links <-
  mf_get_links(
    x = countries,
    df = migr,
    x_id = "adm0_a3_is",
    df_id = c("i", "j")
  )

template("L'effet Spaghetti ", "maps/spaghetti.png")
mf_map(links, col = col, add = TRUE)
mf_map(land,
       col = NA,
       border = "#3b3b3b",
       add = TRUE)
dev.off()

ISO3 <- "FRA"
label = "France"

countr <- countries[, c("adm0_a3_is", "label")]
migrFRA <- migr[migr$j == ISO3, ]
migrFRA$fij <- as.numeric(migrFRA$fij)
maxval = max(migrFRA$fij)
total = round(sum(migrFRA$fij) / 1000000,1)
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "i",
    all.x = TRUE
  )
countr <- countr[-3]
colnames(countr) <- c("id", "label", "fij", "geometry")

knitr::kable(countr[c(0:10),], row.names = F, digits = 1)

template(paste0("En 2019, il y avait ",total, " millions d'étrangers en France"),
         "maps/prop1.png")
#mf_map(countr[countr$id == ISO3,], col = col, border = "red", lwd = 2, add = TRUE)
mf_map(
  countr[countr$id != ISO3, ],
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  val_max = maxval,
  inches = 0.4,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countr[countr$id == ISO3, ],
  col = NA,
  border = "#e36019",
  lwd = 2,
  add = TRUE
)
dev.off()

countr <- countries[, c("adm0_a3_is", "label")]
migrFRA <- migr[migr$i == ISO3, ]
migrFRA$fij <- as.numeric(migrFRA$fij)
total = round(sum(migrFRA$fij) / 1000000,1)
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "j",
    all.x = TRUE
  )
countr <- countr[-3]
colnames(countr) <- c("id", "label", "fij", "geometry")

template(paste0("En 2019, il y avait ",total, " millions de Français à l'étranger"),
         "maps/prop2.png")
mf_map(
  countr[countr$id != ISO3, ],
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  val_max = maxval,
  inches = 0.4,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countr[countr$id == ISO3, ],
  col = NA,
  border = "#e36019",
  lwd = 2,
  add = TRUE
)
dev.off()

ISO3 <- "FRA"
label = "France"
migrtoFRA <- migr[migr$j == ISO3,]
migrtoFRA$fij <- as.numeric(migrtoFRA$fij)

links <-
  mf_get_links(
    x = countries,
    df = migrtoFRA,
    x_id = "adm0_a3_is",
    df_id = c("i", "j")
  )

template(
  paste0("Origine des personnes étrangères vivant en ", label, " en 2019"),
  "maps/links1.png"
)
mf_map(
  links,
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  inches = 10,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countries[countries$adm0_a3_is == ISO3, ],
  col = "#4e4f4f",
  border = col,
  lwd = 1.5,
  add = TRUE
)
dev.off()

tabflow2 <- read.csv("data/migr2.csv")

fij <- (tabflow2$fij)

mean <- mean(fij)     #as Tobler

#Q3<-quantile(flow$fij,0.75)   #25% of the most important migrations
#Q95<-quantile(flow$fij, 0.95) # 5% of the most important migrations
#Q98<-quantile(flow$fij, 0.98) # 2% of the most important migrations

ISO3 <- "FRA"
label = "France"
migrFRA <- migr[migr$j == ISO3,]
migrFRA$fij <- as.numeric(migrFRA$fij)
migrFRA <-
  rbind.data.frame(migrFRA, c(
    i = ISO3,
    j = ISO3,
    fij = sum(as.numeric(migrFRA$fij))
  ))
countr <- countries[, "adm0_a3_is"]
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "i",
    all.x = TRUE
  )
colnames(countr) <- c("i", "j", "fij", "geometry")

knitr::kable(countr[c(0:10),], row.names = F, digits = 1)

library("packcircles")

dots = countr
st_geometry(dots) <-
  st_centroid(sf::st_geometry(dots), of_largest_polygon = TRUE)
dots <- data.frame(dots$i, dots["fij"], st_coordinates(dots))
dots = dots[, c("dots.i", "X", "Y", "fij")]
colnames(dots) <- c("id", "x", "y", "v")
dots <- dots[!is.na(dots$v), ]

k = 700000 # pour ajuster la taille des cercles
itermax = 10 # nombre d'iterations
delta = 35000
dat.init <- dots[, c("x", "y", "v", "id")]
dat.init$v <- sqrt(as.numeric(dat.init$v) * k)
simulation <- circleRepelLayout(
  x = dat.init,
  xysizecols = 1:3,
  wrap = FALSE,
  sizetype = "radius",
  maxiter = itermax,
  weights = 1
)$layout
circles <- st_buffer(sf::st_as_sf(
  simulation,
  coords = c('x', 'y'),
  crs = sf::st_crs(countries)
),
dist = simulation$radius - delta)

