I am replicating the R code for a published paper about climate change that uses prism package to access and visualize data from the Oregon State PRISM project.
This code stopped running with a error message of:
utils::unzip(outFile, exdir = ofolder) : 'exdir' does not exist
However, there is no unzipping in the code, except the one already defined in the prism package. This code uses two functions in the prism package: 1) get_prism_dailys, and 2) prism_set_dl_dir.
I have already read this page about error of "exdir does not exist". But this code from the published paper does not have an unzipping command.
Can someone help me with this? Code:
lapply(c("tmin","tmax"), function(var){
print(var)
# Set directory to write daily files
prism_set_dl_dir(paste0(dir$prism,"/daily/",var))
# Download files
get_prism_dailys(type = var, minDate = "1981-01-01", maxDate = "2020-12-31", keepZip=FALSE)
})
})
Related
I am trying to use the acs package in R to download Census data for a basic map, but I am unable to download the data and I'm receiving a confusing error message.
My code is as follows:
#Including all packages here in case this is somehow the issue
install.packages(c("choroplethr", "choroplethrMaps", "tidycensus", "tigris", "leaflet", "acs", "sf"))
library(choroplethr)
library(choroplethrMaps)
library(tidycensus)
library(tigris)
library(leaflet)
library(acs)
library(sf)
library(tidyverse)
api.key.install("my_api_key")
SD_geo <- geo.make(state="CA", county = 73, tract = "*", block.group = "*")
median_income <- acs.fetch(endyear = 2015, span = 5, geography = SD_geo, table.number = "B19013", col.names="pretty")
Everything appears to work until the final command, when I receive the following error message:
trying URL 'http://web.mit.edu/eglenn/www/acs/acs-variables/acs_5yr_2015_var.xml.gz'
Content type 'application/xml' length 735879 bytes (718 KB)
downloaded 718 KB
Error in if (url.test["statusMessage"] != "OK") { :
missing value where TRUE/FALSE needed
In addition: Warning message:
In (function (endyear, span = 5, dataset = "acs", keyword, table.name, :
XML variable lookup tables for this request
seem to be missing from ' https://api.census.gov/data/2015/acs5/variables.xml ';
temporarily downloading and using archived copies instead;
since this is *much* slower, recommend running
acs.tables.install()
This is puzzling to me because 1) it appears like something is in fact being downloaded at first? and 2) 'Error in if (url.test["statusMessage"] != "OK") { :
missing value where TRUE/FALSE needed' makes no sense to me. It doesn't align with any of the arguments in the function.
I have tried:
Downloading the tables using acs.tables.install() as recommended in the second half of the error message. Doesn't help.
Changing the endyear and span to be sure that I'm falling within the years of data supported by the API. I seem to be, according to the API documentation. Have also used the package default arguments with no luck.
Using 'variable =' and the code for the variable as found in the official API documentation. This returns only the two lines with the mysterious "Error in if..." message.
Removing colnames = "pretty"
I'm going to just download the datafile as a CSV and read it into R for now, but I'd like to be able to perform this function from the script for future maps. Any information on what's going on here would be appreciated. I am running R version 3.3.2. Also, I'm new to using this package and the API. But I'm following the documentation and can't find evidence that I'm doing anything wrong.
Tutorial I am working off of:
http://zevross.com/blog/2015/10/14/manipulating-and-mapping-us-census-data-in-r-using-the-acs-tigris-and-leaflet-packages-3/#get-the-tabular-data-acs
And documentation of the acs package: http://eglenn.scripts.mit.edu/citystate/wp-content/uploads/2013/02/wpid-working_with_acs_R2.pdf
To follow up on Brandon's comment, version 2.1.1 of the package is now on CRAN, which should resolve this issue.
Your code runs for me. My guess would be that the Census API was temporarily down.
As you loaded tidycensus and you'd like to do some mapping, you might also consider the following code:
library(tidycensus)
census_api_key("your key here") # use `install = TRUE` to install the key
options(tigris_use_cache = TRUE) # optional - to cache the Census shapefile
median_income <- get_acs(geography = "block group",
variables = "B19013_001",
state = "CA", county = "San Diego",
geometry = TRUE)
This will get you the data you need, along with feature geometry for mapping, as a tidy data frame.
