0

我想知道如何从他们的 FTP 站点下载 LEHD 文件。

https://lehd.ces.census.gov/data/lodes/LODES7/

我需要下载多年的数据，包括工作场所和居住地。这些文件定期命名，技术文档可以在这里找到：

https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.2.pdf S000 参考所有劳动力细分 JT00 参考所有工作类型

所以一个典型的文件名是：ca_wac_S000_JT00_2008.csv.gz 在'目录'/URL 的：https ://lehd.ces.census.gov/data/lodes/LODES7/ca/wac/

这段 git-hub 代码似乎是相关的。哈佛教程很有用，并为我提供了一种创建所有文件列表的方法。但我无法让实际下载工作——R.curl 对我不起作用，因为我遇到了 SSL 问题。

扩展代码似乎也不起作用：

install.packages("RCurl")
library(RCurl)
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))   
URL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
x <- getURL(URL)
x
#the above code works.

#my implementation...fails
URL <- "https://lehd.ces.census.gov/data/lodes/LODES7/ca/wac/ca_wac_S000_JT00_2002.csv.gz"
x <- getURL(URL)
#results in following error:
#Error in function (type, msg, asError = TRUE)  : 
# error:14077410:SSL routines:SSL23_GET_SERVER_HELLO:sslv3 alert handshake failure

devtools::session_info() 会话信息 ------------------------------------------ --------------------------------------------- 设定值版本 R 版本3.4.3 (2017-11-30) system x86_64, mingw32 ui RStudio (1.1.383) language (EN) collat​​e English_United States.1252 tz America/Denver
日期 2017-12-17

软件包------------------------------------------------- ------------------------------------------ 包 * 版本日期 来源 acs * 2.1 .2
2017-10-10 CRAN (R 3.4.3) 断言 0.2.0 2017-04-11 CRAN (R 3.4.3) 基础 * 3.4.3 2017-12-06 本地绑定器 0.1 2016-11-13 CRAN ( R 3.4.3) bindrcpp 0.2 2017-06-17 CRAN (R 3.4.3) 类 7.3-14 2015-08-30 CRAN (R 3.4.3) classInt 0.1-24 2017-04-16 CRAN (R 3.4.3 ) 编译器 3.4.3
2017-12-06 本地 curl * 3.1 2017-12-12 CRAN (R 3.4.3) 数据集 * 3.4.3 2017-12-06 本地 DBI 0.7 2017-06-18 CRAN (R 3.4.3) 开发工具 * 1.13。 4 2017-11-09 CRAN (R 3.4.3) 摘要 0.6.13 2017-12-14 CRAN (R 3.4.3) dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.3) e1071 1.6-8 2017-02-02 CRAN (R 3.4.3) 国外 0.8-69 2017-06-22 CRAN (R 3.4.3) gdtools * 0.1.6 2017-09-01 CRAN (R 3.4.3) git2r 0.19.0
2017-07-19 CRAN (R 3.4.3) 胶水 1.2.0 2017-10-29 CRAN (R 3.4.3) 图形 * 3.4.3 2017-12-06 本地 grDevices * 3.4.3 2017-12-06 本地网格 3.4.3 2017-12-06 本地 hms 0.4.0 2017-11-23 CRAN (R 3.4.3) httr 1.3.1 2017-08-20 CRAN (R 3.4.3) 格子 0.20-35 2017-03- 25 CRAN (R 3.4.3) lodes * 0.1.0 2017-12-17 git (@8cca008) magrittr 1.5 2014-11-22 CRAN (R 3.4.3) maptools 0.9-2
2017-03-25 CRAN (R 3.4 .3) memoise 1.1.0 2017-04-21 CRAN (R 3.4.3) 方法 * 3.4.3 2017-12-06 本地 pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.3) plyr 1.8.4 2016-06-08 CRAN (R 3.4.3) 咕噜声 0.2.4 2017-10-18 CRAN (R 3.4.3) R6
2.2.2 2017-06-17 CRAN (R 3.4.3) rappdirs 0.3.1 2016-03-28 CRAN (R 3.4.3) Rcpp 0.12.14 2017-11-23 CRAN (R 3.4.3) 阅读器 1.1。 1 2017-05-16 CRAN (R 3.4.3) rgdal 1.2-16 2017-11-21 CRAN (R 3.4.3) rgeos 0.3-26 2017-10-31 CRAN (R 3.4.3) rlang 0.1.4 2017 -11-05 CRAN (R 3.4.3) sf 0.5-5 2017-10-31 CRAN (R 3.4.3) sp * 1.2-5 2017-06-29 CRAN (R 3.4.3) 统计 * 3.4.3 2017 -12-06 本地字符串 1.1.6 2017-11-17 CRAN (R 3.4.2) stringr * 1.2.0 2017-02-18 CRAN (R 3.4.3) tibble 1.3.4 2017-08-22 CRAN (R 3.4.3) tigris * 0.5.3
2017-05-26 CRAN (R 3.4.3) 工具 3.4.3 2017-12-06 本地
udunits2 0.13 2016-11-17 CRAN (R 3.4.1) 单位 0.4-6
2017-08-27 CRAN (R 3.4.3) utils * 3.4.3 2017-12-06 本地
uuid 0.1-2 2015-07-28 CRAN (R 3.4.1) withr 2.1.0
2017-11-01 CRAN ( R 3.4.3) XML * 3.98-1.9 2017-06-19 CRAN (R 3.4.1)

