In this blog post, I will analyze the Simpsons dataset from TidyTuesday about its guest stars. library(tidyverse) theme_set(theme_bw()) This is my first time using read_delim() to separate |. It is also the first time I recognize read_csv() does not have a delim argument. simpsons <- readr::read_delim("https://raw. Read more

Bob Ross’s paintings are beautiful. In this blog post, I will analyze his painting dataset, which can be downloaded from the link with visualization and PCA. Load the necessary libraries. library(tidyverse) library(ggraph) library(igraph) library(widyr) library(reshape2) library(broom) library(tidytext) library(scales) Use str_to_title() to transform title and then remove " Read more

This blog post analyzes women world cup datasets from TidyTuesday, and here is the link to download the import the datasets. I am personally not into soccer, but this is an excellent opportunity to explore the datasets and hopefully my analysis can shed some light on women world cup and soccer! Read more

This blog post will analyze franchise revenue, and the data is from TidyTuesday Data Science online learning community. You can get the data from this link. library(tidyverse) library(tidytext) library(scales) media <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv") %>% mutate(original_media = str_to_title(original_media), revenue_category = str_to_title(revenue_category)) media ## # A tibble: 321 x 7 ## franchise revenue_category revenue year_created original_media creators ## <chr> <chr> <dbl> <dbl> <chr> <chr> ## 1 A Song of I~ Book Sales 0. Read more

This blog post analyzes ramen rating dataset from TidyTuesday. What is interesting about this blog post is that I analyzed the same dataset a few months ago and you can check it out in this link. Here I work on it again and see how much improvement I have obtained in data science since then. Read more

I came across the wikipedia page on ASA Fellows. Since my Ph.D. advisor is ASA fellow, it is interesting to web scrape the page by using the R package rvest and then analyze the data after obatining it. Load the packages! library(tidyverse) library(rvest) The webpage only contains the full name of each fellow and year. Read more

This blog post will analyze an interesting wine dataset from TidyTuesday. We will visualize the data and use a LASSO model to use words presented in the wine description to make prediction for wine rating. library(tidyverse) library(scales) library(patchwork) library(broom) library(tidytext) library(geofacet) library(glmnet) theme_set(theme_bw()) wine <- read_csv("https://raw. Read more

Plastic products are ubiquitous in our everyday life, and they are always associated with waste and pollution to the global environment. In this blog post, we will analyze three releated dataset from TidyTuesday, shedding some light on how much plastic waste is produced in each country and how it is related to GDP and other key factors. Read more

Nobel Prize is a world-class prize. In this blog post, the prize winners’ information and prize category will be analyzed. The datasets are from the TidyTuesday Project, which is a wonderful online data science learning community for R users. Load the packages and the datasets! Read more

The dataset is from TidyTuesday about teacher-student ratio in each country. Also, this is a great opportunity to harness the package WDI for GDP, population and other key pieces of information about each country. library(tidyverse) library(tidytext) library(scales) library(WDI) theme_set(theme_bw()) ratio <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-07/student_teacher_ratio.csv") ratio ## # A tibble: 5,189 x 8 ## edulit_ind indicator country_code country year student_ratio flag_codes ## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> ## 1 PTRHC_2 Lower Seco~ MRT Mauritania 2013 56. Read more