Laboratorio Bio-demografico

class: center, middle, inverse, title-slide

# Laboratorio Bio-demografico
## Lezione 15 - Misurare le migrazioni
### Nicola Barban Alma Mater Studiorum Università di Bologna Dipartimento di Scienze Statistiche
### 18 Marzo 2021 <img src="../img/UniBo-Universita-di-Bologna.png" style="width:20.0%" />

---

# Migration

## 1. Stock
I dati di *stock* rappresentano il numero di immigrati che vivono in un paese o regione ad una certa **data**.

## 2. Flussi
I dati di *flusso* rappresentano il numero di immigrati che entrano o emigrati che lasciano un paese e regione durante un **periodo** di tempo determinato.

---
# Migration Stock

```r
#install.packages("wpp2019")
library(wpp2019)
data(migration)
data(pop)
```
>Net migration (in thousand) for the specific five-year time period (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0).

---

```r
library(tidyverse)
A=migration[,1:17] %>% 
  filter(name=="Italy") %>% 
  pivot_longer(cols=-c(country_code, name), names_to="period", values_to="net_mig") %>% 
  ggplot(aes(x=period, y=net_mig, group=name))+
  geom_line()+
  theme_bw()+
  labs(title="ITALIA",
       x="Anno",
       y="Net Migration"
    
  )
```

---

```r
A
```

---
# Tengo solo macro-regioni

```r
library(knitr)
kable(migration$name[migration$country_code>900 & migration$country_code<1000 ], type="html")
```

|x                                       |
|:---------------------------------------|
|More developed regions                  |
|Less developed regions                  |
|Least developed countries               |
|Other less developed countries          |
|Less developed regions, excluding China |
|Africa                                  |
|Asia                                    |
|Europe                                  |
|Latin America and the Caribbean         |
|Northern America                        |
|Oceania                                 |
|Sub-Saharan Africa                      |
|Eastern Africa                          |
|Middle Africa                           |
|Southern Africa                         |
|Western Africa                          |
|Northern Africa                         |
|Western Asia                            |
|Central and Southern Asia               |
|Eastern Asia                            |
|South-Eastern Asia                      |
|Caribbean                               |
|Central America                         |
|South America                           |
|Australia/New Zealand                   |
|Melanesia                               |
|Micronesia                              |
|Polynesia                               |
|Eastern Europe                          |
|Northern Europe                         |
|Southern Europe                         |
|Western Europe                          |
---

```r
names(migration)[1:17]<-names(pop)[1:17]
migration_long<- migration[,1:17] %>% 
 pivot_longer(cols=-c(country_code, name), names_to="year", values_to="mig")

pop_long<- pop %>% 
 pivot_longer(cols=-c(country_code, name), names_to="year", values_to="pop") %>% 
 filter(pop>10000) ### seleziono paesi con pop> 10 mil

mig_pop<-pop_long %>% left_join(migration_long,
 by=c("country_code", "name", "year")) %>% 
 mutate(net.mig=1000*mig/pop)
```

---

```r
B<- mig_pop %>% 
 ggplot(aes(x=year, y=net.mig, group=name, label=name))+
 geom_line(col="grey", alpha=0.2)+
 theme_bw()+
 theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
 labs(title="Net migration",
 x="Year",
 y="Net Migration",
 caption="Source: World Population Prospect"
 )
```

---

```r
B+ geom_line(data=mig_pop %>% filter(name=="Italy"), aes(col="Italy"), alpha=0.8, size=1.1)+
   geom_line(data=mig_pop %>% filter(name=="Argentina"), aes(col="Argentina"), alpha=0.8, size=1.1)
```

<img src="lezione15_files/figure-html/unnamed-chunk-7-1.png" width="60%" />
---
# Grafico interattivo

```r
library(plotly)
ggplotly(B)
```

