RO VAA

Data from Busola Electorala between 11 Nov 2016 - 16 Nov 2016

This document provides descriptive statistics, offering an overwiew of the data. It starts by showing the location and other descriptives. If you press Hide, the R code will disappear and you are left with the results. I suggest doing that, it’s easier to read.

library(foreign)
ro <- read.csv("C:/Users/Irina/Desktop/ro2016vaa/roexport2.csv", header = TRUE)

First things first: we want to know how many people went through all 30 statements. We discard the others.

ro <- subset(ro, answered_all_statements != 0)
length(ro$answered_all_statements)

[1] 7310

That leaves us with 7310 people who were able to see their position in the political landscape after going through all statements. You can your position even if you fill out the tool partially, but the results won’t be accurate.

Let’s see where the users come from:
This is additional data we collect, not asked in the survey

ro$country[ro$country==""] <- NA
table(ro$country)

                                      Algeria              Australia                Austria                Belarus 
                     0                      1                      5                     25                      1 
               Belgium Bosnia and Herzegovina               Bulgaria               Cameroon                 Canada 
                    70                      0                      4                      1                     23 
            Cape Verde               Colombia                Croatia                 Cyprus         Czech Republic 
                     1                      1                      1                      1                     11 
               Denmark                Finland                 France                Germany                 Greece 
                    28                      2                     75                    121                      0 
              Guernsey              Hong Kong                Hungary                  India              Indonesia 
                     1                      1                     13                      4                      1 
               Ireland                 Israel                  Italy                  Japan                Lebanon 
                     8                      0                     33                      1                      1 
               Liberia             Luxembourg              Macedonia               Malaysia                  Malta 
                     1                      5                      1                      1                      1 
                Mexico                 Monaco            Netherlands                Nigeria                 Norway 
                     1                      1                     55                      3                      2 
                  Oman                 Poland               Portugal            Puerto Rico                  Qatar 
                     1                      9                      6                      1                      1 
     Republic of Korea  Republic of Lithuania    Republic of Moldova                Romania                 Serbia 
                     1                      2                      5                   6377                      4 
             Singapore        Slovak Republic               Slovenia            South Sudan                  Spain 
                     3                      1                      3                      1                     25 
             Sri Lanka                 Sweden            Switzerland               Thailand                 Turkey 
                     1                     18                     14                      1                      2 
               Ukraine   United Arab Emirates         United Kingdom          United States                unknown 
                     3                      3                    205                     77                     23 
             Venezuela                Vietnam 
                     1                      2 

If you wanna go really crazy, we can see the cities (let me know if you want that)

Here is the browser from which the tool was accessed: (I have no idea if you care for these details)

table(ro$browser)

                    Android           BlackBerry WebKit                      Chrome               Chrome Mobile 
                         83                          12                        3217                        2005 
          Chrome Mobile iOS                    Chromium                        Edge                    Epiphany 
                         16                          12                          60                           1 
                    Firefox              Firefox Mobile                          IE                   IE Mobile 
                        865                          26                          72                           9 
                    Maxthon               Mobile Safari                       Opera                  Opera Mini 
                          4                         566                         114                           2 
               Opera Mobile Pale Moon (Firefox Variant)                      Puffin                      Safari 
                          9                           4                           0                         215 
                  SeaMonkey                  UC Browser                     Vivaldi              Yandex Browser 
                          2                           1                          14                           1 

Now, to more interesting stuff:

let’s have a look at the demographics. We ask these questions in the beginning of the tool, and quite a few people fill in this information. The variables are collected separatelly for people who took the survey on their phone, that’s why there are two variables for gender. You need to look at the last 2 rows, where coded ) are females, code 1 are males. So 5517 men in our sample. This is quite normal, as men are more interested in politics than women.
From the 7310 who went through all the statements, about half filled out the demographic questions

ro$male <- ifelse(ro$background_question_0==0, 1,
           ifelse(ro$background_question_0==1, 0,
                NA))
   
ro$malem <- ifelse(ro$background_question_1_is_mobile==0, 1,
            ifelse(ro$background_question_1_is_mobile==1, 0,
                         NA))
ro$male[is.na(ro$male)] <- ro$malem[is.na(ro$male)]
table(ro$male)

