我在Windows 7中使用RStudio，我在向操作系统释放内存时遇到问题。以下是我的代码。在for循环中：
acs通过临时对象.csv将它们保存在table文件中。table（通常大小：几MB），我使用包pryr来检查内存使用情况。 根据函数mem_used()，在删除table之后，R总是返回到常量内存使用情况;相反，根据Windows任务管理器，rsession.exe（而不是Rstudio）的内存分配在每次迭代时都会增加，并最终导致rsession崩溃。 gc()的使用没有帮助。我已经阅读了很多类似的问题，但似乎唯一解决空闲内存的方法是重启R会话，这似乎很愚蠢。
有什么建议吗？
library(acs)
library(pryr)
# for loop to extract tables from API and save them on API
for (i in 128:length(tablecodes)) {
tryCatch({table <- acs.fetch(table.number = tablecodes[i],endyear = 2014, span=5,
geography = geo.make(state = "NY", county = "*", tract = "*"),
key = "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851",col.names="pretty")},
error = function(e){print("Table skipped") })
# if the table is actually fetched then we save it
if (exists("table", mode="S4")) {
print(paste("Table",i,"fetched")
if (!is.na(table)){
write.csv(estimate(table),paste("./CENSUS_tables/NY/",tablecodes[i],".csv",sep = ""))
}
print(mem_used())
print(mem_change(rm(table)))
gc()
}
}
答案 0 :(得分：4)
我能够确认Windows 7上存在内存问题。（在MacOSX上通过VMware Fusion运行）。它似乎也存在于MacOSX上，虽然内存使用率似乎很平缓[未经证实但表示内存泄漏]。 MacOSX稍微有些棘手，因为如果操作系统看到高使用率，操作系统会压缩内存。
根据上述内容，我的建议是，当您从美国人口普查局下载时，将表格下载集分成较小的组。为什么？那么，查看您正在下载数据的代码以存储在.CSV文件中。因此，短期内的解决方法是打破您正在下载的表列表。您的程序应该能够在一组运行中成功完成。
一个选项是创建一个包装器RScript并使其在N次运行中运行，其中每次调用都会调用一个单独的R会话。即Rscript串行调用N个RSessions，每个会话下载N个文件
NB。基于你的代码和观察到的内存使用情况，我的感觉是你正在下载很多表，因此在R会话中拆分可能是最好的选择。
nb。以下内容适用于Windows 7上的cgiwin。
示例：下载主表01到27 - 如果它们不存在则跳过...
!#/bin/bash
#Ref: https://censusreporter.org/topics/table-codes/
# Params: Primary Table Year Span
for CensusTableCode in $(seq -w 1 27)
do
R --no-save -q --slave < ./PullCensus.R --args B"$CensusTableCode"001 2014 5
done
if (!require(acs)) install.packages("acs")
if (!require(pryr)) install.packages("pryr")
# You can obtain a US Census key from the developer site
# "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
api.key.install(key = "** Secret**")
setwd("~/dev/stackoverflow/37264919")
# Extract Table Structure
#
# B = Detailed Column Breakdown
# 19 = Income (Households and Families)
# 001 =
# A - I = Race
#
args <- commandArgs(trailingOnly = TRUE) # trailingOnly=TRUE means that only your arguments are returned
if ( length(args) != 0 ) {
tableCodes <- args[1]
defEndYear = args[2]
defSpan = args[3]
} else {
tableCodes <- c("B02001")
defEndYear = 2014
defSpan = 5
}
# for loop to extract tables from API and save them on API
for (i in 1:length(tableCodes))
{
tryCatch(
table <- acs.fetch(table.number = tableCodes[i],
endyear = defEndYear,
span = defSpan,
geography = geo.make(state = "NY",
county = "*",
tract = "*"),
col.names = "pretty"),
error = function(e) { print("Table skipped")} )
# if the table is actually fetched then we save it
if (exists("table", mode = "S4"))
{
print(paste("Table", i, "fetched"))
if (!is.na(table))
{
write.csv(estimate(table), paste(defEndYear,"_",tableCodes[i], ".csv", sep = ""))
}
print(mem_used())
print(mem_change(rm(table)))
gc(reset = TRUE)
print(mem_used())
}
}
我希望以上有助于举例。这是一种方法。 ; - ）
吨。
我将查看包源，看看我是否能看到实际上是错的。或者，您自己也可以缩小范围并针对包提出错误。
我的感觉是，可能有助于提供一个有效的代码示例来构建上述解决方法。为什么？这里的目的是提供一个人们可能用来测试和考虑正在发生的事情的例子。为什么？嗯，这使您更容易理解您的问题和意图。
基本上，（据我所知），您从美国人口普查网站批量下载美国人口普查数据。表格代码用于指定您要下载的数据。好吧，所以我刚刚创建了一组表代码并测试了内存使用情况，看看是否开始按照你的解释使用内存。
library(acs)
library(pryr)
library(tigris)
library(stringr) # to pad fips codes
library(maptools)
# You can obtain a US Census key from the developer site
# "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
api.key.install(key = "<INSERT KEY HERE>")
