conduct analyses as part of their jobs. It appears that users who conduct analyses as part of their jobs ran a larger percentage of functions classified as “modeling”, “exploratory”, and “communication”, whereas those who do not ran a larger percentage of “setup” functions. Of note, among those who do not conduct analyses as part of their job, there were 0 functions used that classify as “communication”. Had this experiment been run on a larger scale, we could potentially draw inference on the differences between these two groups and how they choose to code.
Static Analysis
This second example demonstrates how to use the matahari
and tidycode
packages to analyze data from a retrospective study, or static R
scripts. Here, we use the read_rfiles() function from the
tidycode
package. This wraps the dance_recital() matahari
function and allows for multiple file paths or URLs to be read,
resulting in a tidy data frame. As an example, we are going to scrape
all of the .R files from two of the most widely used data manipulation
packages, the data.table
package (Dowle and Srinivasan 2019) and
the dplyr
package. We are going to use the gh package
(Bryan and Wickham 2017) to scrape these
files from GitHub.
Setup
We access the files via GitHub using the gh() function
from the gh
package. This gives a list of download URLs that can be passed to the
read_rfiles() function from the tidycode
package.
library(tidyverse)
library(gh)
library(tidycode)
dplyr_code <- gh("/repos/tidyverse/dplyr/contents/R") %>%
purrr::map("download_url") %>%
read_rfiles()
datatable_code <- gh("/repos/Rdatatable/data.table/contents/R") %>%
purrr::map("download_url") %>%
read_rfiles()Data Cleaning
We can combine these two tidy data frames. We will do some small data
manipulation, removing R calls that were either NULL or
character. For example, in the dplyr
package some .R files just reference data frames as a character
string.
pkg_data <- bind_rows(
list(
dplyr = dplyr_code,
datatable = datatable_code
),
.id = "pkg"
) %>%
filter(
!map_lgl(expr, is.null),
!map_lgl(expr, is.character)
)Analyze R functions
Now we can use the tidycode
unnest_calls() function to create a tidy data frame of the
individual functions along with the arguments used to create both
packages. Notice here we are not performing an anti join on “stop
functions”. For this analysis, we are interested in examining some key
differences in the commonly used functions contained the two packages.
Common operators may actually be of interest, so we do not want to drop
them from the data frame. We can count the functions by package.
func_counts <- pkg_data %>%
unnest_calls(expr) %>%
count(pkg, func, sort = TRUE)
func_counts#> # A tibble: 1,163 x 3
#> pkg func n
#> <chr> <chr> <int>
#> 1 datatable = 1640
#> 2 dplyr <- 1634
#> 3 datatable if 1590
#> 4 datatable { 1172
#> 5 dplyr { 1047
#> 6 dplyr function 724
#> 7 datatable ! 616
#> 8 datatable <- 579
#> 9 datatable [ 564
#> 10 datatable length 557
#> # ... with 1,153 more rowsUsing this data frame, we can visualize which functions are most commonly called in each package.
top_funcs <- func_counts %>%
group_by(pkg) %>%
top_n(10) %>%
ungroup() %>%
arrange(pkg, n) %>%
mutate(i = row_number())
ggplot(top_funcs, aes(i, n, fill = pkg)) +
theme_bw() +
geom_col(show.legend = FALSE) +
facet_wrap(~pkg, scales = "free") +
scale_x_continuous(
element_blank(),
breaks = top_funcs$i,
labels = top_funcs$func,
expand = c(0, 0)
) +
coord_flip()
We can glean a few interesting details from Figure 5. First, the data.table
authors sometimes use the = as an assignment operator,
resulting in this being the most frequent function used. The dplyr
authors always use <- for assignment, therefore this is
the most frequent function seen in this package (Wickham 2019). Additionally, the dplyr
authors often create modular code as a combination of small functions to
complete specific tasks. This may explain why function is
the third most frequent R call in this package, and less prevalent in
the data.table
package. This just serves as a glimpse of what can be accomplished with
these tools.
Discussion
We have designed a framework to analyze the data analysis pipeline and created two R packages that allow for the study of data analysis code conducted in R. We present two packages, matahari, a package for logging everything that is typed in the R console or in an R script, and tidycode, a package with tools to allow for analyzing R calls in a tidy manner. These tools can be applied both to prospective studies, where a researcher can intentionally record code typed by participants, and retrospectively, where the researcher can retrospectively analyze code. We believe that these tools will help shape the next phase of reproducibility and replicability, allowing the analysis of code to inform data science pedagogy, examine how collaborates conduct data analyses, and explore how current software tools are being utilized.
Acknowledgements
We would like to extend a special thank you to the members of the Leek Lab at Johns Hopkins Bloomberg School of Public Health as well as volunteers who used the “classify” shiny application for helping classify R functions.