The sample SAS and Stata code provided here is intended for use with certain datasets in the National Neighborhood Data Archive (NaNDA). NaNDA (https://www.openicpsr.org/openicpsr/nanda) contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk was previously available on the UDS Mapper website, which is no longer active. An archived copy of the ZIP-code-to-ZCTA crosswalk file has been included here. Sample SAS and Stata code are provided for merging the UDS mapper crosswalk with NaNDA datasets.

This dataset consists of DOHGS merged data from the 19 research flights with the C-130 over the Southeast U.S. between June 1 and July 15, 2013, as part of the Southeast Atmosphere Study (SAS). Merged data files have been created, combining all observations on the C-130 to a common time base for each flight. Version R5 (created Jan 21, 2015) of the merges includes all data available as of Jan 12. Start and stop times taken from the DOHGS file, midtime calculated from them. Averaging and missing value treatment as in 1-min merge.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

NIS 2002-2011 Within Year Merge

• Each year of the NIS has a Core, Hospital and Severity file: File Level ID Core discharge KEY, HOSPID Hospital hospital HOSPID Severity discharge KEY, HOSPID

1. The 2 dischrage-level files will trimmed down to desired variables and then merged by KEY and saved into a temporary SAS dataset.
2. The hospital file will be trimmed and then merged into the core-severity and saved into a permanent SAS dataset with following notation: NIS_YYYY
3. Working directory cleared after every year since very large datasets.

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

The New York City Department of Health and Mental Hygiene, with support from the National Center for Health Statistics, conducted the New York City Health and Nutrition Examination Survey (NYC HANES) to improve disease surveillance and establish citywide estimates for several previously unmeasured health conditions from which reduction targets could be set and incorporated into health policy planning initiatives. NYC HANES also provides important new information about the prevalence and control of chronic disease precursors, such as undiagnosed hypertension, hypercholesterolemia, and impaired fasting glucose, which allow chronic disease programs to monitor more proximate health events and rapidly evaluate primary intervention efforts. Study findings are used by the public health community in New York City, as well as by researchers and clinicians, to better target resources to the health needs of the population. The NYC HANES data consist of the following six datasets: (1) Study Participant File (SPfile), (2) Computer-Assisted Personal Interview (CAPI), (3) Audio Computer-Assisted Self-Interview (ACASI), (4) Composite International Diagnostic Interview(CIDI), (5) Examination Component, and (6) Laboratory Component. The Study Participant File contains variables necessary for all analyses, therefore, when using the other datasets, they should be merged to this file. Variable P_ID is the unique identifier used to merge all datasets. Merging information from multiple NYC HANES datasets using SP_ID ensures that the appropriate information for each SP is linked correctly. (SAS datasets must be sorted by SP_ID prior to merging.) Please note that NYC HANES datasets may not have the same number of records for each component because some participants did not complete each component. Demographic variables include race/ethnicity, Hispanic origin, age, body weight, gender, education level, marital status, and country of birth.

A novel features set that uniquely characterizes the object’s shape, and takes into account the object’s highlight-shadow geometrical relations.

Dataset

This dataset was created by Arslan Gabdulkhakov

Released under Apache 2.0

Contents

analyze the area resource file (arf) with r the arf is fun to say out loud. it's also a single county-level data table with about 6,000 variables, produced by the united states health services and resources administration (hrsa). the file contains health information and statistics for over 3,000 us counties. like many government agencies, hrsa provides only a sas importation script and an as cii file. this new github repository contains two scripts: 2011-2012 arf - download.R download the zipped area resource file directly onto your local computer load the entire table into a temporary sql database save the condensed file as an R data file (.rda), comma-separated value file (.csv), and/or stata-readable file (.dta). 2011-2012 arf - analysis examples.R limit the arf to the variables necessary for your analysis sum up a few county-level statistics merge the arf onto other data sets, using both fips and ssa county codes create a sweet county-level map click here to view these two scripts for mo re detail about the area resource file (arf), visit: the arf home page the hrsa data warehouse notes: the arf may not be a survey data set itself, but it's particularly useful to merge onto other survey data. confidential to sas, spss, stata, and sudaan users: time to put down the abacus. time to transition to r. :D

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

Context

The The datasets used in the new analyses are available to the public on the NHTSA website.

The documentation for the data sets can be downloaded from the same site. The best source of info for the CDS data is the NASS CDS Coding & Editing Manual and the NASS CDS Analytical User’s Manual. Both may be found here
Code Books

https://crashstats.nhtsa.dot.gov/#/DocumentTypeList/5

The four SAS data sets used are as follows;

Content

"Occupant Assessment" - Providing information about the occupant and injuries

"General Vehicle" - Providing information about the impact speeds and the vehicle model

"Vehicle Exterior" - Provides the direction of impact

"Accident Description" - Provides the "ratio inflation factor" used for weighting the data.

These data sets are provided for each year. Individual cases are distinguished by year of accident, primary sampling unit, and case ID. The case IDs refer to the accident; within accident individual cars are identified by a "vehicle number" and within vehicles, each occupant is identified by an "occupant number". Using these identifiers, the user may combine the data sets.

