README.md

In titaniumtroop/aRlegislation: A Dataset of Arkansas Legislation from 2001-2019

aRlegislation

This package provides a dataset of Arkansas legislation from 2001-2019 for data exploration, natural language processing, and graph analysis. Pre-calculated topic models are provided; however, these files are large, so only a few are included in the package build.

The primary datasets is structured as a nested tibble containing distinct legislative sessions, nested by cycle (every two years) and session type. Because the package uses nested tibbles, the tidyr package is required to use the dataset:

library(aRlegislation)
library(tidyr) # needed for nest/unnest operations

legislation dataset

The structure of the legislation dataset is:

head(legislation)

## # A tibble: 6 x 5
##   cycle session acts                 lawmakers           sponsorship         
##   <dbl> <chr>   <list>               <list>              <list>              
## 1  2001 R       <tibble [1,843 × 6]> <tibble [136 × 11]> <tibble [4,814 × 2]>
## 2  2001 S1      <tibble [2 × 6]>     <tibble [86 × 11]>  <tibble [86 × 2]>   
## 3  2003 R       <tibble [1,816 × 6]> <tibble [135 × 11]> <tibble [5,683 × 2]>
## 4  2003 S1      <tibble [63 × 6]>    <tibble [6 × 11]>   <tibble [66 × 2]>   
## 5  2003 S2      <tibble [110 × 6]>   <tibble [124 × 11]> <tibble [473 × 2]>  
## 6  2005 R       <tibble [2,325 × 6]> <tibble [135 × 11]> <tibble [6,128 × 2]>

There are three different datasets for each legislative cycle and session:

• acts: contains act number, bill number, title, subtitle, text, and partisanship
• lawmakers: contains demographic information about elected senators and representatives such as chamber, political party, and district
• sponsorship: a simple table containing act number and sponsor name

The vignettes provide more detail about each type of nested tibble, and also provide an example or two of analyses that can be performed using each type of tibble.

lawmakers dataset

The structure of the lawmakers dataset is:

head(lawmakers)

## # A tibble: 6 x 4
##   sponsor_full_name count sessions          corruption
##   <chr>             <int> <list>            <lgl>     
## 1 Aaron Burkes          1 <tibble [1 × 12]> FALSE     
## 2 Aaron Pilkington      4 <tibble [4 × 12]> FALSE     
## 3 Alan Clark            7 <tibble [7 × 12]> FALSE     
## 4 Allen Kerr            5 <tibble [5 × 12]> FALSE     
## 5 Allen Maxwell         6 <tibble [6 × 12]> FALSE     
## 6 Alvin Simes           4 <tibble [4 × 12]> FALSE

This dataset provides entity resolution for the sponsors of legislation across the cycles and sessions contained in the legislation dataset. Per-cycle/session information is nested in the dataframe. A corruption flag has been added for each legislator; the source code contains links in the comments to the news articles and U.S. attorney press releases announcing public corruption charges/convictions.

legislation_corpus dataset

The structure of the legislation_corpus dataset is:

head(legislation_corpus)

## # A tibble: 6 x 5
## # Groups:   cycle, session [6]
##   cycle session title_corpus text_corpus text_stemmed
##   <dbl> <chr>   <list>       <list>      <list>      
## 1  2001 R       <VCorpus>    <VCorpus>   <VCorpus>   
## 2  2001 S1      <VCorpus>    <VCorpus>   <VCorpus>   
## 3  2003 R       <VCorpus>    <VCorpus>   <VCorpus>   
## 4  2003 S1      <VCorpus>    <VCorpus>   <VCorpus>   
## 5  2003 S2      <VCorpus>    <VCorpus>   <VCorpus>   
## 6  2005 R       <VCorpus>    <VCorpus>   <VCorpus>

This dataset provides various VCorpus objects based on the act titles and text in the legislation dataset. There is also a stemmed version that is useful for conducting topical analyses.

Topic modeling

A large amount of topic modeling has been conducted on the dataset; this is a time-consuming process, so various functions are provided to load topic models and other models derived from them. Those functions are:

• load_topic_models: loads the LDA Gibbs objects for the specified range of topics (currently 100-203)
• load_lawmaker_models: loads the dot product of the sparse lawmaker sponsorship matrix and the LDA Gibbs gamma matrix for the specified range of topics; this represents a “career view” topic model for each lawmaker
• load_lawmaker_session_models: loads the ot product of the sparse, by-session lawmaker sponsorship matrix and the LDA Gibbs gamma matrix for the specified range of topics; this represents a “session view” topic model for each lawmaker’s activity over time

Note: As noted above, only a few topic models are provided in the package build in order to keep the size down. Additional topic models in the data/*_model subdirectories can be downloaded if needed. In particular, the topic models are large – on the order of 1.6GB. Use care when downloading, loading, and processing.

Future Plans

The legislature meets once a year, plus special sessions, so the dataset can be updated as each additional session concludes.

This dataset is essentially an aggregation of distinct datasets across each cycle/session of the legislature. This works well for seeing trends across cycles/sessions, but it doesn’t give much visibility into how individual lawmakers act as they get re-elected, gain experience, and perhaps graduate from House to Senate as term limits force them out of office. As such, entity resolution of lawmakers across cycles/sessions will likely be the focus of the next major version of this dataset.

titaniumtroop/aRlegislation documentation built on May 4, 2020, 3:24 a.m.