   0    1 
1262 2420 

Now we move on to age. I’ll create age categories, 18-29 coded 1, 30-49 coded 2, 50-64 coded 3 and 65+ coded 4

ro$age <- 2016 - (ro$background_question_2 + 1910)
ro$age[ro$age < 18] <- NA
ro$age[ro$age >= 18 & ro$age <= 29] <- 1
ro$age[ro$age >= 30 & ro$age <= 49] <- 2
ro$age[ro$age >= 50 & ro$age <= 64] <- 3
ro$age[ro$age >= 65] <- 4
ro$agea <- 2016 - (ro$background_question_3_is_mobile + 1910)
ro$agea[ro$agea < 18] <- NA
ro$agea[ro$agea >= 18 & ro$agea <= 29] <- 1
ro$agea[ro$agea >= 30 & ro$agea <= 49] <- 2
ro$agea[ro$agea >= 50 & ro$agea <= 64] <- 3
ro$agea[ro$agea >= 65] <- 4
ro$age[is.na(ro$age)] <- ro$agea[is.na(ro$age)]
table(ro$age)

   1    2    3    4 
1602 1792  223   56 

We move on to education 0 - no education, 1 - elementary school, 2 - high school, 3 - university degree, master and PhD, 4 - post doctoral studies

ro$edu <- ifelse(ro$background_question_4==0, 0, 
          ifelse(ro$background_question_4==1, 1,
          ifelse(ro$background_question_4==2, 2,
          ifelse(ro$background_question_4==4, 4,
          ifelse(ro$background_question_4=="Facultate\nMasterat/Doctorat", 3,
          ifelse(ro$background_question_4=="Posztgraduális végzettség ", 4,
                 NA))))))
ro$edum <- ifelse(ro$background_question_5_is_mobile==0, 0, 
           ifelse(ro$background_question_5_is_mobile==1, 1,
           ifelse(ro$background_question_5_is_mobile==2, 2,
           ifelse(ro$background_question_5_is_mobile==4, 4,
           ifelse(ro$background_question_5_is_mobile=="Facultate\nMasterat/Doctorat", 3,
           ifelse(ro$background_question_5_is_mobile=="Posztgraduális végzettség ", 4,
                 NA))))))
ro$edu[is.na(ro$edu)] <- ro$edum[is.na(ro$edu)]
table(ro$edu)

   0    1    2    3    4 
   9   43  661 2671  218 

As expected, mostly highly educated people.

Now, we can see he city where people intend to vote (for this type of election, you can only vote in the electoral circumscription you belong to)

