I'm using ggmap to find locations. Some locations generates error. For example,
library(ggmap)
loc = 'Blue Grass Airport'
geocode(loc, output = c("more"))
results in
Error in data.frame(long_name = "Blue Grass Airport", short_name = "Blue Grass Airport", :
arguments imply differing number of rows: 1, 0
It's ok if I can't get results for some locations, but I'm trying to work on 100 locations in a list. So is there a way to get NA instead of error and keep things go on? E.g.,
library(ggmap)
loc = c('Blue Grass Airport', 'Boston MA', 'NYC')
geocode(loc, output = c("more"))
should generate
NA
Result for Boston
Result for New York City
You can make use of the R tryCatch() function to handle these errors gracefully:
loc = 'Blue Grass Airport'
x <- tryCatch(geocode(loc, output = c("more")),
warning = function(w) {
print("warning");
# handle warning here
},
error = function(e) {
print("error");
# handle error here
})
If you intend to loop over locations explicitly using a for loop or using an apply function, then tryCatch() should also come in handy.
Related
I need to run the whatNWISdata() function in the dataRetrieval package (https://waterdata.usgs.gov/blog/dataretrieval/) with the parameter code 62614 for every state and Washington D.C., except for Alaska and Hawaii.
library(dataRetrieval)
stateCdsubset <- subset(stateCd, STATE=="01"|STATE>="04"&STATE<="13"|STATE>="16"&STATE<="56")
for(st in stateCdsubset$STUSAB){
USASites <- whatNWISdata(stateCd = st, parameterCd = "62614")}
But I keep getting the error "no loop for break/next, jumping to top level." When I try to run it with every state, as below:
library(dataRetrieval)
for(st in stateCd$STATE){
USASites <- whatNWISdata(stateCd = st, parameterCd = "62614")}
I get the error "Error in if (values["stateCd"] == "UM") { :
missing value where TRUE/FALSE needed." I'm not sure what I'm doing wrong.
I have a working program which pulls addresses from a list in Excel and geocodes them using a Google API, but anytime it gets to an address with an apartment, unit, or unfindable address, it stops the program.
I can't get a workable tryCatch routine going inside my loop. :(
Here is the Code:
library("readxl")
library(ggplot2)
library(ggmap)
fileToLoad <- file.choose(new = TRUE)
origAddress <- read_excel(fileToLoad, sheet = "Sheet1")
geocoded <- data.frame(stringsAsFactors = FALSE)
for(i in 1:nrow(origAddress))
{
# Print("Working...")
result <- geocode(origAddress$addresses[i], output = "latlona", source = "google")
origAddress$lon[i] <- as.numeric(result[1])
origAddress$lat[i] <- as.numeric(result[2])
origAddress$geoAddress[i] <- as.character(result[3])
}
write.csv(origAddress, "geocoded1.csv", row.names=FALSE)
And here is the Error message:
Warning: Geocoding "[removed address]" failed with error:
You must use an API key to authenticate each request to Google Maps Platform APIs. For additional information, please refer to http://g.co/dev/maps-no-account
Error: Can't subset columns that don't exist.
x Location 3 doesn't exist.
i There are only 2 columns.
Run `rlang::last_error()` to see where the error occurred.
In addition: Warning messages:
1: Unknown or uninitialised column: `lon`.
2: Unknown or uninitialised column: `lat`.
3: Unknown or uninitialised column: `geoAddress`.
Now, this is not an API key error because the key works in calls after the error -- and it stops at any address that ends in a number after the street name.
I'm going to be processing batches of thousands of addresses every month and they are not all going to be perfect, so what I need is to be able to skip these bad addresses, put "NA" in the lon/lat columns, and move on.
I'm new to R and can't make a workable error handling routine to handle these types of mistakes. can anyone point me in the right direction? Thanks in advance.
When geocode fails to find an address and output = "latlona", the address field is not returned. You code can be made to work with the following modification.
