# install.packages("ggplot2", dependencies=TRUE, type="win.binary")
# install.packages("ggmap", dependencies=TRUE, type="win.binary")
# install.packages("rworldmap")
# install.packages("rworldxtra")
# install.packages("devtools", dependencies=TRUE, type="win.binary")
# devtools::install_github("dkahle/ggmap")
library(ggplot2)
library(ggmap)
library(rworldmap)
library(rworldxtra)

################################################ BALICEK GGMAP ################################################
# zdroje map: stamen, google, osm a cloudmade
# k dnesnimu dni pouze stamen nevyzaduje api key
# priklad pouziti

ethiopia1 <- get_stamenmap(c(left = 30, bottom = 0, right = 50, top = 20), zoom = 5, maptype = "toner-lite")
# note: vsimnete si, ze vysledkem get_map/get_stamenmap je specialni typ objektu - rastrovy objekt
str(ethiopia1)                              # prvni radek hexadecimalni kody barev
ggmap(ethiopia1)

ethiopia2 <- get_map(c(left = 30, bottom = 0, right = 50, top = 20), zoom = 5, maptype = "watercolor", source = "stamen")
ggmap(ethiopia2)

# pozor u vetsich map - zejmena pokud chceme detailnejsi typ mapy ! 
# priklad, zobrazeni mapy Chicaga
bbox <- c(left = -87.936287, bottom = 41.679835, right = -87.447052, top = 42.000835)
ggmap(get_stamenmap(bbox, zoom = 11))
ggmap(get_stamenmap(bbox, zoom = 12))

# ruzne mapove typy
ggmap(get_stamenmap(bbox, maptype = "terrain", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "terrain-background", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "terrain-lines", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-2010", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-2011", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-background", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-hybrid", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-labels", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-lines", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "toner-lite", zoom = 11))
ggmap(get_stamenmap(bbox, maptype = "watercolor", zoom = 11))

# # chceme zobrazit lokality: Dolni Vestonice, Brno a Prahu
# Note: pokud se Vam nechce rucne prevadet jednotky, napr.: https://prevodyonline.eu/cz/souradnice.html
# Souradnice
lat <- c(48.883, 49.200, 50.083)
long <- c(16.633, 16.617, 14.417)
df <- as.data.frame(cbind(long,lat))

naleziste <- get_map(c(left = 11, bottom = 48, right = 20, top = 51.5), zoom = 5, 
                     maptype = "terrain-background", source = "stamen")
ggmap(naleziste)
# note: zoom - ovlivnuje detailnost mapy - cim vetsi, tim detailnejsi

# pridame body
ggmap(naleziste) + geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24)
# nechceme legendu
# Note: protoze byl pouzit shape 24 coz je trojuhelnicek s vyplni, je mozne pouzit parametr aes fill

# nechceme legendu
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) +
  guides(fill=FALSE, alpha=FALSE, size=FALSE)

# chceme body spojit useckou
# moznost 1 funkce geom_line
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_line(aes(x = long, y = lat), col = "tomato", data = df)

# moznost 2 funkce geom_path
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df, aes(x = long, y = lat), col = "darkblue")

# POZOR:geom_line spojuje data podle toho, jak jsou zadana, tzn. zadame-li body v poradi 1, 3, 2 spoji je 
# v tomto poradi, zatimco geom_path spojuje data podle osy x (podle poradi na ose x)!

# note: vsimnete si jak jsou data spojena - maji V tvar, pokud bychom chteli jenom dva body pr. druhy a treti
df2 <- df[2:3,]
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df2, aes(x = long, y = lat), col = "gold", size = 2, lineend = "round", linetype = 2)
# size - velikost cary
# lineend = "round" - konce cary chceme zaoblene
# linetype - typ čáry
# je mozne tuto caru jakkoliv upravovat - pr. udelame z ni sipku
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df2, aes(x = long, y = lat), col = "gold", size = 2, lineend = "round", arrow = arrow())
# sipku muzeme take upravovat
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8),fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df2, aes(x = long, y = lat), col = "gold", size = 1, lineend = "round", 
            arrow = arrow(angle = 15, type = "closed"))
# sipka na obou stranach primky (parametr ends)
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "red", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df2, aes(x = long, y = lat), col = "gold", size = 1, lineend = "round",
            arrow = arrow(angle = 15, ends = "both", type = "closed"))

