data-raw/cartogram_processing.R
In mccormackandrew/mapcan: Tools for Plotting Canadian Choropleth Maps and Choropleth Alternatives
# Paths may have changed, when spdfs were moved to data-raw
## First run shapefile_processing.R, these cartograms will work with the
## compressed shapefiles created in that script
## To install Rcartogram and getcartr ----------------------
# First `brew install fftw` in command line, then:
# devtools::install_github("omegahat/Rcartogram")
# devtools::install_github('chrisbrunsdon/getcartr',subdir='getcartr')
library(pacman)
p_load(tidyverse, rgdal, rgeos, rmapshaper,
mapproj, sf, sp, RColorBrewer,
Rcartogram, getcartr, devtools)
## Make provinces and territories cartogram--------------------
# Import map data
load("data-raw/simplified_shapefiles/provinces_territories_spdf.rda")
load("data/province_pop_annual.rda")
# Merge in riding population and election data
provinces_territories <- merge(provinces_territories_spdf,
province_pop_annual[province_pop_annual$year == 2016, ],
by.x="PRENAME",by.y="province")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
provinces_territories$area <- gArea(provinces_territories, byid=TRUE) / (1000000)
# Generate cartogram
provinces_territories_carto <- getcartr::quick.carto(provinces_territories, provinces_territories$population, res = 256)
# Use buffer so fortify works
provinces_territories_carto <- gBuffer(provinces_territories_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
provinces_territories_carto <- fortify(provinces_territories_carto,
region = "PRENAME")
provinces_territories_carto <- left_join(provinces_territories_carto,
province_pop_annual[province_pop_annual$year == 2016, ],
by = c("id" = "province"))
names(provinces_territories_carto) <- c("long", "lat", "order", "hole", "piece", "pr_english", "group", "year", "population")
provinces_territories_carto$pr_alpha <- dplyr::recode(provinces_territories_carto$pr_english,
`Newfoundland and Labrador` = "NL",
`Prince Edward Island` = "PE",
`Nova Scotia` = "NS",
`New Brunswick` = "NB",
`Quebec` = "QC",
`Ontario` = "ON",
`Manitoba` = "MB",
`Saskatchewan` = "SK",
`Alberta` = "AB",
`British Columbia` = "BC",
`Yukon` = "YT",
`Northwest Territories` = "NT",
`Nunavut` = "NU")
provinces_territories_carto$pr_french <- dplyr::recode(provinces_territories_carto$pr_alpha,
`NL` = "Terre-Neuve-et-Labrador",
`PE` = "Île-du-Prince-Édouard",
`NS` = "Nouvelle-Écosse",
`NB` = "Nouveau-Brunswick",
`QC` = "Québec",
`ON` = "Ontario",
`MB` = "Manitoba",
`SK` = "Saskatchewan",
`AB` = "Alberta",
`BC` = "Colombie-Britannique",
`YT` = "Yukon",
`NT` = "Territoires du Nord-Ouest",
`NU` = "Nunavut")
provinces_territories_carto$pr_sgc_code <- dplyr::recode(provinces_territories_carto$pr_alpha,
`NL` = 10L,
`PE` = 11L,
`NS` = 12L,
`NB` = 13L,
`QC` = 24L,
`ON` = 35L,
`MB` = 46L,
`SK` = 47L,
`AB` = 48L,
`BC` = 59L,
`YT` = 60L,
`NT` = 61L,
`NU` = 62L)
provinces_territories_carto <- provinces_territories_carto %>%
dplyr::select(-year)
# CONVERT TO ASCII
provinces_territories_carto <- provinces_territories_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Save data
use_data(provinces_territories_carto, overwrite = T)
