First, set up Google credentials.

library(googlesheets4)

# Prompts a browser pop-up
gs4_auth()

# Once set up, you can automate this process by passing your email
gs4_auth(email = "avamariehoffman@gmail.com")

You can also supply an authorization token directly, but make sure to add any files to your .gitignore!

library(googledrive)
drive_auth(email= "<email>",
           token = readRDS("google-sheets-token.rds")) # Saved in a file

Specify the sheet name if necessary:

sheet_dat_oceania <- read_sheet(sheet_url, sheet = "Oceania")

✔ Reading from "gapminder".

✔ Range ''Oceania''.

head(sheet_dat_oceania)

# A tibble: 6 × 6
  country   continent  year lifeExp      pop gdpPercap
  <chr>     <chr>     <dbl>   <dbl>    <dbl>     <dbl>
1 Australia Oceania    1952    69.1  8691212    10040.
2 Australia Oceania    1957    70.3  9712569    10950.
3 Australia Oceania    1962    70.9 10794968    12217.
4 Australia Oceania    1967    71.1 11872264    14526.
5 Australia Oceania    1972    71.9 13177000    16789.
6 Australia Oceania    1977    73.5 14074100    18334.

read_sheet(sheet_url, sheet = "Oceania", range =  cell_rows(1:4))

✔ Reading from "gapminder".

✔ Range ''Oceania'!1:4'.

# A tibble: 3 × 6
  country   continent  year lifeExp      pop gdpPercap
  <chr>     <chr>     <dbl>   <dbl>    <dbl>     <dbl>
1 Australia Oceania    1952    69.1  8691212    10040.
2 Australia Oceania    1957    70.3  9712569    10950.
3 Australia Oceania    1962    70.9 10794968    12217.

read_sheet(sheet_url, sheet = "Oceania", range =  cell_cols("A:B"))

✔ Reading from "gapminder".

✔ Range ''Oceania'!A:B'.

# A tibble: 24 × 2
   country   continent
   <chr>     <chr>    
 1 Australia Oceania  
 2 Australia Oceania  
 3 Australia Oceania  
 4 Australia Oceania  
 5 Australia Oceania  
 6 Australia Oceania  
 7 Australia Oceania  
 8 Australia Oceania  
 9 Australia Oceania  
10 Australia Oceania  
# ℹ 14 more rows

List out the sheet names using sheet_names().

sheet_names(sheet_url)

[1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania" 

Iterate through the sheet names with purrr:

gapminder_sheets <- sheet_names(sheet_url)

data_list <- map(gapminder_sheets, ~ read_sheet(sheet_url, sheet = .x))

✔ Reading from "gapminder".

✔ Range ''Africa''.

✔ Reading from "gapminder".

✔ Range ''Americas''.

✔ Reading from "gapminder".

✔ Range ''Asia''.

✔ Reading from "gapminder".

✔ Range ''Europe''.

✔ Reading from "gapminder".

✔ Range ''Oceania''.

Check out the list:

str(data_list)

