Estimating political ideology

In this guided coding session, we will apply a latent space model to the network of users following 10 or more political accounts in the US. We will explore whether we can use this method to derive valid estimates of political ideology. This exercise is based on our paper published in Psychological Science.

The first is to load the matrix of users following political accounts. If you want to see how the data was collected, as well as the code to create it, you can go here. We will load the Matrix package to deal with this sparse matrix, and tweetscores to estimate correspondence analysis.

library(Matrix)
#devtools::install_github("pablobarbera/twitter_ideology/pkg/tweetscores")
library(tweetscores)
## Loading required package: R2WinBUGS
## Loading required package: coda
## Loading required package: boot
# adjacency matrix
load("../data/US-follower-network.rdata")
dim(y)
## [1] 168620    585
y[1:5,1:5]
## 5 x 5 sparse Matrix of class "ngCMatrix"
##            ABC ACLU AEI ajam algore
## 387756785    |    .   .    .      |
## 32487224     |    .   .    |      |
## 178006237    |    .   .    .      .
## 328863802    |    .   .    .      .
## 1258024164   |    .   |    .      .
# data about columns
users <- read.csv("../data/accounts-twitter-data.csv")
head(users)
##         twitter     id_str   screen_name       twitter_name
## 1           abc   28785486           ABC           ABC News
## 2          aclu   13393052          ACLU      ACLU National
## 3           aei   30864583           AEI                AEI
## 4          ajam 1178700896          ajam Al Jazeera America
## 5        algore   17220934        algore            Al Gore
## 6 andercrenshaw   20209807 AnderCrenshaw     Ander Crenshaw
##                                                                                                                                                      description
## 1                                                                             See the whole picture with @ABC News. Join us on Facebook: https://t.co/ewMNZ54axm
## 2                  The ACLU is a nonprofit, nonpartisan, legal and advocacy organization devoted to protecting the basic civil liberties of everyone in America.
## 3 Cherish freedom? The power of enterprise? Opportunity for all? It's these core beliefs that drive the scholars and staff at the American Enterprise Institute.
## 4                                             Reporting unbiased, fact-based and in-depth journalism that gets you closer to the people at the heart of the news
## 5                                                                                                                                                               
## 6                                                                                                                                      Member of Congress, FL-04
##   followers_count statuses_count friends_count
## 1         6821473         137736           827
## 2          267514          23420          1017
## 3           58496          38594          6275
## 4          327491          36468           272
## 5         2980631           1873            28
## 6           10252           1151           221
##                       created_at                  location   id   bioid
## 1 Sat Apr 04 12:40:32 +0000 2009 New York City / Worldwide   NA    <NA>
## 2 Tue Feb 12 16:27:34 +0000 2008            All 50 states    NA    <NA>
## 3 Mon Apr 13 13:33:33 +0000 2009            Washington, DC   NA    <NA>
## 4 Thu Feb 14 11:45:59 +0000 2013                        US   NA    <NA>
## 5 Thu Nov 06 22:21:18 +0000 2008             Nashville, TN   NA    <NA>
## 6 Fri Feb 06 01:48:11 +0000 2009          Washington, D.C. 1643 C001045
##             name gender              type      party     facebook
## 1           <NA>   <NA>     Media Outlets       <NA>         <NA>
## 2           <NA>   <NA>   Interest groups       <NA>         <NA>
## 3           <NA>   <NA>   Interest groups       <NA>         <NA>
## 4           <NA>   <NA>     Media Outlets       <NA>         <NA>
## 5           <NA>   <NA> Other Politicians       <NA>         <NA>
## 6 Ander Crenshaw      M          Congress Republican 200388204657
##            youtube
## 1             <NA>
## 2             <NA>
## 3             <NA>
## 4             <NA>
## 5             <NA>
## 6 RepAnderCrenshaw
table(users$type)
## 
##          Congress   Interest groups       Journalists     Media Outlets 
##               516                10                10                33 
## Other Politicians Primary Candidate 
##                15                 7

One of the advantages of correspondence analysis with respect to other methods is that we can add “supplementary columns” – parts of the matrix not included in the estimation of the latent space, but that can be then projected onto that same latent space. We will take advantage of this to make sure we only train the latent space model with Members of Congress; then we will add the other accounts.

