HW4: Univariate Statistics
Megan Georges
11/23/2021
Description of the Dataset and Variables
2017 Australian Marraige Law
A postal survey was sent to Australians asking the question: Should the law be changed to allow same-sex couples to marry? The data was reported by the Australian Bureau of Statistics using Excel.
Variables
- Divisions (Australian States/Territories)
- Cities (within the Divisions)
- Response_Clear_Yes: those who clearly selected Yes in support of the new marraige law
- Response_Clear_No: those who clearly selected No to the new marraige law
- Response_Not_Clear: those in which researchers could not determine which answer the respondent intended to select
- Non_Response: those who did not return the survey or select any of the responses
Tidying and Cleaning the Data
Read the data into R
ausmarraige <- read_excel("../../DACSS601_R/Data/australian_marriage_law_postal_survey_2017_-_response_final.xls", "Table 2", skip=6) ## New names:
## * `` -> ...1
## * no. -> no....2
## * `%` -> `%...3`
## * no. -> no....4
## * `%` -> `%...5`
## * ...head(ausmarraige, 10)## # A tibble: 10 x 16
## ...1 no....2 `%...3` no....4 `%...5` no....6 `%...7` ...8 no....9 `%...10`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>
## 1 New S~ NA NA NA NA NA NA NA NA NA
## 2 Banks 37736 44.9 46343 55.1 84079 100 NA 84079 79.9
## 3 Barton 37153 43.6 47984 56.4 85137 100 NA 85137 77.8
## 4 Benne~ 42943 49.8 43215 50.2 86158 100 NA 86158 81
## 5 Berow~ 48471 54.6 40369 45.4 88840 100 NA 88840 84.5
## 6 Blaxl~ 20406 26.1 57926 73.9 78332 100 NA 78332 75
## 7 Bradf~ 53681 60.6 34927 39.4 88608 100 NA 88608 83.5
## 8 Calare 54091 60.2 35779 39.8 89870 100 NA 89870 77.8
## 9 Chifl~ 32871 41.3 46702 58.7 79573 100 NA 79573 73.7
## 10 Cook 47505 55 38804 45 86309 100 NA 86309 82
## # ... with 6 more variables: no....11 <dbl>, %...12 <dbl>, no....13 <dbl>,
## # %...14 <dbl>, no....15 <dbl>, %...16 <dbl>View column names
colnames(ausmarraige)## [1] "...1" "no....2" "%...3" "no....4" "%...5" "no....6"
## [7] "%...7" "...8" "no....9" "%...10" "no....11" "%...12"
## [13] "no....13" "%...14" "no....15" "%...16"Select the columns that we want to keep and rename them
ausmarraige1 <- select(ausmarraige, "...1", "no....2", "no....4", "no....11", "no....13")%>%
rename(Cities=...1, Response_Clear_Yes=no....2, Response_Clear_No=no....4, Response_Not_Clear=no....11, Non_Response=no....13)%>%
drop_na(Cities)
head(ausmarraige1)## # A tibble: 6 x 5
## Cities Response_Clear_Y~ Response_Clear_~ Response_Not_Cl~ Non_Response
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 New South Wa~ NA NA NA NA
## 2 Banks 37736 46343 247 20928
## 3 Barton 37153 47984 226 24008
## 4 Bennelong 42943 43215 244 19973
## 5 Berowra 48471 40369 212 16038
## 6 Blaxland 20406 57926 220 25883Remove division totals and footnotes
ausmarraige1 <- ausmarraige1 %>%
filter(!str_detect(Cities, "Total"))%>%
filter(!str_starts(Cities, "\\("))%>%
filter(Cities != "Australia")%>%
filter(!str_starts(Cities, "\\©"))Create new column for the divisions
ausmarraige1 <- ausmarraige1 %>%
mutate(Divisions = case_when(
str_ends(Cities, "Divisions") ~ Cities
))
ausmarraige2 <- ausmarraige1[, c("Divisions", "Cities", "Response_Clear_Yes", "Response_Clear_No", "Response_Not_Clear", "Non_Response")]
head(ausmarraige2)## # A tibble: 6 x 6
## Divisions Cities Response_Clear_~ Response_Clear_~ Response_Not_Cl~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 New South Wales Divisions New S~ NA NA NA
## 2 <NA> Banks 37736 46343 247
## 3 <NA> Barton 37153 47984 226
## 4 <NA> Benne~ 42943 43215 244
## 5 <NA> Berow~ 48471 40369 212
## 6 <NA> Blaxl~ 20406 57926 220
## # ... with 1 more variable: Non_Response <dbl>Continue to fix the Divisions column using fill
ausmarraige2 <- fill(ausmarraige2, Divisions, .direction = c("down"))Remove the Divisions from the Cities column using filter
ausmarraige2 <- filter(ausmarraige2, !str_detect(Cities, "Divisions"))
head(ausmarraige2)## # A tibble: 6 x 6
## Divisions Cities Response_Clear_~ Response_Clear_~ Response_Not_Cl~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 New South Wales Divisions Banks 37736 46343 247
## 2 New South Wales Divisions Barton 37153 47984 226
## 3 New South Wales Divisions Bennelong 42943 43215 244
## 4 New South Wales Divisions Berowra 48471 40369 212
## 5 New South Wales Divisions Blaxland 20406 57926 220
## 6 New South Wales Divisions Bradfield 53681 34927 202
## # ... with 1 more variable: Non_Response <dbl>write_csv(ausmarraige2, file = "../../DACSS601_R/Data/ausmar.csv", col_names = TRUE)Create a column to show Totals for each City
austotals <- ausmarraige2 %>%
mutate(Total=rowsum(Response_Clear_Yes, Response_Clear_No, Response_Not_Clear, Non_Response))## Warning in if (reorder) ugroup <- sort(ugroup, na.last = TRUE, method =
## "quick"): the condition has length > 1 and only the first element will be usedhead (austotals)## # A tibble: 6 x 7
## Divisions Cities Response_Clear_~ Response_Clear_~ Response_Not_Cl~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 New South Wales Divisions Banks 37736 46343 247
## 2 New South Wales Divisions Barton 37153 47984 226
## 3 New South Wales Divisions Bennelong 42943 43215 244
## 4 New South Wales Divisions Berowra 48471 40369 212
## 5 New South Wales Divisions Blaxland 20406 57926 220
## 6 New South Wales Divisions Bradfield 53681 34927 202
## # ... with 2 more variables: Non_Response <dbl>, Total <dbl[,1]>Summary Descriptives of the Variables
Divisions and Cities
Australia comprises of 6 states and 2 territories (thus, 8 Divisions). The number of Cities in each Division is displayed below:
select(ausmarraige2, Divisions)%>%
table()## .