circles$v = dots$v
circles$id = dots$id

# Links

dots$j = "FRA"

links <-
  mf_get_links(
    x = circles,
    df = migrFRA,
    x_id = "id",
    df_id = c("i", "j")
  )
links$fij = as.numeric(links$fij)

template("Les étrangers en France, 2019", "maps/migrexplorer1.png")

col2 = "#4e4f4f"

mf_map(
  land,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)


mf_map(
  links,
  var = "fij",
  col = col,
  border = "#3b3b3b",
  type = "prop",
  lwd_max = 160,
  leg_pos = "n",
  add = TRUE
)

mf_map(
  circles[circles$id != ISO3, ],
  var = "fij",
  col = col,
  border = "#3b3b3b",
  lwd = 1.5,
  add = TRUE
)

mf_map(
  circles[circles$id == ISO3, ],
  var = "fij",
  col = col2,
  border = col,
  lwd = 2.5,
  add = TRUE
)

t =  circles[circles$id != ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col2,
  overlap = TRUE,
  lines = FALSE
)

t =  circles[circles$id == ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col,
  overlap = TRUE,
  lines = FALSE
)

dev.off()

ISO3 <- "FRA"
label = "France"
migrFRA <- migr[migr$i == ISO3,] # ici
migrFRA$fij <- as.numeric(migrFRA$fij)
migrFRA <-
  rbind.data.frame(migrFRA, c(
    i = ISO3,
    j = ISO3,
    fij = sum(as.numeric(migrFRA$fij))
  ))
countr <- countries[, "adm0_a3_is"]
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "j", # là
    all.x = TRUE
  )
colnames(countr) <- c("i", "j", "fij", "geometry")

dots = countr
st_geometry(dots) <-
  st_centroid(sf::st_geometry(dots), of_largest_polygon = TRUE)
dots <- data.frame(dots$i, dots["fij"], st_coordinates(dots))
dots = dots[, c("dots.i", "X", "Y", "fij")]
colnames(dots) <- c("id", "x", "y", "v")
dots <- dots[!is.na(dots$v), ]

k = 700000 # pour ajuster la taille des cercles
itermax = 10 # nombre d'iterations
delta = 35000
dat.init <- dots[, c("x", "y", "v", "id")]
dat.init$v <- sqrt(as.numeric(dat.init$v) * k)
simulation <- circleRepelLayout(
  x = dat.init,
  xysizecols = 1:3,
  wrap = FALSE,
  sizetype = "radius",
  maxiter = itermax,
  weights = 1
)$layout
circles <- st_buffer(sf::st_as_sf(
  simulation,
  coords = c('x', 'y'),
  crs = sf::st_crs(countries)
),
dist = simulation$radius - delta)

circles$v = dots$v
circles$id = dots$id

# Links

dots$j = "FRA"

links <-
  mf_get_links(
    x = circles,
    df = migrFRA,
    x_id = "id",
    df_id = c("i", "j")
  )
links$fij = as.numeric(links$fij)

template("Les français à l'étranger, 2019", "maps/migrexplorer2.png")

col2 = "#4e4f4f"

mf_map(
  land,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)


mf_map(
  links,
  var = "fij",
  col = col,
  border = "#3b3b3b",
  type = "prop",
  lwd_max = 160,
  leg_pos = "n",
  add = TRUE
)

mf_map(
  circles[circles$id != ISO3, ],
  var = "fij",
  col = col,
  border = "#3b3b3b",
  lwd = 1.5,
  add = TRUE
)

mf_map(
  circles[circles$id == ISO3, ],
  var = "fij",
  col = col2,
  border = col,
  lwd = 2.5,
  add = TRUE
)

t =  circles[circles$id != ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col2,
  overlap = TRUE,
  lines = FALSE
)

t =  circles[circles$id == ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col,
  overlap = TRUE,
  lines = FALSE
)

dev.off()

knitr::kable(countries[c(0:10),c("adm0_a3_is", "label","Code2","Label2")], row.names = F, digits = 1)

subregions <-
  aggregate(countries, by = list(countries$Code2), FUN = head, 1)
subregions <- subregions[, c("Code2", "Label2")]
st_geometry(subregions) <-
  st_cast(subregions$geometry, "MULTIPOLYGON")
colnames(subregions) <- c("id", "label", "geometry")

template("Subregions", "maps/subregions.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = col,
  lwd = 0.5,
  add = TRUE
)
mf_label(
  x = subregions,
  var = "label",
  halo = TRUE,
  bg = "#4e4f4f",
  cex = 0.8,
  col = col,
  overlap = TRUE,
  lines = FALSE
)
dev.off()

