vignettes/README.md
In JeffreyBLewis/Rvoteview: Voteview Data in R
This package provides tools to query and download from the VoteView database. This vignette will demonstrate the different types of queries that can be used, how Rvoteview can be used to do ideal point estimation on a subset of votes using the pscl package and the wnominate package, and how Rvoteview facilitates regression analyses of congressional voting behavior.
- Installation
- Querying the database with
voteview_search
- Downloading roll call data with
voteview_download
- Additional Methods
- Extended Examples
Installation
To install this package, ensure you have devtools installed. If you do not, run install.packages("devtools") and then install from GitHub using
devtools::install_github("voteview/Rvoteview")
For a quick start, see the README in the GitHub repository here.
Querying the database with voteview_search
The first main function of this package is to allow users to search for roll calls. Using a custom query parser, we allow both simple and complex queries to be made to the VoteView database. The simple way uses a set of arguments to build a query within the R package while the complex way allows the user to build a specific query with nested, boolean logic. Both can also be used simultaneously. You can find the full documentation for the query parser here.
Simple text queries
The q argument should be treated similarly to a search box online. You can put in text search terms, specific fields with parameters, or it can be left blank if other arguments are used. The simple usage is to treat the q argument as a way to search all text fields. If you want to search a specific phrase, put the query in quotes. This will essentially look for that exact phrase in many of the text fields in the database. Alternatively, if you search without using quotes, the word will be lemmatized (shortened) and will search an index of the text fields. For example, we can search for "terrorism" exactly or loosely using the index:
library(Rvoteview)
res <- voteview_search("'terrorism'") # exact
res <- voteview_search("terrorism") # index based
You can also search for multiple words:
res <- voteview_search("terrorism iraq") # index based search
Using the text index, the MongoDB that houses the rollcalls will search for the documents for either of these words and return the best matches. In effect, this will return documents that have either "terror" or "iraq" or various shortened versions of those words.
Basic syntax
When using one of the simple queries above, the query parser automatically adds a field to the front of a query that does not specify which field to search. In order to specify a specific field, use the following fieldname:query syntax. To replicate the last example more explicitly, we use the following:
res <- voteview_search("alltext:terrorism iraq")
Unfortunately, due to the way the query parser works, you cannot search for two exact words at the moment or search in two different specific text fields. You can however look within a specific text field.
res <- voteview_search("vote_desc:'iraq'")
Using additional arguments
Users can also use other arguments to search only roll calls that are in a certain chamber of Congress, within a date range, within a certain set of congresses, and within a level of support, defined as the percent of total valid votes that were yea vote. This is especially useful if users only want to return competitive votes. Note that all fields are joined using "AND" logic; for example you search for roll calls using the keyword "tax" AND are in the House but not votes that either use the keyword "tax" OR were held in the House. Also note that the congress field uses "OR" logic within the numeric vector that specifices which congress to search. No roll call can be in two congresses, so it makes no sense to search for roll calls that are in one congress AND in another congress.
## Search for votes with a start date
## Note that because tax is not in quotes, it searches the text index and not for
## exact matches
res <- voteview_search("tax", startdate = "2005-01-01")
#> Query '(tax) AND (startdate:2005-01-01)' returned 823 rollcalls...
## Search for votes with an end date in just the House
res <- voteview_search("tax", enddate = "2005-01-01", chamber = "House")
#> Query '(tax) AND (enddate:2005-01-01) AND (chamber:house)' returned 1580 rollcalls...
## Search for votes with a start date in just the house in the 110th or 112th Congress
res <- voteview_search("tax",
startdate = "2000-12-20",
congress = c(110, 112),
chamber = "House")
#> Query '(tax) AND (startdate:2000-12-20) AND (congress:110 112) AND (chamber:house)' returned 143 rollcalls...
You can always see exactly what search was used to create a set of roll calls by retrieving the 'qstring' attribute of the returned data frame:
attr(res, "qstring")
#> [1] "(tax) AND (startdate:2000-12-20) AND (congress:110 112) AND (chamber:house)"
Building complex queries
As previewed before, users can use the q argument to specify complex queries by specifying which fields to search and how to combine fields using boolean logic. The complete documentation can be found here. In general, the following syntax is used, field:specific phrase (field:other phrase OR field:second phrase).
For example, if you wanted to find votes where 'war' and 'iraq' were present but only up to 1993 and after 2000, you could write it like so:
qString <- "alltext:war iraq (enddate:1993 OR startdate:2000)"
res <- voteview_search(q = qString)
#> Query 'alltext:war iraq (enddate:1993 OR startdate:2000)' returned 2943 rollcalls...
Whenever in doubt, add parentheses to make the query clearer!
Numeric fields can be searched in a similar way, although users can also use square brackets and "to" for ranges of numbers. For example, the query for all votes about taxes in the 100th to 102nd congress could be expressed either using "alltext:taxes congress:100 OR congress:101 OR congress:102" or using "alltext:taxes congress:[100 to 102]". Note that if you want to restrict search to certain dates, the startdate and enddate arguments in the function should be used.
For example, here is a query that will get votes from the 100 to 102nd congress on tax where the percent of the rollcall votes in favor will be between 45 and 55 percent, inclusive.
qString <- "alltext:tax iraq (congress:[100 to 102] AND support:[45 to 55])"
res <- voteview_search(q = qString)
#> Query 'alltext:tax iraq (congress:[100 to 102] AND support:[45 to 55])' returned 36 rollcalls...
Downloading roll call data with voteview_download
The second main function of this package is to allow users to download detailed roll call data into a modified rollcall object from the pscl package. The default usage is to pass voteview_download a vector of roll call id numbers that we return in the voteview_search function.
## Search all votes with the exact phrase "estate tax" in the 105th congress
res <- voteview_search("'estate tax' congress:105")
## Download all estate tax votes
rc <- voteview_download(res$id)
summary(rc)
#> Source: Download from VoteView
#>
#> Number of Legislators: 536
#> Number of Roll Call Votes: 20
#>
#>
#> Using the following codes to represent roll call votes:
#> Yea: 1 2 3
#> Nay: 4 5 6
#> Abstentions: 7 8 9
#> Not In Legislature: 0
#>
#> Party Composition:
#> 100 200 328
#> 252 283 1
#>
#> Vote Summary:
#> Count Percent
#> 0 (notInLegis) 7696 71.8
#> 1 (yea) 1339 12.5
#> 6 (nay) 1645 15.3
#> 7 (missing) 1 0.0
#> 9 (missing) 39 0.4
#>
#> Use summary(rc,verbose=TRUE) for more detailed information.
Importantly, the object we return is a modified rollcall object, in that it may contain additional elements that the authors of the pscl package did not include. Therefore it will work with all of the methods they wrote for rollcall objects as well as some methods we include in this package. The biggest difference between the original rollcall object and what we return is the inclusion of "long" versions of the votes.data and legis.data data frames, described below.
First, because icpsr numbers are not necessarily unique to legislators, we include legis.long.dynamic in the output. For example, when Strom Thurmond changed parties, his icpsr number also changed. However, when building rollcall objects, icpsr numbers are the default. Therefore, legis.long.dynamic contains a record of every legislator-party-congress as a unique id, as well as the relevant covariates.
Second, we include votes.long, a data frame where the rows are legislator-roll calls and contain how each legislator voted on each roll call. This is the long version of the votes matrix included in all rollcall objects.
Additional Methods
We also add three methods that can be used on rollcall objects created by our package.
Joining two rollcall objects
The first function allows for a full outer join of two rollcall objects downloaded from the VoteView database, creating a new rollcall object that is a union of the two. It is called by using the %+% operator. This is especially useful if the user downloaded two roll call objects at separate times and wants to join them together rather than re-download all of the votes at the same time.
## Search all votes with exact phrase "estate tax"
res <- voteview_search("'estate tax' congress:105")
## Download first 10 votes
rc1 <- voteview_download(res$id[1:10])
## Download another 10 votes with some overlap
rc2 <- voteview_download(res$id[5:14])
## Merge them together
rcall <- rc1 %+% rc2
rcall$m # The number of total votes
#> [1] 14
Melting rollcall objects
We also provide a function called melt_rollcall which allows users to produce a long data frame that is essentially the same as votes.long but includes all of the roll call and legislator data on each row.