ro$location <- 0
ro$location[is.na(ro$background_question_8)] <- NA # overwrite 0's with NA's if background_question_8 is NA
ro$location[ro$background_question_8==0]  <- "Alba"
ro$location[ro$background_question_8==1]  <- "Arad"
ro$location[ro$background_question_8==2]  <- "Arges"
ro$location[ro$background_question_8==3]  <- "Bacau"
ro$location[ro$background_question_8==4]  <- "Bihor"
ro$location[ro$background_question_8==5]  <- "Bistrita-Nasaud"
ro$location[ro$background_question_8==6]  <- "Botosani"
ro$location[ro$background_question_8==7]  <- "Brasov"
ro$location[ro$background_question_8==8]  <- "Braila"
ro$location[ro$background_question_8==9]  <- "Bucuresti"
ro$location[ro$background_question_8==10] <- "Buzau"
ro$location[ro$background_question_8==11] <- "Caras-Severin"
ro$location[ro$background_question_8==12] <- "Calarasi"
ro$location[ro$background_question_8==13] <- "Cluj"
ro$location[ro$background_question_8==14] <- "Constanta"
ro$location[ro$background_question_8==15] <- "Covasna"
ro$location[ro$background_question_8==16] <- "Dambovita"
ro$location[ro$background_question_8==17] <- "Dolj"
ro$location[ro$background_question_8==18] <- "Galati"
ro$location[ro$background_question_8==19] <- "Giurgiu"
ro$location[ro$background_question_8==20] <- "Gorj"
ro$location[ro$background_question_8==21] <- "harghita"
ro$location[ro$background_question_8==22] <- "Hunedoara"
ro$location[ro$background_question_8==23] <- "Ialomita"
ro$location[ro$background_question_8==24] <- "Iasi"
ro$location[ro$background_question_8==25] <- "Ilfov"
ro$location[ro$background_question_8==26] <- "Maramures"
ro$location[ro$background_question_8==27] <- "Mehendinti"
ro$location[ro$background_question_8==28] <- "Mures"
ro$location[ro$background_question_8==29] <- "Neamt"
ro$location[ro$background_question_8==30] <- "Olt"
ro$location[ro$background_question_8==31] <- "Prahova"
ro$location[ro$background_question_8==32] <- "Satu Mare"
ro$location[ro$background_question_8==33] <- "Salaj"
ro$location[ro$background_question_8==34] <- "Sibiu"
ro$location[ro$background_question_8==35] <- "Suceava"
ro$location[ro$background_question_8==36] <- "Teleorman"
ro$location[ro$background_question_8==37] <- "Timis"
ro$location[ro$background_question_8==38] <- "Tulcea"
ro$location[ro$background_question_8==39] <- "Vaslui"
ro$location[ro$background_question_8==40] <- "Valcea"
ro$location[ro$background_question_8==41] <- "Vrancea"
ro$location[ro$background_question_8==42] <- "Strainatate"
ro$locationm <- 0
ro$locationm[is.na(ro$background_question_9_is_mobile)] <- NA # overwrite 0's with NA's if background_question_8 is NA
ro$locationm[ro$background_question_9_is_mobile==0]  <- "Alba"
ro$locationm[ro$background_question_9_is_mobile==1]  <- "Arad"
ro$locationm[ro$background_question_9_is_mobile==2]  <- "Arges"
ro$locationm[ro$background_question_9_is_mobile==3]  <- "Bacau"
ro$locationm[ro$background_question_9_is_mobile==4]  <- "Bihor"
ro$locationm[ro$background_question_9_is_mobile==5]  <- "Bistrita-Nasaud"
ro$locationm[ro$background_question_9_is_mobile==6]  <- "Botosani"
ro$locationm[ro$background_question_9_is_mobile==7]  <- "Brasov"
ro$locationm[ro$background_question_9_is_mobile==8]  <- "Braila"
ro$locationm[ro$background_question_9_is_mobile==9]  <- "Bucuresti"
ro$locationm[ro$background_question_9_is_mobile==10] <- "Buzau"
ro$locationm[ro$background_question_9_is_mobile==11] <- "Caras-Severin"
ro$locationm[ro$background_question_9_is_mobile==12] <- "Calarasi"
ro$locationm[ro$background_question_9_is_mobile==13] <- "Cluj"
ro$locationm[ro$background_question_9_is_mobile==14] <- "Constanta"
ro$locationm[ro$background_question_9_is_mobile==15] <- "Covasna"
ro$locationm[ro$background_question_9_is_mobile==16] <- "Dambovita"
ro$locationm[ro$background_question_9_is_mobile==17] <- "Dolj"
ro$locationm[ro$background_question_9_is_mobile==18] <- "Galati"
ro$locationm[ro$background_question_9_is_mobile==19] <- "Giurgiu"
ro$locationm[ro$background_question_9_is_mobile==20] <- "Gorj"
ro$locationm[ro$background_question_9_is_mobile==21] <- "harghita"
ro$locationm[ro$background_question_9_is_mobile==22] <- "Hunedoara"
ro$locationm[ro$background_question_9_is_mobile==23] <- "Ialomita"
ro$locationm[ro$background_question_9_is_mobile==24] <- "Iasi"
ro$locationm[ro$background_question_9_is_mobile==25] <- "Ilfov"
ro$locationm[ro$background_question_9_is_mobile==26] <- "Maramures"
ro$locationm[ro$background_question_9_is_mobile==27] <- "Mehendinti"
ro$locationm[ro$background_question_9_is_mobile==28] <- "Mures"
ro$locationm[ro$background_question_9_is_mobile==29] <- "Neamt"
ro$locationm[ro$background_question_9_is_mobile==30] <- "Olt"
ro$locationm[ro$background_question_9_is_mobile==31] <- "Prahova"
ro$locationm[ro$background_question_9_is_mobile==32] <- "Satu Mare"
ro$locationm[ro$background_question_9_is_mobile==33] <- "Salaj"
ro$locationm[ro$background_question_9_is_mobile==34] <- "Sibiu"
ro$locationm[ro$background_question_9_is_mobile==35] <- "Suceava"
ro$locationm[ro$background_question_9_is_mobile==36] <- "Teleorman"
ro$locationm[ro$background_question_9_is_mobile==37] <- "Timis"
ro$locationm[ro$background_question_9_is_mobile==38] <- "Tulcea"
ro$locationm[ro$background_question_9_is_mobile==39] <- "Vaslui"
ro$locationm[ro$background_question_9_is_mobile==40] <- "Valcea"
ro$locationm[ro$background_question_9_is_mobile==41] <- "Vrancea"
ro$locationm[ro$background_question_9_is_mobile==42] <- "Strainatate"
ro$location[is.na(ro$location)] <- ro$locationm[is.na(ro$location)]
table(ro$location)

           Alba            Arad           Arges           Bacau           Bihor Bistrita-Nasaud        Botosani          Braila 
             56              26              51              43              69              28              13              17 
         Brasov       Bucuresti           Buzau        Calarasi   Caras-Severin            Cluj       Constanta         Covasna 
             98            1471              37              19               9             354             106               5 
      Dambovita            Dolj          Galati         Giurgiu            Gorj        harghita       Hunedoara        Ialomita 
             32              54              42               8              17               9              33              19 
           Iasi           Ilfov       Maramures      Mehendinti           Mures           Neamt             Olt         Prahova 
            153              82              35              18              46              31              18              80 
          Salaj       Satu Mare           Sibiu     Strainatate         Suceava       Teleorman           Timis          Tulcea 
              6              19              66             270              41              25             138              21 
         Valcea          Vaslui         Vrancea 
             25              23              25 

We break down the location according to regions (NUTS2). I am doing this because the ultimate goal is to weight the data (using census data, trying to make the data more representative. this is just the nerd in me)

ro$regiune <- 0
ro$regiune[is.na(ro$location)] <- NA # overwrite 0's with NA's if background_question_8 is NA
# Nord-Vest – RO11; Bihor, Bistrita-Nasaud, Cluj, Maramures, Satu Mare, Salaj
ro$regiune[ro$location=="Bihor"] <-         "RO11"
ro$regiune[ro$location=="Cluj"] <-          "RO11"
ro$regiune[ro$location=="Bistrita-Nasaud"]<-"RO11"
ro$regiune[ro$location=="Maramures"] <-     "RO11"
ro$regiune[ro$location=="Satu Mare"] <-     "RO11"
ro$regiune[ro$location=="Salaj"] <-         "RO11"
# Centru – RO12; Alba, Brasov, Covasna, Harghita, Mures, Sibiu
ro$regiune[ro$location=="Alba"] <-       "RO12"
ro$regiune[ro$location=="Brasov"] <-     "RO12"
ro$regiune[ro$location=="Covasna"] <-    "RO12"
ro$regiune[ro$location=="Harghita"] <-   "RO12"
ro$regiune[ro$location=="Mures"] <-      "RO12"
ro$regiune[ro$location=="Sibiu"] <-      "RO12"
# Nord-Est – RO21; Bacau, Botosani, Iasi, Neamt, Suceava, Vaslui
ro$regiune[ro$location=="Bacau"] <-      "RO21"
ro$regiune[ro$location=="Botosani"] <-   "RO21"
ro$regiune[ro$location=="Iasi"] <-       "RO21"
ro$regiune[ro$location=="Neamt"] <-      "RO21"

After we have an idea about who the users are and where they come from, let’s look into their political affilition. Here we see for which coalition they voted for in the last parliamentary elections of 2012. Here we miss quite a few users, because they dont remember who they voted for.

ro$voterec <- 0
ro$voterec[is.na(ro$background_question_10)] <- NA
ro$voterec <- ifelse(ro$background_question_10==0, "USL",
              ifelse(ro$background_question_10==1, "ARD",
              ifelse(ro$background_question_10==2, "PPDD",
              ifelse(ro$background_question_10==3, "UDMR",
              ifelse(ro$background_question_10==4, "other",
              ifelse(ro$background_question_10==6, "did not vote",
                     NA))))))
ro$voterecm <- 0
ro$voterecm[is.na(ro$background_question_11_is_mobile)] <- NA
ro$voterecm <- ifelse(ro$background_question_11_is_mobile==0, "USL",
               ifelse(ro$background_question_11_is_mobile==1, "ARD",
               ifelse(ro$background_question_11_is_mobile==2, "PPDD",
               ifelse(ro$background_question_11_is_mobile==3, "UDMR",
               ifelse(ro$background_question_11_is_mobile==4, "other",
               ifelse(ro$background_question_11_is_mobile==6, "did not vote",
                         NA))))))
ro$voterec[is.na(ro$voterec)] <- ro$voterecm[is.na(ro$voterec)]
table(ro$voterec)

         ARD did not vote        other         PPDD         UDMR          USL 
         461          863          253           40           51          707 

We can see who they intend to vote for, which is quite exciting

table(ro$popup_question_6)