Occupant Assessment

As a reminder: Occupant Assessment provides information about the occupant and injuries squired. Variables used from the "Occupant Assessment" data sets include;

"AGE" - 'AGE OF OCCUPANT'

"BAGAVAIL" - 'AIR BAG SYSTEM AVAILABILITY'

"BAGDEPLY" - 'AIR BAG SYSTEM DEPLOYED'

"BAGFAIL" - 'AIR BAG SYSTEM FAILURE'

"CASEID" - 'CASE NUMBER - STRATUM'

"CASENO" - 'CASE SEQUENCE NUMBER'

"CHTYPE" - 'TYPE OF CHILD SAFETY SEAT'

"DEATH" - 'TIME TO DEATH'

"HEIGHT" - 'HEIGHT OF OCCUPANT'

"HOSPSTAY" - 'HOSPITAL STAY'

"INJSEV" - 'INJURY SEVERITY (POLICE RATING)'

"MANUSE" - 'MANUAL BELT SYSTEM USE'

"OCCNO" - 'OCCUPANT NUMBER'

"PSU" - 'PRIMARY SAMPLING UNIT NUMBER'

"ROLE" - 'OCCUPANT'S ROLE'

"SEATPOS" - 'OCCUPANT'S SEAT POSITION'

"SEX" - 'OCCUPANT'S SEX'

"TREATMNT" - 'TREATMENT - MORTALITY'

"VEHNO" - 'VEHICLE NUMBER'

"WEIGHT" - 'OCCUPANT'S WEIGHT'

General Vehicle

As a reminder: the "general vehicle" file, provided information about the impact speeds and the vehicle model. The variables used to create data set for General Vehicle data include;

"PSU" - 'PRIMARY SAMPLING UNIT NUMBER'

"CASEID" - 'CASE NUMBER - STRATUM'

"VEHNO" - 'VEHICLE NUMBER'

"BODYTYPE" - 'VEHICLE BODY TYPE'

"MODELYR" - 'VEHICLE MODEL YEAR'

"DVTOTAL" - 'TOTAL DELTA V'

"DVBASIS" - 'BASIS FOR TOTAL DELTA V (HIGHEST)'

"DVEST" - 'ESTIMATED HIGHEST DELTA V'

"MAKE" - 'VEHICLE MAKE'

"Vehicle Exterior"

As a reminder: the "Vehicle Exterior" data provides the direction of impact.

The variables used to create this data table include;

"PSU" - 'PRIMARY SAMPLING UNIT NUMBER'

"DOF1" - 'DIRECTION OF FORCE (HIGHEST)'

"CASEID" -'CASE NUMBER - STRATUM'

"GAD1" - 'DEFORMATION LOCATION (HIGHEST)'

"Accident Description"

As a reminder: The "Accident Description" data table provides the "ratio inflation factor" used for weighting the data.

The variables used to create the data table were;

"PSU" - 'PRIMARY SAMPLING UNIT NUMBER'

"STRATIF" - 'CASE STRATUM'

"CASEID" - 'CASE NUMBER - STRATUM'

Acknowledgements

Thanks to Nass for making their datasets free to the public by year.

Inspiration

I was inspired to create this dataset to analyse safety. My question to you is can you find any information that might be valid to the general public based on this dataset that I created by combining the years from 2004 - 2014.

This series of code and corresponding data files are intended for use in cognitive decline and Alzheimer’s disease and related dementias (ADRD) research. The files include twelve years of cleaned datasets derived from the 2007-2018 years of the National Health Interview Survey (NHIS). NHIS is a nationally representative study aimed at monitoring the health of the non-institutionalized United States population. The provided datasets include sociodemographic information on respondents’ age, sex, race, and marital status from the Sample Adult Files, cognition variables from the Sample Adult files and, in applicable years, merged cognition data from the Adult Functioning and Disability (AFD) supplement. The files were constructed to allow for users to append multiple years of data for longitudinal analysis. Brief and detailed summaries of the variables available in these datasets along with more detailed descriptions of performed calculations can be found in the provided data dictionaries. Users may also refer to the provided “Overview of variables across years” document to see which variables are available each year. SAS, Stata, and CSV data file formats are provided as are the full coding scripts used in Stata.

Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. ‘Penn G2’) putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems. Methods The requested information is described ad nauseum in the Materials & Methods section of the ‘Related Works.’

On 2. Nov., the author mistakenly uploaded a raw data file. Within, the first worksheet/tab titled MSR contained all 475 lines of MSR and vegetative index data. However, consideration for abidance of ANOVA assumptions precluded a small number of dependent variable observations, as employ of garden variety transformations were unsuccessful. Specifically, for percent reflectance of 510-, 560-, 610-, 660-, 760-, and 810-nm spectra; 2, 2, 2, 3, 3, and 4 observations were omitted as missing data, respectively. Likewise, since the dark green color index (DGCI) is calculated by 460, 560, and 660-nm reflectance, five (5) DGCI observations were conceded as missing data. Results described in the ‘Related Works’ report 510-, 560-, 610-, 660-, 760-, and 810-nm reflectance means and inference from 473-, 473-, 473-, 472-, 472-, and 471-observation datasets, respectively. No data were replaced and degree of freedom penalties were incurred in analysis reported in ‘Related Works.’ Likewise, the daily clipping yield data, dCY (2nd worksheet/tab) in the original 2 Nov. file upload, contained 150 observations. The statistical model and analysis of dCY data described in the ‘Related Works’ results report means and inference from a 148-observation dataset. The SAS output for each the reduced (n=148) and full (n=150) datasets are now included in data files. Model diagnostics on the reduced datasets, uploaded 11 Dec., 2022 meet all required assumptions. For the dCY data, the model diagnostics issue and resolution are squarely depicted in the two attached SAS outputs. The same is true for the MSR data, but SAS outputs are not attached. Motivated parties are invited to reanalyze the above-noted dependent variables using the 2 Nov. (full) and 11 Dec. (reduced) data freely available to you in ‘Data Files.’ It is strict Dryad policy that voluntarily uploaded data files not be deleted. Thus, the authors were compelled to append the two regrettably-conflicting datasets with the above explanation, today, 11 Dec. 2022. We hope you have found this explanation helpful and encourage you to forward your questions or comments to Max Schlossberg at mjs38@psu.edu.