données bison futé

get_prev.R

res <- jsonlite::fromJSON("http://www.bison-fute.gouv.fr/previsions/previsions.json")
library(tidyverse)
prev_bf <- tibble(
  days = res$days,
  depts = list(res$deptsLine[1:94]),
  depart = map(res$values, ~ strsplit(.x[1:94], split = ",")) %>% modify_depth(2, 1) %>% simplify_all(),
  retour = map(res$values, ~ strsplit(.x[1:94], split = ",")) %>% modify_depth(2, 2) %>% simplify_all()
) %>%
  unnest()
prev_bf

reprex.md

res <- jsonlite::fromJSON("http://www.bison-fute.gouv.fr/previsions/previsions.json")
library(tidyverse)
prev_bf <- tibble(
  days = res$days,
  depts = list(res$deptsLine[1:94]),
  depart = map(res$values, ~ strsplit(.x[1:94], split = ",")) %>% modify_depth(2, 1) %>% simplify_all(),
  retour = map(res$values, ~ strsplit(.x[1:94], split = ",")) %>% modify_depth(2, 2) %>% simplify_all()
) %>%
  unnest()
prev_bf
#> # A tibble: 39,574 x 4
#>    days       depts depart retour
#>    <chr>      <chr> <chr>  <chr> 
#>  1 15/11/2017 75    V      V     
#>  2 15/11/2017 77    V      V     
#>  3 15/11/2017 78    V      V     
#>  4 15/11/2017 91    V      V     
#>  5 15/11/2017 92    V      V     
#>  6 15/11/2017 93    V      V     
#>  7 15/11/2017 94    V      V     
#>  8 15/11/2017 95    V      V     
#>  9 15/11/2017 2     V      V     
#> 10 15/11/2017 14    V      V     
#> # ... with 39,564 more rows

^{Created on 2018-10-05 by the reprex package (v0.2.1)}