This site started after the release of dplyr 1.0.0 in
2020, in the form of a Rmarkdown where I noted down the changes from
previous versions.
During the years that Rmarkdown grew bigger and bigger, both in size and in scope, not being anymore a changelog of updates but also containing the basics of various functions.
At the end of 2022 I decided to restructure the file, to give it more utility and also to ease its consultation. The end result of that process is this site, as along the weeks I decided to be very throughout and detailed about all the functions that exist in the package.
It was both a way to challenge myself in producing helpful technical
resources in a language that is not my mother tongue and to further my
knowledge of dplyr and of R at large, as I
feel that it is the main tool for a Data Analyst like myself.
This site went through many revisions and verification, still mistakes and typos can always occur. I apologize if those will cause troubles, or if my general elaboration is not sufficiently clear for a complete understanding of the subjects discussed.
The intended audience was… myself, as I planned to use this blog as a personal reference guide for when I have doubts.
Writing it though I was as well imagining a reader that has little
knowledge about dplyr and wants to deepen their
acquaintance with the package.
It is my way to give back to the community, to which I am extremely grateful as it was thanks to similar efforts of others that I could land my first job in the Data Analysis field.
This site is not a course though, as in every page I may use concepts
explained in some other pages so for somebody that wants to learn
dplyr from 0 I feel that this would not be the best place
to start.
With that intended audience in mind, I strove to be as clear and
intelligible as possible, through the following means:
- one information/concept at a time
- an easy example after every information/concept provided
- using the same variables in different examples
- common language instead of technical one, like I always used column
and row instead of variable and observation, never x and y when
referring to objects and so on and so forth.
About the site’s visual presentation, I kept it simple and clean, also because I didn’t nor don’t want to spend too much time on it.
I did not address remote table verbs (https://dplyr.tidyverse.org/reference/index.html#remote-tables)
and joining functions (https://dplyr.tidyverse.org/articles/two-table.html).
For the latter, that is because dplyr 1.1.0 introduced many
changes that I feel I need more working experience to be comfortable
discussing them.
I always used the same data frame for all the examples provided, as I think that keeping that as a constant reduce the cognitive load when learning new concepts.
The data frame I used can be found here: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II
and I chose it primarily for a personal convenience as I was already using it for another project of mine.
A little warning; some examples rely on a subset of the data frame,
so, at a first glance, it might seem like there is a code chunk not
relevant to the topic at hand (the first of those is in the
filter() page, where I explained how the function treats
NAs. To do so, I used the subset of only the 10123G Stock Code).
In some pages (the one for filter() for example) the
side menu has too many elements, so the last ones can’t be accessed and
the only solution is to scroll to the end of the page. I apologize for
that.
I thank https://github.com/shafayetShafee that provided me with the code necessary to show groups information for printed data frames.
For any comment, critique or suggestion you can contact me on my linkedin profile.
This site was last updated on the 27th of August 2023, using the
1.1.2 version of dplyr.
I don’t know if I will keep this site up to date with future
dplyr releases (that you can check here https://dplyr.tidyverse.org/news/index.html), it will
depend on current commitments and interests.
Despite requiring a lot of effort, working on this site was really
engaging, instructive and rewarding. I can see challenging myself again
with other topics, first and foremost gpplot2, but for when
and if I don’t know at this moment in time.