R: do multiple linear regressions in a data table

Question

I have a data table that looks like this (from the CSV) outlining voting data. What I need to know is how many votes come in per day (average) by year, by doing a linear regression over votesneeded ~ dayuntilelection. The slope would be the average votes coming in per day.

How can I run a linear regression function over this dataframe by year?

date,year,daysuntilelection,votesneeded
2018-01-25,2018,9,40
2018-01-29,2018,5,13
2018-01-30,2018,4,-11
2018-02-03,2018,0,-28
2019-01-23,2019,17,81
2019-02-01,2019,8,-4
2019-02-09,2019,0,-44
2020-01-17,2020,22,119
2020-01-24,2020,15,58
2020-01-30,2020,9,12
2020-02-03,2020,5,-4
2020-02-07,2020,1,-12
2021-01-08,2021,29,120
2021-01-26,2021,11,35
2021-01-29,2021,8,17
2021-02-01,2021,5,-2
2021-02-03,2021,3,-8
2021-02-06,2021,0,-10

The preferred output would be a dataframe looking something like this

year     averagevotesperday
2018       8.27
2019       7.40
2020       6.55
2021       4.60

note: full data sets and analyses are at https://github.com/robhanssen/glenlake-elections, for the curious.

utubun · Accepted Answer · 2021-08-19 14:13:19Z

2

Do you need something like this?

library(dplyr) 

dat |>
    group_by(year) |>
    summarize(
        avgVoteDay = coef(lm(votesneeded ~ daysuntilelection))[2]
    )

Output is slightly differs from yours:

# A tibble: 4 x 2
   year avgvote_day
  <int>       <dbl>
1  2018        7.76
2  2019        7.40
3  2020        6.41
4  2021        4.74

answered Aug 19, 2021 at 14:13

utubun

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1 Comment

Rob Hanssen Over a year ago

Thanks! The discrepancy in number is because I ran it individually on the larger dataset. Separate question: is |> the new version of %>% and will be the old pipe be replaced completely?

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