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name: United States of America","year: 1995 net.mig: 3.341314e+01 name: United States of America name: United States of America","year: 2000 net.mig: 1.893747e+01 name: United States of America name: United States of America","year: 2005 net.mig: 1.840297e+01 name: United States of America name: United States of America","year: 2010 net.mig: 1.605674e+01 name: United States of America name: United States of America","year: 2015 net.mig: 1.487800e+01 name: United States of America name: United States of America","year: 2020 net.mig: 1.393426e+01 name: United States of America name: United States of America",null,"year: 1950 net.mig: -6.030497e-01 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1955 net.mig: -1.451956e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1960 net.mig: -3.173981e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1965 net.mig: -1.840863e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1970 net.mig: -2.639184e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1975 net.mig: -1.824775e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1980 net.mig: -3.947507e-01 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1985 net.mig: -1.565951e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1990 net.mig: -2.841430e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 1995 net.mig: -1.744518e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 2000 net.mig: -1.733990e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 2005 net.mig: -1.682195e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 2010 net.mig: 1.343169e+00 name: Upper-middle-income countries name: Upper-middle-income countries","year: 2015 net.mig: -5.735180e-01 name: Upper-middle-income countries name: Upper-middle-income countries","year: 2020 net.mig: -1.655151e+00 name: Upper-middle-income countries name: Upper-middle-income countries",null,"year: 1965 net.mig: 4.410188e+01 name: Uzbekistan name: Uzbekistan","year: 1970 net.mig: 5.849767e+00 name: Uzbekistan name: Uzbekistan","year: 1975 net.mig: 5.201455e+00 name: Uzbekistan name: Uzbekistan","year: 1980 net.mig: -1.164682e+01 name: Uzbekistan name: Uzbekistan","year: 1985 net.mig: -1.971239e+01 name: Uzbekistan name: Uzbekistan","year: 1990 net.mig: -1.582368e+01 name: Uzbekistan name: Uzbekistan","year: 1995 net.mig: -1.064612e+01 name: Uzbekistan name: Uzbekistan","year: 2000 net.mig: -9.781891e+00 name: Uzbekistan name: Uzbekistan","year: 2005 net.mig: -5.302374e+00 name: Uzbekistan name: Uzbekistan","year: 2010 net.mig: -2.330945e+00 name: Uzbekistan name: Uzbekistan","year: 2015 net.mig: -1.432740e+00 name: Uzbekistan name: Uzbekistan","year: 2020 net.mig: -1.324023e+00 name: Uzbekistan name: Uzbekistan",null,"year: 1970 net.mig: 8.248222e-02 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 1975 net.mig: 8.491596e-02 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 1980 net.mig: 9.089343e-02 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 1985 net.mig: 5.311928e-02 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 1990 net.mig: -1.156236e-01 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 1995 net.mig: -2.070121e-01 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 2000 net.mig: -9.399627e-01 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 2005 net.mig: -7.574782e+00 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 2010 net.mig: -1.515615e+01 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 2015 net.mig: -1.085786e+02 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)","year: 2020 net.mig: 5.716807e+01 name: Venezuela (Bolivarian Republic of) name: Venezuela (Bolivarian Republic of)",null,"year: 1950 net.mig: 0.000000e+00 name: Viet Nam name: Viet Nam","year: 1955 net.mig: 0.000000e+00 name: Viet Nam name: Viet Nam","year: 1960 net.mig: 0.000000e+00 name: Viet Nam name: Viet Nam","year: 1965 net.mig: 0.000000e+00 name: Viet Nam name: Viet Nam","year: 1970 net.mig: 