keys <- data.frame(countries[, c("adm0_a3_is", "Code2")])
keys$geometry <- NULL
migr <- merge(x = migr,
              y = keys,
              by.x = "i",
              by.y = "adm0_a3_is")
colnames(migr)[4] <- "subreg_i"
migr <- merge(x = migr,
              y = keys,
              by.x = "j",
              by.y = "adm0_a3_is")
colnames(migr)[5] <- "subreg_j"
migr$id <- paste0(migr$subreg_i, "_", migr$subreg_j)
migr2 <- aggregate(migr$fij, by = list(migr$id), FUN = sum)
migr2$i <- sapply(strsplit(migr2$Group.1, "_"), "[", 1)
migr2$j <- sapply(strsplit(migr2$Group.1, "_"), "[", 2)
migr2 <- migr2[, c("i", "j", "x")]
colnames(migr2)[3] <- "fij"
migr2$fij <- round(migr2$fij / 1000, 0)

knitr::kable(migr2[c(0:10),], row.names = F, digits = 1)

flowsintra <- migr2[migr2$i == migr2$j,c("i","fij")]
colnames(flowsintra) <- c("id","intra")
subregions <- merge(x = subregions, y = flowsintra, by = "id")

knitr::kable(subregions[c(0:10),], row.names = F, digits = 1)

migr2 <- migr2[migr2$i != migr2$j,]
for (k in 1:length(migr2$i)) {
  val1 <- migr2$fij[k]
  val2 <-
    migr2[migr2$i == migr2$j[k] & migr2$j == migr2$i[k], "fij"]
  migr2$interaction[k] <- sum(val1, val2)
}

# Suppression des doublons
interactions = data.frame(matrix(
  ncol = 3,
  nrow = 0,
  dimnames = list(NULL, c("i", "j", "interaction"))
))
for (k in 1:length(migr2$i)) {
  idi = migr2$i[k]
  idj = migr2$j[k]
  test = length(interactions[(interactions$i == idi &
                                interactions$j == idj) |
                               (interactions$i == idj & interactions$j == idi), "interaction"])
  if (test == 0) {
    interactions <-
      rbind(interactions, data.frame(
        i = idi,
        j = idj,
        interaction = migr2$interaction[k]
      ))
  }
}

knitr::kable(interactions[c(0:10),], row.names = F, digits = 1)

mean<-mean(interactions$interaction)

Q90<-quantile(interactions$interaction, 0.90)  #10% des migrations les plus importantes

threshold <- 2000
interactions <- interactions[interactions$interaction >= threshold,]

links <-
  mf_get_links(
    x = subregions,
    df = interactions,
    x_id = "id",
    df_id = c("i", "j")
  )

template("L'Afrique, un continent encore isolé dans la mondialisation", "maps/heran.png")

col2 = "#4e4f4f"

mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)


mf_map(
  links,
  var = "interaction",
  col = col,
  border = "#3b3b3b",
  type = "prop",
  lwd_max = 25,
  leg_pos = "bottomleft",
  leg_title = paste0("Migratons INTER régionales (interactions)\n(A -> B) + (B -> A)\nSeuil : ",threshold, "\nen milliers de personnes"),
  add = TRUE
)

mf_map(
  subregions,
  var = "intra",
  col = "#3b3b3b",
  border = col,
  lwd = 1.5,
  type = "prop",
  symbol = "square",
  leg_pos = "topright",
  leg_title = "Migrations INTRA\nrégionale nen 2019\n(en milliers)",
  add = TRUE
)

mf_label(
  subregions,
  var = "intra",
  halo = FALSE,
  cex = sqrt(as.numeric(subregions$intra) / 12000),
  col = col,
  overlap = TRUE,
  lines = FALSE
)

mf_label(
  links,
  var = "interaction",
  halo = TRUE,
  cex = 0.5,
  col = col2,
  bg = col,
  r = 0.1,
  overlap = FALSE,
  lines = FALSE
)

dev.off()

#subregions <- st_read(system.file("subregions.gpkg", package="flowmapper")) %>% st_transform(crs)
#migr <- read.csv(system.file("migrantstocks2019.csv", package="flowmapper"))

subregions <- st_read(system.file("subregions.gpkg", package="ttt")) %>% 
  st_transform(crs)
migr <- read.csv(system.file("migrantstocks2019.csv", package="ttt"))

