# setup -------------------------------------------------------------------
library(tidyverse)
library(stringr)
library(broom)
library(highcharter)
library(ggrepel)
library(viridis)
library(jbkmisc)
library(hrbrthemes)
theme_set(theme_jbk())
# data --------------------------------------------------------------------
data <- read_csv("../matrices-oea/all.csv")
data <- data %>%
gather(origin, qty, -Destination, -periodo) %>%
rename(destination = Destination, year = periodo) %>%
filter(!is.na(qty))
data <- data %>%
mutate_if(is.character, str_replace_all, "\\(.*\\)", "") %>%
mutate_if(is.character, str_trim)
# destination -------------------------------------------------------------
df_dest <- data %>%
group_by(destination, year) %>%
summarise(qty = sum(qty)) %>%
arrange(destination, year) %>%
mutate(qty_std = qty/first(qty),
year0 = year - 1990)
ggplot(df_dest, aes(year, qty_std)) +
geom_line(aes(group = destination, color = destination),
size = 1.2, alpha = 0.5) +
geom_text_repel(aes(label = destination),
color = "gray60", size = 3,
nudge_x = 10, force = 1,
data = filter(df_dest, year == 2015)) +
scale_color_viridis(discrete = TRUE) +
scale_x_continuous(limits = c(NA, 2020)) +
theme(legend.position = "none") +
labs(
title = "Exploratory Analysis Inmigration"
)
df_dest_country <- nest(df_dest, -destination)
do_model <- function(df) lm(qty_std ~ year0, data = df)
models_dest <- df_dest_country %>%
mutate(
model = map(data, do_model),
glance = map(model, glance),
rsq = map_dbl(glance, "r.squared"),
tidy = map(model, tidy),
augment = map(model, augment)
)
models_dest %>%
unnest(tidy) %>%
select(destination, rsq, term, estimate) %>%
spread(term, estimate) %>%
ggplot(aes(`(Intercept)`, year0)) +
geom_point()
# origin ------------------------------------------------------------------
df_orig <- data %>%
group_by(origin, year) %>%
summarise(qty = sum(qty)) %>%
arrange(origin, year) %>%
mutate(qty_std = qty/first(qty),
year0 = year - 1990)
ggplot(df_orig, aes(year, qty_std)) +
geom_line(aes(group = origin, color = origin),
size = 1.2, alpha = 0.5) +
geom_text_repel(aes(label = origin),
color = "gray60", size = 3,
nudge_x = 10, force = 1,
data = filter(df_orig, year == 2015)) +
scale_color_viridis(discrete = TRUE) +
scale_x_continuous(limits = c(NA, 2020)) +
theme(legend.position = "none") +
labs(
title = "Exploratory Analysis for Migration"
)
df_orig_country <- nest(df_orig, -origin)
models_orig <- df_orig_country %>%
mutate(
model = map(data, do_model),
glance = map(model, glance),
rsq = map_dbl(glance, "r.squared"),
tidy = map(model, tidy),
augment = map(model, augment)
)
models_orig %>%
unnest(tidy) %>%
select(origin, rsq, term, estimate) %>%
spread(term, estimate) %>%
ggplot(aes(`(Intercept)`, year0)) +
geom_point()
# join --------------------------------------------------------------------
models_orig2 <- models_orig %>%
unnest(tidy) %>%
select(origin, term, estimate) %>%
spread(term, estimate)
models_dest2 <- models_dest %>%
unnest(tidy) %>%
select(destination, term, estimate) %>%
spread(term, estimate)
models <- left_join(models_dest2, models_orig2, by = c("destination" = "origin"))
ggplot(models, aes(year0.x, year0.y)) +
geom_point(color = "royalblue", size = 1.5) +
geom_text_repel(aes(label = destination), color = "gray30", size = 3) +
coord_fixed() +
geom_vline(xintercept = 0, color = "gray60") +
geom_hline(yintercept = 0, color = "gray60") +
geom_abline(aes(slope = 1, intercept = 0), color = "gray60") +
scale_x_percent(limits = c(-0.05, 0.20)) +
scale_y_percent(limits = c(-0, 0.15)) +
theme_jbk() +
labs(
x = "Tendencia Inmigración*",
y = "Tendencia Migración*",
caption = "* Tasa de crecimiento de acuerdo a regresión lineal"
)
modelsf <- models %>%
rename(pais = destination, x = year0.x, y = year0.y) %>%
select(pais, x, y) %>%
left_join(
models_orig %>% select(origin, data) %>% rename(pais = origin, orig = data)
) %>%
left_join(
models_dest %>% select(destination, data) %>% rename(pais = destination, dest = data)
) %>%
mutate(
orig = map(orig, "qty"),
dest = map(dest, "qty")
)
modelsf
## # A tibble: 35 x 5
## pais x y orig dest
## <chr> <dbl> <dbl> <list> <list>
## 1 Antigua and Barbuda 0.065338734 0.047913047 <dbl [6]> <dbl [6]>
## 2 Argentina 0.042623675 0.029433725 <dbl [6]> <dbl [6]>
## 3 Bahamas 0.063110428 0.020232016 <dbl [6]> <dbl [6]>
## 4 Barbados 0.004361434 0.008297509 <dbl [6]> <dbl [6]>
## 5 Belize 0.029658758 0.026548648 <dbl [6]> <dbl [6]>
## 6 Bolivia 0.031536471 0.075082670 <dbl [6]> <dbl [6]>
## 7 Brazil 0.030369309 0.046807995 <dbl [6]> <dbl [6]>
## 8 Canada 0.035353155 0.005769499 <dbl [6]> <dbl [6]>
## 9 Chile 0.186127295 0.002686614 <dbl [6]> <dbl [6]>
## 10 Colombia 0.014039708 0.051529150 <dbl [6]> <dbl [6]>
## # ... with 25 more rows
library(highcharter)
hchart(modelsf, "bubble", hcaes(x, y, name = pais), color = "skyblue", maxSize = "3%") %>%
hc_plotOptions(
series = list(
dataLabels = list(enabled = TRUE, format = "{point.name}")
)
) %>%
hc_add_theme(hc_theme_smpl()) %>%
hc_xAxis(title = "Crecimiento Inmigración") %>%
hc_yAxis(title = "Crecimiento Migración") %>%
hc_tooltip(
useHTML = TRUE,
positioner = JS("function () { return { x: 900, y: 25 }; }"),
headerFormat = "{point.pais}",
pointFormatter = JS("
function(){
var thiz = this;
console.log(thiz);
setTimeout(function() {
$('#minichart').highcharts({
title : {
text: ''
},
subtitle: {
text: thiz.pais,
align: 'left'
},
tooltip : {enabled: true},
exporting: {
enabled: false
},
legend: {
enabled: true
},
credits: {
enabled: false
},
series: [{
animation: false,
color: 'blue',
name: 'Migracion',
data: thiz.orig
},{
animation: false,
color: 'red',
name: 'Inmigracion',
data: thiz.dest
}],
yAxis: {
title: ''
},
xAxis: {
title: '',
categories: [1990, 1995, 2000, 2005, 2010, 2015]
}
});
}, 0);
return '<div id=\"minichart\" style=\"width: 300px; height: 200px;\"></div>';
}
")
)