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)
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
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 #>