“fleece” contains helper functions for turning nested lists from JSON data into tidy data.frames. It deals with entries of unequal length and automatically unnests all columns.
You can install the development version of fleece from GitHub with:
# install.packages("devtools")
devtools::install_github("thieled/fleece")Here are two examples how to use fleece::rectangularize() on JSON output from different APIs.
library(fleece)
### Example 1
# In the first example we use the "earthquake.usgs.gov" API to get data about some earthquakes:
url <- "https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2023-03-01&endtime=2023-03-02&minmagnitude=5"
# Call the API and extract the content using httr
con <- httr::GET(url) |> httr::content()
# Note that httr::content already parses the JSON data into a "list" R-object.
# Often, APIs return a number of things. Pick the content that interests you:
con <- con$features
# Feed the list object into rectangularize() and voila:
df <- rectangularize(con)
dplyr::glimpse(df)
#> Rows: 6
#> Columns: 32
#> $ type <chr> "Feature", "Feature", "Feature", "Feature", "Fe…
#> $ id <chr> "us7000jgnz", "us7000jgmv", "us7000jgmu", "us70…
#> $ properties_mag <dbl> 5.0, 5.0, 5.5, 5.0, 5.1, 6.6
#> $ properties_place <chr> "southern Sumatra, Indonesia", "157 km ESE of K…
#> $ properties_time <dbl> 1.677712e+12, 1.677699e+12, 1.677699e+12, 1.677…
#> $ properties_updated <dbl> 1.683511e+12, 1.683511e+12, 1.689795e+12, 1.683…
#> $ properties_tz <lgl> NA, NA, NA, NA, NA, NA
#> $ properties_url <chr> "https://earthquake.usgs.gov/earthquakes/eventp…
#> $ properties_detail <chr> "https://earthquake.usgs.gov/fdsnws/event/1/que…
#> $ properties_felt <int> 2, NA, NA, NA, NA, 4
#> $ properties_cdi <dbl> 3.1, NA, NA, NA, NA, 4.1
#> $ properties_mmi <dbl> NA, NA, 2.980, NA, NA, 2.354
#> $ properties_alert <chr> NA, NA, "green", NA, NA, "green"
#> $ properties_status <chr> "reviewed", "reviewed", "reviewed", "reviewed",…
#> $ properties_tsunami <int> 0, 0, 0, 0, 0, 1
#> $ properties_sig <int> 385, 385, 465, 385, 400, 672
#> $ properties_net <chr> "us", "us", "us", "us", "us", "us"
#> $ properties_code <chr> "7000jgnz", "7000jgmv", "7000jgmu", "7000jgk0",…
#> $ properties_ids <chr> ",us7000jgnz,", ",us7000jgmv,", ",pt23060001,us…
#> $ properties_sources <chr> ",us,", ",us,", ",pt,us,", ",us,", ",us,", ",us…
#> $ properties_types <chr> ",dyfi,origin,phase-data,", ",origin,phase-data…
#> $ properties_nst <int> 206, 60, 103, 110, 39, 156
#> $ properties_dmin <dbl> 3.490, 5.028, 1.965, 0.655, 17.440, 2.725
#> $ properties_rms <dbl> 0.64, 0.64, 0.90, 0.41, 0.58, 0.76
#> $ properties_gap <int> 28, 78, 44, 73, 95, 11
#> $ properties_magType <chr> "mww", "mb", "mww", "mb", "mb", "mww"
#> $ properties_type <chr> "earthquake", "earthquake", "earthquake", "eart…
#> $ properties_title <chr> "M 5.0 - southern Sumatra, Indonesia", "M 5.0 -…
#> $ geometry_type <chr> "Point", "Point", "Point", "Point", "Point", "P…
#> $ geometry_coordinates_1 <dbl> 100.8384, 149.5786, 146.8086, 142.7793, -27.079…
#> $ geometry_coordinates_2 <dbl> -1.5655, 44.4887, 14.1495, 27.4834, -60.3018, -…
#> $ geometry_coordinates_3 <dbl> 93.325, 35.000, 10.404, 16.780, 62.441, 600.933
### Example 2
# Let's get some information about German universities:
url_2 <- "http://universities.hipolabs.com/search?country=Germany"
# Let's use this example to demonstrate how to deal with JSON data that is plain text:
con_2 <- httr::GET(url_2) |> httr::content(as = "text", encoding = "UTF-8")
# Use jsonlite::parse_json() to parse text as an R 'list' object:
con_2 <- con_2 |> jsonlite::parse_json()
# Here, we can use the output right away for rectangularize():
df_2 <- rectangularize(con_2)
dplyr::glimpse(df_2)
#> Rows: 622
#> Columns: 12
#> $ `state-province` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ country <chr> "Germany", "Germany", "Germany", "Germany", "Germany"…
#> $ alpha_two_code <chr> "DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE",…
#> $ name <chr> "AKAD Hochschulen für Berufstätige, Fachhochschule Le…
#> $ domains_1 <chr> "akad.de", "asfh-berlin.de", "augustana.de", "bethel.…
#> $ domains_2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ domains_3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ domains_4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ web_pages_1 <chr> "http://www.akad.de/", "http://www.asfh-berlin.de/", …
#> $ web_pages_2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ web_pages_3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ web_pages_4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…Writing documentation was accelerated by
ChatGPT.
The Adventure-Time like drawing in the logo was created by
Dall-E.
The name is a pun.