## Make provinces (no territories) cartogram--------------------
# Merge in riding population and election data
provinces_noterr <- merge(provinces_territories_spdf[!(provinces_territories_spdf$PREABBR %in% c("Y.T.", "Nvt.", "N.W.T.")), ],
province_pop_annual[province_pop_annual$year == 2016, ],
by.x="PRENAME",by.y="province")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
provinces_noterr$area <- gArea(provinces_noterr, byid=TRUE) / (1000000)
# Generate cartogram
provinces_noterr_carto <- getcartr::quick.carto(provinces_noterr, provinces_noterr$population, res = 256)
# Use buffer so fortify works
provinces_noterr_carto <- gBuffer(provinces_noterr_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
provinces_noterr_carto <- fortify(provinces_noterr_carto,
region = "PRENAME")
provinces_noterr_carto <- left_join(provinces_noterr_carto,
province_pop_annual[province_pop_annual$year == 2016, ],
by = c("id" = "province"))
names(provinces_noterr_carto) <- c("long", "lat", "order", "hole", "piece", "pr_english", "group", "year", "population")
provinces_noterr_carto$pr_alpha <- dplyr::recode(provinces_noterr_carto$pr_english,
`Newfoundland and Labrador` = "NL",
`Prince Edward Island` = "PE",
`Nova Scotia` = "NS",
`New Brunswick` = "NB",
`Quebec` = "QC",
`Ontario` = "ON",
`Manitoba` = "MB",
`Saskatchewan` = "SK",
`Alberta` = "AB",
`British Columbia` = "BC",
`Yukon` = "YT",
`Northwest Territories` = "NT",
`Nunavut` = "NU")
provinces_noterr_carto$pr_french <- dplyr::recode(provinces_noterr_carto$pr_alpha,
`NL` = "Terre-Neuve-et-Labrador",
`PE` = "Île-du-Prince-Édouard",
`NS` = "Nouvelle-Écosse",
`NB` = "Nouveau-Brunswick",
`QC` = "Québec",
`ON` = "Ontario",
`MB` = "Manitoba",
`SK` = "Saskatchewan",
`AB` = "Alberta",
`BC` = "Colombie-Britannique",
`YT` = "Yukon",
`NT` = "Territoires du Nord-Ouest",
`NU` = "Nunavut")
provinces_noterr_carto$pr_sgc_code <- dplyr::recode(provinces_noterr_carto$pr_alpha,
`NL` = 10L,
`PE` = 11L,
`NS` = 12L,
`NB` = 13L,
`QC` = 24L,
`ON` = 35L,
`MB` = 46L,
`SK` = 47L,
`AB` = 48L,
`BC` = 59L,
`YT` = 60L,
`NT` = 61L,
`NU` = 62L)
provinces_noterr_carto <- provinces_noterr_carto %>%
dplyr::select(-year)
# CONVERT TO ASCII
provinces_noterr_carto <- provinces_noterr_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Save data
use_data(provinces_noterr_carto, overwrite = TRUE)
## Make census divisions cartogram--------------------
# Import map data
load("data-raw/simplified_shapefiles/census_divisions_2016_spdf.rda")
load("data/census_pop2016.rda")
# Merge in riding population and election data
census_divisions_2016 <- merge(census_divisions_2016_spdf,
census_pop2016,
by.x = "CDUID" , by.y = "census_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
census_divisions_2016$area <- rgeos::gArea(census_divisions_2016, byid=TRUE) / (1000000)
# Generate cartogram
census_divisions_2016_carto <- getcartr::quick.carto(census_divisions_2016,
census_divisions_2016$population_2016,
res = 256)
# Use buffer so fortify works
census_divisions_2016_carto <- gBuffer(census_divisions_2016_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
census_divisions_2016_carto <- fortify(census_divisions_2016_carto,
region = "CDUID")
census_divisions_2016_carto$id <- as.numeric(census_divisions_2016_carto$id)
census_divisions_2016_carto <- left_join(census_divisions_2016_carto,
census_pop2016,
by = c("id" = "census_code"))
