Income inequality: OECD data

Data In this post I explore income inequality. The data comes from OECD, where inequality is defined as household disposable income per year. Main income inequality markers I use from the dataset are:

TidyTuesday 2022: week 1

Data I’m going to use the data I’m intimately familiar with: medication utilization in Denmark. I will visualize antidepressants use patterns. I’ll use palettes from my {hermitage} package. My favourite palettes so far are madonna_litta and hermitage_1.

Kaggle ML survey 2021

I am a doctoral student. I often wonder what my future holds in this brave and largely liberated according to some world. In EU, although 48% of doctoral graduates were women according to She Figures 2021, only 34% of researchers are women and only 24% of heads of higher education institutions are women.

TidyTuesday Starbucks Data

Data The data I use are available here. Let’s go ✌ I have no initial idea what I want to present and so made several exploratory plots to see what I deal with.

TidyTuesday Spice Girls Data

Data I use data by Jacquie Tran available here. Let’s go ✌ I chose to plot the audio features of Spice Girls tracks: danceability, energy, speechiness, acousticness, valence, liveness, and instrumentalness.

Iterative visualizations with ggplot2: no more copy-pasting

Are you tired of copy-pasting some chunks of your code over and over again? I am, too. Let’s dig into how we can improve our workflow with a bit of tidy evaluation and writing our own functions to avoid copy-pasting.