Skip to main content

Update to Data on Github Post: Solution to an RCurl problem

A reader of my most recent post tried the R code I had written to download the data set of electoral disproportionality from the GitHub repository. However, it didn’t work for them. After entering disproportionality.data <- getURL(url) they got the error message:

Error in function (type, msg, asError = TRUE)  : 
SSL certificate problem, verify that the CA cert is OK. Details:
error:14090086:SSL routines:SSL3_GET_SERVER_CERTIFICATE:certificate verify failed


The Solution

The problem seems to be that they didn’t have a certificate from an appropriate signing agent (see the RCurl FAQ page near the bottom) for more information. If you are really interested in SSL verification this page from redhat is a place to look).

The solution to this problem is pretty straightforward. As the RCurl FAQ page points out you can use the argument ssl.verifypeer = FALSE to skip certificate verification (effectively a man-in-the-middle attack).

So, if you get the above error message just use this new code:

library(RCurl)

url <- "https://raw.github.com/christophergandrud/Disproportionality_Data/master/Disproportionality.csv"

disproportionality.data <- getURL(url, ssl.verifypeer = FALSE)                

disproportionality.data <- read.csv(textConnection(disproportionality.data))

That should work.



Question

I didn’t originally mention this issue, because I didn’t get it when I ran the code on my Mac. When I tried the code on a Windows machine I was able to replicate the error.

Does any reader know why Windows computers (or any other types) lack certificates from an appropriate signing agent needed to download data from GitHub? How can you get one?

Comments

A certificate bundle comes with various browsers as well as with the RCurl library

R-2.15.0\library\RCurl\CurlSSL\cacert.pem

curl <- getCurlHandle(cainfo="cacert.pem",followlocation=TRUE,
cookiefile='cookies.txt',cookiejar='cookies.txt')
url1 <- "https://telogdata.hrsd.com/TWM/DesktopLoginPage.aspx"
out <- getURI(url1,curl=curl)
This comment has been removed by the author.
Unknown said…
A solution is provided at http://tonybreyal.wordpress.com/2011/11/24/source_https-sourcing-an-r-script-from-github/
I adapted the script at https://github.com/bobthecat/codebox/blob/master/source_https.r
Unknown said…
Great, thanks for this.

Popular posts from this blog

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background).LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package.I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object.To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis. The full function is below. To use it follow …

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame.I've found the various R methods for doing this hard to remember and usually need to look at old blogposts. Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command–slide–to the DataCombine R package (v0.1.5).Building on the shift function TszKin Julian posted on his blog, slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data.Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996, 1997. ExamplesNot…

More Corrections the the DPI’s yrcurnt Election Timing Variable: OECD Edition

Previously on The Political Methodologist, I posted updates to the Database of Political Institutions' election timing variable: yrcurnt. That set of corrections was only for the current 28 EU member states.I’ve now expanded the corrections to include most other OECD countries.1 Again, there were many missing elections:Change listCountryChangesAustraliaCorrects missing 1998 election year.CanadaCorrects missing 2000, 2006, 2008, 2011 election years.IcelandCorrects missing 2009 election year.IrelandCorrects missing 2011 election.JapanCorrects missing 2005 and 2012 elections. Corrects misclassification of the 2003 and 2009 elections, which were originally erroneously labeled as being in 2004 and 2008, respectively.  Import into RTo import the most recent corrected version of the data into R simply use:election_time <- rio::import('https://raw.githubusercontent.com/christophergandrud/yrcurnt_corrected/master/data/yrcurnt_original_corrected.csv') Australia, Canada, Iceland, Is…