This is related to here.com and the developer side on a freemium account.
Problem: Is creating an empty dataframe or only ID instead of giving me a full data table with a polygon each row based in the isoline routing based in time.
Below piece could please guide anyone into a way to solve this? Code obtained through this example
set_key(api_key = "12345") # not it
df <- structure(list(address = c("853, Boul. Albiny-Paquette", "790 Boul. Wilfrid-Lavigne",
"495 Vanier", "209, Chemin Aylmer", "585 Boul. Des Grives", "265 Boul. Saint-Raymond",
"725 Boul. Des Hautes-Plaines", "9 Rue Daniel-Johnson", "480 Boul. St-Joseph",
"15 Rue Eddy"), city = c("Mont-Laurier", "Gatineau", "Gatineau",
"Gatineau", "Gatineau", "Gatineau", "Gatineau", "Gatineau", "Gatineau",
"Gatineau"), state = c("QC", "QC", "QC", "QC", "QC", "QC", "QC",
"QC", "QC", "QC"), zip_code = c("J9L 1L6", "J9J 2S5", "J9J 1Z4",
"J9H 1A3", "J9A 3W9", "J9A 3G6", "J8Z 2W9", "J8Z 1X6", "J8Y 3Y7",
"J8X 4B3"), county = c("Antoine-Labelle", "Communauté-Urbaine-de-l'Outaouais",
"Communauté-Urbaine-de-l'Outaouais", "Communauté-Urbaine-de-l'Outaouais",
"Communauté-Urbaine-de-l'Outaouais", "Communauté-Urbaine-de-l'Outaouais",
"Communauté-Urbaine-de-l'Outaouais", "Communauté-Urbaine-de-l'Outaouais",
"Communauté-Urbaine-de-l'Outaouais", "Communauté-Urbaine-de-l'Outaouais"
), country = c("Canada", "Canada", "Canada", "Canada", "Canada",
"Canada", "Canada", "Canada", "Canada", "Canada"), geometry = structure(list(
structure(c(-75.4880697, 46.5512158), class = c("XY", "POINT",
"sfg")), structure(c(-75.8386017, 45.4110993), class = c("XY",
"POINT", "sfg")), structure(c(-75.8070987999999, 45.4161897
), class = c("XY", "POINT", "sfg")), structure(c(-75.8322898,
45.3959535), class = c("XY", "POINT", "sfg")), structure(c(-75.7859960999999,
45.4435358), class = c("XY", "POINT", "sfg")), structure(c(-75.7611743,
45.4269774), class = c("XY", "POINT", "sfg")), structure(c(-75.7719339,
45.474538), class = c("XY", "POINT", "sfg")), structure(c(-75.7475764,
45.4625748), class = c("XY", "POINT", "sfg")), structure(c(-75.7323619,
45.4464210999999), class = c("XY", "POINT", "sfg")), structure(c(-75.7202519,
45.4251762), class = c("XY", "POINT", "sfg"))), class = c("sfc_POINT",
"sfc"), precision = 0, bbox = structure(c(xmin = -75.8386017,
ymin = 45.3959535, xmax = -75.4880697, ymax = 46.5512158), class = "bbox"), crs = structure(list(
input = "WGS84", wkt = "GEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n CS[ellipsoidal,2],\n AXIS[\"geodetic latitude (Lat)\",north,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"geodetic longitude (Lon)\",east,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), sf_column = "geometry", agr = structure(c(store_number = NA_integer_,
store_type = NA_integer_, address = NA_integer_, city = NA_integer_,
state = NA_integer_, zip_code = NA_integer_, phone_number = NA_integer_,
store_hours = NA_integer_, services = NA_integer_, website_address = NA_integer_,
latitude = NA_integer_, longitude = NA_integer_, country_code = NA_integer_,
county = NA_integer_, country = NA_integer_, geo_accuracy = NA_integer_
), class = "factor", levels = c("constant", "aggregate", "identity"
)), row.names = c(NA, 10L), class = "data.frame")
iso <- as.data.frame(c())
for (i in 1:nrow(df)){
iso[i,] <- isoline(
st_sf(df[i,]),
range = (5*60),
range_type = "time",
routing_mode = "fast",
transport_mode = "car",
aggregate = TRUE,
traffic = TRUE
)
print(iso)
iso$store_number[i] <- df$store_number[i]
}
st_write(obj = iso, dsn = "5min_isoline.shp", factorsAsCharacter = T, delete_layer = T)
ERROR:
<[1] id
<0 rows> (or 0-length row.names)
Error in $<-.data.frame(*tmp*, "zip", value = "J9L 1L6") :
replacement has 1 row, data has 0
In addition: Warning messages:
