Attaching packages tidyverse 1.3.0
ggplot2 3.3.3 purrr 0.3.4
tibble 3.0.6 dplyr 1.0.4
tidyr 1.1.2 stringr 1.4.0
readr 1.4.0 forcats 0.5.1
Conflicts tidyverse_conflicts()
dplyr::filter() masks stats::filter()
dplyr::lag() masks stats::lag()
raw_arabica <- read_csv("https://raw.githubusercontent.com/jldbc/coffee-quality-database/master/data/arabica_data_cleaned.csv")
raw_robusta <- read_csv("https://raw.githubusercontent.com/jldbc/coffee-quality-database/master/data/robusta_data_cleaned.csv")
Warning message:
Missing column names filled in: 'X1' [1]
olumn specification
cols(
.default = col_character(),
X1 = col_double(),
Number.of.Bags = col_double(),
Aroma = col_double(),
Flavor = col_double(),
Aftertaste = col_double(),
Acidity = col_double(),
Body = col_double(),
Balance = col_double(),
Uniformity = col_double(),
Clean.Cup = col_double(),
Sweetness = col_double(),
Cupper.Points = col_double(),
Total.Cup.Points = col_double(),
Moisture = col_double(),
Category.One.Defects = col_double(),
Quakers = col_double(),
Category.Two.Defects = col_double(),
altitude_low_meters = 2mcol_double(),
altitude_high_meters = col_double(),
altitude_mean_meters = col_double()
)
ℹ Use spec() for the full column specifications.
Warning message:
Missing column names filled in: 'X1' [1]
Column specification
cols(
.default = col_character(),
X1 = col_double(),
Number.of.Bags = col_double(),
Harvest.Year = col_double(),
Fragrance...Aroma = col_double(),
Flavor = col_double(),
Aftertaste = col_double(),
Salt...Acid = col_double(),
Bitter...Sweet = col_double(),
Mouthfeel = col_double(),
Uniform.Cup = mcol_double(),
Clean.Cup = col_double(),
Balance = col_double(),
Cupper.Points = col_double(),
Total.Cup.Points = col_double(),
Moisture = col_double(),
Category.One.Defects = col_double(),
Quakers = col_double(),
Category.Two.Defects = col_double(),
altitude_low_meters = col_double(),
altitude_high_meters = col_double()
# ... with 1 more columns
)
ℹ Use `spec()` for the full column specifications.
A tibble: 6 44
| X1 | Species | Owner | Country.of.Origin | Farm.Name | Lot.Number | Mill | ICO.Number | Company | Altitude | | Color | Category.Two.Defects | Expiration | Certification.Body | Certification.Address | Certification.Contact | unit_of_measurement | altitude_low_meters | altitude_high_meters | altitude_mean_meters |
|---|
| <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | | <chr> | <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> |
|---|
| 1 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 0 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 2 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 1 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 3 | Arabica | grounds for health admin | Guatemala | san marcos barrancas “san cristobal cuch | NA | NA | NA | NA | 1600 - 1800 m | | NA | 0 | May 31st, 2011 | Specialty Coffee Association | 36d0d00a3724338ba7937c52a378d085f2172daa | 0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 | m | 1600 | 1800 | 1700 |
| 4 | Arabica | yidnekachew dabessa | Ethiopia | yidnekachew dabessa coffee plantation | NA | wolensu | NA | yidnekachew debessa coffee plantation | 1800-2200 | | Green | 2 | March 25th, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1800 | 2200 | 2000 |
