The datasets for this blog post come from TidyTuesday about space launches from various countries in different time intervals. There are two datasets, one is for each space launch detail, and the other one is about space launch agencies worldwide. library(tidyverse) library(scales) library(lubridate) library(tidytext) library(patchwork) theme_set(theme_light()) agencies <- read_csv("https://raw. Read more

The dataset for this blog post is from TidyTuesday titled “TV’s golden age is real.” library(tidyverse) library(lubridate) library(broom) library(scales) theme_set(theme_light()) tv_raw <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-08/IMDb_Economist_tv_ratings.csv")%>% mutate(year = year(date)) tv_raw ## # A tibble: 2,266 x 8 ## titleId seasonNumber title date av_rating share genres year ## <chr> <dbl> <chr> <date> <dbl> <dbl> <chr> <dbl> ## 1 tt2879552 1 11. Read more

The datasets analyzed in this blog post is from TidyTuesday about tweets with hashtag #rstats and #TidyTuesday. library(tidyverse) library(tidytext) library(scales) library(hms) library(lubridate) library(rvest) library(ggrepel) theme_set(theme_light()) tt<- read_rds(url("https://github.com/rfordatascience/tidytuesday/blob/master/data/2019/2019-01-01/tidytuesday_tweets.rds?raw=true")) %>% mutate(time = as_hms(created_at), date = date(created_at), account_age = interval(account_created_at, created_at)/ years(1), week = floor_date(date, "week", week_start = 1)) This is my first time loading a dataset by using read_rds. Read more

The dataset of this blog post is from TidyTuesday about whales and dolphins in the US between 1938 and 2017. library(tidyverse) library(scales) library(lubridate) library(fuzzyjoin) library(broom) library(survival) theme_set(theme_bw()) dolphins <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-12-18/allCetaceanData.csv") %>% select(-X1) %>% mutate(sex = case_when( sex == "F" ~ "Female", sex == "M" ~ "Male", sex == "U" ~ "Unkown", TRUE ~ as. Read more

This blog post analyzes the dataset about the inspections on the restaurants in NYC, and the dataset is obtained from TidyTuesday. library(tidyverse) library(lubridate) library(scales) library(ggthemes) library(widyr) library(broom) theme_set(theme_tufte()) nyr <- read_csv("https://data.cityofnewyork.us/api/views/43nn-pn8j/rows.csv") %>% janitor::clean_names() %>% select(-phone, -grade_date, -record_date, -building, -street) %>% mutate(inspection_date = mdy(inspection_date), cuisine_description = case_when( cuisine_description == "Latin (Cuban, Dominican, Puerto Rican, South & Central American)" ~ "Latin", TRUE ~ as. Read more

This blog post’s data is from TidyTuesday about articles posted on Medium. library(tidyverse) library(lubridate) library(tidytext) library(widyr) library(glmnet) library(broom) library(ggraph) library(igraph) theme_set(theme_bw()) medium <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-12-04/medium_datasci.csv") %>% mutate(year_month = make_datetime(year = year, month = month, day = day)) head(medium) ## # A tibble: 6 x 22 ## x1 title subtitle image author publication year month day reading_time ## <dbl> <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 2 Onlin~ Online a~ 1 Emma ~ NA 2017 8 1 5 ## 2 5 A. Read more

This blog post analyzes the dataset from TidyTuesday about the conditions of bridges in Maryland. It is an interesting dataset to analyze and some maps about the state will be made. This post has an analogous context as one of my previous blog posts about car accident analysis in the same state. Read more

The dataset analyzed in this blog post is from TidyTuesday about Thanksgiving meals. library(tidyverse) library(scales) library(ggraph) library(igraph) library(widyr) theme_set(theme_bw()) tg <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-20/thanksgiving_meals.csv") tg ## # A tibble: 1,058 x 65 ## id celebrate main_dish main_dish_other main_prep main_prep_other stuffing ## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 4. Read more

The dataset for this blog post is from TidyTuesday about malaria situation across the world from different years. In the developed countries, this disease does not exist, yet in the developing countries across the world, especially in Africa, Malaria is still prevalent in some regions. Read more

The dataset for this blog analysis is from TidyTuesday about wind turbine data across different states in the U.S. The dataset contains a number of technical terms that might not be easy to understand, but we can still use data science tools to analyze and visualize the data. Read more