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Data Finagling and Analysis
Created new .csv file for the analyses columns Qs [22, 23, 27] (in order to be less messy extracting columns). Separated out answers to Q20 in order to test whether there is a different in response. Sorted out ANOVA and MANOVA analyses - preliminary results indicate no significant difference on Q22 or Q27 in regards to social issues of importance. Significant diff on Q23 with economy, health and environment - explore means to see where. Could be interesting
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rehughes07
parent
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@ -74,6 +74,7 @@ pie(Q26_freq)
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#very messy as a pie chart - split by type? Or is it important to see crossover
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#very messy as a pie chart - split by type? Or is it important to see crossover
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Could potentially see crossover with crosstabs by type (since response is now binary variable T/F), maybe chi square; perhaps just descriptives
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Could potentially see crossover with crosstabs by type (since response is now binary variable T/F), maybe chi square; perhaps just descriptives
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```{r Q3 bar/pie}
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```{r Q3 bar/pie}
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@ -112,8 +113,9 @@ pie(test2$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics",
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# JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses
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# JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses
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```
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```
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xtabs(Frequency ~ Subject, test2)
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pie(Q3_freq)
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pie(Q3_freq)
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#also not optimal as pie...perhaps bar
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#also not optimal as pie...perhaps bar
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@ -159,9 +161,82 @@ table(Q3_1factor)
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- Make Q20 a factor with 14 levels
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- Make Q20 a factor with 14 levels
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- Collapse 2 Q22 columns into one mean for analyses
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- Collapse 2 Q22 columns into one mean for analyses
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- Analyse 1 way anova Q20 (14 levels) by Q22; Q23[1-2]; Q27[1-7]
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- Analyse 1 way anova Q20 (14 levels) by Q22; Q23[1-2]; Q27[1-7]
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- 1 way within subjects?? Though not all participants ticked every box... Would it then be best to separate them out and do 14 separate analyses with bonferroni correction due to the multiple tests? - could then be 14 different t tests based on whether they ticked each one as important or not... Many analyses but that may be the most straightforward way to go. Factorial mixed ANOVA? 14 predictors, each with 2 levels (yes/no)??
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- 14 predictors, within subjects, 2 levels (yes/no). DV as responses to questions. Q22 would be a factorial between subjects (only 1 option on IVs) ANOVA. Qs 23, 27 would be factorial between subjects MANOVA
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```{r Correlation 1}
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```{r Analyses 1 - As Factor}
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social_issues_data <- read.csv("./data/Q20_data.csv")
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head(social_issues_data)
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# All 14 as factors, with 2 levels: 1=YES, 2=NO
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social_issues_data$brexit <- factor(social_issues_data$brexit, levels = c(1, 2), labels = c("Yes", "No"))