#
# example data
#
origAddress <- data.frame(addresses = c("white house, Washington",
"white house, # 100, Washington",
"white hose, Washington",
"Washington Apartments, Washington, DC 20001",
"1278 7th st nw, washington, dc 20001") )
#
# simple fix for fatal error
#
for(i in 1:nrow(origAddress))
{
result <- geocode(origAddress$addresses[i], output = "latlona",
source = "google")
origAddress$lon[i] <- result$lon[1]
origAddress$lat[i] <- result$lat[1]
origAddress$geoAddress[i] <- ifelse( is.na(result$lon[1]), NA, result$address[1] )
}
However, you mention that some of your addresses may not be exact. Google's geocoding will try to interpret all address you supply. Sometimes it fails and returns NA but other times its interpretation may not be correct so you should always check geocode results.
A simple method which will catch many errors to set output = "more" in geocode and then examine the values returned in the loctype column. If loctype != "rooftop", you may have a problem. Examing the type column will give you more information. This check isn't complete. To do a more complete check, you could use output = "all" to return all data supplied by google for an address but this requires parsing a moderately complex list. You should read more about the data returned by google geocoding at https://developers.google.com/maps/documentation/geocoding/overview
Also, geocode will take at least tens of minutes at least to return results for thousands of addresses. To minimize the response time, you should supply addresses to geocode as a character vector of addresses. A data frame of results is then returned which you can use to update your origAddress data frame and check for errors as shown below.
#
# Solution should check for wrongly interpreted addresses
#
# see https://developers.google.com/maps/documentation/geocoding/overview
# for more information on fields returned by google geocoding
#
# return all addresses in single call to geocode
#
origAddress <- data.frame(addresses = c("white house, Washington", # identified by name
"white hose, Washington", # misspelling
"Washington Apartments, apt 100, Washington, DC 20001", # identified by name of apartment building
"Washington Apartments, # 100, Washington, DC 20001", # invalid apartment number specification
"1206 7th st nw, washington, dc 20001") ) # address on street but no structure with that address
result <- suppressWarnings(geocode(location = origAddress$addresses,
output = "more",
source = "google") )
origAddress <- cbind(origAddress, result[, c("address", "lon","lat","type", "loctype")])
#
# Addresses which need to be checked
#
check_addresses <- origAddress[ origAddress$loctype != "rooftop" |
is.na(origAddress$loctype), ]
I'm trying to use the DoHeatmap function in Seurat to show expression of a number of genes across some defined clusters.
B_cells is my Seurat object.
tfs <- c("PRDM1", "PAX5", "BACH2")
DoHeatmap(B_cells, features=tfs)
I'm getting this error back;
Error in data.frame(group = sort(x = group.use), x = x.divs) :
arguments imply differing number of rows: 10411, 0
When I look at the number of rows and columns in the Seurat object;
nrow(B_cells) = 19651
ncol(B_cells) = 10151
Sorry if this is a silly question but I've been stuck on it for a while now.
edit traceback():
3: stop(gettextf("arguments imply differing number of rows: %s",
paste(unique(nrows), collapse = ", ")), domain = NA)
2: data.frame(group = sort(x = group.use), x = x.divs)
1: DoHeatmap(B_cells, features = genes)
The source code for the DoHeatmap() function can be found at https://github.com/satijalab/seurat/blob/develop/R/visualization.R. The traceback() shows line 363 of visualization.R is causing the error:
if (label) {
x.max <- max(pbuild$layout$panel_params[[1]]$x.range)
# Attempt to pull xdivs from x.major in ggplot2 < 3.3.0; if NULL, pull from the >= 3.3.0 slot
x.divs <- pbuild$layout$panel_params[[1]]$x.major %||% pbuild$layout$panel_params[[1]]$x$break_positions()
x <- data.frame(group = sort(x = group.use), x = x.divs)
...
}
As a workaround to bypass the error try:
DoHeatmap(B_cells, features=tfs, label=FALSE)
I had a similar error. It turns out that there was a problem with my cluster labeling, where one of my clusters ended up with an empty label (""). I found it by when I asked for DimPlot with label=T, and one of the clusters did not have a label. When I went back and re-labeled the clusters correctly, the DoHeatmap error disappeared.
I would like to reverse geo code address and pin code in R
These are the columns
A B C
15.3859085 74.0314209 7J7P92PJ+9H77QGCCCC
I have taken first four rows having columns A B and C among 1000's of rows
df<-ga.data[1:4,]
df <- cbind(df,do.call(rbind,
lapply(1:nrow(df),
function(i)
revgeocode(as.numeric(
df[i,3:1]), output = "more")
[c("administrative_area_level_1","locality","postal_code","address")])))
Error in revgeocode(as.numeric(df[i, 3:1]), output = "more") :
is.numeric(location) && length(location) == 2 is not TRUE
Also is there any other package or approach to find out the address and pincode most welcome
I also tried the following
When I tried using ggmap I got this error
In revgeocode(as.numeric(df[i, c("Latitude", "Longitude")]), output = "address") :
HTTP 400 Bad Request
Also i tried this
revgeocode(c(df$B[1], df$A[1]))
Warning Warning message: In revgeocode(c(df$Longitude[1],
df$Latitude[1])) : HTTP 400 Bad Request
Also I am from India and it does not work for me if i search for lat long of India. If I use lat long of US it gives me the exact address
seems fishy
data <- read.csv(text="ID, Longitude, Latitude
311175, 41.298437, -72.929179
292058, 41.936943, -87.669838
12979, 37.580956, -77.471439")
library(ggmap)
result <- do.call(rbind,
lapply(1:nrow(data),
function(i)revgeocode(as.numeric(data[i,3:2]))))
data <- cbind(data,result)
The current CRAN version of revgeo_0.15 does not have the revgeocode function. If you upgrade to this version, you'll find a revgeo function, which takes longitude, latitude arguments. Your column C should not be passed into the function.
revgeo::revgeo(latitude=df[, 'A'], longitude=df[, 'B'], output='frame')
[1] "Getting geocode data from Photon: http://photon.komoot.de/reverse?lon=74.0314209&lat=15.3859085"
housenumber street city state zip country
1 House Number Not Found Street Not Found Borim Goa Postcode Not Found India
I am trying to use very well written instructions from this blog: https://www.jessesadler.com/post/geocoding-with-r/ to geocode locational data in R including specific cites and cities in Hawaii. I am having issues pulling information from Google. When running mutate_geocode my data runs but no output is gathered. I bypassed this for the time being with manual entry of lat and lon for just one location of my dataset, attempting to trouble shoot. Now, when I use get_googlemap, I get the error message "Error in Download File"
I have tried using mutate_geocode as well as running a loop using geocode. I either do not get output or I get the OVER_QUERY_LIMIT error (which seems to be very classic). After checking my query limit I am nowhere near the limit.
Method 1:
BH <- rename(location, place = Location)
BH_df <- as.data.frame(BH)
location_df <- mutate_geocode(HB, Location)
Method 2:
origAddress <- read.csv("HSMBH.csv", stringsAsFactors = FALSE)
geocoded <- data.frame(stringsAsFactors = FALSE)
for(i in 1:nrow(origAddress))
{
result <- geocode(HB$Location[i], output = "latlona", source = "google")
HB$lon[i] <- as.character(result[1])
HB$lat[i] <- as.character(result[2])
HB$geoAddress[i] <- as.character(result[3])
}
Post Manual Entry of lon and lat points I run in to this error:
map <- get_googlemap(center = c(-158.114, 21.59), zoom = 4)
I am hoping to gather lat and lon points for my locations, and then be able to use get_googlemap to draft a map with which I can plot density points of occurrences (I have the code for the points already).
Alternatively, you can use a one-liner for rapid geocoding via tmaptools::geocode_OSM():
Data
library(tmaptools)
addresses <- data.frame(address = c("New York", "Berlin", "Huangpu Qu",
"Vienna", "St. Petersburg"),
stringsAsFactors = FALSE)
Code
result <- lapply(addresses[, 1], geocode_OSM)
> result
$address
query lat lon lat_min lat_max lon_min lon_max
1 New York 40.73086 -73.98716 40.47740 40.91618 -74.25909 -73.70018
2 Berlin 52.51704 13.38886 52.35704 52.67704 13.22886 13.54886
3 Huangpu Qu 31.21823 121.48030 31.19020 31.24653 121.45220 121.50596
4 Vienna 48.20835 16.37250 48.04835 48.36835 16.21250 16.53250
5 St. Petersburg 27.77038 -82.66951 27.64364 27.91390 -82.76902 -82.54062
This way, you have both
the centroids (lon, lat) that are important for Google Maps and
boundary boxes (lon_min, lat_min, lon_max, lat_max) that mapping services like OSM or Stamen need.