## Default is to retain all data
rc_long <- melt_rollcall(rcall)
rc_long[1:3, 1:17]
#> id vname vote rollnumber chamber date congress
#> 1 MH10502605 RH1050241 1 241 House 1997-06-26 105
#> 2 MH10515401 RH1050241 6 241 House 1997-06-26 105
#> 3 MH10529717 RH1050241 1 241 House 1997-06-26 105
#> description question
#> 1 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> 2 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> 3 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> yea_count nay_count vote_result codes.Issue
#> 1 197 235 Failed Budget resolution ; Tax rates
#> 2 197 235 Failed Budget resolution ; Tax rates
#> 3 197 235 Failed Budget resolution ; Tax rates
#> codes.Peltzman codes.Clausen nomslope nomintercept
#> 1 Budget General Interest Government Management 5.347 0.495
#> 2 Budget General Interest Government Management 5.347 0.495
#> 3 Budget General Interest Government Management 5.347 0.495
## Retaining fewer columns
rc_long <- melt_rollcall(rcall, votecols = c("chamber", "congress"))
rc_long[1:3, ]
#> id vname vote chamber congress icpsr
#> 1 MH10502605 RH1050241 1 House 105 2605
#> 2 MH10515401 RH1050241 6 House 105 15401
#> 3 MH10529717 RH1050241 1 House 105 29717
#> name party_code state_abbrev cqlabel district_code
#> 1 DINGELL, John David, Jr. 100 MI (MI-16) 16
#> 2 BAKER, Richard Hugh 200 LA (LA-06) 6
#> 3 DAVIS, Danny K. 100 IL (IL-07) 7
Completing interrupted downloads
If your internet connection drops in the middle of a download or you have to interrupt a download for some reason, the voteview_download function should try to complete building the rollcall object with whatever data it has successfully downloaded. While manually interrupting functions in R is tricky and we cannot catch interrupts perfectly, if it does succeed or if your connection does drop, then we store the roll call ids that you were unable to retrieve in the unretrievedids slot of our modified rollcall object. Users can then use the complete_download function to download the unretrieved ids and create a complete rollcall object. For example, imagine the following download stalls as your wireless cuts out at this cute coffee shop that has beans roasted in house but cannot manage a good wireless conenction:
rc_fail <- voteview_download(res$id)
If this fails but still manages to build a rollcall object with whatever ids it was able to retrieve, then we can complete the download with a simple command:
rc <- complete_download(rc_fail)
Again, because of the difficulty with properly catching interrupts in R, this will not always work with manual interrupts, but should work with dropped internet connections.
Retrieving member data
There is also the ability to search the database for members (House Representatives, Senators, and Presidents) using the member_search function. Unfortunately, the syntax is not identical to the syntax when searching for roll calls. Nonetheless, the usage in R is quite simple. There are fields to search members' names, icpsr number, state (either ICPSR state number, two letter postal code, or the full name), the range of congresses to search within, the CQ label of the member, and the chamber to search within.
The function returns a data frame of metadata, with one row for each legislator-congress that is found (these are the unique entries in the database of members). Therefore, if we want to return all unique legislator-congresses where the name 'clinton' appears anywhere in the name fields, we can use the following search:
clintons <- member_search("clinton")
## Drop the bio field because it is quite long
clintons[1:7, names(clintons) != "bio"]
#> id icpsr bioname party_code cqlabel
#> 1 MH00901849 1849 CLINTON, George 13 (NY-02)
#> 2 MH05200311 311 BABBITT, Clinton 100 (WI-01)
#> 3 MH00801849 1849 CLINTON, George 13 (NY-03)
#> 4 MH01001849 1849 CLINTON, George 13 (NY-02)
#> 5 MS00701847 1847 CLINTON, De Witt 13 (NY)
#> 6 MH01806044 6044 MARTINDALE, Henry Clinton 8000 (NY-18)
#> 7 MH01906044 6044 MARTINDALE, Henry Clinton 22 (NY-18)
#> bioImgURL congress state state_abbrev chamber score
#> 1 silhouette.png 9 New York NY House 1.500000
#> 2 000311.jpg 52 Wisconsin WI House 1.500000
#> 3 silhouette.png 8 New York NY House 1.500000
#> 4 silhouette.png 10 New York NY House 1.500000
#> 5 001847.jpg 7 New York NY Senate 1.333333
#> 6 silhouette.png 18 New York NY House 1.333333
#> 7 silhouette.png 19 New York NY House 1.333333
#> congresses seo_name district_code
#> 1 8, 10 george-clinton 2
#> 2 52, 52 clinton-babbitt 1
#> 3 8, 10 george-clinton 3
#> 4 8, 10 george-clinton 2
#> 5 7, 8 de-witt-clinton 0
#> 6 18, 23, 21, 23 henry-clinton-martindale 18
#> 7 18, 23, 21, 23 henry-clinton-martindale 18
#> party_name nominate.dim2 nominate.dim1
#> 1 Democratic-Republican Party 0.235 0.163
#> 2 Democratic Party 0.125 -0.302
#> 3 Democratic-Republican Party 0.235 0.163
#> 4 Democratic-Republican Party 0.235 0.163
#> 5 Democratic-Republican Party -0.528 -0.366
#> 6 Adams-Clay Federalist Party -0.102 0.682
#> 7 Adams Party -0.102 0.682
#> nominate.geo_mean_probability
#> 1 0.712
#> 2 0.645
#> 3 NA
#> 4 NA
#> 5 0.905
#> 6 0.712
#> 7 0.634
It is important to note that if there is no white space in the name field, the database is searched for exact matches for that one word. If there are multiple words we use a text index of all of the name fields and return the best matches.
If you only want to return the first record per ICPSR number, you can set the distinct flag equal to one. This is useful because it limits the size of the object returned and most data is duplicated within ICPSR number. For example, CS DW-NOMINATE scores are constant within ICPSR number, as are names and (usually) party.
clintons <- member_search("clinton",
state = "NY",
distinct = 1)
## Drop the bio field because it is quite long
clintons[, names(clintons) != "bio"]
#> id icpsr bioname party_code cqlabel
#> 1 MH00901849 1849 CLINTON, George 13 (NY-02)
#> 2 MS00701847 1847 CLINTON, De Witt 13 (NY)
#> 3 MH01806044 6044 MARTINDALE, Henry Clinton 8000 (NY-18)
#> 4 MH02701850 1850 CLINTON, James Graham 100 (NY-06)
#> 5 MH04206426 6426 MERRIAM, Clinton Levi 200 (NY-20)
#> 6 MH04305875 5875 MacDOUGALL, Clinton Dugald 200 (NY-25)
#> 7 MS10740105 40105 CLINTON, Hillary Rodham 100 (NY)
#> 8 MH03805707 5707 LITTLEJOHN, De Witt Clinton 200 (NY-22)
#> bioImgURL congress state state_abbrev chamber score
#> 1 silhouette.png 9 New York NY House 1.500000
#> 2 001847.jpg 7 New York NY Senate 1.333333
#> 3 silhouette.png 18 New York NY House 1.333333
#> 4 silhouette.png 27 New York NY House 1.333333
#> 5 006426.jpg 42 New York NY House 1.333333
#> 6 silhouette.png 43 New York NY House 1.333333
#> 7 040105.jpg 107 New York NY Senate 1.333333
#> 8 005707.jpg 38 New York NY House 1.250000
#> congresses seo_name district_code
#> 1 8, 10 george-clinton 2
#> 2 7, 8 de-witt-clinton 0
#> 3 18, 23, 21, 23 henry-clinton-martindale 18
#> 4 27, 28 james-graham-clinton 6
#> 5 42, 43 clinton-levi-merriam 20
#> 6 43, 44 clinton-dugald-macdougall 25
#> 7 107, 111 hillary-rodham-clinton 0
#> 8 38, 38 de-witt-clinton-littlejohn 22
#> party_name nominate.dim2 nominate.dim1
#> 1 Democratic-Republican Party 0.235 0.163
#> 2 Democratic-Republican Party -0.528 -0.366
#> 3 Adams-Clay Federalist Party -0.102 0.682
#> 4 Democratic Party -0.694 -0.262
#> 5 Republican Party -0.774 0.353
#> 6 Republican Party -0.463 0.453
#> 7 Democratic Party -0.243 -0.367
#> 8 Republican Party 0.474 0.454
#> nominate.geo_mean_probability
#> 1 0.712
#> 2 0.905
#> 3 0.712
#> 4 0.789
#> 5 0.679
#> 6 0.699
#> 7 0.833
#> 8 0.729
Some other fields that are not unique to ICPSR number but may vary are the chamber of the representative, their CQ label, and the number of votes they cast. Let's get all the records for Bernie Sanders.
