You are currently browsing the category archive for the ‘Uncategorized’ category.
httr 0.6.0 is now available on CRAN. The httr packages makes it easy to talk to web APIs from R. Learn more in the quick start vignette.
This release is mostly bug fixes and minor improvements. The most important are:
handle_reset(), which allows you to reset the default handle if you get the error “easy handle already used in multi handle”.
write_stream()which lets you process the response from a server as a stream of raw vectors (#143).
VERB()allows to you send a request with a custom http verb.
brew_dr()checks for common problems. It currently checks if your
libcurluses NSS. This is unlikely to work so it gives you some advice on how to fix the problem (thanks to Dirk Eddelbuettel for debugging this problem and suggesting a remedy).
- Added support for Google OAuth2 service accounts. (#119, thanks to help from @siddharthab). See
I’ve also switched from RC to R6 (which should make it easier to extend OAuth classes for non-standard OAuth implementations), and tweaked the use of the backend SSL certificate details bundled with httr. See the release notes for complete details.
ggvis 0.4 is now available on CRAN. You can install it with:
The major features of this release are:
- Boxplots, with
chickwts %>% ggvis(~feed, ~weight) %>% layer_boxplots()
- Better stability when errors occur.
- Better handling of empty data and malformed data.
- More consistent handling of data in compute pipeline functions.
Because of these changes, interactive graphics with dynamic data sources will work more reliably.
Additionally, there are many small improvements and bug fixes under the hood. You can see the full change log here.
RStudio is planning a new Master R Developer Workshop to be taught by Hadley Wickham in the San Francisco Bay Area on January 19-20. This will be the same workshop that Hadley is teaching in September in New York City to a sold out audience.
If you did not get a chance to register for the NYC workshop but wished to, consider attending the January Bay Area workshop. We will open registration once we have planned out all of the event details. If you would like to be notified when registration opens, leave a contact address here.
tidyr is new package that makes it easy to “tidy” your data. Tidy data is data that’s easy to work with: it’s easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R’s hundreds of modelling packages). The two most important properties of tidy data are:
- Each column is a variable.
- Each row is an observation.
Arranging your data in this way makes it easier to work with because you have a consistent way of referring to variables (as column names) and observations (as row indices). When use tidy data and tidy tools, you spend less time worrying about how to feed the output from one function into the input of another, and more time answering your questions about the data.
To tidy messy data, you first identify the variables in your dataset, then use the tools provided by tidyr to move them into columns. tidyr provides three main functions for tidying your messy data:
gather() takes multiple columns, and gathers them into key-value pairs: it makes “wide” data longer. Other names for gather include melt (reshape2), pivot (spreadsheets) and fold (databases). Here’s an example how you might use
gather() on a made-up dataset. In this experiment we’ve given three people two different drugs and recorded their heart rate:
library(tidyr) library(dplyr) messy <- data.frame( name = c("Wilbur", "Petunia", "Gregory"), a = c(67, 80, 64), b = c(56, 90, 50) ) messy #> name a b #> 1 Wilbur 67 56 #> 2 Petunia 80 90 #> 3 Gregory 64 50
We have three variables (name, drug and heartrate), but only name is currently in a column. We use
gather() to gather the a and b columns into key-value pairs of drug and heartrate:
messy %>% gather(drug, heartrate, a:b) #> name drug heartrate #> 1 Wilbur a 67 #> 2 Petunia a 80 #> 3 Gregory a 64 #> 4 Wilbur b 56 #> 5 Petunia b 90 #> 6 Gregory b 50
Sometimes two variables are clumped together in one column.
separate() allows you to tease them apart (
extract() works similarly but uses regexp groups instead of a splitting pattern or position). Take this example from stackoverflow (modified slightly for brevity). We have some measurements of how much time people spend on their phones, measured at two locations (work and home), at two times. Each person has been randomly assigned to either treatment or control.
set.seed(10) messy <- data.frame( id = 1:4, trt = sample(rep(c('control', 'treatment'), each = 2)), work.T1 = runif(4), home.T1 = runif(4), work.T2 = runif(4), home.T2 = runif(4) )
To tidy this data, we first use
gather() to turn columns
home.T2 into a key-value pair of key and time. (Only the first eight rows are shown to save space.)
tidier <- messy %>% gather(key, time, -id, -trt) tidier %>% head(8) #> id trt key time #> 1 1 treatment work.T1 0.08514 #> 2 2 control work.T1 0.22544 #> 3 3 treatment work.T1 0.27453 #> 4 4 control work.T1 0.27231 #> 5 1 treatment home.T1 0.61583 #> 6 2 control home.T1 0.42967 #> 7 3 treatment home.T1 0.65166 #> 8 4 control home.T1 0.56774
Next we use
separate() to split the key into location and time, using a regular expression to describe the character that separates them.
tidy <- tidier %>% separate(key, into = c("location", "time"), sep = "\\.") tidy %>% head(8) #> id trt location time time #> 1 1 treatment work T1 0.08514 #> 2 2 control work T1 0.22544 #> 3 3 treatment work T1 0.27453 #> 4 4 control work T1 0.27231 #> 5 1 treatment home T1 0.61583 #> 6 2 control home T1 0.42967 #> 7 3 treatment home T1 0.65166 #> 8 4 control home T1 0.56774
The last tool,
spread(), takes two columns (a key-value pair) and spreads them in to multiple columns, making “long” data wider. Spread is known by other names in other places: it’s cast in reshape2, unpivot in spreadsheets and unfold in databases.
spread() is used when you have variables that form rows instead of columns. You need
spread() less frequently than
separate() so to learn more, check out the documentation and the demos.
Just as reshape2 did less than reshape, tidyr does less than reshape2. It’s designed specifically for tidying data, not general reshaping. In particular, existing methods only work for data frames, and tidyr never aggregates. This makes each function in tidyr simpler: each function does one thing well. For more complicated operations you can string together multiple simple tidyr and dplyr functions with
You can learn more about the underlying principles in my tidy data paper. To see more examples of data tidying, read the vignette,
vignette("tidy-data"), or check out the demos,
demo(package = "tidyr"). Alternatively, check out some of the great stackoverflow answers that use tidyr. Keep up-to-date with development at http://github.com/hadley/tidyr, report bugs at http://github.com/hadley/tidyr/issues and get help with data manipulation challenges at https://groups.google.com/group/manipulatr. If you ask a question specifically about tidyr on stackoverflow, please tag it with tidyr and I’ll make sure to read it.