## THESE VARIABLES IMPORTED FROM census_pop2016 IN left_join ABOVE. PROBABLY NOT NECESSARY.
#census_divisions_2016_carto$pr_alpha <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "NL",
# `11` = "PE",
# `12` = "NS",
# `13` = "NB",
# `24` = "QC",
# `35` = "ON",
# `46` = "MB",
# `47` = "SK",
# `48` = "AB",
# `59` = "BC",
# `60` = "YT",
# `61` = "NT",
# `62` = "NU")
#census_divisions_2016_carto$pr_english <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "Newfoundland and Labrador",
# `11` = "Prince Edward Island",
# `12` = "Nova Scotia",
# `13` = "New Brunswick",
# `24` = "Quebec",
# `35` = "Ontario",
# `46` = "Manitoba",
# `47` = "Saskatchewan",
# `48` = "Alberta",
# `59` = "British Columbia",
# `60` = "Yukon",
# `61` = "Northwest Territories",
# `62` = "Nunavut")
#census_divisions_2016_carto$pr_french <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "Terre-Neuve-et-Labrador",
# `11` = "Île-du-Prince-Édouard",
# `12` = "Nouvelle-Écosse",
# `13` = "Nouveau-Brunswick",
# `24` = "Québec",
# `35` = "Ontario",
# `46` = "Manitoba",
# `47` = "Saskatchewan",
# `48` = "Alberta",
# `59` = "Colombie-Britannique",
# `60` = "Yukon",
# `61` = "Territoires du Nord-Ouest",
# `62` = "Nunavut")
census_divisions_2016_carto <- census_divisions_2016_carto %>%
mutate(census_code = id) %>%
dplyr::select(-id)
census_divisions_2016_carto$census_code <- as.numeric(census_divisions_2016_carto$census_code)
# CONVERT TO ASCII
census_divisions_2016_carto <- census_divisions_2016_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Reduce number of variables
census_divisions_2016_carto <- census_divisions_2016_carto %>%
dplyr::select(long, lat, order, hole, piece, group, census_code, census_division_name, census_division_type, pr_sgc_code, pr_alpha, pr_english, pr_french)
# Save data
use_data(census_divisions_2016_carto, overwrite = TRUE)
## Make census divisions (no terroritories) cartogram--------------------
# Merge in riding population and election data
census_divisions_2016_noterr <- merge(census_divisions_2016_spdf[!(census_divisions_2016_spdf$PRNAME %in%
c("Northwest Territories / Territoires du Nord-Ouest",
"Nunavut",
"Yukon")), ],
census_pop2016,
by.x = "CDUID" , by.y = "census_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
census_divisions_2016_noterr$area <- gArea(census_divisions_2016_noterr, byid=TRUE) / (1000000)
# Generate cartogram
census_divisions_2016_noterr_carto <- getcartr::quick.carto(census_divisions_2016_noterr,
census_divisions_2016_noterr$population_2016,
res = 256)
# Use buffer so fortify works
census_divisions_2016_noterr_carto <- gBuffer(census_divisions_2016_noterr_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
census_divisions_2016_noterr_carto <- fortify(census_divisions_2016_noterr_carto,
region = "CDUID")
census_divisions_2016_noterr_carto$id <- as.numeric(census_divisions_2016_noterr_carto$id)
census_divisions_2016_noterr_carto <- left_join(census_divisions_2016_noterr_carto,
census_pop2016,
by = c("id" = "census_code"))
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
mutate(census_code = id) %>%
dplyr::select(-id)
# CONVERT TO ASCII
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# REduce number of variables
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
dplyr::select(long, lat, order, hole, piece, group, census_code, census_division_name, census_division_type, pr_sgc_code, pr_alpha, pr_english, pr_french)
# Save data
use_data(census_divisions_2016_noterr_carto, overwrite = T)
## FEDERAL RIDING CARTOGRAM (FOR HEXAGON MAPS)
# Import map data
load("data/federal_ridings_spdf.rda")
load("data/federal_election_results.rda")
fed2016 <- federal_election_results[federal_election_results$election_year == 2015, ]
fed2016 <- fed2016[!(fed2016$pr_alpha %in% c("YK", "NT", "NU")), ]
fedridings <- federal_ridings_spdf[!(federal_ridings_spdf$PRNAME %in% c("Nunavut", "Yukon", "Northwest Territories / Territoires du Nord-Ouest")), ]
# Merge in riding population and election data
ridings_carto <- merge(fedridings,
fed2016,
by.x="FEDUID",by.y="riding_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
ridings_carto$area <- gArea(ridings_carto, byid=TRUE) / (1000000)
# Generate cartogram
ridings_carto_noprov <- getcartr::quick.carto(ridings_carto, ridings_carto$population, res = 256)
saveRDS(ridings_carto_noprov, "data-raw/ridings_carto_noprov.RDS")
# Use buffer so fortify works
ridings_carto <- gBuffer(ridings_carto, byid=TRUE, width=0)
mccormackandrew/mapcan documentation built on June 2, 2019, 6:57 p.m.