sanders <- member_search("sanders",
state = "VT")
## Drop the bio field because it is quite long
sanders[, names(sanders) != "bio"]
#> id icpsr bioname party_code cqlabel bioImgURL
#> 1 MH10229147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 2 MH10329147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 3 MH10429147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 4 MH10529147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 5 MH10629147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 6 MH10729147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 7 MH10829147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 8 MH10929147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 9 MS11029147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 10 MS11129147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 11 MS11229147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 12 MS11329147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 13 MS11429147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 14 MS11529147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> congress state state_abbrev chamber score congresses seo_name
#> 1 102 Vermont VT House 1.5 102, 115 bernard-sanders
#> 2 103 Vermont VT House 1.5 102, 115 bernard-sanders
#> 3 104 Vermont VT House 1.5 102, 115 bernard-sanders
#> 4 105 Vermont VT House 1.5 102, 115 bernard-sanders
#> 5 106 Vermont VT House 1.5 102, 115 bernard-sanders
#> 6 107 Vermont VT House 1.5 102, 115 bernard-sanders
#> 7 108 Vermont VT House 1.5 102, 115 bernard-sanders
#> 8 109 Vermont VT House 1.5 102, 115 bernard-sanders
#> 9 110 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 10 111 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 11 112 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 12 113 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 13 114 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 14 115 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> district_code party_name nominate.dim2 nominate.dim1
#> 1 1 Independent -0.323 -0.516
#> 2 1 Independent -0.323 -0.516
#> 3 1 Independent -0.323 -0.516
#> 4 1 Independent -0.323 -0.516
#> 5 1 Independent -0.323 -0.516
#> 6 1 Independent -0.323 -0.516
#> 7 1 Independent -0.323 -0.516
#> 8 1 Independent -0.323 -0.516
#> 9 0 Independent -0.323 -0.516
#> 10 0 Independent -0.323 -0.516
#> 11 0 Independent -0.323 -0.516
#> 12 0 Independent -0.323 -0.516
#> 13 0 Independent -0.323 -0.516
#> 14 0 Independent -0.323 -0.516
#> nominate.geo_mean_probability
#> 1 0.7602263
#> 2 0.8082525
#> 3 0.8195841
#> 4 0.7916307
#> 5 0.8055633
#> 6 0.8127989
#> 7 0.8060040
#> 8 0.8439096
#> 9 0.8814125
#> 10 0.8918629
#> 11 0.7792373
#> 12 0.8365333
#> 13 0.7983794
#> 14 0.8733797
As you can see Sanders changes chambers between the 109th and 110th congresses and a few other fields differ as well. Nonetheless, most is repeated.
Extended Examples
This section details three different possible uses of the Rvoteview package, showing users from beginning to end how to conduct their own ideal point estimation and use Rvoteview in more traditional regression analysis.
Ideal point estimation
Imagine that we want to estimate ideal points for all legislators voting on foreign policy during the first six months of Obama's presidency.. We will use all roll calls that fit the Clausen category "Foreign and Defense Policy" and are somewhat competitive, meaning between 15 and 85 percent of votes on the floor were yeas.
## Load packages
library(ggplot2) # Load this first so that Rvoteview can use %+%
#> Find out what's changed in ggplot2 at
#> http://github.com/tidyverse/ggplot2/releases.
#>
#> Attaching package: 'ggplot2'
#> The following object is masked from 'package:Rvoteview':
#>
#> %+%
library(Rvoteview)
## Search database for votes that meet our criteria
res <- voteview_search("codes.Clausen:Foreign and Defense Policy support:[15 to 85]",
startdate = "2009-01-20", enddate = "2009-07-20")
#> Query '(codes.Clausen:Foreign and Defense Policy support:[15 to 85]) AND (startdate:2009-01-20) AND (enddate:2009-07-20)' returned 74 rollcalls...
## Download votes into rollcall object
rc <- voteview_download(res$id)
summary(rc)
#> Source: Download from VoteView
#>
#> Number of Legislators: 734
#> Number of Roll Call Votes: 555
#>
#>
#> Using the following codes to represent roll call votes:
#> Yea: 1 2 3
#> Nay: 4 5 6
#> Abstentions: 7 8 9
#> Not In Legislature: 0
#>
#> Party Composition:
#> 100 200 328
#> 359 374 1
#>
#> Vote Summary:
#> Count Percent
#> 0 (notInLegis) 212005 52.0
#> 1 (yea) 92810 22.8
#> 6 (nay) 95350 23.4
#> 7 (missing) 246 0.1
#> 9 (missing) 6959 1.7
#>
#> Use summary(rc,verbose=TRUE) for more detailed information.
Now we use the wnominate package to run an ideal point estimation.
library(wnominate)
#>
#> ## W-NOMINATE Ideal Point Package
#> ## Copyright 2006 -2018
#> ## Keith Poole, Jeffrey Lewis, James Lo, and Royce Carroll
#> ## Support provided by the U.S. National Science Foundation
#> ## NSF Grant SES-0611974
# Find extreme legislators for polarity argument
cons1 <- rc$legis.long.dynamic[which.max(rc$legis.data$dim1), c("name", "icpsr")]
cons2 <- rc$legis.long.dynamic[which.max(rc$legis.data$dim2), c("name", "icpsr")]
defIdeal <- wnominate(rc,
polarity = list("icpsr", c(20753, 20523)))
#>
#> Preparing to run W-NOMINATE...
#>
#> Checking data...
#>
#> ... 9 of 734 total members dropped.
#>
#> All votes meet minimum lopsidedness requirement.
#>
#> Running W-NOMINATE...
#>
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Starting estimation of Beta...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Starting estimation of Beta...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Estimating weights...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Estimating weights...
#> Getting bill parameters...
#> Getting legislator coordinates...
#>
#>
#> W-NOMINATE estimation completed successfully.
#> W-NOMINATE took 210.619 seconds to execute.
This ideal point estimation also returns the estimated points attached the all of the legislator and rollcall metadata already in the rc object! This can be useful in creating custom plots.
## Create text party name
defIdeal$legislators$partyName <- ifelse(defIdeal$legislators$party == 200, "Republican",
ifelse(defIdeal$legislators$party == 100, "Democrat", "Independent"))
ggplot(defIdeal$legislators,
aes(x=coord1D, y=coord2D, color=partyName, label=state_abbrev)) +
geom_text() +
scale_color_manual("Party", values = c("Republican" = "red",
"Democrat" = "blue",
"Independent" = "darkgreen")) +
theme_bw()
#> Warning: Removed 9 rows containing missing values (geom_text).
We see the usual split between Republicans and Democrats.
We can also use the build in plot method from `wnominate to produce some great figures from our estimation.
# Some great plots!
plot(defIdeal)
#> NULL
The rollcall objects we build can also be used in the ideal function in the pscl package.
library(pscl)
defIdeal <- ideal(rc,
d = 2)
We can also use the pscl plot method.
plot(defIdeal)
Regression analysis of roll call behavior
Users can also use the VoteView API to run regression analyses. Let's take the state level opinion data on gay rights that was estimated in Lax and Phillips (2009). They used multilevel regression and poststratification on surveys from 1999-2008 in order to estimate state-level explicit support for gay rights issues. Let's pull down some important bills presented before the 111th congress (2009-2011) and see how state level public opinion in the preceding years predicts voting behavior in the legislature.
Let's see what bills there were in the 111th congress that had to do with homosexuality. We can use a search that will capture quite a few different bills.
## Two separate searches because fields cannot be joined with an OR
res <- voteview_search("codes.Issue:Homosexuality congress:111")
#> Query 'codes.Issue:Homosexuality congress:111' returned 18 rollcalls...
res[1:5, 1:10]
#> id congress chamber rollnumber date yea nay support
#> 1 RH1110218 111 House 218 2009-04-29 234 181 56.39
#> 2 RH1110219 111 House 219 2009-04-29 234 190 55.19
#> 3 RH1110221 111 House 221 2009-04-29 185 241 43.43
#> 4 RH1110222 111 House 222 2009-04-29 249 175 58.73
#> 5 RH1110752 111 House 752 2009-10-06 178 234 43.20
#> vote_result short_description
#> 1 Passed HATE CRIMES (PROC)
#> 2 Passed HATE CRIMES -- RULE
#> 3 Failed HATE CRIMES -- RECOMMIT
#> 4 Passed HATE CRIMES (PASS)
#> 5 Failed HATE CRIMES (PROC)
To focus on actual bills that were of some consequence, let's take the House and Senate don't ask don't tell votes and the hate crimes bill from the House.
dadt <- voteview_download(c("RH1111621", "RS1110678", "RH1110222"))
dadt$vote.data
Now we want to turn this into a long dataframe, where each row is a legislator-vote. We could also then cast this using a standard cast function or the reshape2 package to have each row be a legislator, or each row be a legislator-congress and so on. The longer format will serve our purposes for now. Note that dim1 and dim2 are the Common Space DW-Nominate positions on the first and second ideological dimensions. They are fixed over the legislator's tenure in office.