# choosing supplementary columns
included <- users$twitter[users$type %in% c("Congress")]
supcol <- which(tolower(colnames(y)) %in% included == FALSE)
colnames(y)[supcol] ## these will be excluded
##  [1] "ABC"             "ACLU"            "AEI"            
##  [4] "ajam"            "algore"          "andersoncooper" 
##  [7] "AnnCoulter"      "BBCWorld"        "BernieSanders"  
## [10] "billclinton"     "Bloomberg"       "BreitbartNews"  
## [13] "BrookingsInst"   "BuzzFeedPol"     "CatoInstitute"  
## [16] "CBSNews"         "CNN"             "dailykos"       
## [19] "dccc"            "DRUDGE_REPORT"   "EconUS"         
## [22] "edshow"          "FoxNews"         "GeorgeHWBush"   
## [25] "glaad"           "glennbeck"       "GOP"            
## [28] "GStephanopoulos" "GuardianUS"      "Heritage"       
## [31] "HillaryClinton"  "HouseDemocrats"  "HouseGOP"       
## [34] "HRC"             "HuffPostPol"     "JebBush"        
## [37] "JoeBiden"        "KarlRove"        "limbaugh"       
## [40] "maddow"          "marcorubio"      "megynkelly"     
## [43] "MHarrisPerry"    "MotherJones"     "MSNBC"          
## [46] "NBCNews"         "NewsHour"        "newtgingrich"   
## [49] "NewYorker"       "nprnews"         "NRA"            
## [52] "nytimes"         "OccupyWallSt"    "oreillyfactor"  
## [55] "politico"        "POTUS"           "RANDCorporation"
## [58] "realDonaldTrump" "rushlimbaugh"    "SarahPalinUSA"  
## [61] "seanhannity"     "SenateDems"      "Slate"          
## [64] "StephenAtHome"   "tedcruz"         "theblaze"       
## [67] "TheDailyShow"    "TheDemocrats"    "thinkprogress"  
## [70] "USATODAY"        "washingtonpost"  "WSJ"            
## [73] "YahooNews"

And now we can run the model! We can choose how many dimensions to explore – for now, let’s go with 3.

# fitting CA model
res <- tweetscores::CA(y, nd=3, supcol=supcol)
save(res, file="../data/ca-results.rdata")

The model returns row and column coordinates, which correspond to the estimated positions on the latent space. We will now look at the accounts at the extremes of these distributions to examine the face validity of our results.

load("../data/ca-results.rdata")

# results
head(res$rowcoord)
##            [,1]       [,2]       [,3]
## [1,] -1.4228282  0.8477857 -1.0908167
## [2,] -1.7392680  0.8223242 -0.8110220
## [3,]  0.3418974  0.8563789 -0.4579679
## [4,]  1.1903436 -1.7352990  0.3603156
## [5,]  1.6829450  0.9369488 -1.2848358
## [6,]  0.2886186  0.9863780  2.3789176
head(res$rownames)
## [1] "387756785"          "32487224"           "178006237"         
## [4] "328863802"          "1258024164"         "739605663387660288"
head(res$colcoord)
##            [,1]       [,2]       [,3]
## [1,] -0.1956856  1.0990907 -0.7195807
## [2,] -1.3242049  0.9175900 -0.7386290
## [3,]  1.1307646  0.8041842 -0.8143200
## [4,] -1.1326198  1.0712326 -0.7292713
## [5,] -1.0248791  1.1428772 -0.6337304
## [6,]  0.7769290 -1.3906150  0.0397590
head(res$colnames)
## [1] "ABC"           "ACLU"          "AEI"           "ajam"         
## [5] "algore"        "AnderCrenshaw"
# merging with data
users <- read.csv("../data/accounts-twitter-data.csv")
users <- merge(users, data.frame(
    twitter=tolower(res$colnames), phi1=res$colcoord[,1], 
    phi2=res$colcoord[,2], phi3=res$colcoord[,3], stringsAsFactors=F))