List of 5
 $ : tibble [624 × 6] (S3: tbl_df/tbl/data.frame)
  ..$ country  : chr [1:624] "Algeria" "Algeria" "Algeria" "Algeria" ...
  ..$ continent: chr [1:624] "Africa" "Africa" "Africa" "Africa" ...
  ..$ year     : num [1:624] 1952 1957 1962 1967 1972 ...
  ..$ lifeExp  : num [1:624] 43.1 45.7 48.3 51.4 54.5 ...
  ..$ pop      : num [1:624] 9279525 10270856 11000948 12760499 14760787 ...
  ..$ gdpPercap: num [1:624] 2449 3014 2551 3247 4183 ...
 $ : tibble [300 × 6] (S3: tbl_df/tbl/data.frame)
  ..$ country  : chr [1:300] "Argentina" "Argentina" "Argentina" "Argentina" ...
  ..$ continent: chr [1:300] "Americas" "Americas" "Americas" "Americas" ...
  ..$ year     : num [1:300] 1952 1957 1962 1967 1972 ...
  ..$ lifeExp  : num [1:300] 62.5 64.4 65.1 65.6 67.1 ...
  ..$ pop      : num [1:300] 17876956 19610538 21283783 22934225 24779799 ...
  ..$ gdpPercap: num [1:300] 5911 6857 7133 8053 9443 ...
 $ : tibble [396 × 6] (S3: tbl_df/tbl/data.frame)
  ..$ country  : chr [1:396] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
  ..$ continent: chr [1:396] "Asia" "Asia" "Asia" "Asia" ...
  ..$ year     : num [1:396] 1952 1957 1962 1967 1972 ...
  ..$ lifeExp  : num [1:396] 28.8 30.3 32 34 36.1 ...
  ..$ pop      : num [1:396] 8425333 9240934 10267083 11537966 13079460 ...
  ..$ gdpPercap: num [1:396] 779 821 853 836 740 ...
 $ : tibble [360 × 6] (S3: tbl_df/tbl/data.frame)
  ..$ country  : chr [1:360] "Albania" "Albania" "Albania" "Albania" ...
  ..$ continent: chr [1:360] "Europe" "Europe" "Europe" "Europe" ...
  ..$ year     : num [1:360] 1952 1957 1962 1967 1972 ...
  ..$ lifeExp  : num [1:360] 55.2 59.3 64.8 66.2 67.7 ...
  ..$ pop      : num [1:360] 1282697 1476505 1728137 1984060 2263554 ...
  ..$ gdpPercap: num [1:360] 1601 1942 2313 2760 3313 ...
 $ : tibble [24 × 6] (S3: tbl_df/tbl/data.frame)
  ..$ country  : chr [1:24] "Australia" "Australia" "Australia" "Australia" ...
  ..$ continent: chr [1:24] "Oceania" "Oceania" "Oceania" "Oceania" ...
  ..$ year     : num [1:24] 1952 1957 1962 1967 1972 ...
  ..$ lifeExp  : num [1:24] 69.1 70.3 70.9 71.1 71.9 ...
  ..$ pop      : num [1:24] 8691212 9712569 10794968 11872264 13177000 ...
  ..$ gdpPercap: num [1:24] 10040 10950 12217 14526 16789 ...

Pull out sheets as needed:

data_list[[{sheet}]]
# OR
data_list %>% pluck({sheet})
# OR (if named)
data_list${sheet}

sheet_dat_oceania <- data_list[[5]]

sheet_dat_oceania <- sheet_dat_oceania %>%
  mutate(lifeExp_days = lifeExp * 365)

sheet_out <- gs4_create("Oceania-days", 
                       sheets = list(Oceania_days = sheet_dat_oceania))

✔ Creating new Sheet: "Oceania-days".

# Opens a browser window
gs4_browse(sheet_out)

sheet_append(sheet_out, data = sheet_dat_oceania, sheet = "Oceania_days")

✔ Writing to "Oceania-days".

✔ Appending 24 rows to 'Oceania_days'.

https://jhudatascience.org/tidyversecourse/get-data.html#google-sheets

http://sisbid.github.io/Data-Wrangling/labs/advanced-io-lab.Rmd

https://en.wikipedia.org/wiki/JSON

https://docs.github.com/en/rest/reference/search

dim(jsonData$pokemon)

[1] 151  17

class(jsonData$pokemon)

