Credit report of Merge Sas contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

This dataset consists of TOGA 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, stop and mid times are taken from the TOGA file and used for the averaging period. Averaging and missing value treatment as in 1-min merge.

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

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

Abstract

The main objective of the new agricultural statistics program is to provide timely, accurate, credible and comprehensive agricultural statistics to describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock; which can be used for food and agriculture policy formulation and planning, and for the compilation of national accounts statistics.

In this regard, the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) from November 2015 to October 2016 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Second Economic Development and Poverty Reduction Strategy (EDPRS II) and Vision 2020. This 2016 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipments, use of crop production by agricultural operators and by large scale farmers.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

The 2016 RSAS targeted agricultural operators and large scale farmers operating in Rwanda.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Seasonal Agriculture Survey (SAS) sample is composed of two categories of respondents: agricultural operators1 and large-scale farmers (LSF).

For the 2016 SAS, NISR used as the sampling method a dual frame sampling design combining selected area frame sample3 segments and a list of large-scale farmers.

NISR used also imagery from RNRA with a very high resolution of 25 centimeters to divide the total land of the country into twelve strata. A total number of 540 segments were spread throughout the country as coverage of the survey with 25,346 and 23,286 agricultural operators in Season A and Season B respectively. From these numbers of agricultural operators, sub-samples were selected during the second phases of Seasons A and B.

It is important to note that in each of agricultural season A and B, data collection was undertaken in two phases. Phase I was mainly used to collect data on demographic and social characteristics of interviewees, area under crops, crops planted, rainfall, livestock, etc. Phase II was mainly devoted to the collection of data on yield and production of crops.

Phase I serves at collecting data on area under different types of crops in the screening process, whereas the Phase II is mainly devoted to the collection of data on demographic, social characteristics of interviewees, together with yields of the different crops produced. Enumerated large-scale farmers (LSF) were 558 in both 2015 Season A and B. The LSF were engaged in either crop farming activities only, livestock farming activities only, or both crop and livestock farming activities.

Agricultural operators are the small scale farmers within the sample segments. Every selected segment was firstly screened using the appropriate materials such as the segment maps, GIS devices and the screening form. Using these devices, the enumerators accounted for every plot inside the sample segments. All Tracts6 were classified as either agricultural (cultivated land, pasture, and fallow land) or non-agricultural land (water, forests, roads, rocky and bare soils, and buildings).

During Phase I, a complete enumeration of all farmers having agricultural land and operating within the 540 selected segments was undertaken and a total of 25,495 and 24,911 agricultural operators were enumerated respectively in Seasons A and B. Season C considered only 152 segments, involving 3,445 agricultural operators.

In phase II, 50% of the large-scale farmers were undertaking crop farming activities only and 50% of the large-scale farmers were undertaking both crop and livestock farming and were selected for interview. A sample of 199 and 194 large-scale farmers were interviewed in Seasons A and B, respectively, using a farm questionnaire.

From the agricultural operators enumerated in the sample segments during Phase I, a sample of the agricultural operators was designed for Phase II as follows: 5,502 for Season A, 5,337 for Season B and 644 for Season C. The method of probability proportional to size (PPS) sampling at the national level was used. Furthermore, the total number of enumerated large-scale farmers was 774 in 2016 Season A and 622 in Season B.

The Season C considered 152 segments counting 8,987 agricultural operators from which 963 agricultural operators were selected for survey interviews.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

There were two types of questionnaires used for this survey namely Screening questionnaire and farm questionnaires.

A Screening Questionnaire was used to collect information that enabled identification of an Agricultural Operator or Large Scale Farmer and his or her land use.
Farm questionnaires were of two types: a) Phase I Farm Questionnaire was used to collect data on characteristics of Agricultural Operators, crop identification and area, inputs (seeds, fertilizers, labor, …) for Agricultural Operators and large scale farmers. b) Phase 2 Farm questionnaire was used in the collection of data on crop production and use of production.

It is important to mention that all these Farm Questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. For season C, after screening, an interview was conducted for each selected tract/Agricultural Operator using one consolidated Farm questionnaire. All the surveys questionnaires used were published in both English and Kinyarwanda languages.

Cleaning operations

Data editing took place at different stage. Firstly, the filled questionnaires were repatriated at NISR for office editing and coding before data entry started. Data entry of the completed and checked questionnaires was undertaken at the NISR office by 20 staff trained in using the CSPro software. To ensure appropriate matching of data in the completed questionnaires and plot area measurements from the GIS unit, a LOOKUP file was integrated in the CSPro data entry program to confirm the identification of each agricultural operator or LSF before starting data entry. Thereafter, data were entered in computers, edited and summarized in tables using SPSS and Excel.

Response rate

The response rate for Seasonal Agriculture Survey is 98%.

Data appraisal

All Farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. And in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the Team leader.

This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255).

Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s).

Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.

To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524

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

Genres: Genggong and Kamput. No other notes for side 1. SIDE 2 genre: Rabana (drum ensemble). Title of Item: Merge Pati . Title of Item: Taboh Lilingan. Title of Item: Pahlawan. ; Item contains instrumental music.. Language as given: Sasak

Genre for side 1: Rebana 1. Mondar Mondir 2. Perdjalanan 3.Barangan 4.Sereningan 5.Perewak 6.Tawara 7.Rantjangan 8. Taboh enam 9. Merge Pati SIde 2: 1. Nasehat 2. Selawat 3.Nasehal 4. Rukun Telu Alas 5.Nasehat 6. Nasehat 7.Solo Jakub (singer) "masmira" 8. "Mossim hodjan" 9. Sembahjang 10. Nasehat 11. Lelakak; Item contains instrumental music.. Language as given: Sasak

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