# who is on the extremes
head(users[order(users$phi1),])
##             twitter     id_str     screen_name        twitter_name
## 176   repbarbaralee  248735463   RepBarbaraLee    Rep. Barbara Lee
## 317       repkclark 2293131060       RepKClark     Katherine Clark
## 557     teammoulton 3091316093     teammoulton         TeamMoulton
## 243 repdonnaedwards   82649553 repdonnaedwards Rep Donna F Edwards
## 309    repjohnlewis   29450962    repjohnlewis          John Lewis
## 121    maxinewaters   36686040    MaxineWaters       Maxine Waters
##                                                                                                                                                          description
## 176 Progressive Democrat proudly representing the #EastBay CA-13 in Congress. Working to promote economic & racial justice, peace & human rights in the US & abroad.
## 317                                                                                            Proudly representing the people of the 5th District of Massachusetts.
## 557                                                                                                                    The Office of Congressman Seth Moulton (D-MA)
## 243                                                                                                 Congresswoman representing Maryland's 4th Congressional District
## 309                                                                                                              Congressman, Georgia's Fifth Congressional District
## 121                       Proudly serving the people of California's 43rd District in Congress. Ranking Member of the House Financial Services Committee (@FSCDems).
##     followers_count statuses_count friends_count
## 176           37568           5135         16581
## 317           18740           2314          6989
## 557            2701           1964          3082
## 243           24495           5554          4052
## 309          187503           1595           164
## 121           33529           1667           586
##                         created_at                       location   id
## 176 Mon Feb 07 16:28:28 +0000 2011 Washington, DC and Oakland, CA 1501
## 317 Wed Jan 15 18:53:47 +0000 2014                                2196
## 557 Fri Mar 13 14:26:54 +0000 2015                     Salem, MA  2246
## 243 Thu Oct 15 16:04:37 +0000 2009              Washington, D.C.  1894
## 309 Tue Apr 07 13:49:52 +0000 2009                    Atlanta, GA  688
## 121 Thu Apr 30 15:17:45 +0000 2009   Los Angeles/Washington, D.C. 1205
##       bioid               name gender     type    party
## 176 L000551        Barbara Lee      F Congress Democrat
## 317 C001101 Katherine M. Clark      F Congress Democrat
## 557 M001196       Seth Moulton      M Congress Democrat
## 243 E000290   Donna F. Edwards      F Congress Democrat
## 309 L000287         John Lewis      M Congress Democrat
## 121 W000187      Maxine Waters      F Congress Democrat
##                   facebook          youtube      phi1        phi2
## 176          RepBarbaraLee           RepLee -1.866109 -0.25630881
## 317     CongresswomanClark             <NA> -1.823986  0.06913535
## 557 CongressmanSethMoulton             <NA> -1.790996  0.12005422
## 243           107297211756 RepDonnaFEdwards -1.780105 -0.08978815
## 309           RepJohnLewis     repjohnlewis -1.758453  0.54327009
## 121           MaxineWaters     MaxineWaters -1.710957 -0.15388718
##           phi3
## 176 -1.1605443
## 317 -0.6450868
## 557 -0.6564209
## 243 -0.8188072
## 309 -0.9881209
## 121 -1.2096130
tail(users[order(users$phi1),])
##             twitter     id_str     screen_name     twitter_name
## 20    breitbartnews  457984599   BreitbartNews   Breitbart News
## 289   repjeffduncan  240393970   RepJeffDuncan Rep. Jeff Duncan
## 109        limbaugh   22047070        limbaugh    Rush Limbaugh
## 469    rushlimbaugh  342887079    rushlimbaugh    Rush Limbaugh
## 339 replouiegohmert   22055226 replouiegohmert    Louie Gohmert
## 287 repjbridenstine 1092757885 RepJBridenstine  Jim Bridenstine
##                                                                                                                                                       description
## 20                                                           Forever unverified & still Twitter's top political news publisher. https://t.co/DwOmNovMKU #FreeMilo
## 289                                    Christian, husband, father, former small business owner, and Congressman for South Carolina's Third Congressional District
## 109        The Genuine Twitter feed of Rush Limbaugh. The Rush Limbaugh Show is America's most listened to radio talk show, broadcast on over 600 radio stations.
## 469        The Genuine Twitter feed of Rush Limbaugh. The Rush Limbaugh Show is America's most listened to radio talk show, broadcast on over 600 radio stations.
## 339 Member of Congress, representing the first district of Texas which encompasses over 12 counties stretching nearly 120 miles down the eastern border of Texas.
## 287                                                                 Congressman Jim Bridenstine has the honor of serving Oklahoma's First Congressional District.
##     followers_count statuses_count friends_count
## 20           359233          62771           518
## 289           36954           5084         16373
## 109          309367             70             0
## 469          505633           1178             0
## 339           60995           4562           575
## 287           17841           1401          3491
##                         created_at        location   id   bioid
## 20  Sun Jan 08 01:50:52 +0000 2012                   NA    <NA>
## 289 Wed Jan 19 20:45:16 +0000 2011                 2057 D000615
## 109 Thu Feb 26 19:10:19 +0000 2009 The EIB Network   NA    <NA>
## 469 Tue Jul 26 18:49:34 +0000 2011 The EIB Network   NA    <NA>
## 339 Thu Feb 26 20:14:28 +0000 2009                 1801 G000552
## 287 Tue Jan 15 17:33:38 +0000 2013                 2155 B001283
##                name gender          type      party
## 20             <NA>   <NA> Media Outlets       <NA>
## 289     Jeff Duncan      M      Congress Republican
## 109            <NA>   <NA>   Journalists       <NA>
## 469            <NA>   <NA> Media Outlets       <NA>
## 339   Louie Gohmert      M      Congress Republican
## 287 Jim Bridenstine      M      Congress Republican
##                      facebook           youtube     phi1      phi2
## 20                       <NA>              <NA> 1.519136 1.4878760
## 289             RepJeffDuncan    congjeffduncan 1.574717 0.3240799
## 109                      <NA>              <NA> 1.616891 1.4222837
## 469                      <NA>              <NA> 1.651330 1.4382714
## 339               50375006903       GohmertTX01 1.691886 1.1293991
## 287 CongressmanJimBridenstine RepJimBridenstine 1.701643 0.2190995
##          phi3
## 20  -1.672373
## 289 -1.285980
## 109 -1.655883
## 469 -1.665845
## 339 -1.968084
## 287 -1.304261
head(users[order(users$phi2),])
