This blog post analyzes data from TidyTuesday. Unlike the previous blog posts, this one has some many data to analyze. If we see many datasets, it is natural to think about joining them together. Load the libraries. library(tidyverse) library(scales) library(patchwork) library(ggDoubleHeat) theme_set(theme_bw()) lifetime_earn <- read_csv('https://raw. Read more

In this blog post, I will analyze the data about HBCU enrollment from TidyTuesday. Personally, I don’t know what HBCU stands for prior to analyzing the data, and hopefully I can use data visualization to understand it better. Load the packages and set up the theme for plots: Read more

In this blog, I will analyze a few of the TidyTuesday datasets about the Kenya census. You can get the datasets from here. library(tidyverse) library(janitor) library(scales) theme_set(theme_bw()) gender <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-19/gender.csv') %>% clean_names() %>% filter(county != "Total") %>% mutate(county = str_replace_all(county, "([a-z])([A-Z])", "\\1 \\2")) crops <- read_csv('https://raw. Read more

In this blog post, I will analyze data about art collections and the respective artists. The two datasets come from TidyTuesday. If we have more than one dataset, naturally it would be a natural idea to join them together, if it is able to do so. Read more

In this blog post, I will analyze the cost associated with the transit projects each country spends on. The data comes from TidyTuesday. library(tidyverse) library(countrycode) library(scales) library(patchwork) theme_set(theme_light()) Read the data and process it by renaming some columns and adding new ones. transit_cost <- read_csv('https://raw. Read more

This is an interesting dataset about the McDonald’s Big Mac Pricing around the world. There is a similar background story about Starbucks as people complain some parts of world Starbucks Coffee is more expensive than the other ones. As usual, the data comes from TidyTuesday. Read more

In this blog post, I would like to analyze the Ninja Warrior data from TidyTuesday. I need to claim I know nothing about the show, and this is a perfect opportunity to investigate the data without any background information. The data is relatively simple. Read more

This blog post analyzes historical phone usage around the world. The phone usage is divided into the landline and mobile categories. As usual, the data comes from TidyTuesday. library(tidyverse) library(scales) library(broom) theme_set(theme_light()) mobile <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-10/mobile.csv') landline <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-10/landline.csv') I would like to join mobile and landline together, but total_pop is not lined up well. Read more

This blog post analyzes the IKEA dataset from TidyTuesday. library(tidyverse) library(tidylo) library(tidytext) library(scales) library(widyr) library(ggraph) theme_set(theme_light()) ikea <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv') %>% select(-1) %>% mutate(name = str_to_title(name), old_price = if_else(old_price == "No old price", price, parse_number(old_price))) The typical furniture names per category: ikea %>% count(name, category, sort = T) %>% bind_log_odds(category, name, n) %>% group_by(category) %>% slice_max(log_odds_weighted, n = 10, with_ties = F) %>% ungroup() %>% mutate(name = reorder_within(name, log_odds_weighted, category)) %>% ggplot(aes(log_odds_weighted, name, fill = category)) + geom_col() + scale_y_reordered() + facet_wrap(~category, scales = "free_y") + theme(legend. Read more

The data for this blog post is from TidyTuesday. library(tidyverse) library(geofacet) library(tidytext) library(tidylo) library(scales) library(broom) theme_set(theme_light()) beer_awards <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-20/beer_awards.csv') %>% mutate(state = str_to_upper(state), medal = factor(medal, levels = c("Bronze", "Silver", "Gold"))) beer_awards ## # A tibble: 4,970 x 7 ## medal beer_name brewery city state category year ## <fct> <chr> <chr> <chr> <chr> <chr> <dbl> ## 1 Gold Volksbier Vienna Wibby ~ Long~ CO America~ 2020 ## 2 Silver Oktoberfest Founde~ Gran~ MI America~ 2020 ## 3 Bronze Amber Lager Skippi~ Stau~ VA America~ 2020 ## 4 Gold Lager at World's End Epidem~ Conc~ CA America~ 2020 ## 5 Silver Seismic Tremor Seismi~ Sant~ CA America~ 2020 ## 6 Bronze Lite Thinking Pollya~ Lemo~ IL America~ 2020 ## 7 Gold Beachscape Ventur~ Vent~ CA America~ 2020 ## 8 Silver Imagine a World with Beer Cellars ~ Freeta~ San ~ TX America~ 2020 ## 9 Bronze Pilsner Old To~ Port~ OR America~ 2020 ## 10 Gold Tank 7 Boulev~ Kans~ MO America~ 2020 ## # . Read more