The NCAA dataset is from TidyTuesday. library(tidyverse) library(scales) theme_set(theme_light()) tournament <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-06/tournament.csv') %>% mutate(tourney_finish = factor(tourney_finish, levels = c("1st", "2nd", "RSF", "RF", "NSF", "N2nd", "Champ"))) tournament ## # A tibble: 2,092 x 19 ## year school seed conference conf_w conf_l conf_percent conf_place reg_w ## <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> ## 1 1982 Arizona S~ 4 Western C~ NA NA NA - 23 ## 2 1982 Auburn 7 Southeast~ NA NA NA - 24 ## 3 1982 Cheyney 2 Independe~ NA NA NA - 24 ## 4 1982 Clemson 5 Atlantic ~ 6 3 66. Read more

This blog post analyzes the lyrics from Beyonce and Taylor Swift, and the datasets are from TidyTuesday. library(tidyverse) library(lubridate) library(scales) library(tidytext) library(tidylo) theme_set(theme_light()) beyonce_lyrics <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-29/beyonce_lyrics.csv') taylor_swift_lyrics <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-29/taylor_swift_lyrics.csv') %>% rename(song = Title) %>% janitor::clean_names() sales <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-29/sales.csv') charts <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-29/charts.csv') %>% mutate(re_release = str_remove(re_release, "\\[. Read more

This blog post anlayzes Himalayan Climbing data coming from TidyTuesday. library(tidyverse) library(tidytext) library(widyr) library(ggraph) theme_set(theme_light()) members <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv') expeditions <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/expeditions.csv') %>% mutate(termination_reason = str_to_title(str_remove_all(termination_reason, "\\s\\(.*\\)|or\\s"))) %>% separate_rows(termination_reason, sep = ", ") %>% mutate(success_or_not = if_else(termination_reason == "Success", "success", "not success")) peaks <- read_csv('https://raw. Read more

This blog post analyzes how much money each state in the U.S. spent on kids through various programs between 1997 and 2016 (20-year period). As usual, the data comes from TidyTuesday. library(tidyverse) library(geofacet) library(scales) library(tidytext) theme_set(theme_light()) kids <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv') %>% filter(inf_adj > 0) %>% mutate(variable = str_to_title(str_replace(variable, "_", " "))) %>% left_join(tibble(state = state. Read more

This blog post is an interesting one. It analyzes the show Friends from TidyTuesday. library(tidyverse) library(patchwork) library(tidytext) library(widyr) library(tidylo) theme_set(theme_light()) friends <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-08/friends.csv') friends_emotions <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-08/friends_emotions.csv') friends_info <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-08/friends_info.csv') %>% mutate(title = str_trim(str_remove_all(str_to_title(title), "The One With|The One Where"))) Working on friends_into: How popular is each episode? Read more

This blog post analyzes data about the global crop yields from TidyTuesday. The primary focus of this data processing/visualization is between 1961 to 2020. library(tidyverse) library(countrycode) theme_set(theme_light()) key_crop_yields <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv') fertilizer <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/cereal_crop_yield_vs_fertilizer_application.csv') tractors <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/cereal_yields_vs_tractor_inputs_in_agriculture.csv') land_use <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/land_use_vs_yield_change_in_cereal_production.csv') arable_land <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/arable_land_pin.csv') Working on key_crop_yields: Read more

In this blog post, I will analyze a dataset about the TV show Chopped. Nothing do I know about the show, but this is a great opportunity to dive into a dataset that I do not know any background information. Let’s see what we can find from it. Read more

In this blog post, we will analyze extinct plants. The datasets are from TidyTuesday, where you can find them here. library(tidyverse) library(widyr) library(ggraph) theme_set(theme_light()) plants <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-18/plants.csv') %>% separate(binomial_name, sep = " ", into = c("genus", "species")) %>% mutate(species = str_to_title(species)) actions <- read_csv('https://raw. Read more

In this blog post, I will analyze energy productions among the European countries. As usual, the datasets are from the TidyTuesday project. You can find them here. Since so many countries are involved in the data, this is a great opportunity to harness the ggflags package, making the country flags on visualization. Read more

In this blog post, we will visualize and carry out some machine learning models on the penguin datasets form TidyTuesday. library(tidyverse) library(tidymodels) theme_set(theme_light()) penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv') %>% filter(!is.na(bill_length_mm), !is.na(sex)) %>% mutate(species = factor(species)) penguins_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins_raw.csv') %>% janitor::clean_names() %>% select(-c(stage, region)) %>% mutate(species = str_remove(species, "\\s. Read more