将位置列拆分为邮政编码、纬度和经度

Question

我的数据框中有一列包含邮政编码、纬度和经度

地点

"10007 (40.71363051943297, -74.00913138370635)"
"10002 (40.71612146793143, -73.98583147024613)"
"10012 (40.72553802086304, -73.99789641059084)"
"10009 (40.72664935898081, -73.97911148500697)"

我需要将它们分成三个不同的列，例如邮政编码、纬度和经度。

我试过这样做

extract(Location, c("Zip-Code","Latitude", "Longitude"), "\(([^,]+), ([^)]+)\)")

我想使用经纬度使用ggmap绘制地图

谢谢

Answer 1

s.tmp = "10007 (40.71363051943297, -74.00913138370635)"

对于 ZIP：

gsub('([0-9]+) .*', '\1', s.tmp)

对于纬度：

gsub('.*\((.*),.*', '\1', s.tmp)

经度：

gsub('.*, (.*)\).*', '\1', s.tmp)

Answer 2

基本正则表达式提取：

library(purrr)

c("10007 (40.71363051943297, -74.00913138370635)", "10002 (40.71612146793143, -73.98583147024613)",
  "10012 (40.72553802086304, -73.99789641059084)", "10009 (40.72664935898081, -73.97911148500697)") %>%
  stringi::stri_match_all_regex("([[:digit:]]+)[[:space:]]+\(([[:digit:]\.\-]+),[[:space:]]+([[:digit:]\.\-]+)\)") %>%
  map_df(dplyr::as_data_frame) %>%
  dplyr::select(zip=V2, latitude=V3, longitude=V4)
## # A tibble: 4 × 3
##     zip          latitude          longitude
##   <chr>             <chr>              <chr>
## 1 10007 40.71363051943297 -74.00913138370635
## 2 10002 40.71612146793143 -73.98583147024613
## 3 10012 40.72553802086304 -73.99789641059084
## 4 10009 40.72664935898081 -73.97911148500697

更具可读性：

library(purrr)
library(stringi)
library(dplyr)
library(purrr)

dat <- c("10007 (40.71363051943297, -74.00913138370635)",
         "10002 (40.71612146793143, -73.98583147024613)",
         "10012 (40.72553802086304, -73.99789641059084)", 
         "10009 (40.72664935898081, -73.97911148500697)")

zip <- "([[:digit:]]+)"
num <- "([[:digit:]\.\-]+)"
space <- "[[:space:]]+"
lp <- "\("
rp <- "\)"
comma <- ","

match_str <- zip %s+% space %s+% lp %s+% num %s+% comma %s+% space %s+% num %s+% rp

dat %>%
  stri_match_all_regex(match_str) %>%
  map_df(as_data_frame) %>%
  select(zip=V2, latitude=V3, longitude=V4)

将位置列拆分为邮政编码、纬度和经度

Spliting the location column into Zipcode, Latitude and Longitude

r

dataframe

ggmap

r-markdown