Join us for our new online boookclub session discussing ‘Hands-On Machine Learning with R’ by Bradley Boehmke and Brandon Greenwell!

Monday March 27, we will talk about pt IV of the book: Clustering! From the book:

In PART III of this book we focused on methods for reducing the dimension of our feature space (p). The remaining chapters concern methods for reducing the dimension of our observation space (n); these methods are commonly referred to as *clustering*. *K*-means clustering is one of the most commonly used clustering algorithms for partitioning observations into a set of k groups (i.e. k clusters), where k is pre-specified by the analyst. *k*-means, like other clustering algorithms, tries to classify observations into mutually exclusive groups (or clusters), such that observations within the same cluster are as similar as possible (i.e., high intra-class similarity), whereas observations from different clusters are as dissimilar as possible (i.e., low inter-class similarity). In *k*-means clustering, each cluster is represented by its center (i.e, centroid) which corresponds to the mean of the observation values assigned to the cluster. The procedure used to find these clusters is similar to the *k*-nearest neighbor (KNN) algorithm discussed in Chapter [8](https://bradleyboehmke.github.io/HOML/knn.html#knn); albeit, without the need to predict an average response value.

These chapters (20-22) will be presented by one of the organizers of R Ladies Den Bosch: Martine.

The attentive reader might notice that we skipped part III of the book: dimension reduction. This part contains chapter 17 Principal Components Analysis, 18 Generalised Low Rank models, and 19 Autoencoders. If you are looking for a fun little challenge and would like to present one or more of these chapters, please send us a message!

R-Ladies Utrecht

Veerle van Son

Ale Sure

Gerbrich Ferdinands

Boook Club "Machine Learning with R" - Clustering

Online event

Dear R ladies, BelgradeR colleagues are organizing online Meetup on Tuesday.

Details and link:

**Wrap it up: Make you code more shareable, reusable and reproducible with R packages**
**Speaker: Mike Page**
**N.B.** This is an **online event**.
**Abstract**
R packages are the fundamental unit of reproducible R code. In this talk, Mike is going to make a case that everybody writing R code should be thinking about abstracting their work into this format, from stand-alone analyses to Shiny apps. The talk will be grounded in real-world examples from data science work performed at the British Red Cross, discussing the benefits and costs of approaching everything as an R package. The talk will conclude with some concrete advice on how to improve your workflow using an R package structure.
**Bio**
Mike Page is a data scientist on the Strategic Insight and Foresight team at the British Red Cross. Here, he helps to develop a suite of open source tools, such as the Covid-19 Vulnerability Index, to help operational decision making in the charity and the broader voluntary and community sector. Mike is also the author of several R packages, including geographr and mortyr. In his spare time you can find him mountain biking and rock climbing around the Alps.
**More:**

* [https://www.mikejohnpage.com/](https://www.mikejohnpage.com/)
* [https://github.com/humaniverse](https://github.com/humaniverse)

**Event details:**
18:00 BelgradeR :: Mike Page, Make you code more shareable, reusable and reproducible with R packages
Tuesday, 28 March
Google Meet joining info
Video call link: https://meet.google.com/qok-jrpw-bwv
Or dial: ‪(RS) +381 11 4250145‬ PIN: ‪596 081 071 0655‬#
More phone numbers: https://tel.meet/qok-jrpw-bwv?pin=5960810710655

R-Ladies Belgrade

Goran S. Milovanovic

Jasmina Rankovic

Graziele Ruas

BlgrdR: Make you code more shareable, reusable and reproducible with R packages

Creating a data science portfolio can help you to develop your skills, and well as showcase them to the wider world. However, getting started on making your portfolio can feel like a huge task. In this talk, I'll discuss how I used #TidyTuesday to gradually build an R portfolio.

We'll run through an example of a #TidyTuesday contribution, including:

* downloading data
* initial data exploration
* creating and styling your plots
* sharing your data visualisations and code

.....and just like that, you have a portfolio!

R-Ladies Gaborone

Edson Kambeu

Simisani Ndaba

Boago Seropola

Creating your Data Science Portfolio

We're starting a brand new series walking you through all the steps necessary to prepare, explore, and analyse your data in R!

This first workshop covers the concepts of "tidy data" and data wrangling commands from the tidyverse packages: the essentials for getting your data into the right format for visualisation and model fitting.

This event is suitable for beginners - you should know how to load packages and read data into R. Feel free to take a look at our video that covers these tasks: https://youtu.be/NO6ikdBxSwg

\-\-\-

It's important to us to ensure that R-Ladies Freiburg events are a safe space for anyone of a minority gender, specifically including transgender, gender non-conforming and nonbinary people as well as folks questioning their gender. All are welcome at this meetup but we specifically ask cisgender men to take a passive role and hold comments / questions until after others have had the chance to ask. Thank you for your support!

R-Ladies Freiburg

Julia Müller

Kyla McConnell

Divya Seernani

Tidy data: Wrangling for statistical analysis in R

**SPEAKER**
Dana Roemling (she/they) is a Ph.D. student in Applied Linguistics at the University of Birmingham. Their research interests are in forensic linguistics, language and law, sociolinguistics, and lavender linguistics. Their PhD focuses on dialect features in authorship profiling. Although they worked in IT before doing their PhD, they only really started programming during their PhD studies. Now they have grown to enjoy R.
You can find Dana on her personal web or on Twitter
https://danaroemling.com/
@danaroemling

**Topic**
This workshop is designed to provide you with the basics of visualising map data in R using ggplot2. The workshop will provide a brief introduction into mapping data, which is useful, for example, in regional dialectology or typology - or generally when your data is regionally distributed. We’ll focus on creating maps from scratch and mapping linguistic variables using R so that we can visualise and identify patterns in the data.

**PREPARATION**
The R packages we'll need are: maps, rworldmap, sf, tidyverse, dplyr
They can sometimes be tricky to install, so I suggest you try it beforehand.
The data and code we'll work with will be available here: [https://github.com/danaroemling/mapping](https://github.com/danaroemling/mapping)

**ONLINE PROGRAMME**
The workshop will be held online, starting on 5th April at 16:00 (UK time). Please join us via this Zoom link:
[https://bham-ac-uk.zoom.us/j/89852185657?pwd=VWRXRFhkWTVqK0FjOG1ZVm83VzhXUT09](https://bham-ac-uk.zoom.us/j/89852185657?pwd=VWRXRFhkWTVqK0FjOG1ZVm83VzhXUT09)

CODE OF CONDUCT
Please take a minute to read our code of conduct - we are committed to creating a safe and harassment-free environment:
https://github.com/rladies/.github/blob/master/CODE_OF_CONDUCT.md
R-Ladies events are always free and open to anyone who wants to participate as long as they follow the code of conduct. We are pro-actively inclusive of queer, trans, and all minority identities, with additional sensitivity to intersectional identities. Our priority is to provide a safe community space for anyone identifying as a minority gender who is interested in and/or working with R.

R-Ladies Coventry

Wanyin Li

Sophia

Lisa Van den Broeck

the Basics of Visualising Map Data in R using ggplot2

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