I emailed Ezra Haber Glenn, the author of the package, about this as I was having the same issue. I received a response within 30 minutes and it was after midnight, which I thought was amazing. Long story short, the acs package version 2.1.0 is configured to work with the changes the Census Bureau is making to their API later this summer, and it is currently presenting some problems windows users in the mean time. Ezra is going to be releasing an update with a fix, but in the mean time I reverted back to version 2.0 and it works fine. I'm sure there are a few ways to do this, but I installed the devtools package and ran:
require(devtools)
install_version("acs", version = "2.0", repos = "http://cran.us.r-project.org")
Hope this helps anyone else having a similar issue.
When I run "send2cy" function in R studio, I got error.
# Basic setup
library(igraph)
library(RJSONIO)
library(httr)
dir <- "/currentdir/"
setwd(dir)
port.number = 1234
base.url = paste("http://localhost:", toString(port.number), "/v1", sep="")
print(base.url)
# Load list of edges as Data Frame
network.df <- read.table("./data/eco_EM+TCA.txt")
# Convert it into igraph object
network <- graph.data.frame(network.df,directed=T)
# Remove duplicate edges & loops
g.tca <- simplify(network, remove.multiple=T, remove.loops=T)
# Name it
g.tca$name = "Ecoli TCA Cycle"
# This function will be published as a part of utility package, but not ready yet.
source('./utility/cytoscape_util.R')
# Convert it into Cytosccape.js JSON
cygraph <- toCytoscape(g.tca)
send2cy(cygraph, 'default%20black', 'circular')
Error in file(con, "r") : cannot open the connection
Called from: file(con, "r")
But I didn't find error when I use "send2cy" function from terminal R (Run R from terminal just calling by "R").
Any advice is welcome.
I tested your script with local copies of the network data and utility script, and with updated file paths. The script ran fine for me in R Studio.
Given the error message you are seeing "Error in file..." I suspect the issue is with your local files and file paths... somehow in an R Studio-specific way?
FYI: an updated, consolidated and update set of R scripts for Cytoscape are available here: https://github.com/cytoscape/cytoscape-automation/tree/master/for-scripters/R. I don't think anything has significantly changed, but perhaps trying in a new context will resolve the issue you are facing.
I need to download a custom dataset in an Azure Jupyter/iPython Notebook.
My ultimate goal is to install an R package. To be able to do this the package (the dataset) needs to be downloaded in code. I followed the steps outlined by Andrie de Vries in the comments section of this post: Jupyter Notebooks with R in Azure ML Studio.
Uploading the package as a ZIP file was without problems, but when I run the code in my notebook I get an error:
Error in curl(x$DownloadLocation, handle = h, open = conn): Failure
when receiving data from the peer Traceback:
download.datasets(ws, "plotly_3.6.0.tar.gz.zip")
lapply(1:nrow(datasets), function(j) get_dataset(datasets[j, . ], ...))
FUN(1L[[1L]], ...)
get_dataset(datasets[j, ], ...)
curl(x$DownloadLocation, handle = h, open = conn)
So I simplified my code into:
library("AzureML")
ws <- workspace()
ds <- datasets(ws)
ds$Name
data <- download.datasets(ws, "plotly_3.6.0.tar.gz.zip")
head(data)
Where "plotly_3.6.0.tar.gz.zip" is the name of my dataset of data type "Zip".
Unfortunately this results in the same error.
To rule out data type issues I also tried to download another dataset of mine which is of data type "Dataset". Also the same error.
Now I change the dataset I want to download to one of the sample datasets of AzureML Studio.
"text.preprocessing.zip" is of datatype Zip
data <- download.datasets(ws, "text.preprocessing.zip")
"Flight Delays Data" is of datatype GenericCSV
data <- download.datasets(ws, "Flight Delays Data")
Both of the sample datasets can be downloaded without problems.
So why can't I download my own saved dataset?
I could not find anything helpful in the documentation of the download.datasets function. Not on rdocumentation.org, nor on cran.r-project.org (page 17-18).
Try this:
library(AzureML)
ws <- workspace(
id = "your AzureML ID",
auth = "your AzureML Key"
)
name <- "Name of your saved data"
ws <- workspace()
It seems the error I got was due to a bug in the (then early) Azure ML Studio.
I tried again after the reply of Daniel Prager only to find out my code works as expected without any changes. Adding the id and auth parameters was not needed.
I want to shift some of my most commonly used shapefiles to github. I have a new package in development. It worked when built in RStudio, but when I pull the package from github and run it I get the error "Error in getinfo.shape(filen) : Error opening SHP file". When I look inside the package the shapefiles are in place in the data folder. They're being called by individual functions, e.g.
load_lon = function(){
require(maptools)
lon <<- readShapePoly('data/london_outline_simple.shp', proj4string=CRS('+init=epsg:27700'))
}
Presumably this method means R is wrongly looking for subfolder 'data' in the working directory. But I can't think how else to call them, as data() doesn't support shp. Grateful for advice how to load them in.
In line with #josh-obrien's comment, the answer is to put data files in a subfolder of inst, whereupon they'll be compiled with the code when the package is built. I've put the shapefiles in:
$SOURCEDIR/inst/external/
These are installed via functions such as:
load_lon = function(){
require(maptools)
path = system.file("external/london_outline_simple.shp", package="londonShapefiles")
lon <<- readShapePoly(path, proj4string=CRS('+init=epsg:27700'))
}
Check out the working github package for full working example.
The ZevRoss blog is as follow:
http://zevross.com/blog/2014/04/11/using-r-to-quickly-create-an-interactive-online-map-using-the-leafletr-package/
The code with error is:
# ----- Write data to GeoJSON
leafdat<-paste(downloaddir, "/", filename, ".geojson", sep="")
writeOGR(subdat, leafdat, layer="", driver="GeoJSON")
And the error is:
Error in writeOGR(subdat, leafdat, layer = "", driver = "GeoJSON") :
GDAL Error 3: Cannot open file 'd:/Leaflet/County_2010Census_DP1.geojson'
Because I am a freshman in R, I searched for this problem a lot and didn't get any good answer.
I am using Rstudio R version 3.1.1(2014-07-10) on windows 7 32bit.
My rgdal version is 0.9-1.
The other code in the blog runs successfully, this sentence seems to be the only difficult point.
You could create GeoJSON using leafletR package:
library('leafletR')
Your_GeoJSON <- toGeoJSON(data=YourData, dest=getwd())
I've tried to find a solution for this mysterious error for some time.
Eventually I found this post on the Gdal package errors' tickets site that clarified the problem and gave a solution.
Basically the problem is in the interface between rgdal and Gdal (Gdal changed their way to work and the latest version of rgdal hasn't watched up yet):
writeOGR() calls ogrCheckExists("foo.geojson") to check first if the file exists before creating a new dataset.
In the 1.11 version the OGR GeoJSON driver will emit an error message that this file doesn't exists, whereas previous version didn't emit an error message.
rgdal catches this error as a fatal one and doesn't go to the writing step. This should be fixed in rgdal.
Meanwhile you have an easy workaround : add check_exists = FALSE as a parameter to writeOGR()
Therefore the following code will work:
writeOGR(spDf,'foo.geojson','spDf', driver='GeoJSON',check_exists = FALSE)
Of course if there is already a geojson file with the chosen name at the location writeOGR still fails.
Even though you already have a 'd:' drive on your computer and you have permission to write to that drive, try the following:
--------------------------------
leafdat<-paste(downloaddir, "/", ".geojson", sep="")
> leafdat
> "d:/Leaflet/.geojson"
writeOGR(subdat, leafdat, layer="", driver="GeoJSON")
--------------------------------
Then you may get ".geojson" file on "d:/Leaflet". Change the file name ".geojson" to "County_2010Census_DP1.geojson".