In this blog post, we will analyze the animal overall situations in Australia. These datasets include people complaining animals and how these animals being dealt with afterwards. The data is from the amazing project TidyTuesday run by R for Data Science community. So far I have personally learned a lot from the community, and my R skills have been grown exponentially. Read more

I am obssessed with coffee. I do drink it every day, especially when doing data science work. Although I have a penchant for coffee, little do I know about the techniques and evaluations of coffee beans. In this dataset, we will analyze a coffee dataset from TidyTuesday by visualizing it and using some model on it. Read more

The data sets about X-men for this blog post come from TidyTuesday. Instead of loding them manually this time, I will use the tidytuesdayR package. You can install it by simply typing install.packages("tidytuesdayR") on your console! Before doing any data analysis, I do need to admit that I don't know anything about X-men, meaning there is no any background information I hold. Read more

In this blog post, we will analyze caribou tracking datasets from TidyTuesday. library(tidyverse) library(scales) library(lubridate) library(glue) theme_set(theme_bw()) individuals <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-23/individuals.csv') locations <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-23/locations.csv') Check the missing values for individuals individuals %>% map_dfr(~ mean(is.na(.)) * 100) %>% pivot_longer(everything(), names_to = "var", values_to = "missing_percentage(%)") ## # A tibble: 14 x 2 ## var `missing_percentage(%)` ## <chr> <dbl> ## 1 animal_id 0 ## 2 sex 0 ## 3 life_stage 76. Read more

In this blog post, I will carry out a basic usage of the package purrr in conjunction with the package broom. This blog post was motivated by the README file (link) of purrr. Let's load the packages first and then I will flesh it out. Read more

This blog post is a little similar to the previous post I had (you can check it out here). The previous one is about the African-American achievements, but this one is about slave transportation, census data, African names, as well as some important historic events recorded. Read more

This blog post analyzes the African-American achievements data sets from TidyTuesday. The data sets are webscraped from Wikipeida and some preprocessing steps are needed in order to visualize the data. library(tidyverse) library(tidytext) library(widyr) library(ggraph) library(igraph) theme_set(theme_bw()) firsts <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-09/firsts.csv') %>% mutate(first_accomplishment = str_trim(str_remove_all(accomplishment, "First|African.?American"), "left"), decade = 10 * floor(year/10)) science <- read_csv('https://raw. Read more

In this blog post, some cocktail datasets will be analyzed. I personally don't drink and know little if not all to alcohol, but I know how to analyze data. Therefore, let me dive into the datasets in this blog post to understand more about various kinds of cocktails. Read more

In this blog post, a beach volleyball data set will be analyzed. The structure of the data set is a little different, meaning some kind of reshaping (pivot_longer or wider() and gather()) will be used frequently. The data set is from TidyTuesday. library(tidyverse) library(lubridate) library(broom) theme_set(theme_bw()) bv <- read_csv("https://raw. Read more

This blog post will analyze Volcano datasets provided by TidyTuesday (here is the link). library(tidyverse) library(lubridate) theme_set(theme_bw()) volcano <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/volcano.csv') eruptions <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/eruptions.csv')%>% mutate(start_time = make_date(start_year, start_month, start_day), end_time = make_date(end_year, end_month, end_day), eruption_days = as.integer(end_time - start_time)) events <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/events.csv') tree_rings <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/tree_rings.csv') sulfur <- read_csv('https://raw. Read more