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ATUS/data/atusrostec_2024.dat

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ATUS/docs/Rost2024_info.txt

@@ -0,0 +1,14 @@
+
+The data file atusrost_2024.dat (2024 Roster file) contains 8 variables and 18,828 observations.  
+
+For more information about this file, see the ATUS 2024 Interview Data Dictionary, available online at:  https://www.bls.gov/tus/dictionaries/atusintcodebk24.pdf.
+
+In addition to the data file, three computer programs are contained in atusrost-2024.zip.  If you have SPSS, SAS, or Stata software, you may use these programs to read the data into your statistical software package.  The programs include variable and value labels for most variables.  For information about unlabeled values, please refer to the data dictionary.
+
+The file "atusrost_2024.sps" reads the data into SPSS; some variable names exceed length 8.
+
+The file "atusrost_2024.sas" reads the data into SAS.
+
+The file "atusrost_2024.do" reads the data into Stata.
+
+If you have SAS, SPSS, or Stata software, open the corresponding program, edit the infile (in SAS), file (in SPSS), or import (in Stata) statement to reference the data file on your computer, and run the program. 

ATUS/docs/Rostec2024_info.txt

@@ -0,0 +1,15 @@
+
+The data file atusrostec_2024.dat (2024 Eldercare Roster file) contains 12 variables and 1,738 observations.  
+
+For more information about this file, see the ATUS 2024 Interview Data Dictionary, available online at:  https://www.bls.gov/tus/dictionaries/atusintcodebk24.pdf.
+
+In addition to the data file, three computer programs are contained in atusrostec-2024.zip.  If you have SPSS, SAS, or Stata software, you may use these programs to read the data into your statistical software package.  The programs include variable and value labels for most variables.  For information about unlabeled values, please refer to the data dictionary.
+
+The file "atusrostec_2024.sps" reads the data into SPSS; some variable names exceed length 8.
+
+The file "atusrostec_2024.sas" reads the data into SAS.
+
+The file "atusrostec_2024.do" reads the data into Stata.
+
+If you have SAS, SPSS, or Stata software, open the corresponding program, edit the infile (in SAS), 
+file (in SPSS), or import (in Stata) statement to reference the data file on your computer, and run the program. 

ATUS/docs/Sum2024_info.txt

@@ -0,0 +1,24 @@
+
+The file atussum_2024.dat (2024 Activity Summary file) contains 396 variables and 7,669 observations.
+
+The 2024 Activity Summary file contains data for the total number of minutes that each respondent spent doing each 6-digit activity.  Each record corresponds to a unique respondent, as indicated by a unique value of the variable TUCASEID. 
+
+The variable names identify activities by their 6-digit codes.  The letter "t" precedes the 6-digit codes.  The first and second digits of the 6-digit code correspond to the variable tutier1code; the third and fourth digits of the 6-digit code correspond to the variable tutier2code; and, the fifth and sixth digits of the 6-digit code correspond to the variable tutier3code.  For example, the variable "t040102" refers to the activity with TUTIER1CODE=04, TUTIER2CODE=01, and TUTIER3CODE=02.  
+As documented in the ATUS 2024 Coding Lexicon (https://www.bls.gov/tus/lexicons/lexiconwex2024.pdf), activity code "040102" corresponds to "Caring for and helping nonhousehold members/Caring for and helping nonhousehold children/Reading to or with nonhousehold children." See Chapter 5 of the ATUS User's Guide (https://www.bls.gov/tus/atususersguide.pdf) for more information about the ATUS Coding Lexicon. 
+All "t" activity variables on this file correspond to an activity that at least one survey respondent reported doing for one minute or more.  
+
+In addition to the activity code variables (t010101...t509999), the file atussum_2024.dat includes selected variables (such as age, sex, and labor force status of the respondent) from the Respondent and ATUS-CPS files.  The definitions for variables that begin with the letter "P" or "G" appear in the 2024 ATUS-CPS Data Dictionary, available at https://www.bls.gov/tus/dictionaries/atuscpscodebk24.pdf; the definitions 
+for variables that begin with the letter "T" (other than t010101...t509999) appear in the 2024 ATUS Interview Data Dictionary, available at 
+https://www.bls.gov/tus/atusintcodebk24.pdf.  
+
+In addition to the data file, three computer programs are contained in atussum-2024.zip.  If you have SPSS, SAS, or Stata software, you may use these programs to read the data into your statistical software package.  
+
+The file "atussum_2024.sps" reads the data into SPSS; some variable names exceed length 8.
+
+The file "atussum_2024.sas" reads the data into SAS.
+
+The file "atussum_2024.do" reads the data into Stata.
+
+If you have SAS, SPSS, or Stata software, open the corresponding program, edit the infile (in SAS), file (in SPSS), or import (in Stata) statement to reference the data file on your computer, and run the program. 
+
+