[1] "data.frame"

jsonData$pokemon %>% filter(type == "Fire") %>% select(!(img))

    id num       name type height   weight            candy candy_count         egg spawn_chance avg_spawns
1    4 004 Charmander Fire 0.61 m   8.5 kg Charmander Candy          25        2 km       0.2530      25.30
2    5 005 Charmeleon Fire 1.09 m  19.0 kg Charmander Candy         100 Not in Eggs       0.0120       1.20
3   37 037     Vulpix Fire 0.61 m   9.9 kg     Vulpix Candy          50        5 km       0.2200      22.00
4   38 038  Ninetales Fire 1.09 m  19.9 kg     Vulpix Candy          NA Not in Eggs       0.0077       0.77
5   58 058  Growlithe Fire 0.71 m  19.0 kg  Growlithe Candy          50        5 km       0.9200      92.00
6   59 059   Arcanine Fire 1.91 m 155.0 kg  Growlithe Candy          NA Not in Eggs       0.0170       1.70
7   77 077     Ponyta Fire 0.99 m  30.0 kg     Ponyta Candy          50        5 km       0.5100      51.00
8   78 078   Rapidash Fire 1.70 m  95.0 kg     Ponyta Candy          NA Not in Eggs       0.0110       1.10
9  126 126     Magmar Fire 1.30 m  44.5 kg             None          NA       10 km       0.1000      10.00
10 136 136    Flareon Fire 0.89 m  25.0 kg      Eevee Candy          NA Not in Eggs       0.0170       1.70
   spawn_time multipliers          weaknesses                  next_evolution  prev_evolution
1       08:45        1.65 Water, Ground, Rock 005, 006, Charmeleon, Charizard            NULL
2       19:00        1.79 Water, Ground, Rock                  006, Charizard 004, Charmander
3       13:43  2.74, 2.81 Water, Ground, Rock                  038, Ninetales            NULL
4       01:32        NULL Water, Ground, Rock                            NULL     037, Vulpix
5       03:57  2.31, 2.36 Water, Ground, Rock                   059, Arcanine            NULL
6       03:11        NULL Water, Ground, Rock                            NULL  058, Growlithe
7       02:50  1.48, 1.50 Water, Ground, Rock                   078, Rapidash            NULL
8       04:00        NULL Water, Ground, Rock                            NULL     077, Ponyta
9       20:36        NULL Water, Ground, Rock                            NULL            NULL
10      07:02        NULL Water, Ground, Rock                            NULL      133, Eevee

class(jsonData$pokemon$type) # Can be lists

[1] "list"

jsonData$pokemon$type

[[1]]
[1] "Grass"  "Poison"

[[2]]
[1] "Grass"  "Poison"

[[3]]
[1] "Grass"  "Poison"

[[4]]
[1] "Fire"

[[5]]
[1] "Fire"

[[6]]
[1] "Fire"   "Flying"

[[7]]
[1] "Water"

[[8]]
[1] "Water"

[[9]]
[1] "Water"

[[10]]
[1] "Bug"

[[11]]
[1] "Bug"

[[12]]
[1] "Bug"    "Flying"

[[13]]
[1] "Bug"    "Poison"

[[14]]
[1] "Bug"    "Poison"

[[15]]
[1] "Bug"    "Poison"

[[16]]
[1] "Normal" "Flying"

[[17]]
[1] "Normal" "Flying"

[[18]]
[1] "Normal" "Flying"

[[19]]
[1] "Normal"

[[20]]
[1] "Normal"

[[21]]
[1] "Normal" "Flying"

[[22]]
[1] "Normal" "Flying"

[[23]]
[1] "Poison"

[[24]]
[1] "Poison"

[[25]]
[1] "Electric"

[[26]]
[1] "Electric"

[[27]]
[1] "Ground"

[[28]]
[1] "Ground"

[[29]]
[1] "Poison"

[[30]]
[1] "Poison"

[[31]]
[1] "Poison" "Ground"

[[32]]
[1] "Poison"

[[33]]
[1] "Poison"

[[34]]
[1] "Poison" "Ground"

[[35]]
[1] "Normal"

[[36]]
[1] "Normal"

[[37]]
[1] "Fire"

[[38]]
[1] "Fire"

[[39]]
[1] "Normal"

[[40]]
[1] "Normal"