| 5 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 2 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 6 | Arabica | ji-ae ahn | Brazil | NA | NA | NA | NA | NA | NA | | Bluish-Green | 1 | September 3rd, 2014 | Specialty Coffee Institute of Asia | 726e4891cf2c9a4848768bd34b668124d12c4224 | b70da261fcc84831e3e9620c30a8701540abc200 | m | NA | NA | NA |
colnames(raw_arabica)
colnames(raw_robusta)
- ‘X1’
- ‘Species’
- ‘Owner’
- ‘Country.of.Origin’
- ‘Farm.Name’
- ‘Lot.Number’
- ‘Mill’
- ‘ICO.Number’
- ‘Company’
- ‘Altitude’
- ‘Region’
- ‘Producer’
- ‘Number.of.Bags’
- ‘Bag.Weight’
- ‘In.Country.Partner’
- ‘Harvest.Year’
- ‘Grading.Date’
- ‘Owner.1’
- ‘Variety’
- ‘Processing.Method’
- ‘Aroma’
- ‘Flavor’
- ‘Aftertaste’
- ‘Acidity’
- ‘Body’
- ‘Balance’
- ‘Uniformity’
- ‘Clean.Cup’
- ‘Sweetness’
- ‘Cupper.Points’
- ‘Total.Cup.Points’
- ‘Moisture’
- ‘Category.One.Defects’
- ‘Quakers’
- ‘Color’
- ‘Category.Two.Defects’
- ‘Expiration’
- ‘Certification.Body’
- ‘Certification.Address’
- ‘Certification.Contact’
- ‘unit_of_measurement’
- ‘altitude_low_meters’
- ‘altitude_high_meters’
- ‘altitude_mean_meters’
- ‘X1’
- ‘Species’
- ‘Owner’
- ‘Country.of.Origin’
- ‘Farm.Name’
- ‘Lot.Number’
- ‘Mill’
- ‘ICO.Number’
- ‘Company’
- ‘Altitude’
- ‘Region’
- ‘Producer’
- ‘Number.of.Bags’
- ‘Bag.Weight’
- ‘In.Country.Partner’
- ‘Harvest.Year’
- ‘Grading.Date’
- ‘Owner.1’
- ‘Variety’
- ‘Processing.Method’
- ‘Fragrance…Aroma’
- ‘Flavor’
- ‘Aftertaste’
- ‘Salt…Acid’
- ‘Bitter…Sweet’
- ‘Mouthfeel’
- ‘Uniform.Cup’
- ‘Clean.Cup’
- ‘Balance’
- ‘Cupper.Points’
- ‘Total.Cup.Points’
- ‘Moisture’
- ‘Category.One.Defects’
- ‘Quakers’
- ‘Color’
- ‘Category.Two.Defects’
- ‘Expiration’
- ‘Certification.Body’
- ‘Certification.Address’
- ‘Certification.Contact’
- ‘unit_of_measurement’
- ‘altitude_low_meters’
- ‘altitude_high_meters’
- ‘altitude_mean_meters’
install.packages("tidytuesdayR")
# Either ISO-8601 date or year/week works!
tuesdata <- tidytuesdayR::tt_load('2020-07-07')
tuesdata <- tidytuesdayR::tt_load(2020, week = 28)
coffee_ratings <- tuesdata$coffee_ratings
Installing package into /usr/local/lib/R/site-library
(as lib is unspecified)
--- Compiling #TidyTuesday Information for 2020-07-07 ----
--- There is 1 file available ---
--- Starting Download ---
Downloading file 1 of 1: `coffee_ratings.csv`
--- Download complete ---
--- Compiling #TidyTuesday Information for 2020-07-07 ----
--- There is 1 file available ---
--- Starting Download ---
Downloading file 1 of 1: `coffee_ratings.csv`
--- Download complete ---
R Information
Help files with alias or concept or title matching tidytuesdayR using
fuzzy matching:
tidytuesdayR::available
Listing all available TidyTuesdays
tidytuesdayR::tt_date Get date of TidyTuesday, given the year and
week
tidytuesdayR::tt_download_file
Reads in TidyTuesday datasets from Github repo
tidytuesdayR::tt_load Load TidyTuesday data from Github
tidytuesdayR::tt_load_gh
Load TidyTuesday data from Github
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
R Information
Help files with alias or concept or title matching tidytuesdayR using
fuzzy matching:
tidytuesdayR::available
Listing all available TidyTuesdays
tidytuesdayR::tt_date Get date of TidyTuesday, given the year and
week
tidytuesdayR::tt_download_file
Reads in TidyTuesday datasets from Github repo
tidytuesdayR::tt_load Load TidyTuesday data from Github
tidytuesdayR::tt_load_gh
Load TidyTuesday data from Github
Type '?PKG::FOO' to inspect entries 'PKG::FOO', or 'TYPE?PKG::FOO' for
entries like 'PKG::FOO-TYPE'.