0.000000e+00 name: Viet Nam name: Viet Nam","year: 1975 net.mig: -1.762986e+01 name: Viet Nam name: Viet Nam","year: 1980 net.mig: -6.034504e+00 name: Viet Nam name: Viet Nam","year: 1985 net.mig: -5.457222e+00 name: Viet Nam name: Viet Nam","year: 1990 net.mig: -5.798539e+00 name: Viet Nam name: Viet Nam","year: 1995 net.mig: -2.883456e+00 name: Viet Nam name: Viet Nam","year: 2000 net.mig: -8.146611e+00 name: Viet Nam name: Viet Nam","year: 2005 net.mig: -9.542450e+00 name: Viet Nam name: Viet Nam","year: 2010 net.mig: -4.547115e+00 name: Viet Nam name: Viet Nam","year: 2015 net.mig: -4.316051e+00 name: Viet Nam name: Viet Nam","year: 2020 net.mig: -4.767688e+00 name: Viet Nam name: Viet Nam",null,"year: 1950 net.mig: -7.629611e-01 name: Western Africa name: Western Africa","year: 1955 net.mig: -9.275004e-01 name: Western Africa name: Western Africa","year: 1960 net.mig: -6.754088e-01 name: Western Africa name: Western Africa","year: 1965 net.mig: -1.482951e+00 name: Western Africa name: Western Africa","year: 1970 net.mig: -1.843325e+00 name: Western Africa name: Western Africa","year: 1975 net.mig: -1.231278e+00 name: Western Africa name: Western Africa","year: 1980 net.mig: -7.050710e+00 name: Western Africa name: Western Africa","year: 1985 net.mig: -6.267654e+00 name: Western Africa name: Western Africa","year: 1990 net.mig: -6.327703e+00 name: Western Africa name: Western Africa","year: 1995 net.mig: -2.167048e+00 name: Western Africa name: Western Africa","year: 2000 net.mig: -3.426073e+00 name: Western Africa name: Western Africa","year: 2005 net.mig: -3.671745e+00 name: Western Africa name: Western Africa","year: 2010 net.mig: -4.557840e+00 name: Western Africa name: Western Africa","year: 2015 net.mig: -2.516251e+00 name: Western Africa name: Western Africa","year: 2020 net.mig: -2.149100e+00 name: Western Africa name: Western Africa",null,"year: 1950 net.mig: 5.110168e+00 name: Western Asia name: Western Asia","year: 1955 net.mig: 5.362073e+00 name: Western Asia name: Western Asia","year: 1960 net.mig: 5.241751e+00 name: Western Asia name: Western Asia","year: 1965 net.mig: 8.437282e-01 name: Western Asia name: Western Asia","year: 1970 net.mig: 3.329884e+00 name: Western Asia name: Western Asia","year: 1975 net.mig: 3.990036e+00 name: Western Asia name: Western Asia","year: 1980 net.mig: 9.194113e+00 name: Western Asia name: Western Asia","year: 1985 net.mig: 3.343871e-01 name: Western Asia name: Western Asia","year: 1990 net.mig: 6.466009e-01 name: Western Asia name: Western Asia","year: 1995 net.mig: -5.294698e+00 name: Western Asia name: Western Asia","year: 2000 net.mig: 9.235424e+00 name: Western Asia name: Western Asia","year: 2005 net.mig: 3.090464e+01 name: Western Asia name: Western Asia","year: 2010 net.mig: 1.603434e+01 name: Western Asia name: Western Asia","year: 2015 net.mig: 3.782372e+00 name: Western Asia name: Western Asia","year: 2020 net.mig: 4.611196e+00 name: Western Asia name: Western Asia",null,"year: 1950 net.mig: 1.321478e+00 name: Western Europe name: Western Europe","year: 1955 net.mig: 6.678100e+00 name: Western Europe name: Western Europe","year: 1960 net.mig: 1.601488e+01 name: Western Europe name: Western Europe","year: 1965 net.mig: 1.008864e+01 name: Western Europe name: Western Europe","year: 1970 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---
Carico dati Italiani

```r
library(readr)
IMMprovince <- read_delim("IMMprovince.csv", 
 ";", escape_double = FALSE, trim_ws = TRUE, 
 skip = 114,
 col_names=c("cod.prov","prov", "cod.citt", "citt", "maschi", "femmine", "totale"))
```
---
# calcolo totale per provincia

```r
IMMprovince.tot<- IMMprovince %>% 
 group_by(cod.prov, prov) %>% 
 summarize(tot_stranieri=sum(totale)) %>% 
 ungroup() %>% 
 arrange(-tot_stranieri) %>% 
 mutate(rank_abs=row_number(),
 cod.prov=as.numeric(cod.prov)) 
```
---
# Top 10 province per presenza stranieri

```r
kable(IMMprovince.tot %>% 
 filter(rank_abs<=10) %>% 
 select(prov,tot_stranieri ))
```