[[41]]
[1] "Poison" "Flying"

[[42]]
[1] "Poison" "Flying"

[[43]]
[1] "Grass"  "Poison"

[[44]]
[1] "Grass"  "Poison"

[[45]]
[1] "Grass"  "Poison"

[[46]]
[1] "Bug"   "Grass"

[[47]]
[1] "Bug"   "Grass"

[[48]]
[1] "Bug"    "Poison"

[[49]]
[1] "Bug"    "Poison"

[[50]]
[1] "Ground"

[[51]]
[1] "Ground"

[[52]]
[1] "Normal"

[[53]]
[1] "Normal"

[[54]]
[1] "Water"

[[55]]
[1] "Water"

[[56]]
[1] "Fighting"

[[57]]
[1] "Fighting"

[[58]]
[1] "Fire"

[[59]]
[1] "Fire"

[[60]]
[1] "Water"

[[61]]
[1] "Water"

[[62]]
[1] "Water"    "Fighting"

[[63]]
[1] "Psychic"

[[64]]
[1] "Psychic"

[[65]]
[1] "Psychic"

[[66]]
[1] "Fighting"

[[67]]
[1] "Fighting"

[[68]]
[1] "Fighting"

[[69]]
[1] "Grass"  "Poison"

[[70]]
[1] "Grass"  "Poison"

[[71]]
[1] "Grass"  "Poison"

[[72]]
[1] "Water"  "Poison"

[[73]]
[1] "Water"  "Poison"

[[74]]
[1] "Rock"   "Ground"

[[75]]
[1] "Rock"   "Ground"

[[76]]
[1] "Rock"   "Ground"

[[77]]
[1] "Fire"

[[78]]
[1] "Fire"

[[79]]
[1] "Water"   "Psychic"

[[80]]
[1] "Water"   "Psychic"

[[81]]
[1] "Electric"

[[82]]
[1] "Electric"

[[83]]
[1] "Normal" "Flying"

[[84]]
[1] "Normal" "Flying"

[[85]]
[1] "Normal" "Flying"

[[86]]
[1] "Water"

[[87]]
[1] "Water" "Ice"  

[[88]]
[1] "Poison"

[[89]]
[1] "Poison"

[[90]]
[1] "Water"

[[91]]
[1] "Water" "Ice"  

[[92]]
[1] "Ghost"  "Poison"

[[93]]
[1] "Ghost"  "Poison"

[[94]]
[1] "Ghost"  "Poison"

[[95]]
[1] "Rock"   "Ground"

[[96]]
[1] "Psychic"

[[97]]
[1] "Psychic"

[[98]]
[1] "Water"

[[99]]
[1] "Water"

[[100]]
[1] "Electric"

[[101]]
[1] "Electric"

[[102]]
[1] "Grass"   "Psychic"

[[103]]
[1] "Grass"   "Psychic"

[[104]]
[1] "Ground"

[[105]]
[1] "Ground"

[[106]]
[1] "Fighting"

[[107]]
[1] "Fighting"

[[108]]
[1] "Normal"

[[109]]
[1] "Poison"

[[110]]
[1] "Poison"

[[111]]
[1] "Ground" "Rock"  

[[112]]
[1] "Ground" "Rock"  

[[113]]
[1] "Normal"

[[114]]
[1] "Grass"

[[115]]
[1] "Normal"

[[116]]
[1] "Water"

[[117]]
[1] "Water"

[[118]]
[1] "Water"

[[119]]
[1] "Water"

[[120]]
[1] "Water"

[[121]]
[1] "Water"   "Psychic"

[[122]]
[1] "Psychic"

[[123]]
[1] "Bug"    "Flying"

[[124]]
[1] "Ice"     "Psychic"

[[125]]
[1] "Electric"

[[126]]
[1] "Fire"

[[127]]
[1] "Bug"

[[128]]
[1] "Normal"

