我正在尝试从国家卫生统计中心导入死亡率多因文件，位于此链接：
https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm#Downloadable
这些文件的扩展名是.DUSMCPUB（例如，2020 年的文件名为“VS20MORT.DUSMCPUB_r20220105”）。如何导入这样的文件？我不熟悉该扩展名。
我尝试使用以下代码导入，但它导致我的 R 程序终止。您能为我提供有关如何导入这些类型的文件的建议吗？
VS20MORT <- read_delim("VS20MORT.DUSMCPUB_r20220105")
感谢@Mel G 分享这种方法。当我尝试运行它时，我意识到死亡率文件包含截至 2020 年的一些新变量（即死者的职业和行业）。这是包含新变量的细微变化。
# Install and load necessary packages
# install.packages("sqldf") # Used to read in DUSMCPUB file
# install.packages("dplyr") # Used for tidy data management
library(sqldf)
library(dplyr)
#Increase memory limit to make space for large file
# memory.limit()
memory.limit(size=20000)
# Create dataframe containing variables for column width, name, and end position
columns <- data.frame(widths=c(19,1,40,2,1,1,2,2,1,4,1,2,2,2,2,1,1,1,16,4,1,1,1,
1,34,1,1,4,3,1,3,3,2,1,2,7,7,7,7,7,7,7,7,7,7,7,7,
7,7,7,7,7,7,7,7,36,2,1,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5,5,5,5,5,1,2,1,1,1,1,33,3,1,1,2,315,4,2,4,2))
columns$names <- c("blank1", # tape locations 1-19
"Resident_Status_US", # tape location 20
"blank2",
"Education_1989",
"Education_2003",
"Education_flag",
"Month_of_Death",
"blank3",
"Sex",
"DetailAge",
"Age_Substitution_Flag",
"Age_Recode_52",
"Age_Recode_27",
"Age_Recode_12",
"Infant_Age_Recode_22",
"Place_of_Death_and_Status",
"Marital_Status",
"Day_of_Week_of_Death",
"blank4",
"Current_Data_Year",
"Injury_at_Work",
"Manner_of_Death",
"Method_of_Disposition",
"Autopsy",
"blank5",
"Activity_Code",
"Place_of_Injury",
"ICD_Code_10",
"Cause_Recode_358",
"blank6",
"Cause_Recode_113",
"Infant_Cause_Recode_130",
"Cause_Recode_39",
"blank7",
"Number_Entity_Axis_Conditions",
"Condition_1EA", "Condition_2EA", "Condition_3EA", "Condition_4EA", "Condition_5EA",
"Condition_6EA", "Condition_7EA", "Condition_8EA", "Condition_9EA", "Condition_10EA",
"Condition_11EA", "Condition_12EA", "Condition_13EA", "Condition_14EA", "Condition_15EA",
"Condition_16EA", "Condition_17EA", "Condition_18EA", "Condition_19EA", "Condition_20EA",
"blank8",
"Number_Record_Axis_Conditions",
"blank9",
"Condition_1RA", "Condition_2RA", "Condition_3RA", "Condition_4RA", "Condition_5RA",
"Condition_6RA", "Condition_7RA", "Condition_8RA", "Condition_9RA", "Condition_10RA",
"Condition_11RA", "Condition_12RA", "Condition_13RA", "Condition_14RA", "Condition_15RA",
"Condition_16RA", "Condition_17RA", "Condition_18RA", "Condition_19RA", "Condition_20RA",
"blank10",
"Race",
"Bridged_Race_Flag",
"Race_Imputation_Flag",
"Race_Recode_3",
"Race_Recode_5",
"blank11",
"Hispanic_Origin",
"blank12",
"Hispanic_Origin_9_Race_Recode",
"Race_Recode_40",
"blank13",
"CensusOcc",
"Occ_26",
"CensusInd",
"Ind_23")
# Read in file using parameters from 'columns' dataframe
mort2020<- read.fwf("VS20MORT.DUSMCPUB_r20220105", widths=columns$widths, stringsAsFactors=F)
# Attach column names to variables
colnames(mort2020) <- columns$names
# Remove blank variables
mort2020x <- mort2020 %>% dplyr::select(-starts_with("blank"))
或者，看起来这些文件大部分时间都以 CSV 格式发布：https://www.nber.org/research/data/mortality-data-vital-statistics-nchs-multiple-cause-death-数据。 2020 年尚未到来，但对于其他年份，将 CSV 读入 R 可能比使用 read.fwf 快得多。
数据是固定宽度文件的形式。国家卫生统计中心数据的用户指南包含适当的宽度。我给出的答案是另一个论坛的修改答案，由 @Hack-R 发布。
map <- data.frame(widths=c(19, 1,40,2,1,1,2,2,1,1,1,1,1,1,2,2,2,2,1,1,1,16,4,1,1,1,1,34,1,1,4,
3,1,3,3,2,1,2,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,
36,2,1,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,2,1,1,1,1,33,3,
1,1))
#Set column names
map$cn <- c("blank", # cols 1-19
"res_status", #20
"blank2", # 21-60
"ed_v89",#61-62
"ed_v03",#63
"ed_flag", #64
"death_month", #65-66
"blank3",
"sex",
"age_years",
"age_months",
