Question

我正试图从马来西亚环境部网站获取所有历史空气污染指数数据，该网站将所有工作站的数据分成每天4小时链接，如下所示

http://apims.doe.gov.my/apims/hourly1.php?date=20130701 http://apims.doe.gov.my/apims/hourly2.php?date=20130701

与上面的＆＃39; hourly3.php相同？＆＃39;和＆＃39; hourly4.php？＆＃39;

我对R只有一点熟悉，那么使用XML或scrapeR库最简单的方法是什么？

Answer 1

您可以使用R readHTMLTable功能从上面提供的马来西亚DOE网址中提取HTML表格。以第一个URL为例：

# Make sure you have the XML package installed
library(XML)
url <- "http://apims.doe.gov.my/apims/hourly1.php?date=20130701"
all.tables <- readHTMLTable(url)
# the URL you gave only has one <table> tag
table <- all.tables[[1]]
# and now you have a data frame 'table' which contains the contents
# of the air pollutant table

Answer 2

您可以使用列表操作将所有表格转换为宽数据框：

library(rvest)
library(magrittr)
library(dplyr)

date <- 20130701
rng <- c(1:4)

my_tabs <- lapply(rng, function(i) {
  url <- sprintf("http://apims.doe.gov.my/apims/hourly%d.php?date=%s", i, date)
  pg <- html(url)
  pg %>% html_nodes("table") %>% extract2(1) %>% html_table(header=TRUE)
})

glimpse(plyr::join_all(my_tabs, by=colnames(my_tabs[[1]][1:2])))

## Observations: 52
## Variables:
## $ NEGERI / STATE   (chr) "Johor", "Johor", "Johor", "Johor", "Kedah...
## $ KAWASAN/AREA     (chr) "Kota Tinggi", "Larkin Lama", "Muar", "Pas...
## $ MASA/TIME12:00AM (chr) "63*", "53*", "51*", "55*", "37*", "48*", ...
## $ MASA/TIME01:00AM (chr) "62*", "52*", "52*", "55*", "36*", "48*", ...
## $ MASA/TIME02:00AM (chr) "61*", "51*", "53*", "55*", "35*", "48*", ...
## $ MASA/TIME03:00AM (chr) "60*", "50*", "54*", "55*", "35*", "48*", ...
## $ MASA/TIME04:00AM (chr) "59*", "49*", "54*", "54*", "34*", "47*", ...
## $ MASA/TIME05:00AM (chr) "58*", "48*", "54*", "54*", "34*", "45*", ...
## $ MASA/TIME06:00AM (chr) "57*", "47*", "53*", "53*", "33*", "45*", ...
## $ MASA/TIME07:00AM (chr) "57*", "46*", "52*", "53*", "32*", "45*", ...
## $ MASA/TIME08:00AM (chr) "56*", "45*", "52*", "52*", "32*", "44*", ...
## ...

由于命名与plyr的冲突，我实际上很少加载/使用dplyr，但join_all非常适合这种情况。

您也可能需要长格式的数据：

plyr::join_all(my_tabs, by=colnames(my_tabs[[1]][1:2])) %>% 
  tidyr::gather(masa, nilai, -1, -2) %>%
# better column names
  rename(nigeri=`NEGERI / STATE`, kawasan=`KAWASAN/AREA`) %>%  
# cleanup & convert time (using local timezone)
# make readings numeric; NA will sub for #
  mutate(masa=gsub("MASA/TIME", "", masa), 
         masa=as.POSIXct(sprintf("%s %s", date, masa), format="%Y%m%d %H:%M%p", tz="Asia/Kuala_Lumpur"),
         nilai=as.numeric(gsub("[[:punct:]]+", "", nilai))) -> pollut

head(pollut)
##   nigeri                 kawasan                masa nilai
## 1  Johor             Kota Tinggi 2013-07-01 12:00:00    63
## 2  Johor             Larkin Lama 2013-07-01 12:00:00    53
## 3  Johor                    Muar 2013-07-01 12:00:00    51
## 4  Johor            Pasir Gudang 2013-07-01 12:00:00    55
## 5  Kedah              Alor Setar 2013-07-01 12:00:00    37
## 6  Kedah Bakar Arang, Sg. Petani 2013-07-01 12:00:00    48

使用R在一天内使用4个网址刮取数据

2 个答案: