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# "pib trim.xlsx" es el PIB trimestral a precios encadenados de este enlace:
#https://si3.bcentral.cl/Siete/ES/Siete/Cuadro/CAP_CCNN/MN_CCNN76/CCNN2018_P0_V2/637801082315858005?cbFechaInicio=2000&cbFechaTermino=2024&cbFrecuencia=QUARTERLY&cbCalculo=NONE&cbFechaBase=

pibtrim <- readxl::read_excel("pib trim.xlsx",skip = 1)

colnames(pibtrim) <- c("time", "pib")

pibtrim <- pibtrim %>% 
  mutate(time2 = ymd(str_sub(time, 1, 10))) %>% 

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library(stm)
library(tidytext)
library(ggridges)

n_topicos = 20
n_nuestras = 100

# iteración sobre tópicos
lapply(1:n_topicos, function(i){

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library(tidyverse)
library(haven)
theme_set(theme_bw(base_size = 24))


dat <- read_dta("http://fmwww.bc.edu/ec-p/data/wooldridge/wage1.dta")


dat %>% 
  ggplot() +

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library(foreign)

dat <- read.dta("http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1.dta")

coef(lm(colGPA ~ ACT + hsGPA, data = dat))


coef(lm(colGPA ~ hsGPA, data = dat))

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generate_regression_table <- function(models, se_type = "HC3") {
  require(sandwich)
  require(lmtest)
  
  # Initialize lists to store results
  results_list <- list()
  
  # Loop over each model
  for (i in seq_along(models)) {
    model <- models[[i]]

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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

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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)

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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"

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

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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)),]