## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = FALSE ) ## ----setup, message=FALSE, warning=FALSE-------------------------------------- # library(pct) # library(sf) # library(dplyr) # library(tmap) # tm_shape(pct_regions) + # tm_polygons() + # tm_text("region_name", size = 0.6) ## ----------------------------------------------------------------------------- # region_of_interest = "west-yorkshire" # zones_region = get_pct_zones(region = region_of_interest) # # zones_region = get_pct_zones(region = region_of_interest, geography = "lsoa") # for smaller zones # names(zones_region) # plot(zones_region["bicycle"]) ## ----eval=FALSE, echo=FALSE--------------------------------------------------- # # # tm_shape(zones_region) + # # tm_fill("bicycle", palette = "RdYlBu") + # # tm_shape(pct_regions) + # # tm_borders() + # # tm_text("region_name") # # # reproducible example of fail # # remotes::install_github("mtennekes/tmap") # library(tmap) # u = "https://github.com/npct/pct-outputs-regional-notR/raw/master/commute/lsoa/isle-of-wight/z.geojson" # z = sf::st_read(u) # plot(z["bicycle"]) # qtm(z) # tm_shape(z) + # tm_fill("bicycle", palette = "RdYlBu") # tmap_mode("view") # qtm(z) # # # another region # tmap_mode("plot") # u = "https://github.com/npct/pct-outputs-regional-notR/raw/master/commute/lsoa/west-yorkshire/z.geojson" # z = sf::st_read(u) # plot(z["bicycle"]) # qtm(z) # tm_shape(z) + # tm_fill("bicycle", palette = "RdYlBu") # tmap_mode("view") # qtm(z) # mapview::mapview(z) # # devtools::session_info() ## ----------------------------------------------------------------------------- # unique(zones_region$lad_name) ## ----------------------------------------------------------------------------- # zones = zones_region %>% # filter(lad_name == "Leeds") # tm_shape(zones) + # tm_fill("bicycle", palette = "RdYlBu") ## ----------------------------------------------------------------------------- # scenarios_of_interest = c("govnearmkt_slc", "dutch_slc", "ebike_slc") # tm_shape(zones) + # tm_fill(scenarios_of_interest, palette = "RdYlBu", n = 9, title = "N. cycling") + # tm_facets(nrow = 1, free.scales = FALSE) + # tm_layout(panel.labels = scenarios_of_interest) ## ----------------------------------------------------------------------------- # zones_mode_share = zones %>% # select(scenarios_of_interest) %>% # mutate_at(scenarios_of_interest, .funs = list(~ ./zones$all * 100)) # tm_shape(zones_mode_share) + # tm_fill(scenarios_of_interest, palette = "RdYlBu", title = "% cycling") + # tm_facets(nrow = 1, free.scales = FALSE) + # tm_layout(panel.labels = scenarios_of_interest) ## ----------------------------------------------------------------------------- # zones_region %>% # st_drop_geometry() %>% # group_by(lad_name) %>% # select(`2011 census` = bicycle, c(scenarios_of_interest, "all")) %>% # summarise_all(.funs = ~ round(sum(.)/sum(all)* 100)) %>% # select(-all, `Local Authority / % Cycling in scenario:` = lad_name) %>% # knitr::kable() ## ----national-dl-------------------------------------------------------------- # zones_national = read_sf("https://github.com/npct/pct-outputs-national/raw/master/commute/msoa/z_all.geojson") ## ----------------------------------------------------------------------------- # national_commute_totals = zones_national %>% # st_drop_geometry() %>% # select(all, census_2011 = bicycle, govtarget_slc, dutch_slc) %>% # summarise_all(.funs = ~sum(.)) # national_commute_percentages = national_commute_totals / national_commute_totals$all * 100 ## ----echo=FALSE--------------------------------------------------------------- # knitr::kable(bind_rows(national_commute_totals, national_commute_percentages), digits = 1, # caption = "Total counts and percentages of cycle commuters under different scenarios") ## ----------------------------------------------------------------------------- # r = read.csv(stringsAsFactors = FALSE, text = "area # Greater London # Greater Manchester # Birmingham # Leeds # Glasgow # Liverpool # Newcastle # Bristol # Cardiff # Belfast # Southampton # Sheffield # ") # matching_las = pct_regions_lookup$lad16nm[pct_regions_lookup$lad16nm %in% r$area] # matching_regions = c("london", "greater-manchester") # pct_lookup = pct_regions_lookup %>% # rename(lad_name = lad16nm) # zones_national = inner_join(zones_national, pct_lookup) # zones_national = zones_national %>% # mutate(area = case_when( # region_name == "london" ~ "Greater London", # region_name == "greater-manchester" ~ "Greater Manchester", # lad_name %in% matching_las ~ lad_name, # TRUE ~ "Other" # )) # table(zones_national$area) # zones_aggregated = zones_national %>% # sf::st_drop_geometry() %>% # group_by(area) %>% # summarise( # Commuters = sum(all, na.rm = TRUE), # Bicycle_census = sum(bicycle), # Bicycle_govtarget = sum(govtarget_slc), # Bicycle_godutch = sum(dutch_slc) # ) # # # plot(zones_aggregated["Commuters"], border = NA) # zones_aggregated %>% # inner_join(r, .) %>% # knitr::kable(digits = 0) ## ----------------------------------------------------------------------------- # zones_aggregated_percents = zones_aggregated %>% # mutate_at(vars(-Commuters, -area), funs(./Commuters * 100)) # names(zones_aggregated_percents)[3:5] = paste0(names(zones_aggregated_percents)[3:5], "_percent") # zones_aggregated_percents %>% # inner_join(r, .) %>% # knitr::kable(digits = 1)