4

2 回答 2

4

如果你可以使用 GitHub 可安装的包（在我在 CRAN 上得到这个之前会有点），那么你可以给https://github.com/hrbrmstr/lodes一个机会：

devtools::install_git("https://github.com/hrbrmstr/lodes.git")

library(lodes)
library(dplyr)

de <- read_lodes("de", "od", "aux", "JT00", "2006", "~/Data/lodes")

glimpse(de)
## Observations: 68,284
## Variables: 13
## $ w_geocode  <dbl> 1.000104e+14, 1.000104e+14, 1.000104e+14, 1.000104e+14, 1.000104e+14, 1.000104e+14, 1.000104e+14...
## $ h_geocode  <chr> "240119550001006", "240119550001040", "240299501002080", "240299501003088", "240299503002017", "...
## $ S000       <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ SA01       <int> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, ...
## $ SA02       <int> 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, ...
## $ SA03       <int> 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...
## $ SE01       <int> 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, ...
## $ SE02       <int> 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, ...
## $ SE03       <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, ...
## $ SI01       <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...
## $ SI02       <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ SI03       <int> 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
## $ createdate <int> 20160228, 20160228, 20160228, 20160228, 20160228, 20160228, 20160228, 20160228, 20160228, 201602...

它具有读取和缓存人行横道文件的功能以及读取和缓存单个数据文件的功能。

如果您仍然遇到 SSL 故障，请告诉我，如果是，请将devtools::session_info()或的输出添加sessionInfo()到您的问题中。

于 2017-01-20T11:52:45.893 回答
1

我在这里找到了解决方案。它并不完美，因为它将文件加载到内存中，而不是将它们保存到磁盘。但它确实对我有用。

years.to.download <- c(2002,2004,2014)
options(scipen = 999) # Supress scientific notation so we can see census geocodes
library(plyr); library(dplyr)
library(downloader) # downloads and then runs the source() function on scripts from github
library(R.utils) # load the R.utils package (counts the number of lines in a file quickly)


# Program start ----------------------------------------------------------------
tf <- tempfile(); td <- tempdir() # Create a temporary file and a temporary directory
# Load the download.cache and related functions
# to prevent re-downloading of files once they've been downloaded.
source_url(
  "https://raw.github.com/ajdamico/asdfree/master/Download%20Cache/download%20cache.R",
  prompt = FALSE,
  echo = FALSE
)
# Loop through and download each year specified by the user
for(year in years.to.download) {
  cat("now loading", year, "...", '\n\r')
#-----------Data import: residence area characteristics---------------------  
  # Data import: workplace area characteristics (i.e. job location data)
  # Download each year of data
  # Zipped file to the temporary file on your local disk
  # S000 references all workforce segments
  # JT00 references all job types
  download_cached(
    url = paste0("http://lehd.ces.census.gov/data/lodes/LODES7/ca/wac/ca_wac_S000_JT00_", year, ".csv.gz"),
    destfile = tf,
    mode = 'wb'
  )

# Create a variable to store the wac file for each year
  assign(paste0("wac.", year), read.table(gzfile(tf), header = TRUE, sep = ",",
                                          colClasses = "numeric", stringsAsFactors = FALSE))
  # Remove the temporary file from the local disk
  file.remove(tf)
  # And free up RAM
  gc()

#-----------Data import: residence area characteristics---------------------
  download_cached(
    url = paste0("http://lehd.ces.census.gov/data/lodes/LODES7/ca/rac/ca_rac_S000_JT00_", year, ".csv.gz"),
    destfile = tf,
    mode = 'wb'
  )
    # Create a variable to store the rac file for each year
  assign(paste0("rac.", year), read.table(gzfile(tf), header = TRUE, sep = ",",
                                          colClasses = "numeric", stringsAsFactors = FALSE))
    # Remove the temporary file from the local disk
  file.remove(tf)
    # And free up RAM
  gc()
}
于 2017-01-20T00:33:16.413 回答