# comparing imputations
comp <- abs2013_cd %>%
gather(key = "Variable", value = "Value", -DivisionNm) %>%
mutate(type = "SA1") %>%
bind_rows(abs2013 %>%
select(-UniqueID) %>%
gather("Variable", "Value", -DivisionNm) %>%
mutate(type = "CED")) %>%
spread(key = "type", value = "Value") %>%
filter(!Variable %in% c("EmuneratedElsewhere", "InternetUse"))
comp %>%
mutate(error = abs((CED-SA1)/SA1)) %>%
group_by(Variable) %>%
summarise(avg_error = mean(error)) %>%
filter(avg_error > 0.1) %>%
ggplot(aes(x = Variable, y = avg_error)) +
geom_point() +
theme(axis.text.x = element_text(angle = 60))
#filter(SA1 <= 110) %>%
#ggplot(aes(x = SA1, y = CED)) +
#geom_point(alpha = 0.1)
# Do a percentage difference for each metric and plot distributions
# Aggregating 2016 just using SA1
sa1_list <- centroids_sa1_2016$data %>%
left_join(allocate_sa1_2016, by = c("SA1_MAIN16" = "sa1")) %>%
select(SA1_7DIGIT, electorate) %>%
mutate(SA1_7DIGIT = as.character(SA1_7DIGIT))
sa1_list_abs <- left_join(abs2016_cd %>% mutate(CD = as.character(CD)), sa1_list, by = c("CD" = "SA1_7DIGIT"))
sa1_list_abs %>% filter(electorate == "SYDNEY") %>% select(Population, Indigenous) %>% mutate(Indigenous = ifelse(is.na(Indigenous), 0, Indigenous)) %>% mutate(tot = Population*Indigenous/100) %>% colSums()
abs2016 %>% filter(DivisionNm == "SYDNEY") %>% select(ID, Population, Indigenous) %>% mutate(Total = Population*Indigenous/100)
# Plotting quantiles
quantiles <- abs2016 %>%
select(-c(ends_with("NS"), ID, State)) %>%
gather("key", "value", -DivisionNm) %>%
mutate(year = "2016") %>%
bind_rows(abs2011 %>%
select(-c(ends_with("NS"), UniqueID, State)) %>%
gather("key", "value", -DivisionNm) %>%
mutate(year = "2011")) %>%
bind_rows(abs2013_cd %>%
gather("key", "value", -DivisionNm) %>%
mutate(year = "2013")) %>%
mutate(year = as.factor(year)) %>%
group_by(year, key) %>%
summarise(q0 = min(value),
q1 = quantile(value, probs= 0.25),
q2 = quantile(value, probs= 0.5),
q3 = quantile(value, probs= 0.75),
q4 = max(value)) %>%
gather("quantile", "value", -c(year, key)) %>%
filter(value < 100)
quantiles %>%
ggplot(aes(x = key, y = value)) +
geom_line(aes(group = quantile, col = quantile)) +
guides(col = F)
# Boxplots
abs2016 %>%
select(-c(ends_with("NS"), ID, State)) %>%
mutate(year = "2016") %>%
bind_rows(abs2011 %>%
select(-c(ends_with("NS"), UniqueID, State)) %>%
mutate(year = "2011")) %>%
bind_rows(abs2013_cd %>%
mutate(year = "2013")) %>%
mutate(year = as.factor(year)) %>%
gather("key", "value", -c(year, DivisionNm)) %>%
mutate(value = as.numeric(value)) %>%
ggplot(aes(x = year, y = value)) +
geom_boxplot() +
facet_wrap(~key, scales = "free")
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