walkerke

library(idbr) 
library(ggplot2)
library(dplyr)
library(gganimate)
library(animation)

idb_api_key("Your key goes here")

male <- idb1('NI', 1990:2050, sex = 'male') %>%
  mutate(POP = POP * -1,

walkerke

library(idbr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(countrycode)
library(gganimate)
library(tweenr)

ctrys <- countrycode(c('South Africa', 'Botswana', 'Lesotho', 'Namibia', 'Zimbabwe', 'Swaziland'), 
            'country.name', 'fips104')

walkerke

library(tigris)

us_states <- unique(fips_codes$state)[1:51]

us_places <- rbind_tigris(
  lapply(
    us_states, function(x) {
      places(state = x, cb = TRUE)
    }
  )

walkerke

# Write to a shapefile for loading into CartoDB
writeOGR(tad_merged, dsn = ".", layer = 'fw_parcels', 
         driver = 'ESRI Shapefile', overwrite_layer = TRUE)

walkerke

summary(tad_merged$valsqft)

# Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
# 0.00    5.42   12.06   16.07   23.03 6345.00 
   

quantile(tad_merged$valsqft, probs = seq(0, 1, 1/7))

#   0% 14.28571% 28.57143% 42.85714% 57.14286% 71.42857% 85.71429%      100% 
# 0.00      2.97      6.24      9.83     15.05     21.55     27.91   6344.93 

walkerke

# Merge the TAD data to the parcels based on a common ID, and remove un-matched records
tad_merged <- geo_join(parcels, tad_sub, 'TAXPIN', 'GIS_Link', how = 'inner')

# Drop parcels with an area of 0, and calculate a "value/square foot" column
tad_merged <- tad_merged[tad_merged$CALCULATED != 0, ]

# Calculate value/square foot (converting acres to square feet)
tad_merged$valsqft <- round(tad_merged$Total_Value / (tad_merged$CALCULATED * 43560), 2)

walkerke

# Load in the data with readOGR (warning: slow)
parcels <- readOGR(dsn = ".", layer = 'tad5ft', stringsAsFactors = FALSE)

# Drop undeveloped parcels and unneeded columns
parcels <- parcels[parcels$PARCELTYPE != 2, ]

parcels@data <- parcels@data[c('TAXPIN', 'CALCULATED')]

walkerke

# Set your working directory in RStudio to your data/script directory
# Session > Set Working Directory > To Source File Location

# If necessary, first run 
# install.packages(c('rgdal', 'readr', 'dplyr', 'tigris', 'ggplot2', 'scales'))

library(rgdal) # For loading spatial data
library(readr) # For loading tabular data
library(dplyr) # For processing tabular data
library(tigris) # For joining spatial and tabular data

walkerke

library(idbr) 
library(ggplot2)
library(animation)
library(dplyr)
library(gganimate)

idb_api_key("Your Census API key goes here")

male <- idb1('IN', 2000:2050, sex = 'male') %>%
  mutate(POP = POP * -1,

walkerke

library(tigris)
library(rgeos)
library(stringr)
library(leaflet)

# Pull the ZCTAs
zips <- zctas(cb = TRUE, starts_with = c('75', '76'))

plot(zips)