Year: 2021
Week Date Data Source
1 1 2020-12-29 Bring your own data from 2020!
2 2 2021-01-05 Transit Cost Project TransitCosts.com
3 3 2021-01-12 Art Collections Tate Collection
4 4 2021-01-19 Kenya Census rKenyaCensus
5 5 2021-01-26 Plastic Pollution Break Free from Plastic
6 6 2021-02-02 HBCU Enrollment Data.World & Data.World
7 7 2021-02-09 Wealth and Income Urban Institute & US Census
8 8 2021-02-16 W.E.B. Du Bois Challenge Du Bois Data Challenge
9 9 2021-02-23 Employment and Earnings BLS
10 10 2021-03-02 SuperBowl Ads FiveThirtyEight
11 11 2021-03-09 Bechdel Test FiveThirtyEight
12 12 2021-03-16 Video Games + Sliced Steam
13 13 2021-03-23 UN Votes Harvard Dataverse
14 14 2021-03-30 Makeup Shades The Pudding data
Article
1
2 Transit Costs Case Study
3 Aspect Ratio of Artworks through Time
4 Introducing rKenyaCensus
5 Sarah Sauve
6 HBCU Donations Article
7 Urban Institute
8 Anthony Starks - Recreating Du Bois's data portraits
9 BLS Article
10 FiveThirtyEight
11 FiveThirtyEight
12 SteamCharts
13 Citizen Statistician
14 The Pudding
Year: 2020
Week Date Data
1 1 2019-12-31 Bring your own data from 2019!
2 2 2020-01-07 Australian Fires
3 3 2020-01-14 Passwords
4 4 2020-01-21 Song Genres
5 5 2020-01-28 San Francisco Trees
6 6 2020-02-04 NFL Attendance
7 7 2020-02-11 Hotel Bookings
8 8 2020-02-18 Food's Carbon Footprint
9 9 2020-02-25 Measles Vaccination
10 10 2020-03-03 NHL Goals
11 11 2020-03-10 College Tuition, Diversity, and Pay
12 12 2020-03-17 The Office
13 13 2020-03-24 Traumatic Brain Injury
14 14 2020-03-31 Beer Production
15 15 2020-04-07 Tour de France
16 16 2020-04-14 Best Rap Artists
17 17 2020-04-21 GDPR Violations
18 18 2020-04-28 Broadway Musicals
19 19 2020-05-05 Animal Crossing
20 20 2020-05-12 Volcano Eruptions
21 21 2020-05-19 Beach Volleyball
22 22 2020-05-26 Cocktails
23 23 2020-06-02 Marble Races
24 24 2020-06-09 African-American Achievements
25 25 2020-06-16 African-American History
26 26 2020-06-23 Caribou Locations
27 27 2020-06-30 Claremont Run of X-Men
28 28 2020-07-07 Coffee Ratings
29 29 2020-07-14 Astronaut Database
30 30 2020-07-21 Australian Animal Outcomes
31 31 2020-07-28 Palmer Penguins
32 32 2020-08-04 European Energy
33 33 2020-08-11 Avatar: The Last Airbender
34 34 2020-08-18 Extinct Plants
35 35 2020-08-25 Chopped
36 36 2020-09-01 Global Crop Yields
37 37 2020-09-08 Friends
38 38 2020-09-15 Gov Spending on Kids
39 39 2020-09-22 Himalayan Climbers
40 40 2020-09-29 Beyonce & Taylor Swift Lyrics
41 41 2020-10-06 NCAA Women's Basketball
42 42 2020-10-13 datasauRus dozen
43 43 2020-10-20 Great American Beer Festival Data
44 44 2020-10-27 Canadian Wind Turbines
45 45 2020-11-03 Ikea Furniture
46 46 2020-11-10 Historical Phones
47 47 2020-11-17 Black in Data
48 48 2020-11-24 Washington Trails
49 49 2020-12-01 Toronto Shelters
50 50 2020-12-08 Women of 2020
51 51 2020-12-15 Ninja Warrior
52 52 2020-12-22 Big Mac Index
Source
1
2 Bureau of Meteorology
3 Knowledge is Beautiful
4 spotifyr
5 data.sfgov.org
6 Pro Football Reference
7 Antonio, Almeida, and Nunes, 2019
8 nu3
9 The Wallstreet Journal