##            twitter     id_str    screen_name        twitter_name
## 373   repmoolenaar 2696643955   RepMoolenaar Rep. John Moolenaar
## 395   reprickallen 2964287128   RepRickAllen       Rick W. Allen
## 376    repnewhouse 2930635215    RepNewhouse    Rep Dan Newhouse
## 163     repabraham 2962891515     RepAbraham  Rep. Ralph Abraham
## 199 repbuddycarter 2973870195 RepBuddyCarter        Buddy Carter
## 401  reprobwoodall 2382685057  RepRobWoodall         Rob Woodall
##                                                                                                                                            description
## 373                                      Representing Michigan's Fourth Congressional District. This is the Twitter page of the official House office.
## 395                                                                                         Proudly representing Georgia's 12th Congressional District
## 376                                                                                  Proud to represent Washington's 4th District in the U.S. Congress
## 163 Proudly representing Louisiana's 5th District. Rural family physician, veteran, farmer, pilot, former veterinarian, husband, father & grandfather.
## 199                                                                                               Proudly serving Georgia's 1st Congressional District
## 401        U.S. Representative for the Seventh District of Georgia, and serving as Chairman of the Rules Subcommittee on Legislative & Budget Process.
##     followers_count statuses_count friends_count
## 373            2121            496           288
## 395            2361            474            93
## 376            2675            683          1290
## 163            2354            220            96
## 199            2778            596           244
## 401            2047            404           583
##                         created_at                       location   id
## 373 Thu Jul 31 21:09:14 +0000 2014                                2248
## 395 Tue Jan 06 14:45:18 +0000 2015 Augusta, GA and Washington, DC 2239
## 376 Thu Dec 18 20:19:23 +0000 2014                                2275
## 163 Mon Jan 05 23:01:54 +0000 2015                                2244
## 199 Mon Jan 12 00:33:43 +0000 2015                                2236
## 401 Mon Mar 10 21:01:47 +0000 2014 Lawrenceville, GA; Wash., D.C. 2008
##       bioid                   name gender     type      party
## 373 M001194      John R. Moolenaar      M Congress Republican
## 395 A000372          Rick W. Allen      M Congress Republican
## 376 N000189           Dan Newhouse      M Congress Republican
## 163 A000374      Ralph Lee Abraham      M Congress Republican
## 199 C001103 Earl L. "Buddy" Carter      M Congress Republican
## 401 W000810            Rob Woodall      M Congress Republican
##                    facebook        youtube      phi1      phi2      phi3
## 373            RepMoolenaar           <NA> 0.4461075 -1.955805 0.2951137
## 395    CongressmanRickAllen           <NA> 0.7038408 -1.920077 0.3470801
## 376             RepNewhouse           <NA> 0.4375044 -1.913413 0.3831642
## 163 CongressmanRalphAbraham           <NA> 0.5614295 -1.887226 0.3052271
## 199  congressmanbuddycarter           <NA> 0.6821126 -1.865451 0.2998415
## 401           RepRobWoodall RobWoodallGA07 0.7310445 -1.848905 0.2217110
tail(users[order(users$phi2),])
##           twitter    id_str   screen_name   twitter_name
## 145 oreillyfactor  23970102 oreillyfactor  Bill O'Reilly
## 529    senmikelee  88784440    SenMikeLee       Mike Lee
## 560      theblaze  10774652      theblaze       TheBlaze
## 559      tgowdysc 237348797      TGowdySC     Trey Gowdy
## 8      anncoulter 196168350    AnnCoulter    Ann Coulter
## 20  breitbartnews 457984599 BreitbartNews Breitbart News
##                                                                                                                                                  description
## 145                                                                  Host of The O'Reilly Factor on the Fox News Channel. Tweets from Bill are signed -BO'R.
## 529                               I am a United States Senator from the great state of Utah. Please help me restore constitutional leadership to Washington!
## 560 The digital network for the New American Heartland, delivering thought-provoking news and entertainment to impassioned people who want to impact change.
## 559                                                                                                           Congressman for South Carolina's 4th District.
## 8                                         Author - follow me on #Facebook! http://t.co/i7VTQ5btPI Disregard my earlier claims that I'd never be on Facebook.
## 20                                                      Forever unverified & still Twitter's top political news publisher. https://t.co/DwOmNovMKU #FreeMilo
##     followers_count statuses_count friends_count
## 145         1125240          10017            48
## 529          234999           3844          2402
## 560          547109          68556           168
## 559          296116           1507           413
## 8            916840          19771           576
## 20           359233          62771           518
##                         created_at          location   id   bioid
## 145 Thu Mar 12 15:44:18 +0000 2009 New York, NY, USA   NA    <NA>
## 529 Mon Nov 09 22:47:47 +0000 2009              Utah 2080 L000577
## 560 Sat Dec 01 22:23:57 +0000 2007        Dallas, TX   NA    <NA>
## 559 Wed Jan 12 16:57:04 +0000 2011                   2058 G000566
## 8   Tue Sep 28 14:04:51 +0000 2010   Los Angeles/NYC   NA    <NA>
## 20  Sun Jan 08 01:50:52 +0000 2012                     NA    <NA>
##           name gender          type      party        facebook
## 145       <NA>   <NA>   Journalists       <NA>            <NA>
## 529   Mike Lee      M      Congress Republican  senatormikelee
## 560       <NA>   <NA> Media Outlets       <NA>            <NA>
## 559 Trey Gowdy      M      Congress Republican 143059759084016
## 8         <NA>   <NA>   Journalists       <NA>            <NA>
## 20        <NA>   <NA> Media Outlets       <NA>            <NA>
##            youtube     phi1     phi2      phi3
## 145           <NA> 1.103564 1.442775 -1.323490
## 529 senatormikelee 1.339189 1.453278 -1.220210
## 560           <NA> 1.402451 1.456567 -1.624819
## 559       TGowdySC 1.497954 1.481305 -1.741649
## 8             <NA> 1.200094 1.486881 -1.471109
## 20            <NA> 1.519136 1.487876 -1.672373
# what could the second dimension mean?
plot(users$phi1, users$phi2, type="n")
text(users$phi1, users$phi2, label=substr(users$type, 1, 2))