## Only retain certain columns with respect to the legislator and the vote
dadtLong <- melt_rollcall(dadt,
legiscols = c("name", "state_abbrev","party_code", "dim1", "dim2"),
votecols = c("vname", "date", "chamber"))
head(dadtLong)
#> id vname vote chamber date name
#> 1 MH11102605 RH1111621 1 House 2010-12-15 DINGELL, John David, Jr.
#> 2 MH11115627 RH1111621 6 House 2010-12-15 STEARNS, Clifford Bundy
#> 3 MH11129149 RH1111621 1 House 2010-12-15 MORAN, James P.
#> 4 MH11110713 RH1111621 1 House 2010-12-15 CONYERS, John, Jr.
#> 5 MH11112036 RH1111621 1 House 2010-12-15 OBEY, David Ross
#> 6 MH11115634 RH1111621 1 House 2010-12-15 ROS-LEHTINEN, Ileana
#> party_code state_abbrev dim2 dim1
#> 1 100 MI 0.313 -0.448
#> 2 200 FL -0.075 0.554
#> 3 100 VA -0.203 -0.302
#> 4 100 MI -0.512 -0.658
#> 5 100 WI -0.063 -0.450
#> 6 200 FL -0.225 0.245
Included in the package is a dataframe that links the numeric ICPSR codes to state names and state mail codes. You can load the data by calling data(states). We use this to merge in the proper state names that will be matched to the Lax and Phillips (2009) dataset. Obama appears three times in this dataset and will be dropped in this merge.
data(states)
dadtLong <- merge(dadtLong, states[, c("state_abbrev", "state_name")],
by = "state_abbrev")
dadtLong$state_name <- tolower(dadtLong$state_name)
Now we use the Lax and Phillips (2009) data, which we make available in the package as well under lpOpinion.
data(lpOpinion)
lpOpinion$state <- tolower(lpOpinion$state)
df <- merge(dadtLong, lpOpinion,
by.x = "state_name", by.y = "state")
head(df)
#> state_name state_abbrev id vname vote chamber date
#> 1 alabama AL MH11120901 RH1110222 6 House 2009-04-29
#> 2 alabama AL MH11120301 RH1110222 6 House 2009-04-29
#> 3 alabama AL MH11120300 RH1111621 6 House 2010-12-15
#> 4 alabama AL MH11120301 RH1111621 6 House 2010-12-15
#> 5 alabama AL MH11120900 RH1110222 6 House 2009-04-29
#> 6 alabama AL MH11120302 RH1110222 6 House 2009-04-29
#> name party_code dim2 dim1
#> 1 GRIFFITH, Parker 100 0.522 -0.045
#> 2 ROGERS, Mike Dennis 200 0.443 0.338
#> 3 BONNER, Jr., Josiah Robins (Jo) 200 0.513 0.367
#> 4 ROGERS, Mike Dennis 200 0.443 0.338
#> 5 BRIGHT, Bobby Neal, Sr. 100 0.671 0.067
#> 6 DAVIS, Artur 100 0.454 -0.270
#> secondParentAdoption hateCrimes healthBenefits housing jobs marriage
#> 1 29 61 54 68 53 23
#> 2 29 61 54 68 53 23
#> 3 29 61 54 68 53 23
#> 4 29 61 54 68 53 23
#> 5 29 61 54 68 53 23
#> 6 29 61 54 68 53 23
#> sodomy civilUnions meanOpinion
#> 1 28 34 44
#> 2 28 34 44
#> 3 28 34 44
#> 4 28 34 44
#> 5 28 34 44
#> 6 28 34 44
Now let's build a dichotomous variable that represents whether the legislator voted yea on that bill (1), nay on that bill (0), or abstained (NA).
## Recode votes
df$voteYes <- ifelse(df$vote == 1, 1, ifelse(df$vote == 6, 0, NA))
## Raw votes by party
table(df$party_code, df$voteYes, useNA = "always")
#>
#> 0 1 <NA>
#> 100 32 522 14
#> 200 349 41 9
#> 328 0 1 0
#> <NA> 0 0 0
## Recode party (add independent to democrats)
df$republican <- ifelse(df$party_code == "200", 1, 0)
Let's use meanOpinion from the Lax and Phillips (2009) data, which is the average of pro-gay public opinion sentiment on various dimensions. We will use it in a couple of analyses.
## Simple model
summary(lm(voteYes ~ meanOpinion, data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion, data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.8823 -0.4151 0.1488 0.3669 0.9899
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -1.173622 0.125685 -9.338 <2e-16 ***
#> meanOpinion 0.031151 0.002197 14.181 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.4458 on 943 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.1758, Adjusted R-squared: 0.1749
#> F-statistic: 201.1 on 1 and 943 DF, p-value: < 2.2e-16
## Control for party
summary(lm(voteYes ~ meanOpinion*republican, data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican, data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.93523 -0.07432 -0.02396 0.07956 0.95041
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.077450 0.099228 0.781 0.435
#> meanOpinion 0.014789 0.001687 8.767 < 2e-16 ***
#> republican -0.645973 0.149434 -4.323 1.7e-05 ***
#> meanOpinion:republican -0.002427 0.002645 -0.918 0.359
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2518 on 941 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.7377, Adjusted R-squared: 0.7369
#> F-statistic: 882.3 on 3 and 941 DF, p-value: < 2.2e-16
## Control for ideology
## Note that ideology here has been estimated using these and later votes,
## so interpret the results with some caution
summary(lm(voteYes ~ meanOpinion*republican + dim1 + dim2,
data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican + dim1 + dim2,
#> data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.79008 -0.10750 -0.01185 0.07428 0.93117
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.614458 0.104153 5.900 5.08e-09 ***
#> meanOpinion 0.003512 0.001802 1.949 0.0516 .
#> republican -0.700580 0.143866 -4.870 1.31e-06 ***
#> dim1 -0.374958 0.056626 -6.622 5.97e-11 ***
#> dim2 -0.270507 0.029114 -9.291 < 2e-16 ***
#> meanOpinion:republican 0.003193 0.002487 1.284 0.1995
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2329 on 939 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.776, Adjusted R-squared: 0.7748
#> F-statistic: 650.6 on 5 and 939 DF, p-value: < 2.2e-16
## Now let's look just at repealing don't ask don't tell and add chamber fixed effects
summary(lm(voteYes ~ meanOpinion*republican + dim1 + dim2 + chamber,
data = df[df$vname != "RH1110222", ]))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican + dim1 + dim2 +
#> chamber, data = df[df$vname != "RH1110222", ])
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.79241 -0.10385 -0.01139 0.07537 0.89854
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.756861 0.139289 5.434 8.53e-08 ***
#> meanOpinion 0.001755 0.002402 0.731 0.46541
#> republican -0.986845 0.189052 -5.220 2.60e-07 ***
#> dim1 -0.246299 0.074859 -3.290 0.00107 **
#> dim2 -0.291247 0.038538 -7.557 1.90e-13 ***
#> chamberSenate 0.062868 0.026494 2.373 0.01802 *
#> meanOpinion:republican 0.006346 0.003275 1.938 0.05318 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2268 on 514 degrees of freedom
#> (13 observations deleted due to missingness)
#> Multiple R-squared: 0.7877, Adjusted R-squared: 0.7852
#> F-statistic: 317.8 on 6 and 514 DF, p-value: < 2.2e-16
Even when controlling for ideology and party, it seems that legislators, and especially Republican legislators, are more likely to vote for pro-gay rights bills when their state has a high average level of pro-gay rights sentiment.
JeffreyBLewis/Rvoteview documentation built on Oct. 15, 2019, 10:18 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
This package provides tools to query and download from the VoteView database. This vignette will demonstrate the different types of queries that can be used, how Rvoteview can be used to do ideal point estimation on a subset of votes using the pscl package and the wnominate package, and how Rvoteview facilitates regression analyses of congressional voting behavior.
