Skip to content

Commit

Permalink
feat: Added tidyverse page under software_development/languages/R (#87
Browse files Browse the repository at this point in the history 
)

* feat: Add first draft page on tidyverse

* chore: minor edits and added line breaks

* feat: added a TL;DR to the top of the page

* feat: added example, comparison vs. base R, and more resource links

* refactor: formatted quote to use quote admonition

* fix: revised some information for accuracy and detail

* feat: added skeleton page for ggplot2

* fix: delete duplicate tidyverse/index.md?

* fix: further revisions of content

* refactor: cut down on content a bit

* Update docs/software_development/Languages/R/Tidyverse/index.md

Co-authored-by: Jermiah Joseph <[email protected]>

---------

Co-authored-by: Jermiah Joseph <[email protected]>
  • Loading branch information
@mtran-code @jjjermiah
mtran-code and jjjermiah authored Nov 27, 2024
1 parent 92607f3 commit 1a9c2a1
Show file tree
Hide file tree
Showing 2 changed files with 200 additions and 0 deletions.
10 changes: 10 additions & 0 deletions docs/software_development/Languages/R/Tidyverse/ggplot2.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
# Plotting with `ggplot2`

!!! warning "Page Under Construction"

## See Also

- The [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/)
Textbook - A great resource to understand the underlying framework that
`ggplot2` follows. Authored by Hadley Wickham, Danielle Navarro, and Thomas
Lin Pedersen.
190 changes: 190 additions & 0 deletions docs/software_development/Languages/R/Tidyverse/index.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,190 @@
# Tidyverse

??? abstract "TL;DR"

The [Tidyverse](https://tidyverse.tidyverse.org/) is a suite of R packages
that mesh together with a goal of improving common data science pipeline
steps, namely data import, tidying, manipulation, visualisation, and
programming.

Of the eight core packages, some notable ones include:

* `readr`, for data import and export
* `dplyr`, for data manipulation
* `ggplot2`, for data visualization

The packages also emphasize the use of the pipe, `|>` or `%>%`. When you
pipe an object forward, it is treated as the first parameter in the
next function. For example, the two lines below are equivalent to one
another:

```
colnames(read.csv(filename, header = TRUE))
```
```
filename |> read.csv(header = TRUE) |> colnames()
```

## What is the Tidyverse?

!!! quote "From the [Tidyverse website](https://www.tidyverse.org/):"

"The Tidyverse is an opinionated collection of R packages designed for data
science. All packages share an underlying design philosophy, grammar, and data
structures."

The origins of the Tidyverse began from a paper written by Hadley Wickham in
2014, titled "Tidy Data", in which he describes the aforementioned
design philosophy. (For those interested, you can read the paper here:
[10.18637/jss.v059.i10](https://doi.org/10.18637/jss.v059.i10).) More recent
documentation of the underlying principles guiding Tidyverse packages can be
found in [the tidy tools manifesto](https://tidyverse.tidyverse.org/articles/manifesto.html)
as well as the [Tidy design principles book](https://design.tidyverse.org/).

For an introduction on the Tidyverse, take a look at their [package page](https://tidyverse.tidyverse.org/).

You can see a list of the eight core packages on their site
[here](https://www.tidyverse.org/packages/), and further details within their
respective documentation pages. For a quick visual overview of a specific
package, you can also find its respective cheatsheet on
[this page](https://posit.co/resources/cheatsheets/).

## Why use the Tidyverse?

The Tidyverse is an _opinionated_ collection of packages, meaning that while the
packages all cohesively follow the same design philosophy, they may not work
well with how you prefer to program or for your specific use case. You may
choose to use only certain packages from the Tidyverse, or you may choose to
only use base R and other alternatives.

Some advantages of using the Tidyverse are:

- **Consistency**: All packages in the Tidyverse follow the same design
philosophy, making it easier to learn and use multiple packages. They also
mesh well with one another, making it easier to use multiple packages
together.
- **Piping**: Most of the Tidyverse is designed to be used together with pipes.
The `|>` operator from base R >=4.1 works well for this, or you can use the
`%>%` pipe from the `magrittr` package for more advanced piping options.
- **Readability**: Often Tidyverse code is more human-readable than more complex
R alternatives, especially when using long pipe chains.

