Skip to main content

Updates to repmis: caching downloaded data and Excel data downloading

Over the past few months I’ve added a few improvements to the repmis–miscellaneous functions for reproducible research–R package. I just want to briefly highlight two of them:

  • Caching downloaded data sets.

  • source_XlsxData for downloading data in Excel formatted files.

Both of these capabilities are in repmis version 0.2.9 and greater.

Caching

When working with data sourced directly from the internet, it can be time consuming (and make the data hoster angry) to repeatedly download the data. So, repmis’s source functions (source_data, source_DropboxData, and source_XlsxData) can now cache a downloaded data set by setting the argument cache = TRUE. For example:

DisData <- source_data("http://bit.ly/156oQ7a", cache = TRUE)

When the function is run again, the data set at http://bit.ly/156oQ7a will be loaded locally, rather than downloaded.

To delete the cached data set, simply run the function again with the argument clearCache = TRUE.

source_XlsxData

I recently added the source_XlsxData function to download Excel data sets directly into R. This function works very similarly to the other source functions. There are two differences:

  • You need to specify the sheet argument. This is either the name of one specific sheet in the downloaded Excel workbook or its number (e.g. the first sheet in the workbook would be sheet = 1).

  • You can pass other arguments to the read.xlsx function from the xlsx package.

Here’s a simple example:

RRurl <- 'http://www.carmenreinhart.com/user_uploads/data/22_data.xls'

RRData <- source_XlsxData(url = RRurl, sheet = 2, startRow = 5)

startRow = 5 basically drops the first 4 rows of the sheet.

Comments

Popular posts from this blog

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…

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 …

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox. There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not.Dropbox Public FolderDropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name. Dropbox recent…