Question

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/

我需要为工作场所和居住地点下载多年的数据。这些文件是定期命名的，技术文档可以在这里找到：

https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.2.pdf S000引用所有劳动力细分市场 JT00引用所有作业类型

因此典型的文件名是：ca_wac_S000_JT00_2008.csv.gz 在https://lehd.ces.census.gov/data/lodes/LODES7/ca/wac/

的＆＃39;目录＆＃URL中

This bit of git-hub code seems relevant。 Harvard tutorial非常有用，为我提供了一种创建所有文件列表的方法。但是当我遇到SSL问题时，我无法让实际的下载工作 - R.curl hasn't worked for me。

扩展代码似乎无效：

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）系统x86_64，mingw32 ui RStudio     （1.1.383）语言（EN）整理     English_United States.1252 tz America / Denver
    日期2017-12-17


包   -------------------------------------------------- ----------------------------------------- package * version date source 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）基数* 3.4.3 2017-12-06本地bindr 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当地卷曲* 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）devtools * 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）外国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）备忘录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）purrr 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）readr   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）stats   * 3.4.3 2017-12-06 local stringi 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当地
  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 local
  uuid 0.1-2 2015-07-28 CRAN（R 3.4.1）with 2.1.0
  2017-11-01 CRAN（R 3.4.3）XML * 3.98-1.9 2017-06-19 CRAN（R   3.4.1）

Answer 1

如果你可以使用GitHub可安装的软件包（在我在CRAN上获得它之前会有一点点），那么你可以给https://github.com/hrbrmstr/lodes一个去：

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...

它具有阅读和阅读功能。缓存crosswalk文件和一个读取和缓存单个数据文件的函数。

如果您仍然遇到SSL失败，请与我们联系。如果是，请将devtools::session_info()或sessionInfo()的输出添加到您的问题中。

Answer 2

我找到了解决方案here。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上。但它对我有用。

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()
}

如何使用R从网站下载LEHD数据？

2 个答案: