mtreg · February 15, 2018 18:09
diff --git a/import_map_nyc_cdprofiles.R b/import_map_nyc_cdprofiles.R
 #Import community district boundaries
 #relies on sf package
 #ggplot2 plotting should also work, but needs dev version to deal with sf objects
 cds <- sf::st_read("https://data.cityofnewyork.us/resource/jp9i-3b7y.GeoJSON")

 #import community profile data - note, you can adjust this line to select specific variables [all are imported by default as written]
 cdprofiles <- read.csv("https://planninglabs.carto.com/api/v2/sql?format=csv&q=SELECT%20cartodb_id,the_geom,the_geom_webmercator,shared_puma,future_fp_mhhi,current_fp_mhhi,cd_tot_resunits,future_fp_resunits,future_fp_bldg,current_fp_resunits,cd_tot_bldgs,current_fp_bldg,moe_under18_rate_nyc,moe_under18_rate_boro,total_lot_area,lot_area_other_no_data,lot_area_parking,lot_area_open_space,lot_area_public_facility_institution,lot_area_transportation_utility,lot_area_industrial_manufacturing,lot_area_commercial_office,lot_area_mixed_use,lot_area_res_multifamily_elevator,lot_area_res_multifamily_walkup,lot_area_res_1_2_family_bldg,lots_total,lots_other_no_data,lots_vacant,lots_parking,lots_open_space,lots_public_facility_institution,lots_transportation_utility,lots_industrial_manufacturing,lots_commercial_office,lots_mixed_use,lots_res_multifamily_elevator,lots_res_multifamily_walkup,lots_res_1_2_family_bldg,pct_served_parks,count_public_schools,count_libraries,count_hosp_clinic,count_parks,crime_count_nyc,crime_count_boro,crime_count,pct_clean_strts_nyc,pop_11_15_acs,pop_2010,pop_2000,puma,borocd,future_fp_pop,current_fp_pop,lot_area_vacant,cb_email,cb_website,acs_tooltip,neighborhoods,son_issue_3,son_issue_2,son_issue_1,cd_son_fy2018,shared_puma_cd,cd_short_title,cd_full_title,future_fp_cost_burden,current_fp_cost_burden,future_fp_rent_burden,current_fp_rent_burden,future_fp_permortg,current_fp_permortg,future_fp_ownerocc,current_fp_ownerocc,future_fp_openspace,current_fp_openspace,future_fp_area,current_fp_area,pct_hispanic,pct_other_nh,pct_asian_nh,pct_black_nh,pct_white_nh,female_85_over,male_85_over,female_80_84,male_80_84,female_75_79,male_75_79,female_70_74,male_70_74,female_65_69,male_65_69,female_60_64,male_60_64,female_55_59,male_55_59,female_50_54,male_50_54,female_45_49,male_45_49,female_40_44,male_40_44,female_35_39,male_35_39,female_30_34,male_30_34,female_25_29,male_25_29,female_20_24,male_20_24,female_15_19,male_15_19,female_10_14,male_10_14,female_5_9,male_5_9,female_under_5,male_under_5,moe_over65_rate_nyc,over65_rate_nyc,moe_over65_rate_boro,over65_rate_boro,moe_over65_rate,over65_rate,under18_rate_nyc,under18_rate_boro,moe_under18_rate,under18_rate,moe_lep_rate_nyc,lep_rate_nyc,moe_lep_rate_boro,lep_rate_boro,moe_lep_rate,lep_rate,moe_foreign_born,pct_foreign_born,pct_lot_area_other_no_data,pct_lot_area_vacant,pct_lot_area_parking,pct_lot_area_open_space,pct_lot_area_public_facility_institution,pct_lot_area_transportation_utility,pct_lot_area_industrial_manufacturing,pct_lot_area_commercial_office,pct_lot_area_mixed_use,pct_lot_area_res_multifamily_elevator,pct_lot_area_res_multifamily_walkup,pct_lot_area_res_1_2_family_bldg,crime_per_1000_nyc,crime_per_1000_boro,crime_per_1000,pct_clean_strts_boro,pct_clean_strts,moe_hh_rent_burd_nyc,pct_hh_rent_burd_nyc,moe_hh_rent_burd_boro,pct_hh_rent_burd_boro,moe_hh_rent_burd,pct_hh_rent_burd,moe_mean_commute_nyc,mean_commute_nyc,moe_mean_commute_boro,mean_commute_boro,moe_mean_commute,mean_commute,moe_unemnployment,unemployment_nyc,moe_unemployment_boro,unemployment_boro,moe_unemployment_cd,unemployment_cd,moe_bach_deg_nyc,pct_bach_deg_nyc,moe_bach_deg_boro,pct_bach_deg_boro,moe_bach_deg,pct_bach_deg,poverty_rate_nyc,poverty_rate_boro,moe_poverty_rate,poverty_rate,pop_change_00_10,area_sqmi,acres%20FROM%20community_district_profiles&filename=Queens-7-indicators.csv")

 ## For some easier understanding of the data, data dictionary:
 cdprofile.datadic <- read.csv("https://communityprofiles.planning.nyc.gov/data/cd_profile_data_dictionary.csv")