# Table Codes
#
# While Census Reporter hopes to save you from the details, you may be
# interested to understand some of the rationale behind American Community
# Survey table identifiers.
#
# Detailed Tables
#
# The bulk of the American Community Survey is the over 1400 detailed data
# tables. These tables have reference codes, and knowing how the codes are
# structured can be helpful in knowing which table to use.
#
# Codes start with either the letter B or C, followed by two digits for the
# table subject, then 3 digits that uniquely identify the table. (For a small
# number of technical tables the unique identifier is 4 digits.) In some cases
# additional letters for racial iterations and Puerto Rico-specific tables.
#
# Full and Collapsed Tables
#
# Tables beginning with B have the most detailed column breakdown, while a
# C table for the same numbers will have fewer columns. For example, the
# B02003 table ("Detailed Race") has 71 columns, while the "collapsed
# version," C02003 has only 19 columns. While your instinct may be to want
# as much data as possible, sometimes choosing the C table can simplify
# your analysis.
#
# Table subjects
#
# The first two digits after B/C indicate the broad subject of a table.
# Note that many tables have more than one subject, but this reflects the
# main subject.
#
# 01 Age and Sex
# 02 Race
# 03 Hispanic Origin
# 04 Ancestry
# 05 Foreign Born; Citizenship; Year or Entry; Nativity
# 06 Place of Birth07Residence 1 Year Ago; Migration
# 08 Journey to Work; Workers' Characteristics; Commuting
# 09 Children; Household Relationship
# 10 Grandparents; Grandchildren
# 11 Household Type; Family Type; Subfamilies
# 12 Marital Status and History13Fertility
# 14 School Enrollment
# 15 Educational Attainment
# 16 Language Spoken at Home and Ability to Speak English
# 17 Poverty
# 18 Disability
# 19 Income (Households and Families)
# 20 Earnings (Individuals)
# 21 Veteran Status
# 22 Transfer Programs (Public Assistance)
# 23 Employment Status; Work Experience; Labor Force
# 24 Industry; Occupation; Class of Worker
# 25 Housing Characteristics
# 26 Group Quarters
# 27 Health Insurance
#
# Three groups of tables reflect technical details about how the Census is
# administered. In general, you probably don't need to look at these too
# closely, but if you need to check for possible weaknesses in your data
# analysis, they may come into play.
#
# 00 Unweighted Count
# 98 Quality Measures
# 99 Imputations
#
# Race and Latino Origin
#
# Many tables are provided in multiple racial tabulations. If a table code
# ends in a letter from A-I, that code indicates that the table universe is
# restricted to a subset based on responses to the race or
# Hispanic/Latino-origin questions.
#
# Here is a guide to those codes:
#
# A White alone
# B Black or African American Alone
# C American Indian and Alaska Native Alone
# D Asian Alone
# E Native Hawaiian and Other Pacific Islander Alone
# F Some Other Race Alone
# G Two or More Races
# H White Alone, Not Hispanic or Latino
# I Hispanic or Latino
setwd("~/dev/stackoverflow/37264919")
# Extract Table Structure
#
# B = Detailed Column Breakdown
# 19 = Income (Households and Families)
# 001 =
# A - I = Race
#
tablecodes <- c("B19001", "B19001A", "B19001B", "B19001C", "B19001D",
"B19001E", "B19001F", "B19001G", "B19001H", "B19001I" )