10 HockeyReference.com
11 TuitionTracker.org
12 schrute
13 CDC
14 TTB
15 tdf package
16 BBC Music
17 Privacy Affairs
18 Playbill
19 Villager DB
20 Smithsonian
21 BigTimeStats
22 Kaggle & Kaggle
23 Jelle's Marble Runs
24 Wikipedia & Wikipedia
25 Black Past & Census & Slave Voyages
26 Movebank
27 Claremont Run
28 James LeDoux & Coffee Quality Database
29 Corlett, Stavnichuk & Komarova article
30 RSPCA
31 Gorman, Williams and Fraser, 2014
32 Eurostat Energy
33 appa
34 IUCN Red List
35 Kaggle & IMDB
36 Our World in Data
37 friends R package
38 Urban Institute
39 The Himalayan Database
40 Rosie Baillie and Dr. Sara Stoudt
41 FiveThirtyEight
42 Alberto Cairo
43 Great American Beer Festival
44 open.canada.ca
45 Kaggle
46 Mobile vs Landline subscriptions
47 Black in Data Week
48 WTA
49 opendatatoronto
50 BBC
51 Data.World
52 TheEconomist
Article
1
2 NY Times & BBC
3 Information is Beautiful
4 Kaylin Pavlik
5 SF Weekly
6 Casino.org
7 tidyverts
8 r-tastic by Kasia Kulma
9 The Wall Street Journal
10 Washington Post
11 TuitionTracker.org
12 The Pudding
13 CDC Traumatic Brain Injury Report
14 Brewers Association
15 Alastair Rushworth's blog
16 Simon Jockers at Datawrapper
17 Roel Hogervorst
18 Alex Cookson
19 Polygon
20 Axios & Wikipedia
21 FiveThirtyEight & Wikipedia
22 FiveThirtyEight
23 Randy Olson
24 David Blackwell & Petition for David Blackwell
25 The Guardian
26 B.C. Ministry of Environment
27 Wikipedia - Uncanny X-Men
28 Yorgos Askalidis - TWD
29 Corlett, Stavnichuk & Komarova article
30 RSPCA Report
31 Palmer Penguins
32 Washington Post Energy
33 Exploring Avatar: The Last Airbender transcript data
34 Florent Lavergne infographic
35 Vice
36 Our World in Data
37 ceros interactive article
38 Joshua Rosenberg's tidykids package
39 Alex Cookson blog post
40 Taylor Swift lyrics
41 FiveThirtyEight
42 datasauRus R package
43 2019 GABF Medal Winner Analysis
44 Canada's National Observer
45 FiveThirtyEight
46 Pew Research Smartphone Adoption
47 BlackInData #DataViz
48 TidyX
49 rabble.ca
50 BBC
51 sasukepedia
52 TheEconomist
Year: 2019
Week Date Data
1 1 2019-01-01 #Rstats & #TidyTuesday Tweets
2 2 2019-01-08 TV's Golden Age
3 3 2019-01-15 Space Launches
4 4 2019-01-22 Incarceration Trends
5 5 2019-01-29 Dairy production & Consumption
6 6 2019-02-05 House Price Index & Mortgage Rates
7 7 2019-02-12 Federal R&D Spending
8 8 2019-02-19 US PhD's Awarded
9 9 2019-02-26 French Train Delays
10 10 2019-03-05 Women in the Workplace
11 11 2019-03-12 Board Games
12 12 2019-03-19 Stanford Open Policing Project
13 13 2019-03-26 Seattle Pet Names
14 14 2019-04-02 Seattle Bike Traffic
15 15 2019-04-09 Tennis Grand Slam Champions
16 16 2019-04-16 The Economist Data Viz Mistakes
17 17 2019-04-23 Anime Data
18 18 2019-04-30 Chicago Bird Collisions
19 19 2019-05-07 Global Student to Teacher Ratios
20 20 2019-05-14 Nobel Prize Winners
21 21 2019-05-21 Global Plastic Waste
22 22 2019-05-28 Wine Ratings
23 23 2019-06-04 Ramen Ratings
24 24 2019-06-11 Meteorites
25 25 2019-06-18 Christmas Bird Counts
26 26 2019-06-25 Global UFO Sightings
