TidyTuesday

This is part 2 of TidyTuesday: Cocktails . Below shows how we can use #rstats to write a cocktail recommendation system that takes in a drink and returns a few other cocktails based on similarly mixed ingredients.

Data from #tidytuesday week of 2020-05-26 ( source ) If you are looking for the R script then you can find it here Load packages library(tidyverse) library(ggrepel) library(FactoMineR) Download data bc_raw <- read_csv('https://raw. githubusercontent.

Data from #tidytuesday week of 2020-05-12 ( source ) but plotting in python. Load modules import pandas as pd import matplotlib. pyplot as plt import seaborn as sns Download and parse data volcano_raw = pd.

Data from #tidytuesday week of 2020-05-05 ( source ) Load packages library(tidyverse) library(ggfortify) Download data villagers_raw <- read_csv('https://raw. githubusercontent.

Data from #tidytuesday week of 2020-03-31 ( source ) Load packages library(tidyverse) library(gganimate) library(gifski) Download data beer_states_raw <- read_csv("https://raw. githubusercontent.

The differences between this unsanctioned #tardythursday and the official #tidytuesday : These will publish on Thursday (obviously) The dataset will come from a completely different week of TidyTuesday For a surprise, I’ll code with either #rstats or python (similar to #makeovermonday ) Load modules import pandas as pd import seaborn as sns import matplotlib. pyplot as plt Download and parse data df_raw=pd.

Goal of #makeovermonday is to transform some of my #rstats articles and visualizations to their python equivalent. Original plot for this #tidytuesday dataset can be found here .

Data from #tidytuesday week of 2019-12-17 ( source ) Quick post to showcase the amazing {reticulate} package which has made my life so much easier! Who said you had to choose between R vs Python?

Data from #tidytuesday week of 2019-07-30 ( source ) Load R packages library(tidyverse) library(RColorBrewer) library(scales) Download data steam_raw <- read_csv("https://raw. githubusercontent.

Data from #tidytuesday week of 2019-06-04 ( source ) Load R packages library(tidyverse) library(plotly) Download and parse data frame ramen_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 3/05/2019 ( source ) Load libraries library(tidyverse) library(scales) library(lubridate) jobs_gender <- read_csv("https://raw. githubusercontent.

Instead of a static visualization I decided to build a barebones Shiny app this week. The purpose is to improve the interactivity of the final output - one of my 2019 goals to level up advanced R knowledge.

Analyzing data for #tidytuesday week of 1/29/2019 ( source ) # LOAD PACKAGES library(tidyverse) library(scales) library(lubridate) library(ggmap) library(gganimate) library(ggthemes) library(transformr) library(gifski) library(mapproj) milk_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 1/22/2019 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(lubridate) library(RColorBrewer) prison_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 1/15/2019 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(RColorBrewer) library(forcats) library(scales) library(ebbr) launches_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 01/08/2019 ( source ) # LOAD PACKAGES AND PARSE DATA library(knitr) library(tidyverse) library(RColorBrewer) library(forcats) library(lubridate) library(broom) tv_data_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 01/01/2019 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) library(tidytext) library(topicmodels) tweets_raw <- as_tibble(readRDS("rstats_tweets. rds")) Parse data and identify top users # IDEA BEHIND THIS IS TO FILTER OUT BOTS # FIND TOP USERS top_interactions <- tweets_raw %>% select(screen_name,…

Analyzing data for #tidytuesday week of 12/18/2018 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) library(lubridate) library(tidytext) cetaceans_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 12/11/2018 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) library(lubridate) library(ebbr) nyc_restaurants_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 12/4/2018 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) library(tidytext) library(stringr) articles_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 11/27/2018 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) bridges_raw <- read_csv("https://raw. githubusercontent.

Analyzing data for #tidytuesday week of 11/20/2018 ( source ) # LOAD PACKAGES AND PARSE DATA library(tidyverse) library(scales) library(RColorBrewer) library(forcats) thanksgiving_raw <- read_csv("https://raw. githubusercontent.