Introduction In this post, memorable characteristics of chocolate bars are plotted. These characteristics relate to anything about the bars, e.g. texture, flavour, overall opinion. This data set includes the country of cocoa bean origin for each cho...

Setup Loading the R libraries and data set. # Loading libraries library(geomtextpath) # For adding text to ggplot2 curves library(tidytuesdayR) # For loading data set library(ggbeeswarm) # For creating a beeswarm plot library(tidyverse) # For the gg...

Introduction In this article, the Star Trek voice commands data set from the #TidyTuesday project is used to investigate character diction using text mining, and train a machine learning model to distinguish between people and computers in the data ...

Introduction In this post, the BEA Infrastructure Investment data set from the #TidyTuesday project is used to illustrate variable transformation and the exploreR::masslm() function. The variable for gross infrastructure investment ad...

Summary In this post, a machine learning model is created using this week’s #TidyTuesday data set: CEO Departures. This data set contains descriptions of thousands of CEO departures and reasons for these departures. After filtering missing values fr...

Setup Loading the R libraries and data set. # Loading libraries library(tidyverse) library(tidytext) library(tidytuesdayR) library(forcats) # Loading data set tt

Setup Loading the R libraries and data set. # Loading libraries library(tidyverse) library(tidytuesdayR) # Loading data tt

Setup and data preparation Loading the R libraries and data set. # Loading libraries library(tidyverse) library(tidytuesdayR) library(viridis) library(tidytext) library(forcats) library(ggridges) # Loading data set tt

Setup and data preparation Loading the R libraries and data set. # Loading libraries library(forcats) library(tidytext) library(tidyverse) library(tidytuesdayR) # Loading data tt

Setup Loading the R libraries and data set.

# Loading libraries
library(tidyverse)

# Reading in the raw data from GitHub (I would use "tt_load", but I hit an API
# rate limt)
games <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-16/games.csv')

Plotting Peak vs. Average number of players using 200 observations For this plot, the top 200 observations for average number of players at the same time are selected using slice_max(order_by = avg, n = 200). The peak and average number of players for ...