[[129]]
[1] "Water"

[[130]]
[1] "Water"  "Flying"

[[131]]
[1] "Water" "Ice"  

[[132]]
[1] "Normal"

[[133]]
[1] "Normal"

[[134]]
[1] "Water"

[[135]]
[1] "Electric"

[[136]]
[1] "Fire"

[[137]]
[1] "Normal"

[[138]]
[1] "Rock"  "Water"

[[139]]
[1] "Rock"  "Water"

[[140]]
[1] "Rock"  "Water"

[[141]]
[1] "Rock"  "Water"

[[142]]
[1] "Rock"   "Flying"

[[143]]
[1] "Normal"

[[144]]
[1] "Ice"    "Flying"

[[145]]
[1] "Electric" "Flying"  

[[146]]
[1] "Fire"   "Flying"

[[147]]
[1] "Dragon"

[[148]]
[1] "Dragon"

[[149]]
[1] "Dragon" "Flying"

[[150]]
[1] "Psychic"

[[151]]
[1] "Psychic"

class(jsonData$pokemon$next_evolution[[1]]) # Or lists of data.frames!

[1] "data.frame"

jsonData$pokemon$next_evolution

[[1]]
  num     name
1 002  Ivysaur
2 003 Venusaur

[[2]]
  num     name
1 003 Venusaur

[[3]]
NULL

[[4]]
  num       name
1 005 Charmeleon
2 006  Charizard

[[5]]
  num      name
1 006 Charizard

[[6]]
NULL

[[7]]
  num      name
1 008 Wartortle
2 009 Blastoise

[[8]]
  num      name
1 009 Blastoise

[[9]]
NULL

[[10]]
  num       name
1 011    Metapod
2 012 Butterfree

[[11]]
  num       name
1 012 Butterfree

[[12]]
NULL

[[13]]
  num     name
1 014   Kakuna
2 015 Beedrill

[[14]]
  num     name
1 015 Beedrill

[[15]]
NULL

[[16]]
  num      name
1 017 Pidgeotto
2 018   Pidgeot

[[17]]
  num    name
1 018 Pidgeot

[[18]]
NULL

[[19]]
  num     name
1 020 Raticate

[[20]]
NULL

[[21]]
  num   name
1 022 Fearow

[[22]]
NULL

[[23]]
  num  name
1 024 Arbok

[[24]]
NULL

[[25]]
  num   name
1 026 Raichu

[[26]]
NULL

[[27]]
  num      name
1 028 Sandslash

[[28]]
NULL

[[29]]
  num      name
1 030  Nidorina
2 031 Nidoqueen

[[30]]
  num      name
1 031 Nidoqueen

[[31]]
NULL

[[32]]
  num     name
1 033 Nidorino
2 034 Nidoking

[[33]]
  num     name
1 034 Nidoking

[[34]]
NULL

[[35]]
  num     name
1 036 Clefable

[[36]]
NULL

[[37]]
  num      name
1 038 Ninetales

[[38]]
NULL

[[39]]
  num       name
1 040 Wigglytuff

[[40]]
NULL

[[41]]
  num   name
1 042 Golbat

[[42]]
NULL

[[43]]
  num      name
1 044     Gloom
2 045 Vileplume

[[44]]
  num      name
1 045 Vileplume

[[45]]
NULL

[[46]]
  num     name
1 047 Parasect

[[47]]
NULL

[[48]]
  num     name
1 049 Venomoth

[[49]]
NULL

[[50]]
  num    name
1 051 Dugtrio

[[51]]
NULL

[[52]]
  num    name
1 053 Persian

[[53]]
NULL

[[54]]
  num    name
1 055 Golduck

[[55]]
NULL

[[56]]
  num     name
1 057 Primeape

[[57]]
NULL

[[58]]
  num     name
1 059 Arcanine

[[59]]
NULL

[[60]]
  num      name
1 061 Poliwhirl
2 062 Poliwrath

[[61]]
  num      name
1 062 Poliwrath

[[62]]
NULL

[[63]]
  num     name