#mean_migr<-mean(migr$fij)
Q65_migr<-quantile(migr$fij, 0.65) #1/3 des migrations les plus importantes

threshold <- 1500
migr <- migr[migr$fij >= threshold, ]

knitr::kable(migr[c(0:10),], row.names = F, digits = 1)

flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  plot = FALSE
)

template("ttt_flowmapper$links", "maps/ttt_links.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$links,
       col = col,
       lwd = 3,
       add = TRUE)
dev.off()

template("ttt_flowmapper$circles", "maps/ttt_circles.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$circles, col = col, add = TRUE)
dev.off()

template("ttt_flowmapper$flows", "maps/ttt_arrows.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$flows, col = col, add = TRUE)
dev.off()

template("Visulaisation par défaut", "maps/ttt_flows.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
dev.off()

template("La surface des fleches", "maps/ttt_surface.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  size = "area",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
dev.off()

migr2 <- data.frame(i = integer(), j = integer(), fij = integer())

for (k in 1:length(migr$i)) {
  val1 <- migr$fij[k]
  val2 <- migr[migr$i == migr$j[k] & migr$j == migr$i[k], "fij"]
  val <- sum(val1, val2)
  idi =  migr$i[k]
  idj =  migr$j[k]
  test <-
    length(migr2[(migr2$i == idi &
                    migr2$j == idj) | (migr2$i == idj & migr2$j == idi), "fij"])
  if (test == 0) {
    migr2 <- rbind(migr2, data.frame(i = idi, j = idj, fij = val))
  }
}
migr2 <- migr2[migr2$i != migr2$j, ] 

head(migr2)

template("Interactions", "maps/ttt_interactions.png")
c <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  size = "thickness",
  type = "rect",
  df = migr2,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
dev.off()

intra <- migr[migr$i == migr$j, ]
intra <- intra[, c("i", "fij")]
colnames(intra) <- c("id", "nb")
knitr::kable(intra, row.names = F, digits = 1)

flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  size = "thickness",
  type = "arrows",
  decreasing = FALSE,
  add = TRUE,
  lwd = 1,
  col = col,
  border = "#424242",
  k = NULL,
  k2 = 60,
  df2 = intra,
  df2id = "id",
  df2var = "nb",
  col2 = "#3b3b3b",
  border2 = col,
  plot = FALSE
)

template("Flux inter et flux intra", "maps/interintra.png")

mf_shadow(x = flows$flows, col = "grey70", cex = 0.2, add = TRUE)

mf_map(
  flows$flows,
  var = "fij",
  col = col,
  border = "#3b3b3b",
  leg_pos = "n",
  add = TRUE
)

mf_map(
  flows$circles,
  var = "fij",
  col = "#3b3b3b",
  border = col,
  lwd = 1.5,
  leg_pos = "n",
  add = TRUE
)

mf_label(
  flows$circles,
  var = "nb",
  halo = FALSE,
  cex = sqrt(as.numeric(flows$circles$nb) / 18000),
  #cex = 1,
  col = col,
  overlap = TRUE,
  lines = FALSE
)


mf_label(
  flows$flows,
  var = "fij",
  halo = TRUE,
  cex = 0.7,
  col = col2,
  bg = col,
  r = 0.1,
  overlap = FALSE,
  lines = FALSE
)

# La fonction légende :-)

ttt_flowmapperlegend(
  x = flows,
  title = "Flux inter",
  title2 = "flux intra",
  col = "#4e4f4f",
  txtcol = col
)


dev.off()

tmp <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = "#ffc524",
  border = "#424242",
  border2 = "#ffc524",
  plot = FALSE
)

crs <-
  "+proj=ortho +lat_0=42.5333333333 +lon_0=-72.53333333339999 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"
flows <- smoothr::densify(tmp$flows, n = 30) %>% st_transform(crs)
dots <- st_transform(tmp$circles, crs)
subregions <- st_transform(subregions, crs)
graticule <- st_transform(graticule, crs)
bbox <- st_transform(bbox, crs)

title = "Flux sur Globe"
file =   "maps/ttt_globe.png"

mf_export(
  subregions,
  export = "png",
  width = 1000,
  filename = file,
  res = 96,
  theme = theme,
  expandBB = c(-.02, 0,-.02, 0)
)

mf_map(
  bbox,
  col = "#3b3b3b",
  border = NA,
  lwd = 0.5,
  add = TRUE
)

mf_map(graticule,
       col = "#FFFFFF50",
       lwd = 0.5,
       add = TRUE)

mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)

mf_credits(
  txt = credit,
  pos = "bottomright",
  col = "#1a2640",
  cex = 0.7,
  font = 3,
  bg = "#ffffff30"
)

mf_map(flows, col = col, add = TRUE)

mf_map(dots, col = col, add = TRUE)

mf_title(title)

dev.off()