plot(users$phi2, log(users$followers_count))

cor(users$phi2, log(users$followers_count))
## [1] 0.8376895
# primary candidates
users <- users[order(users$phi1),]
users[users$type=="Primary Candidate",c("screen_name", "phi1")]
##         screen_name       phi1
## 12    BernieSanders -0.8561287
## 78   HillaryClinton -0.7049112
## 158 realDonaldTrump  0.6037760
## 90          JebBush  0.6795725
## 115      marcorubio  0.8182496
## 558         tedcruz  1.0337009
# others
users[users$type=="Media Outlets",c("screen_name", "phi1")]
##        screen_name        phi1
## 58          edshow -1.40767853
## 47        dailykos -1.34378181
## 563  thinkprogress -1.31280856
## 130    MotherJones -1.27878357
## 140        nprnews -1.20470194
## 4             ajam -1.13261980
## 561   TheDailyShow -0.88431403
## 131          MSNBC -0.80740165
## 548          Slate -0.78792817
## 82     HuffPostPol -0.77905989
## 134       NewsHour -0.76835924
## 75      GuardianUS -0.74307070
## 551  StephenAtHome -0.67514666
## 136      NewYorker -0.64087922
## 22     BuzzFeedPol -0.48185706
## 57          EconUS -0.39152095
## 143        nytimes -0.38783170
## 133        NBCNews -0.33676741
## 10        BBCWorld -0.26308016
## 581 washingtonpost -0.24921910
## 34             CNN -0.23811013
## 28         CBSNews -0.21379257
## 152       politico -0.20829247
## 1              ABC -0.19568559
## 584      YahooNews -0.14679930
## 569       USATODAY -0.12099969
## 16       Bloomberg -0.09863787
## 583            WSJ  0.13334464
## 61         FoxNews  0.78584831
## 56   DRUDGE_REPORT  1.01703159
## 560       theblaze  1.40245070
## 20   BreitbartNews  1.51913559
## 469   rushlimbaugh  1.65132965
users[users$type=="Journalists",c("screen_name", "phi1")]
##         screen_name       phi1
## 126    MHarrisPerry -1.3994000
## 114          maddow -0.9590568
## 7    andersoncooper -0.5138587
## 74  GStephanopoulos -0.3928671
## 125      megynkelly  1.0584244
## 145   oreillyfactor  1.1035641
## 473     seanhannity  1.1878288
## 8        AnnCoulter  1.2000942
## 67        glennbeck  1.2471467
## 109        limbaugh  1.6168909
users[users$type=="Other Politicians",c("screen_name", "phi1")]
##        screen_name       phi1
## 51            dccc -1.1964137
## 5           algore -1.0248791
## 562   TheDemocrats -0.9812492
## 98        JoeBiden -0.9721160
## 79  HouseDemocrats -0.8577119
## 153          POTUS -0.8576321
## 476     SenateDems -0.8308369
## 14     billclinton -0.7131847
## 63    GeorgeHWBush  0.6587120
## 80        HouseGOP  0.8463433
## 68             GOP  0.9011265
## 104       KarlRove  0.9136743
## 135   newtgingrich  0.9350012
## 472  SarahPalinUSA  0.9577739