|prov    | tot_stranieri|
|:-------|-------------:|
|Roma    |        509057|
|Milano  |        459131|
|Torino  |        210973|
|Brescia |        151820|
|Napoli  |        127803|
|Firenze |        123466|
|Bologna |        119380|
|Bergamo |        119045|
|Verona  |        106692|
|Padova  |         93372|
---

```r
ISTAT.prov <- read_csv("province.csv", col_types = cols(`Codice provincia` = col_integer()), 
 skip = 1)

Pop.prov <-ISTAT.prov %>% 
 filter(Età=="Totale") %>% 
 mutate(Tot.Pop=`Totale Maschi`+`Totale Femmine`) %>% 
 select(cod.prov="Codice provincia" , "Tot.Pop" )

IMMprovince.pop<- IMMprovince.tot %>% 
 full_join(Pop.prov, by="cod.prov") %>% 
 mutate(prop.str= 100*tot_stranieri/Tot.Pop)
```
---
# Top 10 province per presenza stranieri

```r
IMMprovince.pop<- IMMprovince.pop %>% 
 arrange(-prop.str) %>% 
 mutate(rank_rel=row_number())

kable(IMMprovince.pop %>% 
 filter(rank_rel<=10) %>% 
 select(prov,prop.str ))
```

|prov               | prop.str|
|:------------------|--------:|
|Prato              | 18.48541|
|Piacenza           | 14.45259|
|Parma              | 14.07931|
|Milano             | 14.06080|
|Modena             | 13.06895|
|Mantova            | 12.60816|
|Firenze            | 12.40220|
|Reggio nell'Emilia | 12.12442|
|Brescia            | 12.09300|
|Lodi               | 11.96903|
---
# Calcolo totale per provenienza

```r
IMMprovince.citt<- IMMprovince %>% 
 group_by(citt) %>% 
 summarize(tot_stranieri=sum(totale)) %>% 
 ungroup() %>% 
 arrange(-tot_stranieri) %>% 
 mutate(rank_citt=row_number())

kable(IMMprovince.citt %>% 
 filter(rank_citt<=10) %>% 
 select(citt,tot_stranieri ))
```

|citt                        | tot_stranieri|
|:---------------------------|-------------:|
|Romania                     |       1145718|
|Albania                     |        421591|
|Marocco                     |        414249|
|Cinese, Repubblica Popolare |        288923|
|Ucraina                     |        228560|
|Filippine                   |        157665|
|India                       |        153209|
|Bangladesh                  |        138895|
|Egitto                      |        128095|
|Pakistan                    |        121609|
---
# Trasformo in dataset wide 
### (top 5 provenienze)

```r
IMMprovince_wide<- IMMprovince %>% 
 filter(citt %in% c("Romania", "Albania", "Marocco", "Cinese, Repubblica Popolare", "Ucraina")) %>% select(cod.prov, prov, totale, citt) %>% 
 pivot_wider( names_from=citt, values_from=totale) %>% 
 mutate(cod.prov=as.numeric(cod.prov))
```
---

```r
library(sf)
library(tmap)

geo_prov<-st_read("shapefileProvince/ProvCM01012018_g_WGS84.shp")
```

```
## Reading layer `ProvCM01012018_g_WGS84' from data source `/Users/nicola/Dropbox/Teaching/2021/labSOCIODEMO/webpage/LaboratorioBioDemografico2021/lezione15/shapefileProvince/ProvCM01012018_g_WGS84.shp' using driver `ESRI Shapefile'
## Simple feature collection with 107 features and 12 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 313279.3 ymin: 3933846 xmax: 1312016 ymax: 5220292
## CRS:            32632
```

```r
geo_prov<- geo_prov %>% mutate(cod.prov=COD_PROV) %>% 
 left_join(IMMprovince_wide , by="cod.prov")
```
---

```r
tm_shape(geo_prov) + 
  tm_polygons(col="Romania", palette="Greens", style="cont")
```

<img src="lezione15_files/figure-html/unnamed-chunk-17-1.png" width="60%" />
---

```r
tm_shape(geo_prov) + 
  tm_polygons(col="Albania", palette="Reds", style="cont")
```

---

```r
tm_shape(geo_prov) + 
  tm_polygons(col="Marocco", palette="Blues", style="cont")
```

<img src="lezione15_files/figure-html/unnamed-chunk-19-1.png" width="60%" />
---

```r
tm_shape(geo_prov) + 
  tm_polygons(col="Cinese, Repubblica Popolare", palette="Purples", style="cont")
```

<img src="lezione15_files/figure-html/unnamed-chunk-20-1.png" width="60%" />
---

```r
tm_shape(geo_prov) + 
  tm_polygons(col="Ucraina", palette="Oranges", style="cont")
```