# Note: dalsi argumenty a nastaveni napr. zde: http://ggplot2.tidyverse.org/reference/geom_path.html

# popiseme body pomoci funkce geom_text
windows()
ggmap(naleziste) +
  geom_point(data = df, aes(x = long, y = lat, alpha = 0.8), fill = "darkgreen", size = 5, shape = 24) + 
  guides(fill=FALSE, alpha=FALSE, size=FALSE) + 
  geom_path(data = df, aes(x = long, y = lat), col = "green", size = 1, lineend = "round", 
            arrow = arrow(angle = 15, ends = "both", type = "closed")) + 
  geom_text(data = df, aes(x = long, y = lat, label = c("Dolni Vestonice", "Brno", "Praha")), 
            size = 3, vjust = 0, hjust = -0.5, col = "darkgreen")


################# ANALYZA DAT ################
data_vzorky <- read.table("vzorky_maps.txt",header = T, sep = "\t")
str(data_vzorky)
attach(data_vzorky)
levels(typ)

# nejprve ziskame mapu Houstonu
bbox <- c(left = -95.40, bottom = 29.73, right = -95.34, top = 29.79)
ggmap(get_stamenmap(bbox, zoom = 13))

HoustonMap_2 <- get_map(bbox, zoom = 13, maptype = "terrain-lines", source = "stamen")
ggmap(HoustonMap_2)

# do sam. okna zobrazime mapu Houstonu + pouzijeme funkci geom_point
windows()
ggmap(HoustonMap_2) + geom_point(data = data_vzorky, aes(x = lon, y = lat, alpha = typ),
                                 shape = 19, col = "darkblue")
# samotne vykresleni bodu timto zpusobem neni prilis prehledne
# vykreslime misto bodu "ctverce rozlozeni"
# note: funkce stat_bin2d: rozdeli rovinu (vykreslovanou plochu) na obdelniky, vypocita pocet pripadu v kazdem obdelniku a potom (ve vychozim nastaveni)
# mapuje pocet pripadu na obdelnik. Jedná se o uzitecnou alternativu k funkci geom_point v pritomnosti tzv. overplottingu (mnoho bodu, casto pres sebe a
# tak velmi neprehledne zobrazeni).
windows()
ggmap(HoustonMap_2) +  stat_bin2d(aes(x = lon, y = lat, colour = typ, fill = typ),
                                  size = 0.5, bins = 30, alpha = 0.5, data = data_vzorky)
# nyni chceme zobrazit denzitu rozlozeni (kde se vyskytovalo nejvice nalezu - jedno jakeho typu)
# k tomu pouzijeme funkci stat_density2d
windows()
ggmap(HoustonMap_2) + stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
                                     size = 2, bins = 5, data = data_vzorky,
                                     geom = "polygon")

windows()
ggmap(HoustonMap_2) + stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
                                     size = 2, bins = 8, data = data_vzorky,
                                     geom = "polygon")
# note: parametr bins určuje kolik "vrstev" bude vykreslovano, resp. zjemnuje krok prechodu
# parametr geom urcuje typ geometrickeho objektu ktery vykreslujeme 
# parametr fill = ..level.. je to same jako fill = stat(level) zalezi na verzi ggplot2
# tzn. chceme aesthetics vykreslit na zaklade vypoctu cetnosti vyskytu v tomto pripade nalezu

# pokud nejsme spokojeni s timto zobrazenim, muzeme vyuzit pro barvy napr. balicek RColorBrewer a pomoci nej
# vytvorit heat mapu (note, zaroven zvysime bins na 25 abychom zjemnili prechod)
# install.packages("RColorBrewer")
library(RColorBrewer)
library(ggplot2)

ggmap(HoustonMap_2) + stat_density_2d(data = data_vzorky, aes(x = lon, y = lat,
                                                              fill = ..level..), alpha = 0.2, bins = 25, geom = "polygon") +
  scale_fill_gradientn(colors = brewer.pal(7, "YlOrRd"))

# chceme prizpusobit i alpha statistice vyskytu (note, umistujeme alpha do aes)
ggmap(HoustonMap_2) + stat_density_2d(data = data_vzorky, aes(x = lon, y = lat,
                                                              fill = ..level.., alpha = ..level..), bins = 25, geom = "polygon") +
  scale_fill_gradientn(colors = brewer.pal(7, "BuGn"))

# Note: funkce brewer.pal() - prvni argument rika kolik odlisnych barev v palete chceme pouzit a druhy argument
# nazev palety kterou chceme pouzit pro detailnejsi popis: https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf

# Pripomenuti, nezapominame citovat balicky, kterou pouzivame navic
citation("ggmap")
citation("RColorBrewer")

## DOMACI UKOL 10
# A) Zobrazte na mape Australska mesta: Canberra, Sydney, Melbourne, Brisbane a Perth. 
# A mesta spojte jednou linii.