meteo_nearby_stations() fails when working with tibbles (as opposed to data frames)

[taraskaduk]

Hello there!
I found that meteo_nearby_stations() doesn't seem to deal with tibbles very well, as opposed to data frames. It returns NAs for distance, and if no limit is specified - it returns all available stations.

It is not a problem to convert to a data frame from tibble. It is a problem, however, to diagnose the issue if you don't know what to look for.

Reprex

library(rnoaa)
library(tidyverse)

dataframe <- data.frame(id = c("sydney", "brisbane"),
                        latitude = c(-33.8675, -27.4710),
                        longitude = c(151.2070, 153.0234))
dataframe_stations <- meteo_nearby_stations(dataframe, radius = 10)
dataframe_stations
#> $brisbane
#> # A tibble: 58 x 5
#>    id          name                       latitude longitude distance
#>    <chr>       <chr>                         <dbl>     <dbl>    <dbl>
#>  1 ASN00040839 BRISBANE (BCC) ALERT          -27.5      153.    0.507
#>  2 ASN00040367 ST LUCIA TRAINING FARM        -27.5      153.    0.816
#>  3 ASN00040214 BRISBANE REGIONAL OFFICE      -27.5      153.    1.04 
#>  4 ASN00040359 HIGHGATE HILL                 -27.5      153.    1.52 
#>  5 ASN00040900 NORMANBY RAIL YARD            -27.5      153.    1.61 
#>  6 ASN00040215 BRISBANE BOTANICAL GARDENS    -27.5      153.    1.68 
#>  7 ASN00040911 HILLTOP GARDENS               -27.5      153.    1.86 
#>  8 ASN00040913 BRISBANE                      -27.5      153.    1.88 
#>  9 ASN00040233 MILTON                        -27.5      153     2.36 
#> 10 ASN00040216 BRISBANE SHOW GROUNDS         -27.5      153.    2.47 
#> # … with 48 more rows
#> 
#> $sydney
#> # A tibble: 62 x 5
#>    id          name                          latitude longitude distance
#>    <chr>       <chr>                            <dbl>     <dbl>    <dbl>
#>  1 ASN00066062 SYDNEY (OBSERVATORY HILL)        -33.9      151.    0.778
#>  2 ASN00066006 SYDNEY BOTANIC GARDENS           -33.9      151.    0.843
#>  3 ASN00066015 CROWN ST. RESERVOIR              -33.9      151.    1.87 
#>  4 ASN00066139 PADDINGTON                       -33.9      151.    1.97 
#>  5 ASN00066149 GLEBE POINT SYD. WATER SUPPLY    -33.9      151.    2.19 
#>  6 ASN00066178 BIRCHGROVE SCHOOL                -33.8      151.    2.93 
#>  7 ASN00066075 WAVERTON BOWLING CLUB            -33.8      151.    3.09 
#>  8 ASN00066097 RANWICK BUNNERONG RD             -33.9      151.    3.67 
#>  9 ASN00066033 ALEXANDRIA (HENDERSON ROAD)      -33.9      151.    3.74 
#> 10 ASN00066061 SYDNEY NTH BOWLING CLUB          -33.8      151.    3.86 
#> # … with 52 more rows


tibble <- tibble(id = c("sydney", "brisbane"),
                 latitude = c(-33.8675, -27.4710),
                 longitude = c(151.2070, 153.0234))
tibble_stations <- meteo_nearby_stations(tibble, radius = 10)
#> Warning in meteo_nearby_stations(tibble, radius = 10): NAs introduced by
#> coercion

#> Warning in meteo_nearby_stations(tibble, radius = 10): NAs introduced by
#> coercion
tibble_stations
#> $brisbane
#> # A tibble: 115,072 x 5
#>    id    name  latitude longitude distance
#>    <chr> <chr>    <dbl>     <dbl>    <dbl>
#>  1 <NA>  <NA>        NA        NA       NA
#>  2 <NA>  <NA>        NA        NA       NA
#>  3 <NA>  <NA>        NA        NA       NA
#>  4 <NA>  <NA>        NA        NA       NA
#>  5 <NA>  <NA>        NA        NA       NA
#>  6 <NA>  <NA>        NA        NA       NA
#>  7 <NA>  <NA>        NA        NA       NA
#>  8 <NA>  <NA>        NA        NA       NA
#>  9 <NA>  <NA>        NA        NA       NA
#> 10 <NA>  <NA>        NA        NA       NA
#> # … with 115,062 more rows
#> 
#> $sydney
#> # A tibble: 115,072 x 5
#>    id    name  latitude longitude distance
#>    <chr> <chr>    <dbl>     <dbl>    <dbl>
#>  1 <NA>  <NA>        NA        NA       NA
#>  2 <NA>  <NA>        NA        NA       NA
#>  3 <NA>  <NA>        NA        NA       NA
#>  4 <NA>  <NA>        NA        NA       NA
#>  5 <NA>  <NA>        NA        NA       NA
#>  6 <NA>  <NA>        NA        NA       NA
#>  7 <NA>  <NA>        NA        NA       NA
#>  8 <NA>  <NA>        NA        NA       NA
#>  9 <NA>  <NA>        NA        NA       NA
#> 10 <NA>  <NA>        NA        NA       NA
#> # … with 115,062 more rows

^{Created on 2019-12-27 by the reprex package (v0.3.0)}

[sckott]

thanks for the report @taraskaduk

having a look

[sckott]

should be fixed now. i am just coercing the tibble or data.frame to a data.frame as the first step internally, so we are always dealing with a data.frame