sientifiko

library(tidyverse)
library(patchwork)
theme_set(theme_classic())

paises <- c("Argentina", "Bolivia", "Brasil", "Chile", "Colombia",
            "Costa Rica", "Cuba", "Ecuador", "El Salvador", "Guatemala", "Honduras",
            "México", "Nicaragua", "Panamá", "Paraguay", "Perú", "Rep Dominicana", 
            "Uruguay", "Venezuela")

sientifiko

library(tidyverse)
library(ineq)

options(scipen = 999)

theme_set(theme_classic())

base <- rep(1000, 1000)

lista <- list()

sientifiko

library(tidyverse)
library(rvest)
library(RSelenium)

# funciones pa limpiar
limpieza <- function(objtabla){
  objtabla <- objtabla[-which(is.na(objtabla[,1])), ]
  
  objtabla <- objtabla[c(1:(nrow(objtabla)-5)),]
  

sientifiko

library(XML)
library(RCurl)
library(tm)
library(wordcloud2)
library(stm)
library(pdftools)
library(tidyverse)
library(patchwork)
library(ggwordcloud)

sientifiko

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"

sientifiko

library(tidyverse)
library(directlabels)
theme_set(theme_bw(base_size = 21))
options(scipen = 999)

dat <- readxl::read_excel("mpd2020.xlsx", sheet = 3) %>% 
  filter(country %in% c("Argentina", "Chile", "Brazil","Sweden", "United States",
                        "Norway", "Uruguay", "United Kingdom", "Panama"),
         year >= 1870)

sientifiko

library(tidyverse)
theme_set(theme_bw(base_size = 21))
options(scipen = 999)

dat <- read.csv("dataregionesChile.csv")

# filtrar el año 2020
y2020 <- dat %>% filter(year == 2020)

# generar histograma