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# Paths may have changed, when spdfs were moved to data-raw
## First run shapefile_processing.R, these cartograms will work with the
## compressed shapefiles created in that script
## To install Rcartogram and getcartr ----------------------
# First `brew install fftw` in command line, then:
# devtools::install_github("omegahat/Rcartogram")
# devtools::install_github('chrisbrunsdon/getcartr',subdir='getcartr')
library(pacman)
p_load(tidyverse, rgdal, rgeos, rmapshaper,
mapproj, sf, sp, RColorBrewer,
Rcartogram, getcartr, devtools)
## Make provinces and territories cartogram--------------------
# Import map data
load("data-raw/simplified_shapefiles/provinces_territories_spdf.rda")
load("data/province_pop_annual.rda")
# Merge in riding population and election data
provinces_territories <- merge(provinces_territories_spdf,
province_pop_annual[province_pop_annual$year == 2016, ],
by.x="PRENAME",by.y="province")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
provinces_territories$area <- gArea(provinces_territories, byid=TRUE) / (1000000)
# Generate cartogram
provinces_territories_carto <- getcartr::quick.carto(provinces_territories, provinces_territories$population, res = 256)
# Use buffer so fortify works
provinces_territories_carto <- gBuffer(provinces_territories_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
provinces_territories_carto <- fortify(provinces_territories_carto,
region = "PRENAME")
provinces_territories_carto <- left_join(provinces_territories_carto,
province_pop_annual[province_pop_annual$year == 2016, ],
by = c("id" = "province"))
names(provinces_territories_carto) <- c("long", "lat", "order", "hole", "piece", "pr_english", "group", "year", "population")
provinces_territories_carto$pr_alpha <- dplyr::recode(provinces_territories_carto$pr_english,
`Newfoundland and Labrador` = "NL",
`Prince Edward Island` = "PE",
`Nova Scotia` = "NS",
`New Brunswick` = "NB",
`Quebec` = "QC",
`Ontario` = "ON",
`Manitoba` = "MB",
`Saskatchewan` = "SK",
`Alberta` = "AB",
`British Columbia` = "BC",
`Yukon` = "YT",
`Northwest Territories` = "NT",
`Nunavut` = "NU")
provinces_territories_carto$pr_french <- dplyr::recode(provinces_territories_carto$pr_alpha,
`NL` = "Terre-Neuve-et-Labrador",
`PE` = "Île-du-Prince-Édouard",
`NS` = "Nouvelle-Écosse",
`NB` = "Nouveau-Brunswick",
`QC` = "Québec",
`ON` = "Ontario",
`MB` = "Manitoba",
`SK` = "Saskatchewan",
`AB` = "Alberta",
`BC` = "Colombie-Britannique",
`YT` = "Yukon",
`NT` = "Territoires du Nord-Ouest",
`NU` = "Nunavut")
provinces_territories_carto$pr_sgc_code <- dplyr::recode(provinces_territories_carto$pr_alpha,
`NL` = 10L,
`PE` = 11L,
`NS` = 12L,
`NB` = 13L,
`QC` = 24L,
`ON` = 35L,
`MB` = 46L,
`SK` = 47L,
`AB` = 48L,
`BC` = 59L,
`YT` = 60L,
`NT` = 61L,
`NU` = 62L)
provinces_territories_carto <- provinces_territories_carto %>%
dplyr::select(-year)
# CONVERT TO ASCII
provinces_territories_carto <- provinces_territories_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Save data
use_data(provinces_territories_carto, overwrite = T)
## Make provinces (no territories) cartogram--------------------
# Merge in riding population and election data
provinces_noterr <- merge(provinces_territories_spdf[!(provinces_territories_spdf$PREABBR %in% c("Y.T.", "Nvt.", "N.W.T.")), ],
