gorkang

# Archivo PDF de: 
  # http://ddhh.minjusticia.gob.cl/informacion-sobre-la-situacion-del-pais-desde-el-19-de-octubre
  # 2019-12-05: https://www.scribd.com/document/438470416/Datos-05-12-19#download

# Librerias -----------------------------------------------------------------------------------

  library(tidyverse)
  library(tabulizer)

gorkang

# FROM: https://github.com/JonMinton/COVID-19



DF = tidied_data %>% 
  filter(type == "confirmed") %>% 
  mutate(
    higher = case_when(
      higher == "Iran (Islamic Republic of)" ~ "Iran",
      higher == "Hong Kong SAR"  ~ "Hong Kong",

gorkang

# Libraries ---------------------------------------------------------------

library(tidyverse)
library(data.table)


# Read data ---------------------------------------------------------------

# DOWNLOAD DATA FROM: https://momo.isciii.es/public/momo/dashboard/momo_dashboard.html#datos

gorkang

# Using {rtweet} (https://github.com/ropensci/rtweet) to check how much Trump tweeted in the last few days

# Libraries
library(rtweet)
library(tidyverse)

# Get Twitter token
file_name <- file.path(".rtweet_token5.rds")
twitter_token = readRDS(file = file_name)

gorkang

library(tidyverse)
library(haven)

DF = read_sav("3312.sav") # Datos de http://www.cis.es/cis/opencm/ES/1_encuestas/estudios/ver.jsp?estudio=14551
DIC = lapply(DF, attr, which = "label") %>%  as_tibble() %>% pivot_longer(1:ncol(.)) %>% rename(nombre = value)

DF_plot = 
DF %>% 
  sjlabelled::remove_all_labels() %>% 
  as_tibble() %>%

gorkang

library(tidyverse)
DF = tibble(country = c("a", "a", "b", "b", "b"),
            value = c(1, 1.01, 2, 2.01, 2),
            size_dot = c(4, 2, 1, 3, 6))
  
  
plot1 = ggplot(DF, aes(value, country, fill = country)) +
  geom_point(aes(size = size_dot), shape = 21, alpha = .5, position = position_dodge2(width = .2))#position_jitterdodge(seed = 5))

plot2 = ggplot(DF, aes(value, country, fill = country)) +

gorkang

# Data from: https://gssdataexplorer.norc.org/trends
# Inspiration: https://twitter.com/_cingraham/status/1487094406159110147

library(tidyverse)
library(readxl)
library(geomtextpath)

FILES = list.files(path = "~/Downloads/", pattern = "happy", full.names = TRUE)
DF_raw = map_df(FILES, read_excel, skip = 11)

gorkang

detect_file_encoding <- function(file_path) {
  
  # MUCH SIMPLER, and should work most of the time
  # file_path = "..."
  # ENC = readr::guess_encoding(file_path, n_max = 1000)
  # readr::read_csv(file_path, skip = 12, locale = locale(encoding = ENC$encoding[1]))

  
  library(cli)
  library(dplyr)