1: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 1 has 1 row to replace 0 rows
2: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 2 has 1 row to replace 0 rows
3: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 3 has 1 row to replace 0 rows
4: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 4 has 1 row to replace 0 rows
5: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 5 has 1 row to replace 0 rows
6: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
replacement element 6 has 1 row to replace 0 rows
7: In [<-.data.frame(*tmp*, i, value = list(id = NA, rank = 1, :
provided 6 variables to replace 1 variables >
Results :( <structure(list(id = logical(0)), row.names = integer(0), class = "data.frame")>
The error is not on the HERE library but on the data structure of the data frame used for saving the returned isoline.
> for (i in 1:nrow(df)){
+ result <- isoline(
+ st_sf(df[i,]),
+ range = (5*60),
+ range_type = "time",
+ routing_mode = "fast",
+ transport_mode = "car",
+ aggregate = TRUE,
+ traffic = TRUE
+ )
+ print(result)
+ }
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.5571 ymin: 46.52435 xmax: -75.41428 ymax: 46.58615
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:07 2023-05-22 17:44:07 300 POLYGON ((-75.55676 46.5641...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.8963 ymin: 45.38796 xmax: -75.78884 ymax: 45.44014
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:08 2023-05-22 17:44:08 300 POLYGON ((-75.89626 45.4259...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.8448 ymin: 45.39791 xmax: -75.78129 ymax: 45.44151
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:08 2023-05-22 17:44:08 300 POLYGON ((-75.84476 45.4105...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.86815 ymin: 45.37937 xmax: -75.77717 ymax: 45.4192
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:09 2023-05-22 17:44:09 300 POLYGON ((-75.86811 45.4088...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.82935 ymin: 45.42332 xmax: -75.74078 ymax: 45.45971
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:09 2023-05-22 17:44:09 300 POLYGON ((-75.82931 45.4414...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.80154 ymin: 45.39757 xmax: -75.72601 ymax: 45.4525
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:09 2023-05-22 17:44:09 300 POLYGON ((-75.80146 45.4262...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.81459 ymin: 45.44632 xmax: -75.73013 ymax: 45.51773
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:09 2023-05-22 17:44:09 300 POLYGON ((-75.81425 45.5177...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.78438 ymin: 45.43327 xmax: -75.69855 ymax: 45.48477
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:10 2023-05-22 17:44:10 300 POLYGON ((-75.78429 45.4736...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.78438 ymin: 45.42091 xmax: -75.68687 ymax: 45.47996
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:10 2023-05-22 17:44:10 300 POLYGON ((-75.78429 45.4447...
Simple feature collection with 1 feature and 5 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -75.7679 ymin: 45.40924 xmax: -75.69374 ymax: 45.47104
Geodetic CRS: WGS 84
id rank departure arrival range geometry
1 NA 1 2023-05-22 17:39:10 2023-05-22 17:44:10 300 POLYGON ((-75.76756 45.4154...
And you can even skip the for loop since hereR can process data in bulk, just remember to put aggregate value to FALSE:
result <- isoline(
st_sf(df), # not need to loop through the data frame
range = (5 * 60),
range_type = "time",
routing_mode = "fast",
transport_mode = "car",
aggregate = FALSE, # when aggregate is off it returns 10 results
traffic = TRUE
)
# if in need, convert the feature data back to data frame
as.data.frame(result)