- Installation
- Querying the database with
voteview_search - Downloading roll call data with
voteview_download - Additional Methods
- Extended Examples
Installation
To install this package, ensure you have devtools installed. If you do not, run install.packages("devtools") and then install from GitHub using
devtools::install_github("voteview/Rvoteview")
For a quick start, see the README in the GitHub repository here.
Querying the database with voteview_search
The first main function of this package is to allow users to search for roll calls. Using a custom query parser, we allow both simple and complex queries to be made to the VoteView database. The simple way uses a set of arguments to build a query within the R package while the complex way allows the user to build a specific query with nested, boolean logic. Both can also be used simultaneously. You can find the full documentation for the query parser here.
Simple text queries
The q argument should be treated similarly to a search box online. You can put in text search terms, specific fields with parameters, or it can be left blank if other arguments are used. The simple usage is to treat the q argument as a way to search all text fields. If you want to search a specific phrase, put the query in quotes. This will essentially look for that exact phrase in many of the text fields in the database. Alternatively, if you search without using quotes, the word will be lemmatized (shortened) and will search an index of the text fields. For example, we can search for "terrorism" exactly or loosely using the index:
library(Rvoteview)
res <- voteview_search("'terrorism'") # exact
res <- voteview_search("terrorism") # index based
You can also search for multiple words:
res <- voteview_search("terrorism iraq") # index based search
Using the text index, the MongoDB that houses the rollcalls will search for the documents for either of these words and return the best matches. In effect, this will return documents that have either "terror" or "iraq" or various shortened versions of those words.
Basic syntax
When using one of the simple queries above, the query parser automatically adds a field to the front of a query that does not specify which field to search. In order to specify a specific field, use the following fieldname:query syntax. To replicate the last example more explicitly, we use the following:
res <- voteview_search("alltext:terrorism iraq")
Unfortunately, due to the way the query parser works, you cannot search for two exact words at the moment or search in two different specific text fields. You can however look within a specific text field.
res <- voteview_search("vote_desc:'iraq'")
Using additional arguments
Users can also use other arguments to search only roll calls that are in a certain chamber of Congress, within a date range, within a certain set of congresses, and within a level of support, defined as the percent of total valid votes that were yea vote. This is especially useful if users only want to return competitive votes. Note that all fields are joined using "AND" logic; for example you search for roll calls using the keyword "tax" AND are in the House but not votes that either use the keyword "tax" OR were held in the House. Also note that the congress field uses "OR" logic within the numeric vector that specifices which congress to search. No roll call can be in two congresses, so it makes no sense to search for roll calls that are in one congress AND in another congress.
## Search for votes with a start date
## Note that because tax is not in quotes, it searches the text index and not for
## exact matches
res <- voteview_search("tax", startdate = "2005-01-01")
#> Query '(tax) AND (startdate:2005-01-01)' returned 823 rollcalls...
## Search for votes with an end date in just the House
res <- voteview_search("tax", enddate = "2005-01-01", chamber = "House")
#> Query '(tax) AND (enddate:2005-01-01) AND (chamber:house)' returned 1580 rollcalls...
## Search for votes with a start date in just the house in the 110th or 112th Congress
res <- voteview_search("tax",
startdate = "2000-12-20",
congress = c(110, 112),
chamber = "House")
#> Query '(tax) AND (startdate:2000-12-20) AND (congress:110 112) AND (chamber:house)' returned 143 rollcalls...
You can always see exactly what search was used to create a set of roll calls by retrieving the 'qstring' attribute of the returned data frame:
attr(res, "qstring")
#> [1] "(tax) AND (startdate:2000-12-20) AND (congress:110 112) AND (chamber:house)"
Building complex queries
As previewed before, users can use the q argument to specify complex queries by specifying which fields to search and how to combine fields using boolean logic. The complete documentation can be found here. In general, the following syntax is used, field:specific phrase (field:other phrase OR field:second phrase).
For example, if you wanted to find votes where 'war' and 'iraq' were present but only up to 1993 and after 2000, you could write it like so:
qString <- "alltext:war iraq (enddate:1993 OR startdate:2000)"
res <- voteview_search(q = qString)
#> Query 'alltext:war iraq (enddate:1993 OR startdate:2000)' returned 2943 rollcalls...
Whenever in doubt, add parentheses to make the query clearer!
Numeric fields can be searched in a similar way, although users can also use square brackets and "to" for ranges of numbers. For example, the query for all votes about taxes in the 100th to 102nd congress could be expressed either using "alltext:taxes congress:100 OR congress:101 OR congress:102" or using "alltext:taxes congress:[100 to 102]". Note that if you want to restrict search to certain dates, the startdate and enddate arguments in the function should be used.
For example, here is a query that will get votes from the 100 to 102nd congress on tax where the percent of the rollcall votes in favor will be between 45 and 55 percent, inclusive.
qString <- "alltext:tax iraq (congress:[100 to 102] AND support:[45 to 55])"
res <- voteview_search(q = qString)
#> Query 'alltext:tax iraq (congress:[100 to 102] AND support:[45 to 55])' returned 36 rollcalls...
Downloading roll call data with voteview_download
The second main function of this package is to allow users to download detailed roll call data into a modified rollcall object from the pscl package. The default usage is to pass voteview_download a vector of roll call id numbers that we return in the voteview_search function.
## Search all votes with the exact phrase "estate tax" in the 105th congress
res <- voteview_search("'estate tax' congress:105")
## Download all estate tax votes
rc <- voteview_download(res$id)
summary(rc)
#> Source: Download from VoteView
#>
#> Number of Legislators: 536
#> Number of Roll Call Votes: 20
#>
#>
#> Using the following codes to represent roll call votes:
#> Yea: 1 2 3
#> Nay: 4 5 6
#> Abstentions: 7 8 9
#> Not In Legislature: 0
#>
#> Party Composition:
#> 100 200 328
#> 252 283 1
#>
#> Vote Summary:
#> Count Percent
#> 0 (notInLegis) 7696 71.8
#> 1 (yea) 1339 12.5
#> 6 (nay) 1645 15.3
#> 7 (missing) 1 0.0
#> 9 (missing) 39 0.4
#>
#> Use summary(rc,verbose=TRUE) for more detailed information.
Importantly, the object we return is a modified rollcall object, in that it may contain additional elements that the authors of the pscl package did not include. Therefore it will work with all of the methods they wrote for rollcall objects as well as some methods we include in this package. The biggest difference between the original rollcall object and what we return is the inclusion of "long" versions of the votes.data and legis.data data frames, described below.
First, because icpsr numbers are not necessarily unique to legislators, we include legis.long.dynamic in the output. For example, when Strom Thurmond changed parties, his icpsr number also changed. However, when building rollcall objects, icpsr numbers are the default. Therefore, legis.long.dynamic contains a record of every legislator-party-congress as a unique id, as well as the relevant covariates.
Second, we include votes.long, a data frame where the rows are legislator-roll calls and contain how each legislator voted on each roll call. This is the long version of the votes matrix included in all rollcall objects.
Additional Methods
We also add three methods that can be used on rollcall objects created by our package.
Joining two rollcall objects
The first function allows for a full outer join of two rollcall objects downloaded from the VoteView database, creating a new rollcall object that is a union of the two. It is called by using the %+% operator. This is especially useful if the user downloaded two roll call objects at separate times and wants to join them together rather than re-download all of the votes at the same time.
## Search all votes with exact phrase "estate tax"
res <- voteview_search("'estate tax' congress:105")
## Download first 10 votes
rc1 <- voteview_download(res$id[1:10])
## Download another 10 votes with some overlap
rc2 <- voteview_download(res$id[5:14])
## Merge them together
rcall <- rc1 %+% rc2
rcall$m # The number of total votes
#> [1] 14
Melting rollcall objects
We also provide a function called melt_rollcall which allows users to produce a long data frame that is essentially the same as votes.long but includes all of the roll call and legislator data on each row.