27 27 2019-07-02 Media Franchise Revenues
28 28 2019-07-09 Women's World Cup
29 29 2019-07-16 R4DS Membership
30 30 2019-07-23 Wildlife Strikes
31 31 2019-07-30 Video Games
32 32 2019-08-06 Bob Ross paintings
33 33 2019-08-13 Roman Emperors
34 34 2019-08-20 Nuclear Explosions
35 35 2019-08-27 Simpsons Guest Stars
36 36 2019-09-03 Moore's Law
37 37 2019-09-10 Amusement Park Injuries
38 38 2019-09-17 National Park Visits
39 39 2019-09-24 School Diversity
40 40 2019-10-01 All the Pizza
41 41 2019-10-08 Powerlifting
42 42 2019-10-15 Car Fuel Economy
43 43 2019-10-22 Horror movie ratings
44 44 2019-10-29 NYC Squirrel Census
45 45 2019-11-05 Bike & Walk Commutes
46 46 2019-11-12 CRAN Code
47 47 2019-11-19 NZ Bird of the Year
48 48 2019-11-26 Student Loan Debt
49 49 2019-12-03 Philly Parking Tickets
50 50 2019-12-10 Replicating plots in R
51 51 2019-12-17 Adoptable dogs
52 52 2019-12-24 Christmas Songs
Source
1 rtweet
2 IMDb
3 JSR Launch Vehicle Database
4 Vera Institute
5 USDA
6 FreddieMac & FreddieMac
7 AAAS
8 NSF
9 SNCF
10 Census Bureau & Bureau of Labor
11 Board Game Geeks
12 Stanford Open Policing Project SOPP - arXiv:1706.05678
13 seattle.gov
14 seattle.gov
15 Wikipedia
16 The Economist
17 MyAnimeList
18 Winger et al, 2019
19 UNESCO
20 Kaggle
21 Our World In Data
22 Kaggle
23 TheRamenRater.com
24 NASA
25 Bird Studies Canada
26 NUFORC
27 Wikipedia
28 data.world
29 R4DS Slack
30 FAA
31 Steam Spy
32 FiveThirtyEight
33 Wikipedia / Zonination
34 SIPRI
35 Wikipedia
36 Wikipedia
37 Data.world & Saferparks
38 Data.world
39 NCES
40 Jared Lander & Ludmila Janda, Tyler Richards, DataFiniti
41 OpenPowerlifting.org
42 EPA
43 IMDB
44 Squirrel Census
45 ACS
46 CRAN
47 New Zealand Forest and Bird Org
48 Department of Education
49 Open Data Philly
50 Simply Statistics
51 Petfinder
52 Billboard Top 100
Article
1 stackoverflow.blog
2 The Economist
3 The Economist
4 Vera Institute
5 NPR
6 Fortune
7 New York Times
8 #epibookclub
9 RTL - Today
10 Census Bureau
11 fivethirtyeight
12 SOPP - arXiv:1706.05678
13 Curbed Seattle
14 Seattle Times
15 Financial Times
16 The Economist
17 MyAnimeList
18 Winger et al, 2019
19 Center for Public Education
20 The Economist
21 Our World in Data
22 Vivino
23 Food Republic
24 The Guardian - Meteorite map
25 Hamilton Christmas Bird Count
26 Example Plots
27 reddit/dataisbeautiful post
28 Wikipedia
29 R4DS useR Presentation
30 FAA
31 Liza Wood
32 FiveThirtyEight
33 reddit.com/r/dataisbeautiful
34 Our World in Data
35 Wikipedia
36 Wikipedia
37 Saferparks
38 fivethirtyeight article
39 Washington Post article
40 Tyler Richards on TWD
41 Elias Oziolor
42 Ellis Hughes
43 Stephen Follows
44 CityLab
45 ACS
46 Phillip Massicotte
47 Dragonfly Data Science & Nathan Moore
48 Dignity and Debt
49 NBC Philadelphia
50 Rafael Irizarry
51 The Pudding
52 A Dash of Data
Year: 2018
Week Date Data
1 1 2018-04-02 US Tuition Costs
2 2 2018-04-09 NFL Positional Salaries
3 3 2018-04-16 Global Mortality
4 4 2018-04-23 Australian Salaries by Gender
5 5 2018-04-30 ACS Census Data (2015)
6 6 2018-05-07 Global Coffee Chains
