tecnoexplotacion.R

library(tidyverse)
library(countrycode)
library(gganimate)
library(gifski)

theme_set(theme_classic())

tiempo <- read.csv("annual-working-hours-per-worker.csv")

colnames(tiempo)[4] <- "tiempo"

complex <- read.csv("eci_hs6_hs96.csv")

complex$iso <-complex$Country.ID %>% str_sub(3, 5) %>% toupper()

complex <- complex %>% 
  gather("anno", "eci", 1:23)

complex$anno <- complex$anno %>% 
  str_extract("\\d+") %>% 
  as.numeric()

dat <- tiempo %>% 
  left_join(
    complex %>% select(anno, iso, eci),
    by = c("Code"="iso",
           "Year" = "anno")
  )



dat$region <- countrycode(dat$Code, 
                          origin = "iso3c",
                          destination = "continent")

dat %>% 
  filter(Year >= 1998) %>%
  ggplot() +
  aes(eci, tiempo, color = Code) +
  guides(color = "none") +
  geom_jitter() +
  facet_wrap(.~region) +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ) +
  labs(x="Índice de complejidad económica",
       y="Horas de trabajo anual promedio",
       title = "Relación entre complejidad económica y horas de trabajo",
       subtitle = "Año: {as.integer(frame_time)}",
       caption = "Con base a datos del de OWID y el OEC\n@sientifiko1") +
  transition_time(Year) +
  ease_aes("linear") +
  shadow_mark(alpha = 0.3, size = 0.5) -> p1

animate(p1, fps = 20, duration = 10, width = 1024,  height= 450,
        renderer = gifski_renderer("plotanimado.gif")) 


energia <- read.csv("energy.csv")

colnames(energia)[4] <- "energy"
energia$Entity <- NULL

dat2 <- tiempo %>% 
  left_join(energia,
            by = c("Year",
                   "Code"))

dat2$region <- countrycode(dat2$Code, 
                          origin = "iso3c",
                          destination = "continent")


dat2 %>% 
  filter(Year == 2017) %>%
  ggplot() +
  aes(energy, tiempo, color = Code) +
  guides(color = "none") +
  geom_jitter() +
  facet_wrap(.~region) +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ) +
  labs(x="Uso energía per cápita",
       y="Horas de trabajo anual promedio",
       title = "Relación entre energía y horas de trabajo",
       subtitle = "2017",
       caption = "Con base a datos del de OWID\n@sientifiko1") 


dat2 %>% 
  filter(Year >= 1965) %>%
  ggplot() +
  aes(energy, tiempo, color = Code) +
  guides(color = "none") +
  geom_jitter() +
  facet_wrap(.~region) +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ) +
  labs(x="Uso energía per cápita",
       y="Horas de trabajo anual promedio",
       title = "Relación entre energía y horas de trabajo",
       subtitle = "Año: {as.integer(frame_time)}",
       caption = "Con base a datos del de OWID\n@sientifiko1") +
  transition_time(Year) +
  ease_aes("linear") +
  shadow_mark(alpha = 0.3, size = 0.5) -> p2

animate(p2, fps = 20, duration = 10, width = 1024,  height= 500,
        renderer = gifski_renderer("plot2.gif")) 



lat <- dat2 %>% 
  filter(region == "Americas") %>% 
  na.omit()

lat <- lat %>% 
  group_by(Code) %>% 
  mutate(t_std = scale(tiempo),
         e_std = scale(energy))

lat %>% 
  ggplot() +
  aes(Year, color = Code) +
  guides(color = "none") +
  geom_line(aes(y=t_std, linetype = "Horas trabajo anual\npromedio")) +
  geom_line(aes(y=e_std, linetype = "Energía\n en Kw/H pc")) +
  facet_wrap(.~Code) +
  # scale_y_continuous(trans = "log10") +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ,
        legend.key.size = unit(1.5, 'cm'),
        legend.text = element_text(size = 12)) +
  labs(x="",
       y="Escala en z-score por país",
       linetype = "",
       color = "",
       title = "Relación entre energía y horas de trabajo",
       subtitle = "Latinoamérica",
       caption = "Con base a datos del de OWID\n@sientifiko1") 



asia <- dat2 %>% 
  filter(region == "Asia") %>% 
  na.omit()

asia <- asia %>% 
  group_by(Code) %>% 
  mutate(t_std = scale(tiempo),
         e_std = scale(energy))

asia %>% 
  ggplot() +
  aes(Year, color = Code) +
  guides(color = "none") +
  geom_line(aes(y=t_std, linetype = "Horas trabajo anual\npromedio")) +
  geom_line(aes(y=e_std, linetype = "Energía\n en Kw/H pc")) +
  facet_wrap(.~Code) +
  # scale_y_continuous(trans = "log10") +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ,
        legend.key.size = unit(1.5, 'cm'),
        legend.text = element_text(size = 12)) +
  labs(x="",
       y="Escala en z-score por país",
       linetype = "",
       color = "",
       title = "Relación entre energía y horas de trabajo",
       subtitle = "Asia",
       caption = "Con base a datos del de OWID\n@sientifiko1") 


internet <- read.csv("share-of-individuals-using-the-internet.csv")


colnames(internet)[4] <- "inter"
internet$Entity <- NULL

dat3 <- tiempo %>% 
  left_join(internet,
            by = c("Year",
                   "Code"))

dat3$region <- countrycode(dat3$Code, 
                           origin = "iso3c",
                           destination = "continent")

lat2 <- dat3 %>% 
  filter(region == "Americas") %>% 
  na.omit()

lat2 <- lat2 %>% 
  group_by(Code) %>% 
  mutate(t_std = scale(tiempo),
         e_std = scale(inter))


lat2 %>% 
  ggplot() +
  aes(Year, color = Code) +
  guides(color = "none") +
  geom_line(aes(y=t_std, linetype = "Horas trabajo anual\npromedio")) +
  geom_line(aes(y=e_std, linetype = "% pob escalado")) +
  facet_wrap(.~Code) +
  # scale_y_continuous(trans = "log10") +
  theme(strip.text.x = element_text(size = 14),
        plot.title = element_text(size = 18),
        plot.subtitle = element_text(size = 15) ,
        legend.key.size = unit(1.5, 'cm'),
        legend.text = element_text(size = 12)) +
  labs(x="",
       y="Escala en z-score por país",
       linetype = "",
       color = "",
       title = "Relación entre internet y horas de trabajo",
       subtitle = "Latinoamérica",
       caption = "Con base a datos del de OWID\n@sientifiko1")