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adding simd 2020, ugly but works
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@jvillacampa
jvillacampa committed Jul 9, 2021
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Showing 1 changed file with 67 additions and 39 deletions.
106 changes: 67 additions & 39 deletions Young people in deprived quintile.R
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Expand Up @@ -21,80 +21,108 @@ source("1.indicator_analysis.R") #Normal indicator functions
#Small function to standarize each years info. Function parameters:
#Data is for what basefile to use, list_pos is for the position of the data frame
#simd for which simd variables-year to look at, year for what year is the data created.
prepare_file <- function(dz_list) {
raw_data <<- readRDS(paste0(lookups, "Population/DZ11_pop_basefile.rds")) %>%
filter(age<26 & datazone2011 %in% dz_list & year>2010) %>% group_by(year, datazone2011) %>%
prepare_file <- function(dz_list14, dz_list17) {
pop <- readRDS(paste0(lookups, "Population/DZ11_pop_basefile.rds")) %>%
filter(age<26 & year>2010) %>% group_by(year, datazone2011) %>%
summarise(numerator = sum(denominator, na.rm= T)) %>% ungroup %>%
rename(datazone = datazone2011)

raw_data <<- rbind(
pop %>% filter((datazone %in% dz_list14 & between(year, 2011, 2016))),
pop %>% filter((datazone %in% dz_list17 & year > 2016)))

}

###############################################.
## Part 1 - Format raw data ready for analysis functions ----
###############################################.

simd_data <- readRDS(paste0(cl_out_depr, 'DataZone2011_simd2016.rds')) %>%
simd_data14 <- readRDS(paste0(cl_out_depr, 'DataZone2011_simd2016.rds')) %>%
setNames(tolower(names(.))) %>%
select(datazone2011, simd2016_crime_rank, simd2016_access_rank, simd2016_inc_rank)

simd_data17 <- readRDS(paste0(cl_out_depr, 'DataZone2011_simd2020v2.rds')) %>%
setNames(tolower(names(.))) %>%
select(datazone2011, simd2020v2_crime_rank, simd2020v2_access_rank, simd2020v2_inc_rank)

# Population 25 or under
# Selecting pop years used to create each simd version 2014 for SIMD2016, 2017 for SIMD2020
pop <- readRDS(paste0(lookups, "Population/DZ11_pop_basefile.rds")) %>%
filter(year == "2014") %>% group_by(year, datazone2011) %>%
filter(year %in% c("2014", "2017") & age <26) %>%
group_by(year, datazone2011) %>%
summarise(pop = sum(denominator, na.rm= T)) %>% ungroup

pop_total <- pop %>% group_by(year) %>% summarise(pop = sum(pop, na.rm= T)) %>%
ungroup %>% pull(pop)

cut_breaks <- c(0, pop_total/5, pop_total/5*2, pop_total/5*3, pop_total/5*4, pop_total)
pop14 <- pop %>% filter(year == "2014")
pop17 <- pop %>% filter(year == "2017")

pop_total14 <- pop14 %>% group_by(year) %>%
summarise(pop = sum(pop, na.rm= T)) %>% ungroup %>% pull(pop)

simd_data <- left_join(simd_data, pop, by = c("datazone2011")) %>%
pop_total17 <- pop17 %>% group_by(year) %>%
summarise(pop = sum(pop, na.rm= T)) %>% ungroup %>% pull(pop)

# Creating the population thresholds for each quintile
cut_breaks14 <- c(0, pop_total14/5, pop_total14/5*2, pop_total14/5*3, pop_total14/5*4, pop_total14)
cut_breaks17 <- c(0, pop_total17/5, pop_total17/5*2, pop_total17/5*3, pop_total17/5*4, pop_total17)

# Preparing files for simd 2016
simd_data14 <- left_join(simd_data14, pop14, by = c("datazone2011")) %>%
arrange(simd2016_crime_rank) %>% # crime pop weighted quintile
mutate(cum_pop_crime=cumsum(pop),
crime_quintile = as.numeric(paste(cut(cum_pop_crime, cut_breaks, include.lowest=TRUE,
crime_quintile = as.numeric(paste(cut(cum_pop_crime, cut_breaks14, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
arrange(simd2016_access_rank) %>% # access pop weighted quintile
mutate(cum_pop_access=cumsum(pop),
access_quintile = as.numeric(paste(cut(cum_pop_access, cut_breaks, include.lowest=TRUE,
access_quintile = as.numeric(paste(cut(cum_pop_access, cut_breaks14, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
arrange(simd2016_inc_rank) %>% # income pop weighted quintile
mutate(cum_pop_inc=cumsum(pop),
inc_quintile = as.numeric(paste(cut(cum_pop_inc, cut_breaks, include.lowest=TRUE,
inc_quintile = as.numeric(paste(cut(cum_pop_inc, cut_breaks14, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
select(-starts_with("cum_pop"), -starts_with("simd"))