## Australian Capital Territory Divisions New South Wales Divisions
## 2 47
## Northern Territory Divisions Queensland Divisions
## 2 30
## South Australia Divisions Tasmania Divisions
## 11 5
## Victoria Divisions Western Australia Divisions
## 37 16The total number of eligible participants, by division:
divtotals <- select(austotals, Divisions, Total)
divtotals2 <- divtotals %>%
pivot_wider(names_from = Divisions, values_from = Total, values_fn = sum)
head(divtotals2)## # A tibble: 1 x 8
## `New South Wales Divisions` `Victoria Divis~ `Queensland Div~ `South Australi~
## <dbl> <dbl> <dbl> <dbl>
## 1 2711012 2092928 1501247 527426
## # ... with 4 more variables: Western Australia Divisions <dbl>,
## # Tasmania Divisions <dbl>, Northern Territory Divisions <dbl>,
## # Australian Capital Territory Divisions <dbl>A proportion table of the eligible participants based on Division:
prop.table(divtotals2)## New South Wales Divisions Victoria Divisions Queensland Divisions
## 1 0.3467988 0.2677321 0.1920429
## South Australia Divisions Western Australia Divisions Tasmania Divisions
## 1 0.06746953 0.0914524 0.02110833
## Northern Territory Divisions Australian Capital Territory Divisions
## 1 0.007598327 0.005797565Participant Responses
Descriptive statistics for Response_Clear_Yes:
summarize(ausmarraige2, Mean = mean(Response_Clear_Yes), Median = median(Response_Clear_Yes), Min = min(Response_Clear_Yes), Max = max(Response_Clear_Yes), SD = sd(Response_Clear_Yes), Var = var(Response_Clear_Yes), IQR = IQR(Response_Clear_Yes))## # A tibble: 1 x 7
## Mean Median Min Max SD Var IQR
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 52115. 51782. 19026 89590 12315. 151661815. 15259Descriptive statistics for Response_Clear_No:
summarize(ausmarraige2, Mean = mean(Response_Clear_No), Median = median(Response_Clear_No), Min = min(Response_Clear_No), Max = max(Response_Clear_No), SD = sd(Response_Clear_No), Var = var(Response_Clear_No), IQR = IQR(Response_Clear_No))## # A tibble: 1 x 7
## Mean Median Min Max SD Var IQR
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 32493. 31654. 14860 57926 8263. 68273681. 8274.Descriptive statistics for Response_Not_Clear:
summarize(ausmarraige2, Mean = mean(Response_Not_Clear), Median = median(Response_Not_Clear), Min = min(Response_Not_Clear), Max = max(Response_Not_Clear), SD = sd(Response_Not_Clear), Var = var(Response_Not_Clear), IQR = IQR(Response_Not_Clear))## # A tibble: 1 x 7
## Mean Median Min Max SD Var IQR
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 245. 240 106 377 55.9 3124. 68.8Descriptive statistics for Non_Response:
summarize(ausmarraige2, Mean = mean(Non_Response), Median = median(Non_Response), Min = min(Non_Response), Max = max(Non_Response), SD = sd(Non_Response), Var = var(Non_Response), IQR = IQR(Non_Response))## # A tibble: 1 x 7
## Mean Median Min Max SD Var IQR
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21855. 21416. 13092 35841 4197. 17618721. 5562.Experimenting with a Visualization
Bar graph modeling the total number of each response:
responsetotals <- ausmarraige2 %>%
select(Response_Clear_Yes, Response_Clear_No, Response_Not_Clear, Non_Response)%>%
summarise(Yes=sum(Response_Clear_Yes), No=sum(Response_Clear_No), NotClear=sum(Response_Not_Clear), NonResponse=sum(Non_Response))
rownames(responsetotals) <- c("Total")## Warning: Setting row names on a tibble is deprecated.responsetotals <- pivot_longer(responsetotals, Yes:NonResponse, names_to = "Response", values_to = "Total")
ggplot(data = responsetotals) +
geom_bar(mapping = aes(x = Response, y = Total), stat = "identity") +
labs(title = "Should Australian Law Change to Allow Same-Sex Marraige?", subtitle = "2017 postal survey of Australians", x = "Type of Response", y = "Total # of Responses")