province_pop_annual[province_pop_annual$year == 2016, ],
by.x="PRENAME",by.y="province")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
provinces_noterr$area <- gArea(provinces_noterr, byid=TRUE) / (1000000)
# Generate cartogram
provinces_noterr_carto <- getcartr::quick.carto(provinces_noterr, provinces_noterr$population, res = 256)
# Use buffer so fortify works
provinces_noterr_carto <- gBuffer(provinces_noterr_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
provinces_noterr_carto <- fortify(provinces_noterr_carto,
region = "PRENAME")
provinces_noterr_carto <- left_join(provinces_noterr_carto,
province_pop_annual[province_pop_annual$year == 2016, ],
by = c("id" = "province"))
names(provinces_noterr_carto) <- c("long", "lat", "order", "hole", "piece", "pr_english", "group", "year", "population")
provinces_noterr_carto$pr_alpha <- dplyr::recode(provinces_noterr_carto$pr_english,
`Newfoundland and Labrador` = "NL",
`Prince Edward Island` = "PE",
`Nova Scotia` = "NS",
`New Brunswick` = "NB",
`Quebec` = "QC",
`Ontario` = "ON",
`Manitoba` = "MB",
`Saskatchewan` = "SK",
`Alberta` = "AB",
`British Columbia` = "BC",
`Yukon` = "YT",
`Northwest Territories` = "NT",
`Nunavut` = "NU")
provinces_noterr_carto$pr_french <- dplyr::recode(provinces_noterr_carto$pr_alpha,
`NL` = "Terre-Neuve-et-Labrador",
`PE` = "Île-du-Prince-Édouard",
`NS` = "Nouvelle-Écosse",
`NB` = "Nouveau-Brunswick",
`QC` = "Québec",
`ON` = "Ontario",
`MB` = "Manitoba",
`SK` = "Saskatchewan",
`AB` = "Alberta",
`BC` = "Colombie-Britannique",
`YT` = "Yukon",
`NT` = "Territoires du Nord-Ouest",
`NU` = "Nunavut")
provinces_noterr_carto$pr_sgc_code <- dplyr::recode(provinces_noterr_carto$pr_alpha,
`NL` = 10L,
`PE` = 11L,
`NS` = 12L,
`NB` = 13L,
`QC` = 24L,
`ON` = 35L,
`MB` = 46L,
`SK` = 47L,
`AB` = 48L,
`BC` = 59L,
`YT` = 60L,
`NT` = 61L,
`NU` = 62L)
provinces_noterr_carto <- provinces_noterr_carto %>%
dplyr::select(-year)
# CONVERT TO ASCII
provinces_noterr_carto <- provinces_noterr_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Save data
use_data(provinces_noterr_carto, overwrite = TRUE)
## Make census divisions cartogram--------------------
# Import map data
load("data-raw/simplified_shapefiles/census_divisions_2016_spdf.rda")
load("data/census_pop2016.rda")
# Merge in riding population and election data
census_divisions_2016 <- merge(census_divisions_2016_spdf,
census_pop2016,
by.x = "CDUID" , by.y = "census_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
census_divisions_2016$area <- rgeos::gArea(census_divisions_2016, byid=TRUE) / (1000000)
# Generate cartogram
census_divisions_2016_carto <- getcartr::quick.carto(census_divisions_2016,
census_divisions_2016$population_2016,
res = 256)
# Use buffer so fortify works
census_divisions_2016_carto <- gBuffer(census_divisions_2016_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
census_divisions_2016_carto <- fortify(census_divisions_2016_carto,
region = "CDUID")
census_divisions_2016_carto$id <- as.numeric(census_divisions_2016_carto$id)
census_divisions_2016_carto <- left_join(census_divisions_2016_carto,
census_pop2016,
by = c("id" = "census_code"))
