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

``` r 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 #> #> 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 #> #> 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 #> #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> 7 NA NA NA #> 8 NA NA NA #> 9 NA NA NA #> 10 NA NA NA #> # … with 115,062 more rows #> #> $sydney #> # A tibble: 115,072 x 5 #> id name latitude longitude distance #> #> 1 NA NA NA #> 2 NA NA NA #> 3 NA NA NA #> 4 NA NA NA #> 5 NA NA NA #> 6 NA NA NA #> 7 NA NA NA #> 8 NA NA NA #> 9 NA NA NA #> 10 NA NA NA #> # … with 115,062 more rows ``` ^{Created on 2019-12-27 by the [reprex package](https://reprex.tidyverse.org) (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