7 7 2018-05-14 Star Wars Survey
8 8 2018-05-21 US Honey Production
9 9 2018-05-29 Comic book characters
10 10 2018-06-05 Biketown Bikeshare
11 11 2018-06-12 FIFA World Cup Audience
12 12 2018-06-19 Hurricanes & Puerto Rico
13 13 2018-06-26 Alcohol Consumption
14 14 2018-07-03 Global Life Expectancy
15 15 2018-07-10 Craft Beer USA
16 16 2018-07-17 Exercise USA
17 17 2018-07-23 p-hack-athon collaboration
18 18 2018-07-31 Dallas Animal Shelter FY2017
19 19 2018-08-07 Airline Safety
20 20 2018-08-14 Russian Troll Tweets
21 21 2018-08-21 California Fires
22 22 2018-08-28 NFL Stats
23 23 2018-09-04 Fast Food Calories
24 24 2018-09-11 Cats vs Dogs (USA)
25 25 2018-09-18 US Flights or Hypoxia
26 26 2018-09-25 Global Invasive Species
27 27 2018-10-02 US Births
28 28 2018-10-09 US Voter Turnout
29 29 2018-10-16 College Major & Income
30 30 2018-10-23 Horror Movie Profit
31 31 2018-10-30 R and R package downloads
32 32 2018-11-06 US Wind Farm locations
33 33 2018-11-13 Malaria Data
34 34 2018-11-20 Thanksgiving Dinner or Transgender Day of Remembrance
35 35 2018-11-27 Baltimore Bridges
36 36 2018-12-04 Medium Article Metadata
37 37 2018-12-11 NYC Restaurant inspections
38 38 2018-12-18 Cetaceans Data
Source
1 onlinembapage.com
2 Spotrac.com
3 ourworldindata.org
4 data.gov.au
5 census.gov , Kaggle
6 Starbucks: kaggle.com , Tim Horton: timhortons.com , Dunkin Donuts: odditysoftware.com
7 fivethirtyeight package
8 USDA, Kaggle.com
9 FiveThirtyEight package
10 BiketownPDX
11 FiveThirtyEight package
12 FiveThirtyEight package
13 FiveThirtyEight package
14 ourworldindata.org
15 data.world
16 CDC
17 simplystatistics.org
18 Dallas OpenData
19 FiveThirtyEight Package
20 FiveThirtyEight.com
21 BuzzFeed.com
22 pro-football-reference.com
23 fastfoodnutrition.org
24 data.world
25 faa.govSoaring Society of America
26 Paini et al, 2016griis.org
27 fivethirtyeight package
28 data.world
29 fivethirtyeight/ACS
30 the-numbers.com
31 cran-logs.rstudio.com
32 usgs.gov
33 ourworldindata.orgMalaria Data Challenge
34 fivethirtyeightTDoR
35 Federal Highway Administration
36 Kaggle.com
37 NYC OpenData/NYC Health Department
38 The Pudding
Article
1 onlinembapage.com
2 fivethirtyeight.com
3 ourworldindata.org
4 data.gov.au
5 No article
6 flowingdata.com
7 fivethirtyeight.com
8 Bee Culture
9 FiveThirtyEight.com
10 Biketown cascadiaRconf/cRaggy
11 FiveThirtyEight.com
12 FiveThirtyEight.com
13 FiveThirtyEight.com
14 ourworldindata.org
15 thrillist.com
16 CDC - National Health Statistics Reports
17 p-hack-athon
18 Dallas OpenData FY2017 Summary
19 538 - Airline Safety
20 538 - Russian Troll Tweets
21 BuzzFeed News - California Fires, RMarkdown
22 eldo.co
23 franchiseopportunities.com
24 Washington Post
25 travelweekly.comSSA - Hypoxia
26 Paini et al, 2016griis.org
27 538 - Births
28 Star Tribune
29 fivethirtyeight
30 fivethirtyeight
31 No Article
32 Wind Market Reports
33 ourworldindata.org malariaAtlas
34 fivethirtyeightTDoR
35 Baltimore Sun
36 TidyText package
37 FiveThirtyEight
38 The Pudding
Week Date Data Source
1 1 2020-12-29 Bring your own data from 2020!