## Default is to retain all data
rc_long <- melt_rollcall(rcall)
rc_long[1:3, 1:17]
#> id vname vote rollnumber chamber date congress
#> 1 MH10502605 RH1050241 1 241 House 1997-06-26 105
#> 2 MH10515401 RH1050241 6 241 House 1997-06-26 105
#> 3 MH10529717 RH1050241 1 241 House 1997-06-26 105
#> description question
#> 1 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> 2 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> 3 Rangel of New York Substitute Amendment On Agreeing to the Amendment
#> yea_count nay_count vote_result codes.Issue
#> 1 197 235 Failed Budget resolution ; Tax rates
#> 2 197 235 Failed Budget resolution ; Tax rates
#> 3 197 235 Failed Budget resolution ; Tax rates
#> codes.Peltzman codes.Clausen nomslope nomintercept
#> 1 Budget General Interest Government Management 5.347 0.495
#> 2 Budget General Interest Government Management 5.347 0.495
#> 3 Budget General Interest Government Management 5.347 0.495
## Retaining fewer columns
rc_long <- melt_rollcall(rcall, votecols = c("chamber", "congress"))
rc_long[1:3, ]
#> id vname vote chamber congress icpsr
#> 1 MH10502605 RH1050241 1 House 105 2605
#> 2 MH10515401 RH1050241 6 House 105 15401
#> 3 MH10529717 RH1050241 1 House 105 29717
#> name party_code state_abbrev cqlabel district_code
#> 1 DINGELL, John David, Jr. 100 MI (MI-16) 16
#> 2 BAKER, Richard Hugh 200 LA (LA-06) 6
#> 3 DAVIS, Danny K. 100 IL (IL-07) 7
Completing interrupted downloads
If your internet connection drops in the middle of a download or you have to interrupt a download for some reason, the voteview_download function should try to complete building the rollcall object with whatever data it has successfully downloaded. While manually interrupting functions in R is tricky and we cannot catch interrupts perfectly, if it does succeed or if your connection does drop, then we store the roll call ids that you were unable to retrieve in the unretrievedids slot of our modified rollcall object. Users can then use the complete_download function to download the unretrieved ids and create a complete rollcall object. For example, imagine the following download stalls as your wireless cuts out at this cute coffee shop that has beans roasted in house but cannot manage a good wireless conenction:
rc_fail <- voteview_download(res$id)
If this fails but still manages to build a rollcall object with whatever ids it was able to retrieve, then we can complete the download with a simple command:
rc <- complete_download(rc_fail)
Again, because of the difficulty with properly catching interrupts in R, this will not always work with manual interrupts, but should work with dropped internet connections.
Retrieving member data
There is also the ability to search the database for members (House Representatives, Senators, and Presidents) using the member_search function. Unfortunately, the syntax is not identical to the syntax when searching for roll calls. Nonetheless, the usage in R is quite simple. There are fields to search members' names, icpsr number, state (either ICPSR state number, two letter postal code, or the full name), the range of congresses to search within, the CQ label of the member, and the chamber to search within.
The function returns a data frame of metadata, with one row for each legislator-congress that is found (these are the unique entries in the database of members). Therefore, if we want to return all unique legislator-congresses where the name 'clinton' appears anywhere in the name fields, we can use the following search:
clintons <- member_search("clinton")
## Drop the bio field because it is quite long
clintons[1:7, names(clintons) != "bio"]
#> id icpsr bioname party_code cqlabel
#> 1 MH00901849 1849 CLINTON, George 13 (NY-02)
#> 2 MH05200311 311 BABBITT, Clinton 100 (WI-01)
#> 3 MH00801849 1849 CLINTON, George 13 (NY-03)
#> 4 MH01001849 1849 CLINTON, George 13 (NY-02)
#> 5 MS00701847 1847 CLINTON, De Witt 13 (NY)
#> 6 MH01806044 6044 MARTINDALE, Henry Clinton 8000 (NY-18)
#> 7 MH01906044 6044 MARTINDALE, Henry Clinton 22 (NY-18)
#> bioImgURL congress state state_abbrev chamber score
#> 1 silhouette.png 9 New York NY House 1.500000
#> 2 000311.jpg 52 Wisconsin WI House 1.500000
#> 3 silhouette.png 8 New York NY House 1.500000
#> 4 silhouette.png 10 New York NY House 1.500000
#> 5 001847.jpg 7 New York NY Senate 1.333333
#> 6 silhouette.png 18 New York NY House 1.333333
#> 7 silhouette.png 19 New York NY House 1.333333
#> congresses seo_name district_code
#> 1 8, 10 george-clinton 2
#> 2 52, 52 clinton-babbitt 1
#> 3 8, 10 george-clinton 3
#> 4 8, 10 george-clinton 2
#> 5 7, 8 de-witt-clinton 0
#> 6 18, 23, 21, 23 henry-clinton-martindale 18
#> 7 18, 23, 21, 23 henry-clinton-martindale 18
#> party_name nominate.dim2 nominate.dim1
#> 1 Democratic-Republican Party 0.235 0.163
#> 2 Democratic Party 0.125 -0.302
#> 3 Democratic-Republican Party 0.235 0.163
#> 4 Democratic-Republican Party 0.235 0.163
#> 5 Democratic-Republican Party -0.528 -0.366
#> 6 Adams-Clay Federalist Party -0.102 0.682
#> 7 Adams Party -0.102 0.682
#> nominate.geo_mean_probability
#> 1 0.712
#> 2 0.645
#> 3 NA
#> 4 NA
#> 5 0.905
#> 6 0.712
#> 7 0.634
It is important to note that if there is no white space in the name field, the database is searched for exact matches for that one word. If there are multiple words we use a text index of all of the name fields and return the best matches.
If you only want to return the first record per ICPSR number, you can set the distinct flag equal to one. This is useful because it limits the size of the object returned and most data is duplicated within ICPSR number. For example, CS DW-NOMINATE scores are constant within ICPSR number, as are names and (usually) party.
clintons <- member_search("clinton",
state = "NY",
distinct = 1)
## Drop the bio field because it is quite long
clintons[, names(clintons) != "bio"]
#> id icpsr bioname party_code cqlabel
#> 1 MH00901849 1849 CLINTON, George 13 (NY-02)
#> 2 MS00701847 1847 CLINTON, De Witt 13 (NY)
#> 3 MH01806044 6044 MARTINDALE, Henry Clinton 8000 (NY-18)
#> 4 MH02701850 1850 CLINTON, James Graham 100 (NY-06)
#> 5 MH04206426 6426 MERRIAM, Clinton Levi 200 (NY-20)
#> 6 MH04305875 5875 MacDOUGALL, Clinton Dugald 200 (NY-25)
#> 7 MS10740105 40105 CLINTON, Hillary Rodham 100 (NY)
#> 8 MH03805707 5707 LITTLEJOHN, De Witt Clinton 200 (NY-22)
#> bioImgURL congress state state_abbrev chamber score
#> 1 silhouette.png 9 New York NY House 1.500000
#> 2 001847.jpg 7 New York NY Senate 1.333333
#> 3 silhouette.png 18 New York NY House 1.333333
#> 4 silhouette.png 27 New York NY House 1.333333
#> 5 006426.jpg 42 New York NY House 1.333333
#> 6 silhouette.png 43 New York NY House 1.333333
#> 7 040105.jpg 107 New York NY Senate 1.333333
#> 8 005707.jpg 38 New York NY House 1.250000
#> congresses seo_name district_code
#> 1 8, 10 george-clinton 2
#> 2 7, 8 de-witt-clinton 0
#> 3 18, 23, 21, 23 henry-clinton-martindale 18
#> 4 27, 28 james-graham-clinton 6
#> 5 42, 43 clinton-levi-merriam 20
#> 6 43, 44 clinton-dugald-macdougall 25
#> 7 107, 111 hillary-rodham-clinton 0
#> 8 38, 38 de-witt-clinton-littlejohn 22
#> party_name nominate.dim2 nominate.dim1
#> 1 Democratic-Republican Party 0.235 0.163
#> 2 Democratic-Republican Party -0.528 -0.366
#> 3 Adams-Clay Federalist Party -0.102 0.682
#> 4 Democratic Party -0.694 -0.262
#> 5 Republican Party -0.774 0.353
#> 6 Republican Party -0.463 0.453
#> 7 Democratic Party -0.243 -0.367
#> 8 Republican Party 0.474 0.454
#> nominate.geo_mean_probability
#> 1 0.712
#> 2 0.905
#> 3 0.712
#> 4 0.789
#> 5 0.679
#> 6 0.699
#> 7 0.833
#> 8 0.729
Some other fields that are not unique to ICPSR number but may vary are the chamber of the representative, their CQ label, and the number of votes they cast. Let's get all the records for Bernie Sanders.
