You MUST work within your assigned teams.
Each MIDS team MUST collaborate using GitHub. This is not compulsory for non-MIDSters, however, I would recommend also collaborating using GitHub. A blank repository has been created for this project. Follow this link: https://classroom.github.com/g/xaKEvdM8 to gain access. You should already know how to clone the repository locally once you gain access. The first student to accept the invitation within each team will be responsible for creating the team name. All other members of the team will be able to join the team once the first person has added the team name. You should only join your pre-assigned team. Feel free to create other folders within the repository as needed but you must push your final reports and presentation videos and/or slides to the corresponding folders already created for you.
Each team will create/record a 6 minute video presentation of their findings. Feel free to get creative; fun animations are welcome!
Each team MUST turn in only one report with team members’ names at the top of the report, and the different designations (checker, coordinator, presenter, programmer, and writer).
.pdf.kable, xtable, stargazer, etc.All team members must complete a very short written evaluation, quickly describing the effort put forth by other team members.
Your analyses MUST address the two sets of questions directly. The report should be written so that there is a section which clearly answers the first set of questions for Part I and another section which clearly answers the second set of questions about Part II. If you prefer, you can write two 5-paged reports, one to address each set of questions. Be sure to also include the following in your report:
This data is from: https://stat.duke.edu/datasets/estrogen-bioassay
Estrogens are a group of hormones produced in both the female ovaries and male testes, with larger amounts made in females than in males. They are particularly influential during puberty, menstruation, and pregnancy, but they also help regulate the growth of bones, skin, and other organs and tissues. In general, they have a strong effect of endocrine function by disrupting these functions.
Over the past 10 years, many synthetic compounds and plant products present in the environment have been found to affect hormonal functions in various ways. Those that have estrogenic activity have been labeled as environmental estrogens. There is increasing concern that chemicals in the environment referred to as environmental estrogens may be causing adverse effects through endocrine disruption. Hence, there is a need for new approaches for screening chemicals for endocrine disrupting effects.
The rat uterotrophic bioassay provides one approach for identifying agonists or antagonists of estrogen. An estrogen antagonist is a compound that blocks the binding of estrogen and so blocks the action of estrogen. An estrogen agonist is a compound that enhances the action of estrogen. Rats in this study are either immature or have their ovaries removed and therefore do not produce estrogen.
The point of the study is to use the rats as an assay to test the effect of estrogen agonists and antagonists on a particular hormonal response, the weight of the uterus. This is done by varying the amount of the agonist or antagonist give to the rat. The response is the weight of the uterus, with uterus weight expected to exhibit an increasing dose response trend for chemicals acting as estrogen agonists and with estrogen antagonists acting to block such estrogen effects. It is expected that the uterus gets heavier with the increase of estrogen agonist dose.
The basic design randomizes female rats to treatment groups, with groups consisting of a control group and several groups having increasing doses of the test agent. An international multi-laboratory study was conducted to compare the results of the rat uterotrophic bioassay using a known estrogen agonist (EE) and a known estrogen antagonist (ZM). The main goal of the study was to assess whether the results were consistent across the laboratories.
The data for this part of the project can be found in the file bioassay.txt on Sakai.
Use a multi-level model to answer the following questions of interest.
| Variable | Description |
|---|---|
| protocol: | A = immature female rats dosed by oral gavage (3 days) B = immature female rats dosed by injection (3 days) C = adult ovariectomized female rats dosed by injection (3 days) D = adult ovariectomized female rats dosed by injection (7 days) |
| uterus | Uterus weight (mg) |
| weight | Body weight of rat (g) |
| EE | Dose of estrogen agonist, EE in mg/kg/day |
| ZM | Dose of estrogen antagonist, ZM in mg/kg/day |
| lab | Laboratory at which assay was conducted |
| group | Lab replicate group (6 rats were used per group) |
The North Carolina State Board of Elections (NCSBE) is the agency charged with the administration of the elections process and campaign finance disclosure and compliance. Among other things, they provide voter registration and turnout data online (https://www.ncsbe.gov/index.html, https://www.ncsbe.gov/results-data). Using the NC voter files for the general elections in November 2016, you will attempt to identify/estimate groups that voted in 2016 out of those who registered. Here’s an interesting read on turnout rates for NC in 2016: https://democracync.org/wp-content/uploads/2017/05/WhoVoted2016.pdf.
The data for this part of the project can be found on Sakai. The file voter_stats_20161108.txt contains information about the aggregate number of registered voters by the demographic variables; the data dictionary can be found in the file DataDictionaryForVoterStats.txt. The file history_stats_20161108.txt contains information about the aggregate number of voters who actually voted by the demographic variables.
There are a few million rows in both datasets but you will only work with a subset of those. Take a random sample of 20 counties out of all the counties in both datasets. You should indicate the counties you sampled in your final report. You will need to merge the two files voter_stats_20161108.txt and history_stats_20161108.txt by the common variables for the counties you care about. Take a look at the set of join functions in the dplyr package in R (https://www.rdocumentation.org/packages/dplyr/versions/0.7.8/topics/join) or the merge function in base R. I recommend the functions in dplyr. You may choose to merge the datasets before or after selecting the samples you want, but be careful if you decide to do the latter.
Unfortunately, the data dictionary from the NCSBE does not provide the exact difference between the variables party_cd and voted_party_cd in the history_stats_20161108.txt file (if you are able to find documentation on the difference, do let me know). However, I suspect that the voted party code encodes the information about people who changed their party affiliation as at the registration deadline, whereas the first party code is everyone’s original affiliation. Voters are allowed to change their party affiliation in NC so that lines up. The two variables are thus very similar and only about 0.8% of the rows in the history_stats_20161108.txt file have different values for the two variables. I would suggest using the voted party code (voted_party_cd) for the history_stats_20161108.txt dataset.
You should discard the following variables before merging: election_date,stats_type, and update_date. Also, you can not really merge by or use the voting_method and voting_method_desc variables in your analysis either because that information is only available in the history_stats_20161108.txt data and not the other dataset. That means you should not use those two variables when merging.
Before discarding the variables however, you need to aggregate to make sure that you are merging correctly. As a simple example, suppose 4 males voted in person and 3 males voted by mail, you need to aggregate out the method of voting so that you have 7 males in total. This is because we are unable to separate people who voted by different voting methods in the voter_stats_20161108.txt we want to merge from. So, the simplest way is to use the aggregate function in R. As an example, the code:
aggregated_data <- aggregate(Data$total_voters,
list(Age=Data$age,Party=Data$party_cd),sum)will sum all voters by all age groups and party. You can also use the dplyr package to aggregate in the same way if you prefer that.
Once you have this clean data for the history_stats_20161108.txt file, you should then go ahead to grab the information on total registered voters from voter_stats_20161108.txt, by merging by all variables in history_stats_20161108.txt, except total_voters.
Use a multi-level model to answer the following questions of interest.
40 points: 20 points for each part.