library(sp)
library(sf)
library(tidyr)
library(spatstat.sparse)
library(REdaS)
library(plotly)
co <- read.csv('d://kaggle/dataset_5/countries_of_the_world.csv')
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
df[,2] <- log(as.numeric(df[,2]))
co[,2] <- log(as.numeric(co[,2]))
elev <- read.csv("d://filebox/dataset_4/csv/elevp.csv")
qk <- read.csv('d://filebox/csv/earthquakes.csv')
qk[,2] <- log(as.numeric(qk[,2]))
deg2rad <- function(deg) {
(deg * pi) / (180)
}
fig1 <- function(x,y){
2*x+x+1=y
}
linear_fun<-function(x){
4*x^2+2*x+1
}
nlat <- 100
nlon <- 100
lat <- seq(-180, 180, length.out = nlat)
lon <- seq(-90, 90, length.out = nlon)
lat <- matrix(rep(lat, nlon), nrow = nlat)
lon <- matrix(rep(lon, each = nlat), nrow = nlat)
LAND_ISO <- c("AUT","BEL","BGR","HRV","CYP","CZE","DNK","EST","FIN","FRA","DEU","GRC","HUN","IRL",
"ITA","LVA","LTU","LUX","MLT","NLD","POL","PRT","ROU","SVK","SVN","ESP","SWE","GBR",
"USA","CHN","KOR","JPN","EGY","ARE","JOR","SAU","AUS","KEN","NGA","BRA")
value <- runif(length(LAND_ISO), 1, 20)
df <- data.frame(LAND_ISO, value)
g <- list(
projection = list(type = 'orthographic'),
showland = TRUE,
landcolor = toRGB("LightGrey"),
showocean = TRUE,
oceancolor = toRGB("LightBlue"),
showlakes = TRUE,
lakecolor = toRGB("Blue"),
showrivers = TRUE,
rivercolor = toRGB("Blue"),
resolution = 100,
showcountries = TRUE,
countrycolor = toRGB("Black"),
showlon = TRUE,
loncolor = toRGB("White"),
showlat = TRUE,
latcolor = toRGB("White"),
showcities = TRUE,
citycolor = toRGB("Red"),
showmark = TRUE,
markcolor = toRGB("Green")
)
recruitment_info <- data.frame(Centre = c("CentreA", "CentreB", "CentreC"),
Lat = c(51.51770, 52.48947, 51.45451),
Long = c(-0.100400, -1.898575, -2.587910),
GroupA = c(907, 1910, 4419),
GroupB = c(47, 116, 277), stringsAsFactors = TRUE)
recruitment_info <- recruitment_info %>%
gather(Group, values, Centre, Lat, Long)
dat <- map_data(map = "world", region = "UK")
fig <- plot_geo(df, type = 'scattergeo', mode = 'lines')
fig <- fig %>%
add_sf(
data = sf::st_as_sf(maps::map("world", plot = TRUE, fill = TRUE)),
x = ~ 1.001 * cos(deg2rad(x)) * cos(deg2rad(y)),
y = ~ 1.001 * sin(deg2rad(x)) * cos(deg2rad(y)),
z = ~ 1.001 * sin(deg2rad(y)),
color = I("black"), size = I(1),
hoverinfo = "skip"
) %>%
add_surface(
x = cos(deg2rad(lon)) * cos(deg2rad(lat)),
y = sin(deg2rad(lon)) * cos(deg2rad(lat)),
z = sin(deg2rad(lat)),
surfacecolor = matrix(df$value, nrow = nlat, ncol = nlon),
showscale = TRUE, hoverinfo = "skip",
contours = list(
x = list(highlight = TRUE),
y = list(highlight = TRUE),
z = list(highlight = TRUE)
)) %>%
add_trace(x = df$x, y = df$y, z = df$z, location = ~LAND_ISO,
line = list(shape = "circle"), showlegend = TRUE,
hoverinfo = "skip") %>%
add_trace(
z = ~value, locations = ~LAND_ISO,
color = ~value, colors = 'Purples' ) %>%
add_trace(
type = "choroplethmapbox",
geojson = paste(c(
"https://gist.githubusercontent.com/cpsievert/",
"7cdcb444fb2670bd2767d349379ae886/raw/",
"cf5631bfd2e385891bb0a9788a179d7f023bf6c8/",
"us-states.json"
), collapse = ""),
locations = row.names(state.x77),
z = state.x77[, "Population"] / state.x77[, "Area"],
span = I(0)
) %>%
add_trace(
type = "choroplethmapbox",
geojson = paste(c("https://raw.githubusercontent.com/mlampros/DataSets/master/california.geojson"),
collapse = ""),
locations = row.names(state.x77),
z = state.x77[, "Population"] / state.x77[, "Area"],
span = I(1)
) %>%
add_segments(x = -cos(15), y = -180 ,xend = -360 ,yend = sin(45)) %>%
add_segments(x = -100, xend = -50, y = 50, yend = 75) %>%
layout(geo = g)
fig