   0    1    2    3    4    5 
 137   87  372   37 1931  263 

ro$voteint <- ifelse(ro$popup_question_6==0, "PSD",
              ifelse(ro$popup_question_6==1, "ALDE",
              ifelse(ro$popup_question_6==2, "PNL",
              ifelse(ro$popup_question_6==3, "UDMR",
              ifelse(ro$popup_question_6==4, "USR",
              ifelse(ro$popup_question_6==5, "other",
                            NA))))))
table(ro$voteint)

 ALDE other   PNL   PSD  UDMR   USR 
   87   263   372   137    37  1931 

Next, we can see how users answered on the statements. 1 means “Completely dissagree, 3,”Neither agree nor disagree“, 5”“Completely agree”, -1 “No opinion”

Free market makes the health system to function better
Competitia economica libera face sistemul de sanatate sa functioneze mai eficient

table(ro$statement_0)

  -1    1    2    3    4    5 
 248  387 1022 1604 2712 1337 

The number of employees in the public sector should be reduced
Numarul angajatilor din sectorul public ar trebui redus

table(ro$statement_1)

  -1    1    2    3    4    5 
  71  267  732 1125 2561 2554 

The state should intervene in the economy as little as possible
Statul ar trebui sa intervina cat mai putin posibil in economie

table(ro$statement_2)

  -1    1    2    3    4    5 
  64  472 1456 1570 2443 1305 

Romania should adopt a progressive taxation system
Romania ar trebui sa adopte un sistem de taxare progresiva

table(ro$statement_3)

  -1    1    2    3    4    5 
 216  762 1178 1079 2727 1348 

Foreign investors are a threat to Romania’s national sovereignty
Investitorii straini sunt o amenintare la suveranitatea nationala a Romaniei

table(ro$statement_4)

  -1    1    2    3    4    5 
  54 3800 2141  828  318  169 

VTA should be reduced under 20%
TVA trebuie redus sub pragul de 20%

table(ro$statement_5)

  -1    1    2    3    4    5 
 171  144  573 1863 2925 1634 

Romania should never adopt the euro
Romania ar trebui sa nu adopte niciodata moneda Euro

table(ro$statement_6)

  -1    1    2    3    4    5 
 136 1990 2154 1608  862  560 

International actors (such as EU or USA) have the right to intervene in Romania’s internal affairs if the state of democracy is threatend
Partenerii internationali (precum UE sau SUA) pot interveni in afacerile interne ale Romaniei atunci cand exista o amenintare la adresa democratiei

table(ro$statement_7)

  -1    1    2    3    4    5 
  68  974 1277 1147 2803 1041 

The strategic partnership with USA is essential for Romania’s national security
Parteneriatul strategic cu SUA este esential pentru securitatea nationala

table(ro$statement_8)

  -1    1    2    3    4    5 
  70  266  428 1162 3056 2328 

Women should be able to decide on any matters related to abortion
Femeile ar trebui sa aiba libertatea de a decide asupra chestiunilor legate de avort

table(ro$statement_9)

  -1    1    2    3    4    5 
  41  197  206  321 1610 4935 

Romania should not receive refugees that try to enter the EU
Romania ar trebui sa accepte mai multi migranti care intra in UE, ca semn de solidaritate

table(ro$statement_10)

  -1    1    2    3    4    5 
  86 1302 1374 1910 1759  879 

Gay couples should have the same rights as the heterosexual ones
Cuplurile homosexuale ar trebui sa se bucure de aceleasi drepturi ca si cuplurile heterosexuale

table(ro$statement_11)

  -1    1    2    3    4    5 
  62  812  579  837 1603 3417 

Smoking should be prohibited in all the public places, even the open-space ones
Fumatul trebuie interzis in toate locurile publice, chiar si cele deschise

table(ro$statement_12)

  -1    1    2    3    4    5 
  47 1150 1873 1155 1444 1641 

Immigrants should adopt the values and culture of Romania
Imigrantii ar trebui sa se adapteze la valorile si cultura Romaniei

table(ro$statement_13)

  -1    1    2    3    4    5 
  52  142  486 1204 2873 2553 

Romania should pursue state unification with Moldova
Romania ar trebui sa urmareasca reunificarea statala cu Republica Moldova

table(ro$statement_14)

  -1    1    2    3    4    5 
 135 1016 1427 2203 1535  994 

A territorial reform should include the creation of an autonomous Hungarian region
O reforma teritoriala ar trebui sa includa crearea unei regiuni maghiare autonome

table(ro$statement_15)