1 064  Kadabra
2 065 Alakazam

[[64]]
  num     name
1 065 Alakazam

[[65]]
NULL

[[66]]
  num    name
1 067 Machoke
2 068 Machamp

[[67]]
  num    name
1 068 Machamp

[[68]]
NULL

[[69]]
  num       name
1 070 Weepinbell
2 071 Victreebel

[[70]]
  num       name
1 071 Victreebel

[[71]]
NULL

[[72]]
  num       name
1 073 Tentacruel

[[73]]
NULL

[[74]]
  num     name
1 075 Graveler
2 076    Golem

[[75]]
  num  name
1 076 Golem

[[76]]
NULL

[[77]]
  num     name
1 078 Rapidash

[[78]]
NULL

[[79]]
  num    name
1 080 Slowbro

[[80]]
NULL

[[81]]
  num     name
1 082 Magneton

[[82]]
NULL

[[83]]
NULL

[[84]]
  num   name
1 085 Dodrio

[[85]]
NULL

[[86]]
  num    name
1 087 Dewgong

[[87]]
NULL

[[88]]
  num name
1 089  Muk

[[89]]
NULL

[[90]]
  num     name
1 091 Cloyster

[[91]]
NULL

[[92]]
  num    name
1 093 Haunter
2 094  Gengar

[[93]]
  num   name
1 094 Gengar

[[94]]
NULL

[[95]]
NULL

[[96]]
  num  name
1 097 Hypno

[[97]]
NULL

[[98]]
  num    name
1 099 Kingler

[[99]]
NULL

[[100]]
  num      name
1 101 Electrode

[[101]]
NULL

[[102]]
  num      name
1 103 Exeggutor

[[103]]
NULL

[[104]]
  num    name
1 105 Marowak

[[105]]
NULL

[[106]]
NULL

[[107]]
NULL

[[108]]
NULL

[[109]]
  num    name
1 110 Weezing

[[110]]
NULL

[[111]]
  num   name
1 112 Rhydon

[[112]]
NULL

[[113]]
NULL

[[114]]
NULL

[[115]]
NULL

[[116]]
  num   name
1 117 Seadra

[[117]]
NULL

[[118]]
  num    name
1 119 Seaking

[[119]]
NULL

[[120]]
  num    name
1 121 Starmie

[[121]]
NULL

[[122]]
NULL

[[123]]
NULL

[[124]]
NULL

[[125]]
NULL

[[126]]
NULL

[[127]]
NULL

[[128]]
NULL

[[129]]
  num     name
1 130 Gyarados

[[130]]
NULL

[[131]]
NULL

[[132]]
NULL

[[133]]
  num     name
1 134 Vaporeon
2 135  Jolteon
3 136  Flareon

[[134]]
NULL

[[135]]
NULL

[[136]]
NULL

[[137]]
NULL

[[138]]
  num    name
1 139 Omastar

[[139]]
NULL

[[140]]
  num     name
1 141 Kabutops

[[141]]
NULL

[[142]]
NULL

[[143]]
NULL

[[144]]
NULL

[[145]]
NULL

[[146]]
NULL

[[147]]
  num      name
1 148 Dragonair
2 149 Dragonite

[[148]]
  num      name
1 149 Dragonite

[[149]]
NULL

[[150]]
NULL

[[151]]
NULL

http://bowtie-bio.sourceforge.net/recount/

https://github.com/simonmunzert/rscraping-jsm-2016/blob/c04fd91fec711df65c838e07723125155a7f2cda/02-scraping-with-rvest.r

https://rvest.tidyverse.org/articles/selectorgadget.html

recount_url <- "http://bowtie-bio.sourceforge.net/recount/"
# install.packages("rvest")
library(rvest)
htmlfile <- read_html(recount_url)

nds <- html_nodes(htmlfile, xpath = '//*[@id="recounttab"]/table')
dat <- html_table(nds)
dat <- as.data.frame(dat)
head(dat)

