使用 R 语言从 PDF 文档中提取表格

library(rvest)
library(tidyverse)
read_html('https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/') -> html
# 创建 pdf 文件夹
dir.create("pdf")
html %>% 
  html_nodes("a") %>% 
  html_attr("href") %>% 
  as_tibble() %>% 
  dplyr::filter(str_detect(value, "\\.pdf")) %>% 
  mutate(value = paste0("https://www.who.int", value)) %>% 
  mutate(dest = str_match(value, "situation-reports/(.*)\\?")[,2],
         dest = paste0("pdf/", dest)) %>% 
  distinct() -> links
for(i in 1:nrow(links)) {
  download.file(links$value[i], links$dest[i])
}

install.packages('rJava')

install.packages("tabulizer")

# 使用 tabulizer 包
library(tabulizer)
library(purrr)
library(tidyr)
library(tidyverse)
f <- "pdf/20200523-covid-19-sitrep-124.pdf"
# Table1 的第一页需要手动选择区域
extract_areas(f, pages = 4, output = "data.frame") -> df1
df1 %>% 
  as.data.frame() %>% 
  as_tibble() %>% 
  slice(-1) %>% 
  set_names(c("Reporting_Country_Territory_Area", 
              "Total_confirmed_cases", 
              "Total_confirmed_new_cases",
              "Total_deaths",
              "Total_new_deaths",
              "Transmission_classification",
              "Days_since_last_reported_case")) %>% 
  mutate(continent = ifelse(Total_confirmed_cases == "", 
                            Reporting_Country_Territory_Area, NA)) %>% 
  select(continent, everything()) %>% 
  fill(continent) %>% 
  dplyr::filter(!is.na(Reporting_Country_Territory_Area)) %>% 
  dplyr::filter(!is.na(Total_confirmed_cases)) %>% 
  dplyr::filter(!is.na(Total_confirmed_new_cases)) %>% 
  dplyr::filter(!str_detect(Total_confirmed_new_cases,
                            "[a-z]")) %>% 
  mutate(Total_confirmed_cases = str_replace_all(Total_confirmed_cases,
                                                 " ", ""),
         Total_confirmed_cases = str_remove_all(Total_confirmed_cases, " "),
         Total_deaths = str_remove_all(Total_deaths, " "),
         Total_new_deaths = str_remove_all(Total_new_deaths, " "),
         Days_since_last_reported_case = str_remove_all(Days_since_last_reported_case, " ")) %>% 
  type_convert() %>% 
  dplyr::filter(!is.na(Total_confirmed_cases)) -> page1
# 剩下的页就好办了
extract_tables(f, pages = 5:13, 
               guess = FALSE, output = "data.frame") -> dfl
rbind(
  dfl[[1]],
  dfl[[2]],
  dfl[[3]],
  dfl[[4]],
  dfl[[5]],
  dfl[[6]],
  dfl[[7]],
  dfl[[8]],
  dfl[[9]]
) %>% 
  as_tibble() %>% 
  set_names(c("Reporting_Country_Territory_Area", 
              "Total_confirmed_cases", 
              "Total_confirmed_new_cases",
              "Total_deaths",
              "Total_new_deaths",
              "Transmission_classification",
              "Days_since_last_reported_case")) %>% 
  mutate(continent = ifelse(Total_confirmed_cases == "", 
                            Reporting_Country_Territory_Area, NA)) %>% 
  select(continent, everything()) %>% 
  fill(continent) %>% 
  type_convert() %>% 
  dplyr::filter(!is.na(Reporting_Country_Territory_Area)) %>% 
  dplyr::filter(!str_detect(Total_confirmed_new_cases,
                            "[a-z]")) %>% 
  mutate(Total_confirmed_cases = str_replace_all(Total_confirmed_cases,
                                                 " ", ""),
         Total_confirmed_cases = str_remove_all(Total_confirmed_cases, " "),
         Total_deaths = str_remove_all(Total_deaths, " "),
         Total_new_deaths = str_remove_all(Total_new_deaths, " "),
         Days_since_last_reported_case = str_remove_all(Days_since_last_reported_case, " ")) %>% 
  type_convert() %>% 
  dplyr::filter(!is.na(Total_confirmed_cases)) %>% 
  mutate(Total_confirmed_new_cases = as.numeric(Total_confirmed_new_cases)) -> page2
# 爬取最后一页
# Table1 的最一页需要手动选择区域
extract_areas(f, pages = 14, output = "data.frame") -> df2
df2 %>% 
  as.data.frame() %>% 
  as_tibble() %>% 
  slice(-1) %>% 
  set_names(c("Reporting_Country_Territory_Area", 
              "Total_confirmed_cases", 
              "Total_confirmed_new_cases",
              "Total_deaths",
              "Total_new_deaths",
              "Transmission_classification",
              "Days_since_last_reported_case")) %>% 
  mutate(Reporting_Country_Territory_Area = case_when(
    Reporting_Country_Territory_Area == "(Commonwealth of the)" ~ "Northern Mariana Islands(Commonwealth of the)",
    T ~ Reporting_Country_Territory_Area
  )) %>% 
  slice(-1) %>% 
  mutate(continent = ifelse(Total_confirmed_cases == "", 
                            Reporting_Country_Territory_Area, NA)) %>% 
  select(continent, everything()) %>% 
  fill(continent) %>% 
  dplyr::filter(!is.na(Reporting_Country_Territory_Area)) %>% 
  dplyr::filter(!is.na(Total_confirmed_cases)) %>% 
  dplyr::filter(!is.na(Total_confirmed_new_cases)) %>% 
  dplyr::filter(!str_detect(Total_confirmed_new_cases,
                            "[a-z]")) %>% 
  mutate(Total_confirmed_cases = str_replace_all(Total_confirmed_cases,
                                                 " ", ""),
         Total_confirmed_cases = str_remove_all(Total_confirmed_cases, " "),
         Total_deaths = str_remove_all(Total_deaths, " "),
         Total_new_deaths = str_remove_all(Total_new_deaths, " "),
         Days_since_last_reported_case = str_remove_all(Days_since_last_reported_case, " "),
         Total_confirmed_new_cases = str_remove_all(Total_confirmed_new_cases, " ")) %>% 
  type_convert() %>%