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class(social_issues_data$brexit)
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#social_issues_data[ ,4:5] <- factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No"))
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#Did not work; made 2 columns "NA" so am going through to make factors individually
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### OR ###
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#social_issues_data[ ,4:5] <- lapply(social_issues_data[ ,4:5], factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No")))
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social_issues_data$economy <- factor(social_issues_data$economy, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$immigration <- factor(social_issues_data$immigration, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$crime <- factor(social_issues_data$crime, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$health <- factor(social_issues_data$health, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$education <- factor(social_issues_data$education, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$housing <- factor(social_issues_data$housing, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$welfare <- factor(social_issues_data$welfare, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$defence <- factor(social_issues_data$defence, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$environment <- factor(social_issues_data$environment, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$tax <- factor(social_issues_data$tax, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$pensions <- factor(social_issues_data$pensions, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$family.life <- factor(social_issues_data$family.life, levels = c(1, 2), labels = c("Yes", "No"))
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social_issues_data$transport <- factor(social_issues_data$transport, levels = c(1, 2), labels = c("Yes", "No"))
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```
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``` {r Analyses 2 - ANOVA and MANOVA}
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## Q22; Q23[1-2]; Q27[1-7]
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#Q22_average
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#Q23_1, Q23_2
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#Q27_1 - Q27_7
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#t.test to see if difference in one variable - Q22_average
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hist(social_issues_data$Q22_average)
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t.test(Q22_average~brexit, data = social_issues_data, paired = FALSE)
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#no significant difference between scores on Q22, and whether they thought brexit was important
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Q_22test <- aov(Q22_average ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
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summary(Q_22test)
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#no significant different between scores on Q22 and their opinion on social issues
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Q_23test <- manova(cbind(Q23_1, Q23_2) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
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summary(Q_23test)
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#significant difference between scores on Q23 with economy, health, and environment
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econ <- aggregate(cbind(Q23_1, Q23_2) ~ economy, data = social_issues_data, FUN = mean)
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health <- aggregate(cbind(Q23_1, Q23_2) ~ health, data = social_issues_data, FUN = mean)
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env <- aggregate(cbind(Q23_1, Q23_2) ~ environment, data = social_issues_data, FUN = mean)
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#SORT OUT MEANS FOR THIS -- interesting pattern viewed with means
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Q_27test <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data)
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summary(Q_27test)
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#No significant difference in responses to Q27 based on what they considered important
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```
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```
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- RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27, [Q24, Q25, Q30]
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- RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27, [Q24, Q25, Q30]
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86
data/Q20_data.csv
Normal file
86
data/Q20_data.csv
Normal file
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@ -0,0 +1,86 @@
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Q20,Q20_recode,brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport,Q22_1,Q22_2,Q22_average,Q23_1,Q23_2,Q27_1,Q27_2,Q27_3,Q27_4,Q27_5,Q27_6,Q27_7
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"health,education,environment","5,6,10",2,2,2,2,1,1,2,2,2,1,2,2,2,2,4,4,4,3,5,4,4,4,4,4,4,4
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"economy,immigration,crime,health,education,housing,welfare,defence,environment,family life,transport","2,3,4,5,6,7,8,9,10,13,14",2,1,1,1,1,1,1,1,1,1,2,2,1,1,4,3,3.5,2,5,4,5,5,3,4,4,4
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"crime,health,housing,welfare,environment","4,5,7,8,10",2,2,2,1,1,2,1,1,2,1,2,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,2,3,4,5,4,3,2,3,2,2,2
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"health,education,housing,welfare,environment,family life","5,6,7,8,10,13",2,2,2,2,1,1,1,1,2,1,2,2,1,2,4,2,3,1,4,5,4,2,5,4,3,3
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"brexit,economy,immigration,crime,health,education,housing,welfare,environment,tax,pensions,family life","1,2,3,4,5,6,7,8,10,11,12,13",1,1,1,1,1,1,1,1,2,1,1,1,1,2,4,3,3.5,3,3,4,4,2,3,2,3,2
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"crime,housing,environment","4,7,10",2,2,2,1,2,2,1,2,2,1,2,2,2,2,5,5,5,4,5,4,4,4,4,4,4,4
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,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,5,5,5,1,5,5,5,5,5,5,5
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"economy,crime,health,welfare,environment","2,4,5,8,10",2,1,2,1,1,2,2,1,2,1,2,2,2,2,5,5,5,5,5,5,5,5,5,5,5,4
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"crime,health,education,housing,welfare,environment,family life","4,5,6,7,8,10,13",2,2,2,1,1,1,1,1,2,1,2,2,1,2,4,4,4,3,5,4,4,4,4,4,4,4
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"immigration,crime,health,education,housing,welfare,environment,family life","3,4,5,6,7,8,10,13",2,2,1,1,1,1,1,1,2,1,2,2,1,2,2,2,2,2,4,5,4,2,3,2,3,2
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"health,education,environment","5,6,10",2,2,2,2,1,1,2,2,2,1,2,2,2,2,2,2,2,4,4,3,4,2,4,4,4,3
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"immigration,crime,health,housing,welfare,family life","3,4,5,7,8,13",2,2,1,1,1,2,1,1,2,2,2,2,1,2,4,4,4,3,4,4,4,4,4,4,5,4
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"economy,immigration,health,education,housing,environment,pensions","2,3,5,6,7,10,12",2,1,1,2,1,1,1,2,2,1,2,1,2,2,3,3,3,2,4,3,3,3,4,4,4,5
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"brexit,economy,immigration,crime,health,education,housing,welfare,environment,family life","1,2,3,4,5,6,7,8,10,13",1,1,1,1,1,1,1,1,2,1,2,2,1,2,5,4,4.5,2,4,4,4,2,5,3,4,3
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"immigration,crime,health,education,welfare,family life","3,4,5,6,8,13",2,2,1,1,1,1,2,1,2,2,2,2,1,2,4,2,3,2,3,4,4,3,2,3,3,3
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"health,education,housing,welfare,environment,pensions,family life,transport","5,6,7,8,10,12,13,14",2,2,2,2,1,1,1,1,2,1,2,1,1,1,5,5,5,2,5,5,5,2,5,5,5,2
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"economy,education,environment","2,6,10",2,1,2,2,2,1,2,2,2,1,2,2,2,2,4,4,4,2,4,4,4,4,3,4,4,4
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"health,education,welfare,environment","5,6,8,10",2,2,2,2,1,1,2,1,2,1,2,2,2,2,4,2,3,2,3,2,2,2,1,2,3,1
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"brexit,economy,immigration,health,education,housing,welfare,environment","1,2,3,5,6,7,8,10",1,1,1,2,1,1,1,1,2,1,2,2,2,2,4,2,3,1,5,4,4,4,4,4,2,2