2 2 2021-01-05 Transit Cost Project TransitCosts.com
3 3 2021-01-12 Art Collections Tate Collection
4 4 2021-01-19 Kenya Census rKenyaCensus
5 5 2021-01-26 Plastic Pollution Break Free from Plastic
6 6 2021-02-02 HBCU Enrollment Data.World & Data.World
7 7 2021-02-09 Wealth and Income Urban Institute & US Census
8 8 2021-02-16 W.E.B. Du Bois Challenge Du Bois Data Challenge
9 9 2021-02-23 Employment and Earnings BLS
10 10 2021-03-02 SuperBowl Ads FiveThirtyEight
11 11 2021-03-09 Bechdel Test FiveThirtyEight
12 12 2021-03-16 Video Games + Sliced Steam
13 13 2021-03-23 UN Votes Harvard Dataverse
14 14 2021-03-30 Makeup Shades The Pudding data
Article
1
2 Transit Costs Case Study
3 Aspect Ratio of Artworks through Time
4 Introducing rKenyaCensus
5 Sarah Sauve
6 HBCU Donations Article
7 Urban Institute
8 Anthony Starks - Recreating Du Bois's data portraits
9 BLS Article
10 FiveThirtyEight
11 FiveThirtyEight
12 SteamCharts
13 Citizen Statistician
14 The Pudding
colnames(tuesdata$coffee_ratings)
- ‘total_cup_points’
- ‘species’
- ‘owner’
- ‘country_of_origin’
- ‘farm_name’
- ‘lot_number’
- ‘mill’
- ‘ico_number’
- ‘company’
- ‘altitude’
- ‘region’
- ‘producer’
- ‘number_of_bags’
- ‘bag_weight’
- ‘in_country_partner’
- ‘harvest_year’
- ‘grading_date’
- ‘owner_1’
- ‘variety’
- ‘processing_method’
- ‘aroma’
- ‘flavor’
- ‘aftertaste’
- ‘acidity’
- ‘body’
- ‘balance’
- ‘uniformity’
- ‘clean_cup’
- ‘sweetness’
- ‘cupper_points’
- ‘moisture’
- ‘category_one_defects’
- ‘quakers’
- ‘color’
- ‘category_two_defects’
- ‘expiration’
- ‘certification_body’
- ‘certification_address’
- ‘certification_contact’
- ‘unit_of_measurement’
- ‘altitude_low_meters’
- ‘altitude_high_meters’
- ‘altitude_mean_meters’
head(tuesdata$coffee_ratings)
A tibble: 6 43
| total_cup_points | species | owner | country_of_origin | farm_name | lot_number | mill | ico_number | company | altitude | | color | category_two_defects | expiration | certification_body | certification_address | certification_contact | unit_of_measurement | altitude_low_meters | altitude_high_meters | altitude_mean_meters |
|---|
| <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | | <chr> | <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> |
|---|
| 90.58 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 0 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 89.92 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 1 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 89.75 | Arabica | grounds for health admin | Guatemala | san marcos barrancas “san cristobal cuch | NA | NA | NA | NA | 1600 - 1800 m | | NA | 0 | May 31st, 2011 | Specialty Coffee Association | 36d0d00a3724338ba7937c52a378d085f2172daa | 0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 | m | 1600 | 1800 | 1700 |