  -1    1    2    3    4    5 
  89 3340 2276 1049  409  147 

The state should give privileged status to the orthodox church
Statul ar trebui sa acorde un statut privilegiat Bisericii Ortodoxe

table(ro$statement_16)

  -1    1    2    3    4    5 
  41 5248 1265  442  198  116 

The electoral treshold should be lowered, so smaller parties can gain access to the parliament
Pragul electoral ar trebui redus pentru a permite intrarea partidelor mai mici în parlament

table(ro$statement_17)

  -1    1    2    3    4    5 
  93  529  859  979 2536 2314 

The electoral reform should include lowering the number of signatures needed for candidacy for all election types
Numarul de semnaturi pentru candidatura la toate tipurile de alegeri ar trebui redus

table(ro$statement_18)

  -1    1    2    3    4    5 
 113  343  672  922 2440 2820 

The number of polling stations for diaspora should be increased for all election types
Numarul de sectii de votare in diaspora trebuie marit pentru toate tipurile de alegeri

table(ro$statement_19)

  -1    1    2    3    4    5 
  61  126  228  674 2547 3674 

The postal vote should be introduced for all election types
Votul prin corespondenta ar trebui introdus pentru toate tipurile de alegeri

table(ro$statement_20)

  -1    1    2    3    4    5 
  78  187  276  599 2304 3866 

The digitalization of the judicial process should be continued by publishing on-line all the decisions
Digitalizarea procesului judiciar trebuie continuata prin publicarea on-line a tuturor hotararilor judecatoresti

table(ro$statement_21)

  -1    1    2    3    4    5 
  58   44   64  227 2216 4701 

Government’s public information must be accessible online
Informatiile publice ale guvernului ar trebui sa fie accesibile on-line

table(ro$statement_22)

  -1    1    2    3    4    5 
  20   19   20   62 1675 5514 

The open vote should be introduced for all decisions made in the parliament
Votul deschis trebuie introdus pentru toate deciziile luate in Parlament

table(ro$statement_23)

  -1    1    2    3    4    5 
 100   69  282  550 1929 4380 

Politicians prosecuted for corruption should give up any public function
Politicienii urmariti penal pentru fapte de coruptie trebuie sa renunte la orice functie publica

table(ro$statement_24)

  -1    1    2    3    4    5 
  31  171  340  452 1179 5137 

MPs should enjoy immunity rights only for votes and declarations
Parlamentarii ar trebui sa aiba imunitate doar pentru voturi si declaratii

table(ro$statement_25)

  -1    1    2    3    4    5 
 112  213  333  649 2088 3915 

Governments formed exclusivelly from non-partisan tehnocrats work better than those formed from politicians
Guvernele formate exclusiv din tehnocrati neafiliati politic functioneaza mai bine decat cele formate din politicieni

table(ro$statement_26)

  -1    1    2    3    4    5 
 111  544  651 2034 2125 1845 

Prosecutors have too much power in prosecuting citizens
Procurorii au prea multa putere în ceea ce priveste anchetarea cetatenilor

table(ro$statement_27)

  -1    1    2    3    4    5 
 281  903 2078 2604  995  449 

The Mechanism of Cooperation and Verification, which tracks progress in the judiciary system reform should be maintained
Mecanismul de Cooperare si Verificare, prin care se verifica progresele in reformarea justitiei, ar trebui inlaturat

table(ro$statement_28)

  -1    1    2    3    4    5 
 523 1735 2513 1812  472  255 

Romania should work on strengthening its diplomatic relations with Rusia
Romania ar trebui sa isi consolideze relatiile diplomatice cu Rusia

table(ro$statement_29)

  -1    1    2    3    4    5 
 199  854 1048 2378 2377  454 

Interest in politics 0 very much, 3 not at all

ro$popup_question_2[ro$popup_question_2==4] <- NA
table(ro$popup_question_2)

   0    1    2    3 
1538 1142  199   37 

Left-right self-positioning, 0 left 10 right

ro$popup_question_3[ro$popup_question_3==11] <- NA
ro$popup_question_3[ro$popup_question_3==12] <- NA
table(ro$popup_question_3)

  0   1   2   3   4   5   6   7   8   9  10 
 28  62  64 161 173 455 299 555 501 183 337 

Evaluation of Ciolos’ government performance 0 very good, 4 very bad, 5 don’t know

table(ro$popup_question_4)

   0    1    2    3    4    5 
1197 1168   97   25  272  138