ATUS/docs/Who2024_info.txt

@@ -0,0 +1,15 @@
+
+The data file atuswho_2024.dat (2024 Who file) contains 5 variables and 167,653 observations.  
+
+For more information about this file, see the ATUS 2024 Interview Data Dictionary, available online at:  https://www.bls.gov/tus/dictionaries/atusintcodebk24.pdf.
+
+In addition to the data file, three computer programs are contained in atuswho-2024.zip.  If you have SPSS, SAS, or Stata software, you may use these programs to read the data into your statistical software package.  The programs include variable and value labels for most variables.  For information about unlabeled values, please refer to the data dictionary.
+
+The file "atuswho_2024.sps" reads the data into SPSS; some variable names exceed length 8.
+
+The file "atuswho_2024.sas" reads the data into SAS.
+
+The file "atuswho_2024.do" reads the data into Stata.
+
+If you have SAS, SPSS, or Stata software, open the corresponding program, edit the infile (in SAS), 
+file (in SPSS), or import (in Stata) statement to reference the data file on your computer, and run the program. 

NSFG/docs/NSFG_2022_2023_FemPregPUFData_readme.txt

@@ -0,0 +1,53 @@
+Dear NSFG Data User, 
+
+We would like to bring to your attention that when importing a CSV data file into SAS or R, the 
+software by default determines the variable attributes (i.e., data type and length) based on the 
+first few observations, which may be an insufficient number of observations if these include few 
+or no responses. Variables with a length of two or more (e.g., R's age at interview AGE_R with values 15-49) 
+and with many missing values can get truncated to length of one (e.g., values 10–12 will be read 
+as 1) or be given an incorrect data type. In NSFG a variable may have many missing values in cases where most of the 
+respondents are out of universe for the question. 
+
+------
+
+Data users can set the number of observations that the software uses for determining the length of 
+variables in the CSV dataset. 
+
+For SAS, at least half the number of observations in the data file 
+may be reasonable and using all the observations in the file may be necessary if variables are not
+read correctly. 
+
+For R, specifying how to read the variable attribute depends on whether you use 
+standard R or the readr R library or an R library that includes readr such as tidyverse. To confirm 
+that your software correctly created your data file from the CSV file, we advise that you compare 
+the frequencies from your file to the frequencies provided in the codebooks on the website (and 
+focus on those variables with few observations). 
+
+------
+
+Options to change the default:
+
+SAS
+In SAS, add the option ‘guessingrows’ to the import statement. The 2022-2023 NSFG Female Pregnancy File has 8247
+observations and the example below specifies to use 4,000 observations to determine the length:
+
+proc import datafile="NSFG_2022_2023_FemPregPUFData.csv" out=female dbms=csv; getnames=yes; guessingrows=MAX; run;
+
+------
+
+Standard R
+The read.csv function will read the entire file and wait until the end to determine variable attributes. 
+The default that converts text strings into enumerated values can be disabled. The example below reads 
+the entire 2022-2023 Female Pregnancy CSV file and does not convert text strings into factors but leaves them as 
+text strings. 
+
+2022_2023_FemPregData <- read.csv("NSFG_2022_2023_FemPregPUFData.csv", stringsAsFactors=FALSE)
+
+------
+
+R Library(readr)
+The read_csv function by default only reads in 1,000 records to determine variable attributes and will not 
+convert strings to factors. If using read_csv, add the argument ‘guess_max.’ The 2022-2023 NSFG Female Respondent file has 
+8247 observations and the example below specifies to use 8247 observations to determine the data type.
+
+2022_2023_FemPregData <- read_csv("NSFG_2022_2023_FemPregPUFData.csv", guess_max = Inf)

