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 layer displays areas across the U.S. that are under severe weather conditions or other hazards and risks which have the potential to impact vulnerable populations or the functionality of critical infrastructure. About This LayerSevere weather conditions, hazards and risks are estimated based on live data feeds extracted from various authoritative sources and that are compared against predefined thresholds; an area is characterized as a potential risk if the conditions in this area exceed this threshold (see table below). The WoW layer is displayed alongside POST’s relative population exposure rank. The relative exposure of the population is calculated based on the US National Grid System (USNG) as the unit of analysis, in a spatial resolution of 1km2. To determine potential impacts on vulnerable populations, POST relies on socio-economic and demographic data made available by LandScan USA 2019 and collected by the American Community Survey (ACS) of the U.S. Census Bureau (e.g., number of elderly people, unemployment rate, number of people on public assistance or food stamps, number of mobile housing units). These data have been aggregated or disaggregated from the administrative block group or census tract division to the USNG division. POST then calculates a weighted Population Vulnerability Score (PVS) for each affected USNG cell (within each risk area as determined in the WoW layer), which in turn, are translated into a relative population exposure rank (values range between 1 and 5, where 1 is the highest exposure rank). The WOW and POST layers are updated every 4 hours.See WoW_Threats for a table with threats and thresholds used in WoW.

Context

This dataset looks at the demographics of World of Warcraft players–gender, age, sexuality, etc.–and how they play the game–role, race, class–to see if there is an association between any of them. Specifically, I was interested in how gender and sexuality affects the gender of the character they play, but there are many other things to look at. This data was gathered through a Google Forms survey, which was then posted on Reddit, Tumblr, Twitter, and my WoW guild's Discord server.

Content

There are 14 columns, 100 rows (not including the titles). 12 of those columns were gathered from a Google Forms survey, and the last two were added by hand. They are:

• Timestamp: Useless. Just when the survey was completed.
• Gender: The gender of the player.
• Sexuality: The sexuality of the player.
• Age: Age of the player.
• Country: Country the player lives in.
• Main: The gender of character the player mains
• Faction: The faction the player mains.
• Server: The server(s) the player mains.
• Role: The role(s) the player mains (DPS, Healer, Tank, or any combination therein).
• Class: The class(es) the player mains. This was a question where the respondent check any number of boxes, so there are many different ways it could be answered. Hard to analyze.
• Race: The race(s) the player mains. Same as class.
• Max: The number of 110s (max level) the player has. Only numerical variable in the dataset.
• Attracted: The gender the player is attracted to.
• Type: The "type" of person the player is. Combines gender and sexuality ("gay woman", "bi male", etc.)

Acknowledgements

This dataset belongs to me. I created the survey and compiled the data. However, I would like to thank stormwind-keep on Tumblr and earth2gem on Twitter for helping me get the survey out to a broader audience.

Inspiration

I already ran a bunch of my own analyses using R, but I could not find a good way to analyze the Class and Race variables. If anyone can figure that one out, please do.