"age_3",
"age_4",
"age_sub_flag",
"age_recode_52",
"age_recode_27",
"age_recode_12",
"infant_age_recode_22",
"place_of_death",
"marital_status",
"death_day",
"blank4",
"current_year",
"work_injury",
"death_manner",
"disposition",
"autopsy",
"blank5",
"activity_code",
"place_injured",
"icd_cause_of_death",
"cause_recode358",
"blank6",
"cause_recode113",
"infant_cause_recode130",
"cause_recode39",
"blank7",
"num_entity_axis",
"cond1","cond2","cond3","cond4","cond5","cond6","cond7","cond8","cond9","cond10",
"cond11","cond12","cond13","cond14","cond15","cond16","cond17","cond18","cond19",
"cond20",
"blank7",
"num_rec_axis_cond",
"blank8",
"acond1", "acond2", "acond3", "acond4", "acond5", "acond6", "acond7",
"acond8", "acond9", "acond10", "acond11", "acond12", "acond13", "acond14",
"acond15", "acond16", "acond17", "acond18", "acond19", "acond20",
"blank9",
"race",
"bridged_race_flag",
"race_imp_flag",
"race_recode3",
"race_recode5",
"blank10",
"hisp",
"blank11",
"hisp_recode")
#Import the file
mort2020 <- read_fwf("./data/original/VS20MORT.DUSMCPUB_r20220105", fwf_widths(map$widths, map$cn))
更新2022年数据：
#Increase memory limit to make space for large file
# memory.limit()
memory.limit(size=20000)
# Create dataframe containing variables for column width, name, and end position
columns <- data.frame(widths=c(18,1,1,42,1,1,2,2,1,4,1,2,2,2,2,1,1,1,16,4,1,1,1,
1,34,1,1,4,3,1,3,3,2,1,2,7,7,7,7,7,7,7,7,7,7,7,7,
7,7,7,7,7,7,7,7,36,2,1,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5,5,5,5,5,4,1,1,1,33,3,2,2,315,4,2,4,2))
columns$names <- c("blank1", # tape locations 1-18
"record_type", #19
"Resident_Status_US", # tape location 20
"blank2",
"Education_2003", #1
"Education_flag", #1
"Month_of_Death", #2
"blank3", #2
"Sex", #1
"DetailAge",#4
"Age_Substitution_Flag", #1
"Age_Recode_52", # 2
"Age_Recode_27", #2
"Age_Recode_12", #2
"Infant_Age_Recode_22", #2
"Place_of_Death_and_Status", #1
"Marital_Status", #1
"Day_of_Week_of_Death", #1
"blank4", #16
"Current_Data_Year", #4
"Injury_at_Work",#1
"Manner_of_Death", #1
"Method_of_Disposition", #1
"Autopsy", #1
"blank5", #34
"Activity_Code", #1
"Place_of_Injury", #1
"ICD_Code_10", #4
"Cause_Recode_358", #3
"blank6", #1
"Cause_Recode_113", #3
"Infant_Cause_Recode_130", #3
"Cause_Recode_39", #2
"blank7", #1
"Number_Entity_Axis_Conditions", #2
"Condition_1EA", "Condition_2EA", "Condition_3EA", "Condition_4EA", "Condition_5EA",
"Condition_6EA", "Condition_7EA", "Condition_8EA", "Condition_9EA", "Condition_10EA",
"Condition_11EA", "Condition_12EA", "Condition_13EA", "Condition_14EA", "Condition_15EA",
"Condition_16EA", "Condition_17EA", "Condition_18EA", "Condition_19EA", "Condition_20EA",
"blank8",
"Number_Record_Axis_Conditions",
"blank9",
"Condition_1RA", "Condition_2RA", "Condition_3RA", "Condition_4RA", "Condition_5RA",
"Condition_6RA", "Condition_7RA", "Condition_8RA", "Condition_9RA", "Condition_10RA",
"Condition_11RA", "Condition_12RA", "Condition_13RA", "Condition_14RA", "Condition_15RA",
"Condition_16RA", "Condition_17RA", "Condition_18RA", "Condition_19RA", "Condition_20RA",
"blank10", #4
"race_imputed", #1
"blank10_1", #1,
"race_recod_6", #1
"blank11" #33
"Hispanic_Origin",#3
"Hispanic_Origin_9_Race_Recode", #2
"Race_Recode_40",#2
"blank13", #315
"CensusOcc", # 4
"Occ_26", #2
"CensusInd", #4
"Ind_23") #2
# Read in file using parameters from 'columns' dataframe
mort2022<- read.fwf(here::here(data_path, "VS22MORT.DUSMCPUB_r20240307"),
widths=columns$widths, stringsAsFactors=F)
# Attach column names to variables
colnames(mort2022) <- columns$names
# Remove blank variables
mort2020x <- mort2022 %>% dplyr::select(-starts_with("blank")) %>%clean_names()