NSFG/docs/NSFG_2022_2023_FemRespPUFData_readme.txt

@@ -0,0 +1,53 @@
+Dear NSFG Data User, 
+
+We would like to bring to your attention that when importing a CSV data file into SAS or R, the 
+software by default determines the variable attributes (i.e., data type and length) based on the 
+first few observations, which may be an insufficient number of observations if these include few 
+or no responses. Variables with a length of two or more (e.g., R's age at interview AGE_R with values 15-49) 
+and with many missing values can get truncated to length of one (e.g., values 10–12 will be read 
+as 1) or be given an incorrect data type. In NSFG a variable may have many missing values in cases where most of the 
+respondents are out of universe for the question. 
+
+------
+
+Data users can set the number of observations that the software uses for determining the length of 
+variables in the CSV dataset. 
+
+For SAS, at least half the number of observations in the data file 
+may be reasonable and using all the observations in the file may be necessary if variables are not
+read correctly. 
+
+For R, specifying how to read the variable attribute depends on whether you use 
+standard R or the readr R library or an R library that includes readr such as tidyverse. To confirm 
+that your software correctly created your data file from the CSV file, we advise that you compare 
+the frequencies from your file to the frequencies provided in the codebooks on the website (and 
+focus on those variables with few observations). 
+
+------
+
+Options to change the default:
+
+SAS
+In SAS, add the option ‘guessingrows’ to the import statement. The 2022-2023 NSFG Female Respondent File has 5586 
+observations and the example below specifies to use 2,000 observations to determine the length:
+
+proc import datafile="NSFG_2022_2023_FemRespPUFData.csv" out=female dbms=csv; getnames=yes; guessingrows=MAX; run;
+
+------
+
+Standard R
+The read.csv function will read the entire file and wait until the end to determine variable attributes. 
+The default that converts text strings into enumerated values can be disabled. The example below reads 
+the entire 2022-2023 Female Respondent CSV file and does not convert text strings into factors but leaves them as 
+text strings. 
+
+2022_2023_FemRespData <- read.csv("NSFG_2022_2023_FemRespPUFData.csv", stringsAsFactors=FALSE)
+
+------
+
+R Library(readr)
+The read_csv function by default only reads in 1,000 records to determine variable attributes and will not 
+convert strings to factors. If using read_csv, add the argument ‘guess_max.’ The 2022-2023 NSFG Female Respondent file has 
+5586 observations and the example below specifies to use 5586 observations to determine the data type.
+
+2022_2023_FemRespData <- read_csv("NSFG_2022_2023_FemRespPUFData.csv", guess_max = Inf)

NSFG/docs/NSFG_2022_2023_MaleRespPUFData_readme.txt

@@ -0,0 +1,53 @@
+Dear NSFG Data User, 
+
+We would like to bring to your attention that when importing a CSV data file into SAS or R, the 
+software by default determines the variable attributes (i.e., data type and length) based on the 
+first few observations, which may be an insufficient number of observations if these include few 
+or no responses. Variables with a length of two or more (e.g., R's age at interview AGE_R with values 15-49) 
+and with many missing values can get truncated to length of one (e.g., values 10–12 will be read 
+as 1) or be given an incorrect data type. In NSFG a variable may have many missing values in cases where most of the 
+respondents are out of universe for the question. 
+
+------
+
+Data users can set the number of observations that the software uses for determining the length of 
+variables in the CSV dataset. 
+
+For SAS, at least half the number of observations in the data file 
+may be reasonable and using all the observations in the file may be necessary if variables are not
+read correctly. 
+
+For R, specifying how to read the variable attribute depends on whether you use 
+standard R or the readr R library or an R library that includes readr such as tidyverse. To confirm 
+that your software correctly created your data file from the CSV file, we advise that you compare 
+the frequencies from your file to the frequencies provided in the codebooks on the website (and 
+focus on those variables with few observations). 
+
+------
+
+Options to change the default:
+
+SAS
+In SAS, add the option ‘guessingrows’ to the import statement. The 2022-2023 NSFG Male Respondent File has 4371
+observations and the example below specifies to use 2,000 observations to determine the length:
+
+proc import datafile="NSFG_2022_2023_MaleRespPUFData.csv" out=female dbms=csv; getnames=yes; guessingrows=MAX; run;
+
+------
+
+Standard R
+The read.csv function will read the entire file and wait until the end to determine variable attributes. 
+The default that converts text strings into enumerated values can be disabled. The example below reads 
+the entire 2022-2023 Male Respondent CSV file and does not convert text strings into factors but leaves them as 
+text strings. 
+
+2022_2023_MaleRespData <- read.csv("NSFG_2022_2023_MaleRespPUFData.csv", stringsAsFactors=FALSE)
+
+------
+
+R Library(readr)
+The read_csv function by default only reads in 1,000 records to determine variable attributes and will not 
+convert strings to factors. If using read_csv, add the argument ‘guess_max.’ The 2022-2023 NSFG Male Respondent file has 
+4371 observations and the example below specifies to use 4371 observations to determine the data type.
+
+2022_2023_MaleRespData <- read_csv("NSFG_2022_2023_MaleRespPUFData.csv", guess_max = Inf)