spatial_course.R

# Slides: https://github.com/ITSLeeds/TDS/blob/master/slides/madrid-slides.Rmd
# Exercises http://git.io/tds2dayex

library(sf)
library(dplyr)
library(stplanr)      # geographic transport data package
library(tmap)
library(osmdata)
library(tidyverse)
library(stats19)
library(pct)

u = "https://www.openstreetmap.org/trace/2992569/data"
download.file(u, "track.gpx")
tracks <- sf::read_sf("track.gpx", layer = 'track_points')

mapview::mapview(tracks)

library(stplanr)

f <- flow[1:3, 1:5]
f_tidy <- flow %>% select(1:5) %>% slice(1:3) %>% as_tibble()

f_tidy

z <- zones_sf

l <- od2line(f, zones_sf)
plot(l)

line2route(l[2, ])

od <- pct::get_od()

iow <- pct::get_pct_centroids("isle-of-wight")

plot(iow)
mapview::mapview(iow)

eyes = c(2.3,4,3.7,4)
eyes = matrix(eyes, ncol = 2, byrow = T)
mouth = c(2,2,2.5,1.3,3,1,3.5,1.3,4,2)
mouth = matrix(mouth, ncol = 2, byrow = T)
plot(eyes, type = "p", main = "Smile you're using R",
     cex = 2, xlim = c(0,5), ylim = c(0,5))
lines(mouth, type = "l", col = "red")


library(sf)
iow <- pct::get_pct_zones('isle-of-wight')

sel = iow$all > 3000 # create a subsetting object
iow_large = iow[sel, ] # subset areas with a popualtion over 100,000
iow_2 = iow[iow$geo_name == "Isle of Wight 002",] # subset based on 'equality' query
five_in_name = iow[grepl(pattern = "5", x = iow$geo_name), ] # subset based on text string match
iow_first_and_third_column = iow[c(1, 3)]
iow_just_all = iow["all"]

file.edit("attribute-operations.R")

# Solutions

iow_small <-
  iow %>%
  filter(all < 3000)

iow_small <-
  iow_small %>% 
  mutate(sel_high_car = car_driver > median(car_driver))

iow_small %>% 
  count(grepl("1", geo_name))

iow_small %>%
  select(car_driver) %>% 
  plot()

iow_small %>% 
  mutate(pop_density = as.numeric(st_area(select(iow_small, all)))) %>% 
  select(pop_density) %>% 
  plot()

iow_small %>% 
  mutate(perc_bike = (bicycle / all)) %>% 
  plot()

iow_small %>% 
  filter(foot > 500) %>% 
  mutate(prop_car = car_driver / all) %>% 
  select(prop_car) %>% 
  plot()

st_crs(iow)

iow_projected = st_transform(iow, 27700) # transfrom to projected
st_crs(iow_projected)
iow_lanlot <- st_transform(iow, 4326)

st_crs(iow_lanlot)

iow_cents <- pct::get_pct_centroids(region = "isle-of-wight", geography = "lsoa")
iow_cents2 <- iow_cents[iow_2, ]

plot(iow$geometry)
plot(iow_cents, col = "black", add = TRUE)
plot(iow_cents2, col = "red", add = TRUE)

st_difference(iow_cents, iow_large) %>% plot()

iow_cents[iow_2, , op = st_disjoint] %>% plot()

library(spData)

plot()

plot(nz$geom)
plot(nz_height['elevation'], add = TRUE)

nz_islands <-
  nz %>% 
  group_by(Island) %>% 
  summarize(Population = sum(Population))

plot(nz_islands)

canterbury <-
  nz %>% 
  filter(Name == "Canterbury")

plot(nz$geom)
plot(nz_height[canterbury, 'elevation'], add = TRUE)

nz_height[canterbury, 'elevation'] %>% nrow()

sf::st_join(nz, nz_height) %>% 
  count(Name) %>% 
  arrange(desc(n))

nz %>% 
  filter(Name == "West Coast") %>%
  {plot(nz_height[., 'elevation'], add = TRUE)}

library(tmap)

iow %>% 
  select(all, bicycle) %>% 
  plot()

iow %>% 
  select(all, bicycle) %>% 
  tm_shape() +
  tm_polygons(c('all', 'bicycle'))

europe <-
  world %>% 
  filter(continent == "Europe", !is.na(iso_a2)) %>% 
  left_join(worldbank_df, by = "iso_a2") %>% 
  select(name, subregion, gdpPercap, HDI, pop_growth) %>% 
  st_transform("+proj=aea +lat_1=20 +lat_2=-23 +lat_0=0 +lon_0=25")

europe %>% 
  tm_shape() +
  tm_polygons()

europe %>% 
  select(gdpPercap) %>% 
  plot()

europe %>% 
  mutate(gdp_groups = case_when(gdpPercap > 30000 ~ "High",
                                between(gdpPercap, 30000, 20000) ~ "Mid",
                                ~ "Low"))
  tm_shape() +
  tm_polygons("gdpPercap")