R binding for TheyWorkForYou
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DESCRIPTION
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README.md

twfyR

Jack Blumenau

About the package

This package provides an R binding for the TheyWorkForYou API. This package is still in the very early stages of development. Use with caution.

How to install

The package is currently only available on github.

library(devtools)
install_github("jblumenau/twfyR")
library(twfyR)

Set your API key

Use of the API is dependent on obtaining an API key from TheyWorkForYou. This is easily done here.

Before trying to get any data using the R functions in this package, you will need to use the set_twfy_key() function.

my_key <- "api_key_from_twfy"
set_twfy_key(my_key)

Calling the API

Calls to the twfy API are made via a series of get functions, such as getMPs() and getDebates(). Note that currently the package is incomplete in various ways, the most notable of which is that it does not yet include bindings for all of the calls made available by TWFY. Hopefully these will be added in the not-too-distant future...

Each of these functions takes a number of arguments to refine the results that are returned by the API. In each case, the function attempts to convert the XML data returned by the call into an R data object (normally a data.frame but sometimes a list (and sometimes even a list of data.frames)).

Let's try getting the current MPs serving in parliament. This is the default output of the getMPs() function:

current_mps <- getMPs()

str(current_mps)
## List of 2
##  $ members:'data.frame': 650 obs. of  5 variables:
##   ..$ member_id   : Factor w/ 650 levels "40706","40707",..: 1 2 3 4 5 6 7 8 9 10 ...
##   ..$ person_id   : Factor w/ 650 levels "10001","10008",..: 279 280 210 388 233 15 342 285 35 157 ...
##   ..$ name        : Factor w/ 650 levels "Adam Afriyie",..: 81 342 576 344 345 464 100 286 289 264 ...
##   ..$ party       : Factor w/ 14 levels "Conservative",..: 5 5 5 1 1 5 5 1 2 5 ...
##   ..$ constituency: Factor w/ 650 levels "Aberavon","Aberconwy",..: 290 559 603 413 457 387 386 31 317 642 ...
##  $ office :'data.frame': 1070 obs. of  9 variables:
##   ..$ member_id   : Factor w/ 650 levels "40706","40707",..: 1 1 2 3 4 5 5 6 6 6 ...
##   ..$ person_id   : Factor w/ 650 levels "10001","10008",..: 279 279 280 210 388 233 233 15 15 15 ...
##   ..$ name        : Factor w/ 650 levels "Adam Afriyie",..: 81 81 342 576 344 345 345 464 464 464 ...
##   ..$ party       : Factor w/ 14 levels "Conservative",..: 5 5 5 5 1 1 1 5 5 5 ...
##   ..$ constituency: Factor w/ 650 levels "Aberavon","Aberconwy",..: 290 290 559 603 413 457 457 387 387 387 ...
##   ..$ position    : Factor w/ 263 levels "Member","Shadow Minister (Public Health)",..: 1 1 1 2 NA 3 4 1 1 1 ...
##   ..$ department  : Factor w/ 57 levels "Public Accounts Committee",..: 2 1 3 4 NA 4 4 5 7 6 ...
##   ..$ from_date   : Date[1:1070], format: "2010-10-11" ...
##   ..$ to_date     : Date[1:1070], format: "2017-01-05" ...

The output is a list of 2 data.frames. The first is a data.frame with unique rows for each current MP. The second is a data.frame of each parliamentary position that each current member holds.

Let's try retreiving the speeches of one member of parliament. We'll use the getDebates() function, and use type = "commons" to specify that we wish to see speeches made in the House of Commons, and person = 10001 to choose our MP of interest (here it is Dianne Abbott).

speeches <- getDebates(person = 10001, type = "commons")

class(speeches)
## [1] "data.frame"

dim(speeches)
## [1] 1000   27

The default behaviour of the getDebates() function is to return 1000 results. But if we wish to receive all the speeches ever made by a given person, we can set complete_call = TRUE. Note, however, that this specification will submit several requests to the twfy API, and is a quick way to use up your call allowance if done too often.

Finally, let's do something with these speeches using the excellent quanteda library.

library(quanteda)

speech_corpus <- corpus(speeches$body)

speech_dfm <- dfm(speech_corpus, remove = c("will", stopwords("english")),
             removePunct = TRUE)

speech_dfm <- dfm_trim(speech_dfm, max_count = 400, min_count = 10)

speech_idf <- tfidf(speech_dfm)

textplot_wordcloud(speech_idf, min.req = 10, random.order = FALSE, rot.per = .25, 
                   colors = RColorBrewer::brewer.pal(8,"Dark2"))