| 89.00 | Arabica | yidnekachew dabessa | Ethiopia | yidnekachew dabessa coffee plantation | NA | wolensu | NA | yidnekachew debessa coffee plantation | 1800-2200 | | Green | 2 | March 25th, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1800 | 2200 | 2000 |
| 88.83 | Arabica | metad plc | Ethiopia | metad plc | NA | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | | Green | 2 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950 | 2200 | 2075 |
| 88.83 | Arabica | ji-ae ahn | Brazil | NA | NA | NA | NA | NA | NA | | Bluish-Green | 1 | September 3rd, 2014 | Specialty Coffee Institute of Asia | 726e4891cf2c9a4848768bd34b668124d12c4224 | b70da261fcc84831e3e9620c30a8701540abc200 | m | NA | NA | NA |
tuesdata$coffee_ratings %>% select(c("country_of_origin", "total_cup_points")) %>%
group_by(country_of_origin) %>%
summarise(mu_points = mean(total_cup_points), count = n()) %>%
arrange(desc(mu_points))
A tibble: 37 3
| country_of_origin | mu_points | count |
|---|
| <chr> | <dbl> | <int> |
|---|
| Papua New Guinea | 85.75000 | 1 |
| Ethiopia | 85.48409 | 44 |
| Japan | 84.67000 | 1 |
| United States | 84.43300 | 10 |
| Kenya | 84.30960 | 25 |
| Panama | 83.70750 | 4 |
| Uganda | 83.45194 | 36 |
| Colombia | 83.10656 | 183 |
| El Salvador | 83.05286 | 21 |
| China | 82.92750 | 16 |
| Rwanda | 82.83000 | 1 |
| Costa Rica | 82.78902 | 51 |
| Thailand | 82.57375 | 32 |
| Indonesia | 82.56550 | 20 |
| Peru | 82.52600 | 10 |
| Brazil | 82.40591 | 132 |
| Tanzania, United Republic Of | 82.36950 | 40 |
| Taiwan | 82.00133 | 75 |
| Zambia | 81.92000 | 1 |
| Guatemala | 81.84657 | 181 |
| Laos | 81.83333 | 3 |
| Burundi | 81.83000 | 2 |
| United States (Hawaii) | 81.82041 | 73 |
| United States (Puerto Rico) | 81.72750 | 4 |
| Malawi | 81.71182 | 11 |
| Vietnam | 81.20875 | 8 |
| India | 81.08286 | 14 |
| Mexico | 80.89008 | 236 |
| Philippines | 80.83400 | 5 |
| Myanmar | 80.75000 | 8 |
| Mauritius | 80.50000 | 1 |
| Nicaragua | 80.45808 | 26 |
| Ecuador | 80.22000 | 3 |
| Honduras | 79.35755 | 53 |
| Cote d?Ivoire | 79.33000 | 1 |
| NA | 79.08000 | 1 |
| Haiti | 77.18000 | 6 |
tuesdata$coffee_ratings %>% select(country_of_origin) %>% table()
.
Brazil Burundi
132 2
China Colombia
16 183
Costa Rica Cote d?Ivoire
51 1
Ecuador El Salvador
3 21
Ethiopia Guatemala
44 181
Haiti Honduras
6 53
India Indonesia
14 20
Japan Kenya
1 25
Laos Malawi
3 11
Mauritius Mexico
1 236
Myanmar Nicaragua
8 26
Panama Papua New Guinea
4 1
Peru Philippines
10 5
Rwanda Taiwan
1 75
Tanzania, United Republic Of Thailand
40 32
Uganda United States
36 10
United States (Hawaii) United States (Puerto Rico)
73 4
Vietnam Zambia
8 1
tuesdata$coffee_ratings %>% select(c("altitude_mean_meters", "total_cup_points")) %>% plot()