crime_dz <- simd_data %>% filter(crime_quintile == "5") %>% pull(datazone2011)
inc_dz <- simd_data %>% filter(inc_quintile == "5") %>% pull(datazone2011)
access_dz <- simd_data %>% filter(access_quintile == "5") %>% pull(datazone2011)
# Preparing files for simd 2020
simd_data17 <- left_join(simd_data17, pop17, by = c("datazone2011")) %>%
arrange(simd2020v2_crime_rank) %>% # crime pop weighted quintile
mutate(cum_pop_crime=cumsum(pop),
crime_quintile = as.numeric(paste(cut(cum_pop_crime, cut_breaks17, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
arrange(simd2020v2_access_rank) %>% # access pop weighted quintile
mutate(cum_pop_access=cumsum(pop),
access_quintile = as.numeric(paste(cut(cum_pop_access, cut_breaks17, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
arrange(simd2020v2_inc_rank) %>% # income pop weighted quintile
mutate(cum_pop_inc=cumsum(pop),
inc_quintile = as.numeric(paste(cut(cum_pop_inc, cut_breaks17, include.lowest=TRUE,
labels=c("5", "4", "3", "2", "1"))))) %>%
select(-starts_with("cum_pop"), -starts_with("simd"))


saveRDS(prepare_file(inc_dz), paste0(data_folder, "Prepared Data/young_people_income_raw.rds"))
saveRDS(prepare_file(crime_dz), paste0(data_folder, "Prepared Data/young_people_crime_raw.rds"))
saveRDS(prepare_file(access_dz), paste0(data_folder, "Prepared Data/young_people_access_raw.rds"))
crime_dz14 <- simd_data14 %>% filter(crime_quintile == "5") %>% pull(datazone2011)
inc_dz14 <- simd_data14 %>% filter(inc_quintile == "5") %>% pull(datazone2011)
access_dz14 <- simd_data14 %>% filter(access_quintile == "5") %>% pull(datazone2011)
crime_dz17 <- simd_data17 %>% filter(crime_quintile == "5") %>% pull(datazone2011)
inc_dz17 <- simd_data17 %>% filter(inc_quintile == "5") %>% pull(datazone2011)
access_dz17 <- simd_data17 %>% filter(access_quintile == "5") %>% pull(datazone2011)

saveRDS(prepare_file(inc_dz14, inc_dz17),
paste0(data_folder, "Prepared Data/young_people_income_raw.rds"))
saveRDS(prepare_file(crime_dz14, crime_dz17),
paste0(data_folder, "Prepared Data/young_people_crime_raw.rds"))
saveRDS(prepare_file(access_dz14, access_dz17),
paste0(data_folder, "Prepared Data/young_people_access_raw.rds"))

###############################################.
## Part 2 - Calling the analysis functions ----
###############################################.
###############################################.
# Crime
analyze_first(filename = "young_people_crime", geography = "datazone11", measure = "percent",
yearstart = 2011, yearend = 2018, time_agg = 1, pop = "DZ11_pop_under26")

analyze_second(filename = "young_people_crime", measure = "percent", time_agg = 1,
ind_id = 13005, year_type = "calendar", qa = F)
filenames <- c("young_people_crime", "young_people_access", "young_people_income")
# Running functions for the three indicators
mapply(analyze_first, filename = filenames, geography = "datazone11", measure = "percent",
yearstart = 2011, yearend = 2019, time_agg = 1, pop = "DZ11_pop_under26")

###############################################.
# Access
analyze_first(filename = "young_people_access", geography = "datazone11", measure = "percent",
yearstart = 2011, yearend = 2018, time_agg = 1, pop = "DZ11_pop_under26")

analyze_second(filename = "young_people_access", measure = "percent", time_agg = 1,
ind_id = 13003, year_type = "calendar", qa = F)

###############################################.
# Income
analyze_first(filename = "young_people_income", geography = "datazone11", measure = "percent",
yearstart = 2011, yearend = 2018, time_agg = 1, pop = "DZ11_pop_under26")
mapply(analyze_second(filename = "young_people_crime", measure = "percent", time_agg = 1,
ind_id = c(13005, 13003, 13004), year_type = "calendar", qa = F))

analyze_second(filename = "young_people_income", measure = "percent", time_agg = 1,
ind_id = 13004, year_type = "calendar", qa = F)
## END

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