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,3,5,5,4,5,5,5,5
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,5,5,5,3,4,4,4,5,5,4,5
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,4,4,5,3,3,4,3,3
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,2,3,5,5,4,5,5,5,4
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"economy,immigration,health,education,environment","2,3,5,6,10",2,1,1,2,1,1,2,2,2,1,2,2,2,2,5,4,4.5,2,3,5,5,4,4,5,5,4
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"economy,health,education,housing,environment,family life","2,5,6,7,10,13",2,1,2,2,1,1,1,2,2,1,2,2,1,2,5,5,5,4,4,5,5,5,5,5,5,5
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"immigration,crime,health,education,housing,environment,family life","3,4,5,6,7,10,13",2,2,1,1,1,1,1,2,2,1,2,2,1,2,4,2,3,2,5,5,5,4,2,4,2,2
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"health,education,housing,welfare,environment,family life","5,6,7,8,10,13",2,2,2,2,1,1,1,1,2,1,2,2,1,2,4,4,4,2,4,4,4,2,4,4,4,3
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"crime,health,education,welfare,environment,family life","4,5,6,8,10,13",2,2,2,1,1,1,2,1,2,1,2,2,1,2,4,2,3,2,1,5,5,2,4,4,4,4
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"immigration,crime,health,education,welfare,environment,tax,transport","3,4,5,6,8,10,11,14",2,2,1,1,1,1,2,1,2,1,1,2,2,1,4,2,3,4,4,5,5,4,4,2,4,2
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"crime,health,education,housing,welfare,family life","4,5,6,7,8,13",2,2,2,1,1,1,1,1,2,2,2,2,1,2,4,4,4,2,2,4,4,4,1,2,2,2
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,3,3.5,3,4,4,5,3,4,5,5,3
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"economy,immigration,crime,health,education,housing,welfare,environment,family life","2,3,4,5,6,7,8,10,13",2,1,1,1,1,1,1,1,2,1,2,2,1,2,5,4,4.5,4,5,5,5,5,4,4,4,4
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"immigration,health,education,housing,welfare,environment","3,5,6,7,8,10",2,2,1,2,1,1,1,1,2,1,2,2,2,2,4,4,4,4,4,4,5,4,5,5,5,4
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"immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","3,4,5,6,7,8,9,10,11,12,13,14",2,2,1,1,1,1,1,1,1,1,1,1,1,1,4,4,4,2,3,4,4,3,5,4,4,4
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"immigration,education,family life","3,6,13",2,2,1,2,2,1,2,2,2,2,2,2,1,2,4,3,3.5,4,3,4,2,4,1,2,2,2
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"economy,immigration,health,education,environment,family life","2,3,5,6,10,13",2,1,1,2,1,1,2,2,2,1,2,2,1,2,4,3,3.5,4,5,5,5,2,4,4,5,2
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"brexit,economy,immigration,crime,health,education,housing,environment,family life","1,2,3,4,5,6,7,10,13",1,1,1,1,1,1,1,2,2,1,2,2,1,2,5,5,5,4,4,3,3,4,4,4,4,4
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"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,5,4,2,3,4,3,2
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"immigration,health,education,welfare,environment,tax","3,5,6,8,10,11",2,2,1,2,1,1,2,1,2,1,1,2,2,2,5,4,4.5,4,3,4,5,5,3,2,4,4
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"immigration,health,education,housing,environment,family life","3,5,6,7,10,13",2,2,1,2,1,1,1,2,2,1,2,2,1,2,4,4,4,4,5,5,5,4,4,5,5,4
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"health,education,welfare,environment,family life","5,6,8,10,13",2,2,2,2,1,1,2,1,2,1,2,2,1,2,3,3,3,3,4,3,3,3,3,3,3,3
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"economy,immigration,crime,health,education,housing,welfare,environment,tax,pensions,family life,transport","2,3,4,5,6,7,8,10,11,12,13,14",2,1,1,1,1,1,1,1,2,1,1,1,1,1,4,3,3.5,4,5,4,5,4,4,4,4,4
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"health,education,housing,environment,family life","5,6,7,10,13",2,2,2,2,1,1,1,2,2,1,2,2,1,2,4,4,4,3,3,4,4,3,3,4,4,3
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,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,5,5,4,3,,,,,,,
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,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,4,4.5,4,3,5,5,5,5,5,5,4
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"immigration,crime,health,education,housing,welfare,environment,family life,transport","3,4,5,6,7,8,10,13,14",2,2,1,1,1,1,1,1,2,1,2,2,1,1,1,1,1,1,5,5,5,2,2,4,4,2
