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Oct 7 • 6 tweets • 4 min read
(1/n) I'm sure that there are tons of #DataViz & #rmarkdown tips & tricks out there. So in this thread, I would just like to share a bit of my coding adventures. 🤓💻
(2/n) Before discovering the wonderfulness of ggplot2, my figures were confined to the "limits" of MS Excel. While it is definitely possible to create coherent plots in Excel, it does get kinda clunky when you have more complex data. And what about overlaying my plots?? 🤨
(3/n) Cue ggplot2! Its super intuitive, customizable & compatible with tons of extensions that "enhances" one's plotting capabilities.

I've also been using @CedScherer AMAZING guide, which takes your plots to a whole other level. 🤩
cedricscherer.com/2019/08/05/a-g…
@CedScherer (4/n) I definitely didn't get the hang of coding instantaneously and I really vibe with @science_irl's experience. For sure it's daunting at the start but trust in the process and you'll get there!
@CedScherer @science_irl (5/n) So I've been using #rmarkdown for about 2 years and counting. As a student, I find it really useful in demonstrating data analyses to my supervisors and collaborators thanks to its consistent and standardised reporting styles.
@CedScherer @science_irl (6/n) For students in our experimental design course (& a refresher for anyone interested), we've a tutorial on some basics in #rmarkdown and #DataViz. At the end is a compiled list of useful resources for anyone who is getting started too. ongxinrui.github.io/website/rmarkd…

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More from @WeAreRLadies

We are R-Ladies Profile picture
Oct 6
(1/n) Let's touch a bit on analysing social media data with R. I've dabbled in this a little, mostly for Twitter data.

Thanks to @TwitterDev, you can apply for Academic Research Access (developer.twitter.com/en/products/tw…) to gather data through their API endpoints. How neat is that!
@TwitterDev (2/n) There are a couple of packages for downloading and working with Twitter data.

So far, I've been using academictwitteR, which has super useful vignettes and straight forward functions!

cran.r-project.org/web/packages/a…
@TwitterDev (3/n) Here's a simple demonstration! After setting your tokens (see cran.r-project.org/web/packages/a…), use get_all_tweets() to obtain tweets based on your queries.

Here I'm getting tweets from @WeAreRLadies in December 2021 😊 R codes for downloading and viewing tweets with the academic
Read 4 tweets
We are R-Ladies Profile picture
Oct 6
(1/n) Why do we do literature reviews? Apart from it forming a PhD chapter (as most of us would kinda have to do), literature reviews give us an overview of our research topic(s). Talking about the INs and OUTs of Topic XYZ~ 📚📖📑

Cartoon by @twisteddoodles
@twisteddoodles (2/n) Well, instead of manually downloading and trawling through every single paper (really??), did you know that you can use R for literature reviews?

An example is litsearchr by @ElizaGrames. Check out it's amazing versatilities here! elizagrames.github.io/litsearchr/
@twisteddoodles @ElizaGrames (3/n) There are many more packages too. Here are a couple that I've come across:

More by @ElizaGrames
synthesisr
topictagger
metaverse

other packages:
bibliometrix
revtools
Adjutant
metagear

More links:
elizagrames.github.io/litsearchr/lit…
bibliometrix.org/vignettes/Intr…
scientificallysound.org/2022/03/22/met…
Read 4 tweets
We are R-Ladies Profile picture
Oct 5
(1/n) It's time for some dung beetle "juicy-ness" again~

My first attempt at using R for spatial data is to map the occurrences of dung beetles in Sabah, Malaysia and Singapore! Image showing the maps of Singapore and Sabah, Malaysia. It
(2/n) If you would like some background as to what motivated this, check out our cool GBIF-funded project here! 😎gbif.org/project/BIFA6_…
(3/n) My ambitious goal was to make the occurrence maps interactive! With some googling, I found many resources using Leaflet, an open-source JavaScript library for interactive mapping. Best of all, the leaflet R package is super versatile and allows for easy map customizability.
Read 6 tweets
We are R-Ladies Profile picture
Oct 5
(1/n) Today's topic is working with spatial data in R! TBH I'm pretty new to this and my spatial data experience has mostly been with using GIS software like ArcGIS, QGIS.

It was only this year where I really started dabbling in using R for spatial data mapping. 🗺️📌 Image of a woman thinking about spatial maps and spatial dat
(2/n) While prepping for today's topic, I came across this amazing guide (geobgu.xyz/presentations/…).

What really caught my attention is the history of spatial analysis in R and that R's capabilities in handling spatial data is increasingly improving! 🤩
(3/n) What I really like about using R for spatial data is that its a single environment for data processing, analysing and mapping

I can also use other packages for data manipulation etc before translating them into spatial objects with more specific spatial packages. 🤓
Read 5 tweets
We are R-Ladies Profile picture
Oct 4
(1/n) Now, let's chat about ecological networks! These networks show the interactions (edges) between species (nodes) in a given ecosystem or community. Common examples include food webs, plant-pollinator networks and host-parasite networks. Example of an aquatic food web. Image obtained from Wikimedi
(2/n) So, I'm studying the interaction networks between dung beetles and mammals whose dung they feed on. Why you may ask?

With a variety of feeding and nesting habits, dung beetles provide tons of ecosystem benefits such as nutrient recycling and secondary seed dispersal.
(3/n) Constructing these networks in tropical forests allows us to see the impacts of deforestation, hunting etc. through loss of interactions and simplified networks.

E.g. we expect to see simpler networks in disturbed sites with lesser mammals and thus lesser dung beetles.
Read 9 tweets
We are R-Ladies Profile picture
Oct 4
(1/n) Today, we are delving into network analyses and visualizations! These are super useful tools that enable us to understand the relationships (edges) between objects (nodes) that we specify.
(2/n) Take social media data for example, we can use network analyses to see how connected we are to one another. Each node would represent a user and edges represent the relationships (e.g. follow status) between users. Example of a social media network showing how social media u
(3/n) In #rstats, there are lots of resources for such analyses and visualising networks!

Below, I've listed a couple of tutorials and packages that I've used - mainly for ecological networks.

What resources have you been using for network analyses? Do add on!
Read 4 tweets

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