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Completed code for question ONE
Co-authored-by: Elaineeechen <113189403+Elaineeechen@users.noreply.github.com>
pushing code 2
Completed code for question THREE
Finished Contributions QMD
Completed Version of Standards.qmd
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Submission Checklist
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standards.qmdcompleted -
contributions.qmdcompleted - All documents rendered
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Nice work team. This was a simple and straightforward analysis.
Transforming Data
| Standard | Score | Comments |
|---|---|---|
| Demonstrates an ability to subset data | 1 | |
| Demonstrates an ability to aggregate data | 1 | |
| Demonstrates an ability to interpret the results of data wrangling | 1 |
Joining Data
| Standard | Score | Comments |
|---|---|---|
| Demonstrates an ability to join to data frames | 1 | |
| Demonstrates an ability to select the most appropriate type of join | 1 | |
| Demonstrates an ability to reflect upon ethical concerns of joining information across data frames | 1 |
GitHub
| Standard | Score | Comments |
|---|---|---|
| Demonstrates an ability to delegate tasks effectively via Issues | 1 | |
| Demonstrates an ability to collaborate across multiple GitHub branches | 1 | |
| Demonstrates an ability to review collaborators' code | 1 |
| candidate_loc_donations <- | ||
| candidates |> | ||
| select(cand_id, cand_name, cand_st) | ||
| candidate_loc_donations |
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In this analysis, I think it would have been nice to have a summary table of the number of candidates in each state.
| contributions |> | ||
| select(cand_id, name, state, transaction_amt, transaction_tp, tran_id) |> | ||
| filter(transaction_tp == "24E") |> | ||
| arrange(desc(transaction_amt)) |
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In this analysis, I think it would have been nice to have a summary table of the number/amount of contributions in each state.
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| #Are contributors donating support money to candidates from their own states? |
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It's a really interesting question and simple analysis here.
| # Blog post | ||
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| Compose your blog post here... | ||
| The question we wanted to answer through the data analytical process was investigating if contributors supporting candidates were particularly biased to candidates from their own state. When looking at the contributions data set, we saw information on the contributors and the amount of money they donated in the 2016 election cycle. Then, when looking at the candidates data set we were able to see the data on each candidate and the amount of donations they got from each contributor in the 2015-2016 election cycle. In the first data chunk, we wrangled the data from the contributions data set to find out what state each contributor was located in, and how much money they donated throughout the election. In the second chuck, we did a similar wrangling, looking at the candidate data set, finding out location and donation size. Lastly, in the third data set, we joined the two wrangled data frames in order to see if there was any overlap between candidates and contributors. |
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What is this dataset? How would you explain it to someone unfamiliar with the fec16.
| inner_join(candidate_loc_donations, by = "cand_id") |> | ||
| mutate(did_the_states_match = case_when(state == cand_st ~ "Yes", TRUE ~ "No")) |> | ||
| group_by(did_the_states_match) |> | ||
| summarize(number_of_yes_or_no = n()) |
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Could be cool to also consider percent of total
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