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,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,2,3,1,3,,,,,,,
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,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,2,4,,,,,,,
|
||||||
|
"immigration,health,education,housing,welfare,environment,family life","3,5,6,7,8,10,13",2,2,1,2,1,1,1,1,2,1,2,2,1,2,4,4,4,3,5,4,4,4,3,4,3,3
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,,,,,,,
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,3,5,5,5,5,5,5,5,5
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,4,4.5,2,3,4,4,4,3,4,4,4
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,2.5,3,5,5,4,4,3,4,3,3
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,2,2,4,4,3,4,4,4,4
|
||||||
|
"economy,health,education,housing,environment","2,5,6,7,10",2,1,2,2,1,1,1,2,2,1,2,2,2,2,4,4,4,4,5,4,5,5,5,4,5,5
|
||||||
|
"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,2,3.5,2,3,3,3,4,1,2,2,2
|
||||||
|
"crime,health,education,welfare,defence,environment,family life","4,5,6,8,9,10,13",2,2,2,1,1,1,2,1,1,1,2,2,1,2,5,4,4.5,5,5,5,5,4,4,5,4,4
|
||||||
|
"immigration,crime,health,education,housing,welfare,environment,tax,family life","3,4,5,6,7,8,10,11,13",2,2,1,1,1,1,1,1,2,1,1,2,1,2,4,2,3,1,5,5,5,4,5,5,5,5
|
||||||
|
"health,education,welfare,environment,family life","5,6,8,10,13",2,2,2,2,1,1,2,1,2,1,2,2,1,2,4,3,3.5,2,5,4,4,3,4,3,3,2
|
||||||
|
"health,education,welfare,environment,family life","5,6,8,10,13",2,2,2,2,1,1,2,1,2,1,2,2,1,2,1,2,1.5,1,5,5,5,5,5,5,5,4
|
||||||
|
"crime,health,education,housing,welfare,pensions,family life","4,5,6,7,8,12,13",2,2,2,1,1,1,1,1,2,2,2,1,1,2,4,4,4,3,3,4,5,3,2,3,4,3
|
||||||
|
"immigration,crime,health,education,welfare,environment,family life","3,4,5,6,8,10,13",2,2,1,1,1,1,2,1,2,1,2,2,1,2,5,4,4.5,2,2,5,4,4,4,2,5,2
|
||||||
|
"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,2,3.5,2,5,4,4,2,4,4,4,2
|
||||||
|
"crime,health,education,environment,family life","4,5,6,10,13",2,2,2,1,1,1,2,2,2,1,2,2,1,2,4,4,4,4,3,4,4,4,4,4,4,3
|
||||||
|
"immigration,crime,health,education,housing,welfare,environment,family life","3,4,5,6,7,8,10,13",2,2,1,1,1,1,1,1,2,1,2,2,1,2,4,2,3,2,4,2,4,2,4,2,4,2
|
||||||
|
"health,education,housing,welfare,environment,tax","5,6,7,8,10,11",2,2,2,2,1,1,1,1,2,1,1,2,2,2,5,4,4.5,2,3,4,4,3,4,4,4,3
|
||||||
|
housing,7,2,2,2,2,2,2,1,2,2,2,2,2,2,2,3,3,3,3,3,4,4,4,3,4,5,4
|
||||||
|
"crime,education,family life","4,6,13",2,2,2,1,2,1,2,2,2,2,2,2,1,2,4,2,3,2,3,4,3,3,4,4,4,2
|
||||||
|
"economy,crime,health,education,housing,welfare,environment,family life,transport","2,4,5,6,7,8,10,13,14",2,1,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,5,3,3,3,1,3,3,3
|
||||||
|
"economy,immigration,health,education,welfare,defence,tax,pensions,family life","2,3,5,6,8,9,11,12,13",2,1,1,2,1,1,2,1,1,2,1,1,1,2,2,2,2,1,4,3,2,2,4,3,4,2
|
||||||
|
"immigration,crime,health,education,housing,welfare,environment,tax,pensions,family life,transport","3,4,5,6,7,8,10,11,12,13,14",2,2,1,1,1,1,1,1,2,1,1,1,1,1,4,4,4,2,4,2,2,2,1,2,4,2
|
||||||
|
"economy,crime,health,education,housing,welfare,environment,transport","2,4,5,6,7,8,10,14",2,1,2,1,1,1,1,1,2,1,2,2,2,1,3,3,3,2,4,4,4,2,2,4,4,2
|
||||||
|
"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,3,4,4,4,3,4,4,4
|
||||||
|
"health,education,welfare,environment","5,6,8,10",2,2,2,2,1,1,2,1,2,1,2,2,2,2,5,4,4.5,5,5,5,4,4,3,4,4,4
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,4,2,5,4,4,4,4,4,4
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,2,2.5,1,1,5,3,4,2,4,4,4
|
||||||
|
"health,education,housing,environment","5,6,7,10",2,2,2,2,1,1,1,2,2,1,2,2,2,2,2,2,2,2,4,3,2,2,1,2,2,1
|
||||||
|
"brexit,immigration,crime,health,education,housing,welfare,environment,family life","1,3,4,5,6,7,8,10,13",1,2,1,1,1,1,1,1,2,1,2,2,1,2,2,2,2,1,5,4,4,3,4,4,3,3
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,3,3.5,3,4,3,4,3,2,3,3,4
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,4,4.5,5,5,4,4,4,4,4,4,4
|
||||||
|
"economy,health,education,environment,family life","2,5,6,10,13",2,1,2,2,1,1,2,2,2,1,2,2,1,2,4,4,4,5,5,5,5,5,4,4,5,5
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1.5,1,3,,,,,,,
|
||||||
|
"immigration,crime,health,environment","3,4,5,10",2,2,1,1,1,2,2,2,2,1,2,2,2,2,1,1,1,1,5,1,3,1,1,1,3,1
|
||||||
|
,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,3,3.5,3,3,,,,,,,
|
||||||
|
Loading…
Reference in a new issue