## THESE VARIABLES IMPORTED FROM census_pop2016 IN left_join ABOVE. PROBABLY NOT NECESSARY.
#census_divisions_2016_carto$pr_alpha <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "NL",
# `11` = "PE",
# `12` = "NS",
# `13` = "NB",
# `24` = "QC",
# `35` = "ON",
# `46` = "MB",
# `47` = "SK",
# `48` = "AB",
# `59` = "BC",
# `60` = "YT",
# `61` = "NT",
# `62` = "NU")
#census_divisions_2016_carto$pr_english <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "Newfoundland and Labrador",
# `11` = "Prince Edward Island",
# `12` = "Nova Scotia",
# `13` = "New Brunswick",
# `24` = "Quebec",
# `35` = "Ontario",
# `46` = "Manitoba",
# `47` = "Saskatchewan",
# `48` = "Alberta",
# `59` = "British Columbia",
# `60` = "Yukon",
# `61` = "Northwest Territories",
# `62` = "Nunavut")
#census_divisions_2016_carto$pr_french <- dplyr::recode(census_divisions_2016_carto$province_sgc_code,
# `10` = "Terre-Neuve-et-Labrador",
# `11` = "Île-du-Prince-Édouard",
# `12` = "Nouvelle-Écosse",
# `13` = "Nouveau-Brunswick",
# `24` = "Québec",
# `35` = "Ontario",
# `46` = "Manitoba",
# `47` = "Saskatchewan",
# `48` = "Alberta",
# `59` = "Colombie-Britannique",
# `60` = "Yukon",
# `61` = "Territoires du Nord-Ouest",
# `62` = "Nunavut")
census_divisions_2016_carto <- census_divisions_2016_carto %>%
mutate(census_code = id) %>%
dplyr::select(-id)
census_divisions_2016_carto$census_code <- as.numeric(census_divisions_2016_carto$census_code)
# CONVERT TO ASCII
census_divisions_2016_carto <- census_divisions_2016_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# Reduce number of variables
census_divisions_2016_carto <- census_divisions_2016_carto %>%
dplyr::select(long, lat, order, hole, piece, group, census_code, census_division_name, census_division_type, pr_sgc_code, pr_alpha, pr_english, pr_french)
# Save data
use_data(census_divisions_2016_carto, overwrite = TRUE)
## Make census divisions (no terroritories) cartogram--------------------
# Merge in riding population and election data
census_divisions_2016_noterr <- merge(census_divisions_2016_spdf[!(census_divisions_2016_spdf$PRNAME %in%
c("Northwest Territories / Territoires du Nord-Ouest",
"Nunavut",
"Yukon")), ],
census_pop2016,
by.x = "CDUID" , by.y = "census_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
census_divisions_2016_noterr$area <- gArea(census_divisions_2016_noterr, byid=TRUE) / (1000000)
# Generate cartogram
census_divisions_2016_noterr_carto <- getcartr::quick.carto(census_divisions_2016_noterr,
census_divisions_2016_noterr$population_2016,
res = 256)
# Use buffer so fortify works
census_divisions_2016_noterr_carto <- gBuffer(census_divisions_2016_noterr_carto, byid=TRUE, width=0)
# Fortify into dataframe that ggplot can use
census_divisions_2016_noterr_carto <- fortify(census_divisions_2016_noterr_carto,
region = "CDUID")
census_divisions_2016_noterr_carto$id <- as.numeric(census_divisions_2016_noterr_carto$id)
census_divisions_2016_noterr_carto <- left_join(census_divisions_2016_noterr_carto,
census_pop2016,
by = c("id" = "census_code"))
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
mutate(census_code = id) %>%
dplyr::select(-id)
# CONVERT TO ASCII
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
mutate_if(is.character, stringi::stri_enc_toascii)
# REduce number of variables
census_divisions_2016_noterr_carto <- census_divisions_2016_noterr_carto %>%
dplyr::select(long, lat, order, hole, piece, group, census_code, census_division_name, census_division_type, pr_sgc_code, pr_alpha, pr_english, pr_french)
# Save data
use_data(census_divisions_2016_noterr_carto, overwrite = T)
## FEDERAL RIDING CARTOGRAM (FOR HEXAGON MAPS)
# Import map data
load("data/federal_ridings_spdf.rda")
load("data/federal_election_results.rda")
fed2016 <- federal_election_results[federal_election_results$election_year == 2015, ]
fed2016 <- fed2016[!(fed2016$pr_alpha %in% c("YK", "NT", "NU")), ]
fedridings <- federal_ridings_spdf[!(federal_ridings_spdf$PRNAME %in% c("Nunavut", "Yukon", "Northwest Territories / Territoires du Nord-Ouest")), ]
# Merge in riding population and election data
ridings_carto <- merge(fedridings,
fed2016,
by.x="FEDUID",by.y="riding_code")
# Compute area of each federal riding in km^2 area (unit: meters -> convert to square km)
ridings_carto$area <- gArea(ridings_carto, byid=TRUE) / (1000000)
# Generate cartogram
ridings_carto_noprov <- getcartr::quick.carto(ridings_carto, ridings_carto$population, res = 256)
saveRDS(ridings_carto_noprov, "data-raw/ridings_carto_noprov.RDS")
# Use buffer so fortify works
ridings_carto <- gBuffer(ridings_carto, byid=TRUE, width=0)
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