The reason why we started with Members of Congress is that we do have good measures of ideology for these accounts – let’s now examine the convergent validity of our results by exploring how correlated our estimates are with these external measures of ideology.

house <- read.csv("../data/house.csv", stringsAsFactors=F); house$chamber <- "House"
senate <- read.csv("../data/senate.csv", stringsAsFactors=F); senate$chamber <- "Senate"
ideal <- rbind(house[,c("nameid", "idealPoint", "chamber")], 
            senate[,c("nameid", "idealPoint", "chamber")])
names(ideal) <- c("bioid", "ideal", "chamber")
users <- merge(users, ideal)

# validation
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.1
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dd <- group_by(users, chamber, party)
summarize(dd, cor(ideal, phi1))
## # A tibble: 5 x 3
## # Groups:   chamber [?]
##   chamber       party `cor(ideal, phi1)`
##     <chr>      <fctr>              <dbl>
## 1   House    Democrat          0.6039404
## 2   House  Republican          0.4975957
## 3  Senate    Democrat          0.6736305
## 4  Senate Independent                 NA
## 5  Senate  Republican          0.5784615
library(ggplot2)
p <- ggplot(users, aes(x=ideal, y=phi1, color=party))
pq <- p + geom_point() + facet_wrap(~ chamber) +
    scale_color_manual(values=c("blue", "green", "red"))
pq

p <- ggplot(users, aes(x=phi1, fill=party))
pq <- p + geom_density() + facet_wrap(~ chamber) +
    scale_fill_manual(values=c("blue", "green", "red"))
pq