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Download palmerpenguins data from Environmental Data Initiative

Source: vignettes/download.Rmd
download.Rmd

Download data from a URL

You can also get this penguin data directly from the Environmental Data Initiative (EDI) using the R code below. Thanks to Julien Brun for the reminder and code below to access & combine them (will get you same data as penguins_raw):

# Adelie penguin data from: https://doi.org/10.6073/pasta/abc50eed9138b75f54eaada0841b9b86

uri_adelie <- "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.3&entityid=002f3893385f710df69eeebe893144ff"

# Gentoo penguin data from: https://doi.org/10.6073/pasta/2b1cff60f81640f182433d23e68541ce

uri_gentoo <- "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.220.3&entityid=e03b43c924f226486f2f0ab6709d2381"

# Chinstrap penguin data from: https://doi.org/10.6073/pasta/409c808f8fc9899d02401bdb04580af7
uri_chinstrap <- "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.221.2&entityid=fe853aa8f7a59aa84cdd3197619ef462"

# Combining the URIs
uris <- c(uri_adelie, uri_gentoo, uri_chinstrap)

# Downloading and importing data
library(purrr)
library(readr)
penguins_lter <- map_dfr(uris, read_csv)

Compare to package data

Compared to ?penguins_raw:

head(penguins_raw)
#> # A tibble: 6 × 17
#>   study…¹ Sampl…² Species Region Island Stage Indiv…³ Clutc…⁴ `Date Egg` Culme…⁵
#>   <chr>     <dbl> <chr>   <chr>  <chr>  <chr> <chr>   <chr>   <date>       <dbl>
#> 1 PAL0708       1 Adelie… Anvers Torge… Adul… N1A1    Yes     2007-11-11    39.1
#> 2 PAL0708       2 Adelie… Anvers Torge… Adul… N1A2    Yes     2007-11-11    39.5
#> 3 PAL0708       3 Adelie… Anvers Torge… Adul… N2A1    Yes     2007-11-16    40.3
#> 4 PAL0708       4 Adelie… Anvers Torge… Adul… N2A2    Yes     2007-11-16    NA  
#> 5 PAL0708       5 Adelie… Anvers Torge… Adul… N3A1    Yes     2007-11-16    36.7
#> 6 PAL0708       6 Adelie… Anvers Torge… Adul… N3A2    Yes     2007-11-16    39.3
#> # … with 7 more variables: `Culmen Depth (mm)` <dbl>,
#> #   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#> #   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>, and
#> #   abbreviated variable names ¹​studyName, ²​`Sample Number`, ³​`Individual ID`,
#> #   ⁴​`Clutch Completion`, ⁵​`Culmen Length (mm)`
#> # ℹ Use `colnames()` to see all variable names

But if that looks too messy for you, ?penguins is simpler and tidier:

head(penguins)
#> # A tibble: 6 × 8
#>   species island    bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
#>   <fct>   <fct>              <dbl>         <dbl>       <int>   <int> <fct> <int>
#> 1 Adelie  Torgersen           39.1          18.7         181    3750 male   2007
#> 2 Adelie  Torgersen           39.5          17.4         186    3800 fema…  2007
#> 3 Adelie  Torgersen           40.3          18           195    3250 fema…  2007
#> 4 Adelie  Torgersen           NA            NA            NA      NA NA     2007
#> 5 Adelie  Torgersen           36.7          19.3         193    3450 fema…  2007
#> 6 Adelie  Torgersen           39.3          20.6         190    3650 male   2007
#> # … with abbreviated variable names ¹​flipper_length_mm, ²​body_mass_g