Some disadvantages of using the Tidyverse are:

- **Learning curve**: The Tidyverse has a learning curve, especially if you are
new to R or programming in general. If switching over from base R, there
might also be a learning curve to relearn how to do things you are already
familiar with because of how differently things are designed.
- **Stability**: The Tidyverse is constantly evolving, and packages may be
updated or deprecated. While base R tries to emphasize stability across
updates, the Tidyverse packages are actively developed, and updates may
introduce changes that improve functionality but could impact existing code.
- **Compatibility**: The Tidyverse is not the only way to do things in R, and as
well, there are countless other packages that may not play nicely with
Tidyverse-oriented data structures or tibbles. In these cases you may have
to convert back and forth between tibbles and data frames, which can be
cumbersome.

## Introductory Example

Say we have a CSV file containing a table, and we want to:

1. Read the file into R;
2. Perform some manipulations on the data; and
3. Write out the new table to a different CSV file.

First, let's define some common paths for input and output:

```r
infile <- "rawdata/my_df.csv"
outfile <- "procdata/my_df.csv"
```

Using these paths, let's compare the code for what this whole process might look
like.

=== "Base R"

```r
# Read the CSV file
my_df <- read.csv(infile)

# Filter to only rows where value > 10
my_df_filtered <- my_df[my_df$value > 10, ]

# Select only specific columns
my_df_selected <- my_df_filtered[, c("id", "value")]

# Write the output out to a new CSV file
write.csv(my_df_selected, outfile, row.names = FALSE)
```

* Note: You could also overwrite the `my_df` variable each time you filter
rows and select columns instead of creating new intermediary data frames,
especially if you have no need to keep the original or intermediary data.

=== "Tidyverse"

```r
my_df <- infile |> # Start with the path, piping forward into the "pipeline"
read_csv() |> # From `readr`, reads a csv at the path into a tibble
filter(value > 10) |> # From `dplyr`, filters to only rows that match condition
select(id, value) |> # From `dplyr`, selects only specified columns by name
write_csv(outfile) # From `readr`, writes the tibble to a csv file path
```

* Note: While technically not required, setting `my_df` to the pipeline will
save the final pipeline result to that variable name. The function
`write_csv()` invisibly returns the same tibble piped into it, thus it is
saved as `my_df`.

Arguably, the human-readability of the code became a bit better, even if you're
not necessarily familiar with the Tidyverse. Note that the `read.csv()` function
is different from the `read_csv()` function, as well as `write.csv()` and
`write_csv()`. Often base R uses dot notation for their variables and functions,
e.g. `as.data.frame()`, whilst Tidyverse convention is to use snakecase, e.g.
`as_tibble()`.

## Tidyverse vs. Base R

It's important to differentiate the R Tidyverse packages and design from the
base R language itself. The R programming language is developed and maintained
by the R Core team and [R Foundation](https://www.r-project.org/foundation/),
whilst the Tidyverse (as well as a multitude of other R programming tools and
packages) are developed and maintained by [Posit, PBC](https://posit.co/)
(formerly RStudio, Inc) on top of the R language.

If you're familiar with R, you're almost certainly familiar with the RStudio
application, the IDE developed by Posit. Hadley Wickham, the author of the Tidy
Data paper, also happens to be the Chief Scientist at Posit. If you're a fan of
the Tidyverse, also check out Posit's other useful tools like [Positron](https://positron.posit.co/),
[Quarto](https://quarto.org/), [Shiny](https://shiny.posit.co/), and [pak](https://pak.r-lib.org/).

## Additional Resources

### Articles

- [Welcome to the Tidyverse](https://tidyverse.tidyverse.org/articles/paper.html)
&mdash; A brief paper introducing the Tidyverse, from the package vignette.
- [Tidyverse](https://en.wikipedia.org/wiki/tidyverse) Wikipedia page.
- [Writing performant code with tidy tools](https://www.tidyverse.org/blog/2023/04/performant-packages/)
&mdash; An interesting read on analysing code performance and how to
consider alternatives; especially relevant to package development.

### Learning

- [R for Data Science](https://r4ds.hadley.nz/) Textbook &mdash; The go-to
textbook for learning R and the Tidyverse, authored by Hadley Wickham, Mine
Çetinkaya-Rundel, and Garrett Grolemund.
- [Swirl](https://swirlstats.com/) &mdash; An interactive learning platform for
R.
- Specifically, check out [this course](https://swirlstats.com/scn/getclean.html)
for a quick (and interactive!) taste of working with `dplyr` and `tidyr`.

### Packages

- [Tidymodels](https://www.tidymodels.org) &mdash; A collection of packages
for modeling and machine learning in R.
- [Pharmaverse](https://pharmaverse.org/) &mdash; A connected network of
companies and individuals working to promote collaborative development of
curated open source R packages for clinical reporting usage in pharma.

0 comments on commit 1a9c2a1

Please sign in to comment.