Recently, I came across the blog post by Emily Robinson titled Lesser Known Stars of the Tidyverse, and here is the link. After reading through the blog, I indeed learned some “hidden” functions I had not been aware of, and knowing how to harness them in my everyday data science work would definitely make my life easier, as I encountered similar situations from time to time that had I known some of these “less known stars” I would process data more quickly. Read more

Project Introduction The dataset is from Kaggle, which indicates that accessing to drinking water is a basic human right. Many places, however, still cannot provide potable water to their residents. In this research project, hopefully, we can use some machine learning algorithms to shed some light on detecting the water quality based on this dataset. Read more

Introduction This dataset is about Amazon Bestsellers between 2009 and 2019 from Kaggle with 50 books every year along with information related to each book (ratings, author(s) and pricing, etc.). It would be interesting to dive into this dataset to visualize it and use some machine learning algorithms to predict the bestsellers’ rating based on some selected features. Read more

Background Introduction As an avid R user for years, I’ve been using tidyverse ecosystem to do my data science work daily and from time to time would come across something new from the tools I use frequently. Last night, as I was building up one of my shiny apps/dashboards by adding test of association of tweets collected from different sampling methods, I found out one of the differences between dplyr::summarize() and dplyr::summarise(). Read more

Data Introduction & Processing This dataset is from Kaggle, and it is about the trending Youtube statistics in the U.S. with more than 40000 rows and 16 columns. After loading the tidyverse library, we can take a peek on what the dataset looks like. Read more