 ## Merge the two datasets [spatial and tabular]
 cd.cp <- merge(cds, cdprofiles, by.x="boro_cd", by.y="borocd")

 plot(cd.cp["pct_hh_rent_burd"])
	#Import community district boundaries
	#relies on sf package
	#ggplot2 plotting should also work, but needs dev version to deal with sf objects
	cds <- sf::st_read("https://data.cityofnewyork.us/resource/jp9i-3b7y.GeoJSON")

	#import community profile data - note, you can adjust this line to select specific variables [all are imported by default as written]
	cdprofiles <- read.csv("https://planninglabs.carto.com/api/v2/sql?format=csv&q=SELECT%20cartodb_id,the_geom,the_geom_webmercator,shared_puma,future_fp_mhhi,current_fp_mhhi,cd_tot_resunits,future_fp_resunits,future_fp_bldg,current_fp_resunits,cd_tot_bldgs,current_fp_bldg,moe_under18_rate_nyc,moe_under18_rate_boro,total_lot_area,lot_area_other_no_data,lot_area_parking,lot_area_open_space,lot_area_public_facility_institution,lot_area_transportation_utility,lot_area_industrial_manufacturing,lot_area_commercial_office,lot_area_mixed_use,lot_area_res_multifamily_elevator,lot_area_res_multifamily_walkup,lot_area_res_1_2_family_bldg,lots_total,lots_other_no_data,lots_vacant,lots_parking,lots_open_space,lots_public_facility_institution,lots_transportation_utility,lots_industrial_manufacturing,lots_commercial_office,lots_mixed_use,lots_res_multifamily_elevator,lots_res_multifamily_walkup,lots_res_1_2_family_bldg,pct_served_parks,count_public_schools,count_libraries,count_hosp_clinic,count_parks,crime_count_nyc,crime_count_boro,crime_count,pct_clean_strts_nyc,pop_11_15_acs,pop_2010,pop_2000,puma,borocd,future_fp_pop,current_fp_pop,lot_area_vacant,cb_email,cb_website,acs_tooltip,neighborhoods,son_issue_3,son_issue_2,son_issue_1,cd_son_fy2018,shared_puma_cd,cd_short_title,cd_full_title,future_fp_cost_burden,current_fp_cost_burden,future_fp_rent_burden,current_fp_rent_burden,future_fp_permortg,current_fp_permortg,future_fp_ownerocc,current_fp_ownerocc,future_fp_openspace,current_fp_openspace,future_fp_area,current_fp_area,pct_hispanic,pct_other_nh,pct_asian_nh,pct_black_nh,pct_white_nh,female_85_over,male_85_over,female_80_84,male_80_84,female_75_79,male_75_79,female_70_74,male_70_74,female_65_69,male_65_69,female_60_64,male_60_64,female_55_59,male_55_59,female_50_54,male_50_54,female_45_49,male_45_49,female_40_44,male_40_44,female_35_39,male_35_39,female_30_34,male_30_34,female_25_29,male_25_29,female_20_24,male_20_24,female_15_19,male_15_19,female_10_14,male_10_14,female_5_9,male_5_9,female_under_5,male_under_5,moe_over65_rate_nyc,over65_rate_nyc,moe_over65_rate_boro,over65_rate_boro,moe_over65_rate,over65_rate,under18_rate_nyc,under18_rate_boro,moe_under18_rate,under18_rate,moe_lep_rate_nyc,lep_rate_nyc,moe_lep_rate_boro,lep_rate_boro,moe_lep_rate,lep_rate,moe_foreign_born,pct_foreign_born,pct_lot_area_other_no_data,pct_lot_area_vacant,pct_lot_area_parking,pct_lot_area_open_space,pct_lot_area_public_facility_institution,pct_lot_area_transportation_utility,pct_lot_area_industrial_manufacturing,pct_lot_area_commercial_office,pct_lot_area_mixed_use,pct_lot_area_res_multifamily_elevator,pct_lot_area_res_multifamily_walkup,pct_lot_area_res_1_2_family_bldg,crime_per_1000_nyc,crime_per_1000_boro,crime_per_1000,pct_clean_strts_boro,pct_clean_strts,moe_hh_rent_burd_nyc,pct_hh_rent_burd_nyc,moe_hh_rent_burd_boro,pct_hh_rent_burd_boro,moe_hh_rent_burd,pct_hh_rent_burd,moe_mean_commute_nyc,mean_commute_nyc,moe_mean_commute_boro,mean_commute_boro,moe_mean_commute,mean_commute,moe_unemnployment,unemployment_nyc,moe_unemployment_boro,unemployment_boro,moe_unemployment_cd,unemployment_cd,moe_bach_deg_nyc,pct_bach_deg_nyc,moe_bach_deg_boro,pct_bach_deg_boro,moe_bach_deg,pct_bach_deg,poverty_rate_nyc,poverty_rate_boro,moe_poverty_rate,poverty_rate,pop_change_00_10,area_sqmi,acres%20FROM%20community_district_profiles&filename=Queens-7-indicators.csv")

	## For some easier understanding of the data, data dictionary:
	cdprofile.datadic <- read.csv("https://communityprofiles.planning.nyc.gov/data/cd_profile_data_dictionary.csv")

	## Merge the two datasets [spatial and tabular]
	cd.cp <- merge(cds, cdprofiles, by.x="boro_cd", by.y="borocd")

	plot(cd.cp["pct_hh_rent_burd"])