# for loop to extract tables from API and save them on API
for (i in 1:length(tablecodes))
{
print(tablecodes[i])
tryCatch(
table <- acs.fetch(table.number = tablecodes[i],
endyear = 2014,
span = 5,
geography = geo.make(state = "NY",
county = "*",
tract = "*"),
col.names = "pretty"),
error = function(e) { print("Table skipped")} )
# if the table is actually fetched then we save it
if (exists("table", mode="S4"))
{
print(paste("Table", i, "fetched"))
if (!is.na(table))
{
write.csv(estimate(table), paste("T",tablecodes[i], ".csv", sep = ""))
}
print(mem_used())
print(mem_change(rm(table)))
gc()
print(mem_used())
}
}
> library(acs)
> library(pryr)
> library(tigris)
> library(stringr) # to pad fips codes
> library(maptools)
> # You can obtain a US Census key from the developer site
> # "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
> api.key.install(key = "...secret...")
>
...
> setwd("~/dev/stackoverflow/37264919")
>
> # Extract Table Structure
> #
> # B = Detailed Column Breakdown
> # 19 = Income (Households and Families)
> # 001 =
> # A - I = Race
> #
> tablecodes <- c("B19001", "B19001A", "B19001B", "B19001C", "B19001D",
+ "B19001E", "B19001F", "B19001G", "B19001H", "B19001I" )
>
> # for loop to extract tables from API and save them on API
> for (i in 1:length(tablecodes))
+ {
+ print(tablecodes[i])
+ tryCatch(
+ table <- acs.fetch(table.number = tablecodes[i],
+ endyear = 2014,
+ span = 5,
+ geography = geo.make(state = "NY",
+ county = "*",
+ tract = "*"),
+ col.names = "pretty"),
+ error = function(e) { print("Table skipped")} )
+
+ # if the table is actually fetched then we save it
+ if (exists("table", mode="S4"))
+ {
+ print(paste("Table", i, "fetched"))
+ if (!is.na(table))
+ {
+ write.csv(estimate(table), paste("T",tablecodes[i], ".csv", sep = ""))
+ }
+ print(mem_used())
+ print(mem_change(rm(table)))
+ gc()
+ print(mem_used())
+ }
+ }
[1] "B19001"
[1] "Table 1 fetched"
95.4 MB
-1.88 MB
93.6 MB
[1] "B19001A"
[1] "Table 2 fetched"
95.4 MB
-1.88 MB
93.6 MB
[1] "B19001B"
[1] "Table 3 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001C"
[1] "Table 4 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001D"
[1] "Table 5 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001E"
[1] "Table 6 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001F"
[1] "Table 7 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001G"
[1] "Table 8 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001H"
[1] "Table 9 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001I"
[1] "Table 10 fetched"
95.5 MB
-1.88 MB
93.6 MB
>ll
total 8520
drwxr-xr-x@ 13 hidden staff 442B Oct 17 20:41 .
drwxr-xr-x@ 40 hidden staff 1.3K Oct 17 23:17 ..
-rw-r--r--@ 1 hidden staff 4.4K Oct 17 23:43 37264919.R
-rw-r--r--@ 1 hidden staff 492K Oct 17 23:50 TB19001.csv
-rw-r--r--@ 1 hidden staff 472K Oct 17 23:51 TB19001A.csv
-rw-r--r--@ 1 hidden staff 414K Oct 17 23:51 TB19001B.csv
-rw-r--r--@ 1 hidden staff 387K Oct 17 23:51 TB19001C.csv
-rw-r--r--@ 1 hidden staff 403K Oct 17 23:51 TB19001D.csv
-rw-r--r--@ 1 hidden staff 386K Oct 17 23:51 TB19001E.csv
-rw-r--r--@ 1 hidden staff 402K Oct 17 23:51 TB19001F.csv
-rw-r--r--@ 1 hidden staff 393K Oct 17 23:52 TB19001G.csv
-rw-r--r--@ 1 hidden staff 465K Oct 17 23:44 TB19001H.csv
-rw-r--r--@ 1 hidden staff 417K Oct 17 23:44 TB19001I.csv