sanders <- member_search("sanders",
state = "VT")
## Drop the bio field because it is quite long
sanders[, names(sanders) != "bio"]
#> id icpsr bioname party_code cqlabel bioImgURL
#> 1 MH10229147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 2 MH10329147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 3 MH10429147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 4 MH10529147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 5 MH10629147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 6 MH10729147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 7 MH10829147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 8 MH10929147 29147 SANDERS, Bernard 328 (VT-01) 029147.jpg
#> 9 MS11029147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 10 MS11129147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 11 MS11229147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 12 MS11329147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 13 MS11429147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> 14 MS11529147 29147 SANDERS, Bernard 328 (VT) 029147.jpg
#> congress state state_abbrev chamber score congresses seo_name
#> 1 102 Vermont VT House 1.5 102, 115 bernard-sanders
#> 2 103 Vermont VT House 1.5 102, 115 bernard-sanders
#> 3 104 Vermont VT House 1.5 102, 115 bernard-sanders
#> 4 105 Vermont VT House 1.5 102, 115 bernard-sanders
#> 5 106 Vermont VT House 1.5 102, 115 bernard-sanders
#> 6 107 Vermont VT House 1.5 102, 115 bernard-sanders
#> 7 108 Vermont VT House 1.5 102, 115 bernard-sanders
#> 8 109 Vermont VT House 1.5 102, 115 bernard-sanders
#> 9 110 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 10 111 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 11 112 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 12 113 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 13 114 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> 14 115 Vermont VT Senate 1.5 102, 115 bernard-sanders
#> district_code party_name nominate.dim2 nominate.dim1
#> 1 1 Independent -0.323 -0.516
#> 2 1 Independent -0.323 -0.516
#> 3 1 Independent -0.323 -0.516
#> 4 1 Independent -0.323 -0.516
#> 5 1 Independent -0.323 -0.516
#> 6 1 Independent -0.323 -0.516
#> 7 1 Independent -0.323 -0.516
#> 8 1 Independent -0.323 -0.516
#> 9 0 Independent -0.323 -0.516
#> 10 0 Independent -0.323 -0.516
#> 11 0 Independent -0.323 -0.516
#> 12 0 Independent -0.323 -0.516
#> 13 0 Independent -0.323 -0.516
#> 14 0 Independent -0.323 -0.516
#> nominate.geo_mean_probability
#> 1 0.7602263
#> 2 0.8082525
#> 3 0.8195841
#> 4 0.7916307
#> 5 0.8055633
#> 6 0.8127989
#> 7 0.8060040
#> 8 0.8439096
#> 9 0.8814125
#> 10 0.8918629
#> 11 0.7792373
#> 12 0.8365333
#> 13 0.7983794
#> 14 0.8733797
As you can see Sanders changes chambers between the 109th and 110th congresses and a few other fields differ as well. Nonetheless, most is repeated.
Extended Examples
This section details three different possible uses of the Rvoteview package, showing users from beginning to end how to conduct their own ideal point estimation and use Rvoteview in more traditional regression analysis.
Ideal point estimation
Imagine that we want to estimate ideal points for all legislators voting on foreign policy during the first six months of Obama's presidency.. We will use all roll calls that fit the Clausen category "Foreign and Defense Policy" and are somewhat competitive, meaning between 15 and 85 percent of votes on the floor were yeas.
## Load packages
library(ggplot2) # Load this first so that Rvoteview can use %+%
#> Find out what's changed in ggplot2 at
#> http://github.com/tidyverse/ggplot2/releases.
#>
#> Attaching package: 'ggplot2'
#> The following object is masked from 'package:Rvoteview':
#>
#> %+%
library(Rvoteview)
## Search database for votes that meet our criteria
res <- voteview_search("codes.Clausen:Foreign and Defense Policy support:[15 to 85]",
startdate = "2009-01-20", enddate = "2009-07-20")
#> Query '(codes.Clausen:Foreign and Defense Policy support:[15 to 85]) AND (startdate:2009-01-20) AND (enddate:2009-07-20)' returned 74 rollcalls...
## Download votes into rollcall object
rc <- voteview_download(res$id)
summary(rc)
#> Source: Download from VoteView
#>
#> Number of Legislators: 734
#> Number of Roll Call Votes: 555
#>
#>
#> Using the following codes to represent roll call votes:
#> Yea: 1 2 3
#> Nay: 4 5 6
#> Abstentions: 7 8 9
#> Not In Legislature: 0
#>
#> Party Composition:
#> 100 200 328
#> 359 374 1
#>
#> Vote Summary:
#> Count Percent
#> 0 (notInLegis) 212005 52.0
#> 1 (yea) 92810 22.8
#> 6 (nay) 95350 23.4
#> 7 (missing) 246 0.1
#> 9 (missing) 6959 1.7
#>
#> Use summary(rc,verbose=TRUE) for more detailed information.
Now we use the wnominate package to run an ideal point estimation.
library(wnominate)
#>
#> ## W-NOMINATE Ideal Point Package
#> ## Copyright 2006 -2018
#> ## Keith Poole, Jeffrey Lewis, James Lo, and Royce Carroll
#> ## Support provided by the U.S. National Science Foundation
#> ## NSF Grant SES-0611974
# Find extreme legislators for polarity argument
cons1 <- rc$legis.long.dynamic[which.max(rc$legis.data$dim1), c("name", "icpsr")]
cons2 <- rc$legis.long.dynamic[which.max(rc$legis.data$dim2), c("name", "icpsr")]
defIdeal <- wnominate(rc,
polarity = list("icpsr", c(20753, 20523)))
#>
#> Preparing to run W-NOMINATE...
#>
#> Checking data...
#>
#> ... 9 of 734 total members dropped.
#>
#> All votes meet minimum lopsidedness requirement.
#>
#> Running W-NOMINATE...
#>
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Starting estimation of Beta...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Starting estimation of Beta...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Estimating weights...
#> Getting bill parameters...
#> Getting legislator coordinates...
#> Estimating weights...
#> Getting bill parameters...
#> Getting legislator coordinates...
#>
#>
#> W-NOMINATE estimation completed successfully.
#> W-NOMINATE took 210.619 seconds to execute.
This ideal point estimation also returns the estimated points attached the all of the legislator and rollcall metadata already in the rc object! This can be useful in creating custom plots.
## Create text party name
defIdeal$legislators$partyName <- ifelse(defIdeal$legislators$party == 200, "Republican",
ifelse(defIdeal$legislators$party == 100, "Democrat", "Independent"))
ggplot(defIdeal$legislators,
aes(x=coord1D, y=coord2D, color=partyName, label=state_abbrev)) +
geom_text() +
scale_color_manual("Party", values = c("Republican" = "red",
"Democrat" = "blue",
"Independent" = "darkgreen")) +
theme_bw()
#> Warning: Removed 9 rows containing missing values (geom_text).
We see the usual split between Republicans and Democrats.
We can also use the build in plot method from `wnominate to produce some great figures from our estimation.
# Some great plots!
plot(defIdeal)
#> NULL
The rollcall objects we build can also be used in the ideal function in the pscl package.
library(pscl)
defIdeal <- ideal(rc,
d = 2)
We can also use the pscl plot method.
plot(defIdeal)
Regression analysis of roll call behavior
Users can also use the VoteView API to run regression analyses. Let's take the state level opinion data on gay rights that was estimated in Lax and Phillips (2009). They used multilevel regression and poststratification on surveys from 1999-2008 in order to estimate state-level explicit support for gay rights issues. Let's pull down some important bills presented before the 111th congress (2009-2011) and see how state level public opinion in the preceding years predicts voting behavior in the legislature.
Let's see what bills there were in the 111th congress that had to do with homosexuality. We can use a search that will capture quite a few different bills.
## Two separate searches because fields cannot be joined with an OR
res <- voteview_search("codes.Issue:Homosexuality congress:111")
#> Query 'codes.Issue:Homosexuality congress:111' returned 18 rollcalls...
res[1:5, 1:10]
#> id congress chamber rollnumber date yea nay support
#> 1 RH1110218 111 House 218 2009-04-29 234 181 56.39
#> 2 RH1110219 111 House 219 2009-04-29 234 190 55.19
#> 3 RH1110221 111 House 221 2009-04-29 185 241 43.43
#> 4 RH1110222 111 House 222 2009-04-29 249 175 58.73
#> 5 RH1110752 111 House 752 2009-10-06 178 234 43.20
#> vote_result short_description
#> 1 Passed HATE CRIMES (PROC)
#> 2 Passed HATE CRIMES -- RULE
#> 3 Failed HATE CRIMES -- RECOMMIT
#> 4 Passed HATE CRIMES (PASS)
#> 5 Failed HATE CRIMES (PROC)
To focus on actual bills that were of some consequence, let's take the House and Senate don't ask don't tell votes and the hate crimes bill from the House.
dadt <- voteview_download(c("RH1111621", "RS1110678", "RH1110222"))
dadt$vote.data
Now we want to turn this into a long dataframe, where each row is a legislator-vote. We could also then cast this using a standard cast function or the reshape2 package to have each row be a legislator, or each row be a legislator-congress and so on. The longer format will serve our purposes for now. Note that dim1 and dim2 are the Common Space DW-Nominate positions on the first and second ideological dimensions. They are fixed over the legislator's tenure in office.
## Only retain certain columns with respect to the legislator and the vote
dadtLong <- melt_rollcall(dadt,
legiscols = c("name", "state_abbrev","party_code", "dim1", "dim2"),
votecols = c("vname", "date", "chamber"))
head(dadtLong)
#> id vname vote chamber date name
#> 1 MH11102605 RH1111621 1 House 2010-12-15 DINGELL, John David, Jr.
#> 2 MH11115627 RH1111621 6 House 2010-12-15 STEARNS, Clifford Bundy
#> 3 MH11129149 RH1111621 1 House 2010-12-15 MORAN, James P.
#> 4 MH11110713 RH1111621 1 House 2010-12-15 CONYERS, John, Jr.
#> 5 MH11112036 RH1111621 1 House 2010-12-15 OBEY, David Ross
#> 6 MH11115634 RH1111621 1 House 2010-12-15 ROS-LEHTINEN, Ileana
#> party_code state_abbrev dim2 dim1
#> 1 100 MI 0.313 -0.448
#> 2 200 FL -0.075 0.554
#> 3 100 VA -0.203 -0.302
#> 4 100 MI -0.512 -0.658
#> 5 100 WI -0.063 -0.450
#> 6 200 FL -0.225 0.245
Included in the package is a dataframe that links the numeric ICPSR codes to state names and state mail codes. You can load the data by calling data(states). We use this to merge in the proper state names that will be matched to the Lax and Phillips (2009) dataset. Obama appears three times in this dataset and will be dropped in this merge.
data(states)
dadtLong <- merge(dadtLong, states[, c("state_abbrev", "state_name")],
by = "state_abbrev")
dadtLong$state_name <- tolower(dadtLong$state_name)
Now we use the Lax and Phillips (2009) data, which we make available in the package as well under lpOpinion.
data(lpOpinion)
lpOpinion$state <- tolower(lpOpinion$state)
df <- merge(dadtLong, lpOpinion,
by.x = "state_name", by.y = "state")
head(df)
#> state_name state_abbrev id vname vote chamber date
#> 1 alabama AL MH11120901 RH1110222 6 House 2009-04-29
#> 2 alabama AL MH11120301 RH1110222 6 House 2009-04-29
#> 3 alabama AL MH11120300 RH1111621 6 House 2010-12-15
#> 4 alabama AL MH11120301 RH1111621 6 House 2010-12-15
#> 5 alabama AL MH11120900 RH1110222 6 House 2009-04-29
#> 6 alabama AL MH11120302 RH1110222 6 House 2009-04-29
#> name party_code dim2 dim1
#> 1 GRIFFITH, Parker 100 0.522 -0.045
#> 2 ROGERS, Mike Dennis 200 0.443 0.338
#> 3 BONNER, Jr., Josiah Robins (Jo) 200 0.513 0.367
#> 4 ROGERS, Mike Dennis 200 0.443 0.338
#> 5 BRIGHT, Bobby Neal, Sr. 100 0.671 0.067
#> 6 DAVIS, Artur 100 0.454 -0.270
#> secondParentAdoption hateCrimes healthBenefits housing jobs marriage
#> 1 29 61 54 68 53 23
#> 2 29 61 54 68 53 23
#> 3 29 61 54 68 53 23
#> 4 29 61 54 68 53 23
#> 5 29 61 54 68 53 23
#> 6 29 61 54 68 53 23
#> sodomy civilUnions meanOpinion
#> 1 28 34 44
#> 2 28 34 44
#> 3 28 34 44
#> 4 28 34 44
#> 5 28 34 44
#> 6 28 34 44
Now let's build a dichotomous variable that represents whether the legislator voted yea on that bill (1), nay on that bill (0), or abstained (NA).
## Recode votes
df$voteYes <- ifelse(df$vote == 1, 1, ifelse(df$vote == 6, 0, NA))
## Raw votes by party
table(df$party_code, df$voteYes, useNA = "always")
#>
#> 0 1 <NA>
#> 100 32 522 14
#> 200 349 41 9
#> 328 0 1 0
#> <NA> 0 0 0
## Recode party (add independent to democrats)
df$republican <- ifelse(df$party_code == "200", 1, 0)
Let's use meanOpinion from the Lax and Phillips (2009) data, which is the average of pro-gay public opinion sentiment on various dimensions. We will use it in a couple of analyses.
## Simple model
summary(lm(voteYes ~ meanOpinion, data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion, data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.8823 -0.4151 0.1488 0.3669 0.9899
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -1.173622 0.125685 -9.338 <2e-16 ***
#> meanOpinion 0.031151 0.002197 14.181 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.4458 on 943 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.1758, Adjusted R-squared: 0.1749
#> F-statistic: 201.1 on 1 and 943 DF, p-value: < 2.2e-16
## Control for party
summary(lm(voteYes ~ meanOpinion*republican, data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican, data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.93523 -0.07432 -0.02396 0.07956 0.95041
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.077450 0.099228 0.781 0.435
#> meanOpinion 0.014789 0.001687 8.767 < 2e-16 ***
#> republican -0.645973 0.149434 -4.323 1.7e-05 ***
#> meanOpinion:republican -0.002427 0.002645 -0.918 0.359
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2518 on 941 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.7377, Adjusted R-squared: 0.7369
#> F-statistic: 882.3 on 3 and 941 DF, p-value: < 2.2e-16
## Control for ideology
## Note that ideology here has been estimated using these and later votes,
## so interpret the results with some caution
summary(lm(voteYes ~ meanOpinion*republican + dim1 + dim2,
data = df))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican + dim1 + dim2,
#> data = df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.79008 -0.10750 -0.01185 0.07428 0.93117
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.614458 0.104153 5.900 5.08e-09 ***
#> meanOpinion 0.003512 0.001802 1.949 0.0516 .
#> republican -0.700580 0.143866 -4.870 1.31e-06 ***
#> dim1 -0.374958 0.056626 -6.622 5.97e-11 ***
#> dim2 -0.270507 0.029114 -9.291 < 2e-16 ***
#> meanOpinion:republican 0.003193 0.002487 1.284 0.1995
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2329 on 939 degrees of freedom
#> (23 observations deleted due to missingness)
#> Multiple R-squared: 0.776, Adjusted R-squared: 0.7748
#> F-statistic: 650.6 on 5 and 939 DF, p-value: < 2.2e-16
## Now let's look just at repealing don't ask don't tell and add chamber fixed effects
summary(lm(voteYes ~ meanOpinion*republican + dim1 + dim2 + chamber,
data = df[df$vname != "RH1110222", ]))
#>
#> Call:
#> lm(formula = voteYes ~ meanOpinion * republican + dim1 + dim2 +
#> chamber, data = df[df$vname != "RH1110222", ])
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.79241 -0.10385 -0.01139 0.07537 0.89854
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.756861 0.139289 5.434 8.53e-08 ***
#> meanOpinion 0.001755 0.002402 0.731 0.46541
#> republican -0.986845 0.189052 -5.220 2.60e-07 ***
#> dim1 -0.246299 0.074859 -3.290 0.00107 **
#> dim2 -0.291247 0.038538 -7.557 1.90e-13 ***
#> chamberSenate 0.062868 0.026494 2.373 0.01802 *
#> meanOpinion:republican 0.006346 0.003275 1.938 0.05318 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2268 on 514 degrees of freedom
#> (13 observations deleted due to missingness)
#> Multiple R-squared: 0.7877, Adjusted R-squared: 0.7852
#> F-statistic: 317.8 on 6 and 514 DF, p-value: < 2.2e-16
Even when controlling for ideology and party, it seems that legislators, and especially Republican legislators, are more likely to vote for pro-gay rights bills when their state has a high average level of pro-gay rights sentiment.
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