       X1                                         X2      X3                               X4
1   Study                                       PMID Species  Number of biological replicates
2 bodymap not published, but publicly available here   human                               19
3  cheung                                   20856902   human                               41
4    core                                   19056941   human                                2
5   gilad                                   20009012   human                                6
6    maqc                                   20167110   human 14 (technical)**  2 (biological)
                                X5              X6              X7              X8
1 Number of uniquely aligned reads   ExpressionSet     Count table Phenotype table
2                    2,197,622,796            link            link            link
3                      834,584,950            link            link            link
4                        8,670,342            link            link            link
5                       41,356,738            link            link            link
6                       71,970,164 original pooled original pooled original pooled
                                               X9
1                                           Notes
2 Illumina Human BodyMap 2.0 -- tissue comparison
3                                    HapMap - CEU
4                                lung fibroblasts
5                        liver; males and femlaes
6                              experiment: MAQC-2

colnames(dat) <- as.character(dat[1,])
dat <- dat[-1,]
head(dat)

       Study                                       PMID Species  Number of biological replicates
2    bodymap not published, but publicly available here   human                               19
3     cheung                                   20856902   human                               41
4       core                                   19056941   human                                2
5      gilad                                   20009012   human                                6
6       maqc                                   20167110   human 14 (technical)**  2 (biological)
7 montgomery                                   20220756   human                               60
  Number of uniquely aligned reads   ExpressionSet     Count table Phenotype table
2                    2,197,622,796            link            link            link
3                      834,584,950            link            link            link
4                        8,670,342            link            link            link
5                       41,356,738            link            link            link
6                       71,970,164 original pooled original pooled original pooled
7                     *886,468,054            link            link            link
                                            Notes
2 Illumina Human BodyMap 2.0 -- tissue comparison
3                                    HapMap - CEU
4                                lung fibroblasts
5                        liver; males and femlaes
6                              experiment: MAQC-2
7                                    HapMap - CEU

https://slate.com/culture/2020/04/whitney-museum-new-york-apartment-exhibit-creators-interview.html

https://doi.org/10.1016/j.dib.2020.106178

https://techcrunch.com/2017/04/28/someone-scraped-40000-tinder-selfies-to-make-a-facial-dataset-for-ai-experiments/

https://on.wsj.com/3hzeu9i

https://developers.facebook.com/

http://www.ncbi.nlm.nih.gov/books/NBK25501/ https://www.ncbi.nlm.nih.gov/home/develop/api/

https://ropensci.org/packages/

tidycensus package: https://walker-data.com/tidycensus/articles/basic-usage.html

https://github.com/ThatCopy/catAPI/wiki/Usage

#install.packages("httr")
library(httr)

# Requests a random cat fact
query_url <- "https://thatcopy.pw/catapi/rest/"

req <- GET(query_url)
content(req)

{html_document}
<html>
[1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">\n<title>Loading...</title>\n< ...
[2] <body><script type="text/javascript">window.location.replace('https://thatcopy.pw/catapi/rest/?ch=1&js=ey ...

https://www.rdocumentation.org/packages/twitteR/versions/1.1.9

# install.packages("twitteR")
library(twitteR)
# Supplied by Twitter
setup_twitter_oauth("API key", "API secret")

searchTwitter("crab cake", geocode="39.290692,-76.610221,5mi")

https://developer.twitter.com/en/portal/petition/academic/is-it-right-for-you

https://walker-data.com/tidycensus/articles/basic-usage.html

# install.packages("tidycensus")
library(tidycensus)
# Supplied by census.gov
census_api_key("YOUR API KEY GOES HERE")

get_decennial(geography = "state", 
                       variables = "P013001", # code for median age
                       year = 2010)