<img src="lezione15_files/figure-html/unnamed-chunk-21-1.png" width="60%" />
---
# Dati di flusso
https://science.sciencemag.org/content/343/6178/1520

>Migrant “stock” data—the number of people living in a country other than the one in which they were born—are frequently used to understand contemporary trends in international migration, but the data are severely limited. Abel and Sander (p. 1520) present a set of global bilateral migration flows estimated from sequential stock data in 5-year intervals.** The percentage of the world population moving over 5-year periods has not shown dramatic changes between 1995 and 2010. **People from individual African countries tended to move within the continent, whereas people from Europe tended to move to very diverse locations.**

---
# Chord diagrams

https://guyabel.com/post/animated-directional-chord-diagrams/

```r
library(migest)

d0 <- read_csv(system.file("imr", "reg_flow.csv", package = "migest"))
d0
```

```
## # A tibble: 891 x 4
## year0 orig_reg dest_reg flow
## <dbl> <chr> <chr> <dbl>
## 1 1960 Africa Africa 1377791
## 2 1960 Africa Eastern Asia 5952
## 3 1960 Africa Eastern Europe & Central Asia 7303
## 4 1960 Africa Europe 919252
## 5 1960 Africa Latin America & Caribbean 15796
## 6 1960 Africa Northern America 82463
## 7 1960 Africa Oceania 32825
## 8 1960 Africa Southern Asia 35603
## 9 1960 Africa Western Asia 106580
## 10 1960 Eastern Asia Africa 37301
## # … with 881 more rows
```
---

```r
d1 <- read_csv(system.file("vidwp", "reg_plot.csv", package = "migest"))
d1
```

```
## # A tibble: 9 x 5
## region order1 col1 reg1 reg2 
## <chr> <dbl> <chr> <chr> <chr> 
## 1 Northern America 1 #40A4D8 Northern America 
## 2 Africa 2 #33BEB7 Africa NA 
## 3 Europe 3 #B2C224 Europe NA 
## 4 Eastern Europe & Central Asia 4 #FECC2F Eastern Europe & Central Asia
## 5 Western Asia 5 #FBA127 Western Asia 
## 6 Southern Asia 6 #F66320 Southern Asia 
## 7 Eastern Asia 7 #DB3937 Eastern Asia 
## 8 Oceania 8 #A463D7 Oceania NA 
## 9 Latin America & Caribbean 9 #0C5BCE Latin America & Caribbean
```
---
# grafico Base

```r
library(circlize)
chordDiagram(x = d0  %>% filter(year0==1960) %>% 
               select(orig_reg,dest_reg,flow),
             )
```

