CERN-SPS. NA4/BCDMS collaboration. Plab 100 - 280 GEV/C. These are data from the BCDMS collaboration on F2 and R=SIG(L)/SIG(T) with a hydrogen target. The statistics are very large (1.8 million events). The ranges of X,Q**2 are 0.06& lt;X& lt;0.8 and 7& lt;Q**2& lt;260 GeV**2. The F2 data show a distinct difference from the data on F2 proton taken by the EMC.. The publication lists values of F2 corresponding to R=0 and R=R(QCD) at each of the four energies, 100, 120, 200 and 280 GeV. As well as the statistical errors also given are 5 factors representing the effects of estimated systematic errors on F2 associated with (1) beam momentum calibration, (2) magnetic field calibration, (3) spectrometer resolution, (4) detector and trigger inefficiencies, and (5) relative normalisation uncertainty of data taken from external and internal targets. This record contains our attempt to merge these data at different energies using the statistical errors as weight factors. The final one-sigma systematic errors given here have been calculated using a prescription from the authors involving calculation of new merged F2 values for each of the systematic errors applied individually, and the combining in quadrature the differences in the new merged F2 values and the original F2. The individual F2 values at each energy are given in separate database records (& lt;a href=http://durpdg.dur.ac.uk/scripts/reacsearch.csh/TESTREAC/red+3021& gt; RED = 3021 & lt;/a& gt;). PLAB=100 GeV/c. These are the data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a hydrogen target. The statistics are very large (1.8 million events). The ranges of X, Q**2 are 0.06& lt;X& lt;0.8 and 7& lt;Q**2& lt;260 GeV**2. The F2 data show a distinct difference from the data on F2 proton taken by the EMC. In the preprint are listed values of F2 corresponding to R=0 and R=R(QCD) at each of the four energies, 100, 120, 200 and 280 GeV. Also listed are 5 systematic errors associated with beam momentum calibration, magnetic field calibration, spectrometer resolution, detector and trigger inefficiencies and relative normalisationuncertainty.. The sytematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. Themethod involves taking the quadratic sum of the errors from each source.. The record (& lt;a href=http://durpdg.dur.ac.uk/scripts/reacsearch.csh/TESTREAC/red+3019& gt; RED = 3019 & lt;/a& gt;) contains our attempt to merge these data at different energies using the statistical errors as weight factors. PLAB=120 GeV/c. These are the data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a hydrogen target. The statistics are very large (1.8 million events). The ranges of X, Q**2 are 0.06& lt;X& lt;0.8 and 7& lt;Q**2& lt;260 GeV**2. The F2 data show a distinct difference from the data on F2 proton taken by the EMC. In the preprint are listed values of F2 corresponding to R=0 and R=R(QCD) at each of the four energies, 100, 120, 200 and 280 GeV. Also listed are 5 systematic errors associated with beam momentum calibration, magnetic field calibration, spectrometer resolution, detector and trigger inefficiencies and relative normalisationuncertainty. The sytematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. Themethod involves taking the quadratic sum of the errors from each source. The record (& lt;a href=http://durpdg.dur.ac.uk/scripts/reacsearch.csh/TESTREAC/red+3019& gt; RED = 3019 & lt;/a& gt;) contains our attempt to merge these data at different energies using the statistical errors as weight factors. PLAB=200 GeV/c. These are the data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a hydrogen target. The statistics are very large (1.8 million events). The ranges of X, Q**2 are 0.06& lt;X& lt;0.8 and 7& lt;Q**2& lt;260 GeV**2. The F2 data show a distinct difference from the data on F2 proton taken by the EMC. In the preprint are listed values of F2 corresponding to R=0 and R=R(QCD) at each of the four energies, 100, 120, 200 and 280 GeV. Also listed are 5 systematic errors associated with beam momentum calibration, magnetic field calibration, spectrometer resolution, detector and trigger inefficiencies and relative normalisationuncertainty. The sytematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. Themethod involves taking the quadratic sum of the errors from each source. The record (& lt;a href=http://durpdg.dur.ac.uk/scripts/reacsearch.csh/TESTREAC/red+3019& gt; RED = 3019 & lt;/a& gt;) contains our attempt to merge these data at different energies using the statistical errors as weight factors. PLAB=280 GeV/c. These are the data...

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...

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

This dataset is intended to accompany the paper "Designing Types for R, Empirically" (@ OOPSLA'20, link to paper). This data was obtained by running the Typetracer (aka propagatr) dynamic analysis tool (link to tool) on the test, example, and vignette code of a corpus of >400 extensively used R packages. Specifically, this dataset contains: function type traces for >400 R packages (raw-traces.tar.gz); trace data processed into a more readable/usable form (processed-traces.tar.gz), which was used in obtaining results in the paper; inferred type declarations for the >400 R packages using various strategies to merge the processed traces (see type-declarations-* directories), and finally; contract assertion data from running the reverse dependencies of these packages and checking function usage against the declared types (contract-assertion-reverse-dependencies.tar.gz). A preprint of the paper is also included, which summarizes our findings. Fair warning Re: data size: the raw traces, once uncompressed, take up nearly 600GB. The already processed traces are in the 10s of GB, which should be more manageable for a consumer-grade computer.