<img src="lezione15_files/figure-html/unnamed-chunk-24-1.png" width="40%" />
---

---
# Aggiungo colori e altri dettagli

```r
dev.off()
```

```
## null device 
##           1
```

```r
chordDiagram(x = d0  %>% 
               filter(year0==1960) %>% 
               select(orig_reg,dest_reg,flow),
             directional = 1, order = d1$region,
             grid.col = d1$col1, annotationTrack = "grid",
             transparency = 0.25,  annotationTrackHeight = c(0.05, 0.1),
             direction.type = c("diffHeight", "arrows"), link.arr.type = "big.arrow",
             diffHeight  = -0.04, link.sort = TRUE, link.largest.ontop = TRUE)
```
---
# Aggiungo etichette

```r
circos.track(track.index = 1, bg.border = NA, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    sector.index = get.cell.meta.data("sector.index")
    reg1 = d1 %>% filter(region == sector.index) %>% pull(reg1)
    reg2 = d1 %>% filter(region == sector.index) %>% pull(reg2)
    
    circos.text(x = mean(xlim), y = ifelse(is.na(reg2), 3, 4),
                labels = reg1, facing = "bending", cex = 1.1)
    circos.text(x = mean(xlim), y = 2.75, labels = reg2, facing = "bending", cex = 1.1)
    circos.axis(h = "top", labels.cex = 0.8,
                labels.niceFacing = FALSE, labels.pos.adjust = FALSE)
  })
```
---
# Creare animazioni
> This creates larger data frame d2, with 100 observations for each corridor, one for each frame in the animation. In the original data d0 there are only 11 observations for each corridor, one for each five-year period.

```r
library(tweenr)

d2 <- d0 %>%
 mutate(corridor = paste(orig_reg, dest_reg, sep = " -> ")) %>%
 select(corridor, year0, flow) %>%
 mutate(ease = "linear") %>%
 tween_elements(time = "year0", group = "corridor", ease = "ease", nframes = 100) %>%
 tbl_df()
```
---
# ordino il dataset

```r
d2 <- d2 %>%
 separate(col = .group, into = c("orig_reg", "dest_reg"), sep = " -> ") %>%
 select(orig_reg, dest_reg, flow, everything()) %>%
 mutate(flow = flow/1e06)
d2
```

```
## # A tibble: 8,181 x 5
## orig_reg dest_reg flow year0 .frame
## <chr> <chr> <dbl> <dbl> <int>
## 1 Africa Africa 1.38 1960 0
## 2 Africa Eastern Asia 0.00595 1960 0
## 3 Africa Eastern Europe & Central Asia 0.00730 1960 0
## 4 Africa Europe 0.919 1960 0
## 5 Africa Latin America & Caribbean 0.0158 1960 0
## 6 Africa Northern America 0.0825 1960 0
## 7 Africa Oceania 0.0328 1960 0
## 8 Africa Southern Asia 0.0356 1960 0
## 9 Africa Western Asia 0.107 1960 0
## 10 Eastern Asia Africa 0.0373 1960 0
## # … with 8,171 more rows
```
---
# creo un grafico per ogni frame

```r
dev.off()
dir.create("./plot-gif/")

library(circlize)
for(f in unique(d2$.frame)){
  # open a PNG plotting device
  png(file = paste0("./plot-gif/globalchord", f, ".png"), height = 7, width = 7, 
      units = "in", res = 500)
  
  # initialise the circos plot
  circos.clear()
  par(mar = rep(0, 4), cex=1)
  circos.par(start.degree = 90, track.margin=c(-0.1, 0.1), 
             gap.degree = 4, points.overflow.warning = FALSE)

# plot the chord diagram
  chordDiagram(d2 %>% 
                 filter( .frame == f) %>% 
                 select(orig_reg,dest_reg,flow ), directional = 1, order = d1$region,
               grid.col = d1$col1, annotationTrack = "grid",
               transparency = 0.25,  annotationTrackHeight = c(0.05, 0.1),
               direction.type = c("diffHeight", "arrows"), link.arr.type = "big.arrow",
               diffHeight  = -0.04, link.sort = TRUE, link.largest.ontop = TRUE)
  
  # add labels and axis
  circos.track(track.index = 1, bg.border = NA, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    sector.index = get.cell.meta.data("sector.index")
    reg1 = d1 %>% filter(region == sector.index) %>% pull(reg1)
    reg2 = d1 %>% filter(region == sector.index) %>% pull(reg2)
    
    circos.text(x = mean(xlim), y = ifelse(is.na(reg2), 3, 4),
                labels = reg1, facing = "bending", cex = 1.1)
    circos.text(x = mean(xlim), y = 2.75, labels = reg2, facing = "bending", cex = 1.1)
    circos.axis(h = "top", labels.cex = 0.8,
                labels.niceFacing = FALSE, labels.pos.adjust = FALSE)
  })
  
  text(-.7,1,glue('Migrant Population: {d2 %>% filter(.frame==f) %>% select(year0) %>% summarize(mean(year0)) %>% round(0)} '))
    text(.8,1,glue('{d2 %>% filter(.frame==f) %>% select(flow) %>% summarize(sum(flow)) %>% round(1)} million'))
  # close plotting device
  dev.off()
}
```
---
# Creo una GIF

```r
library(magick)

img <- image_read(path = "./plot-gif/globalchord0.png")
for(f in unique(d2$.frame)[-1]){
 img0 <- image_read(path = paste0("./plot-gif/globalchord",f,".png"))
 img <- c(img, img0)
 message(f)
}

img1 <- image_scale(image = img, geometry = "720x720")

ani0 <- image_animate(image = img1, fps = 10)
image_write(image = ani0, path = "./plot-gif/globalchord.gif")
```
---
<img src="./plot-gif/globalchord.gif" alt="" class="center" width="550" height="550" >