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Get it Dunn is a small regional run that got extra attention when a runner, Nichole Porath, made the Guiness Book of World Records for the fastest time pushing a double stroller in a half marathon. This dataset contains results from the 2017 and 2018 races.

Usage

get_it_dunn_run

Format

A data frame with 978 observations on the following 10 variables.

date

Date of the run.

race

Run distance.

bib_num

Bib number of the runner.

first_name

First name of the runner.

last_initial

Initial of the runner's last name.

sex

Sex of the runner.

age

Age of the runner.

city

City of residence.

state

State of residence.

run_time_minutes

Run time, in minutes.

Source

Data were collected from GSE Timing: 2018 data, 2017 race data.

Examples


d <- subset(
  get_it_dunn_run,
  race == "5k" & date == "2018-05-12" &
    !is.na(age) & state %in% c("MN", "WI")
)
head(d)
#> # A tibble: 6 × 10
#>   date       race  bib_num first_name last_initial sex     age city      state
#>   <chr>      <chr>   <int> <chr>      <chr>        <chr> <dbl> <chr>     <chr>
#> 1 2018-05-12 5k          1 Jeff       A            M        59 MENOMONIE WI   
#> 2 2018-05-12 5k          2 Julie      A            F        58 Menomonie WI   
#> 3 2018-05-12 5k          3 Amy        A            F        31 Elmwood   WI   
#> 4 2018-05-12 5k          4 Ashley     A            F        33 Cadott    WI   
#> 5 2018-05-12 5k          6 Bob        A            M        60 Boyd      WI   
#> 6 2018-05-12 5k          7 Eric       A            M        30 Boyd      WI   
#> # … with 1 more variable: run_time_minutes <dbl>
m <- lm(run_time_minutes ~ sex + age + state, d)
summary(m)
#> 
#> Call:
#> lm(formula = run_time_minutes ~ sex + age + state, data = d)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -18.109  -8.470  -2.064   7.760  31.646 
#> 
#> Coefficients:
#>             Estimate Std. Error t value             Pr(>|t|)    
#> (Intercept) 38.94177    2.61812  14.874 < 0.0000000000000002 ***
#> sexM        -5.36736    1.13188  -4.742           0.00000298 ***
#> age          0.11232    0.03148   3.569             0.000404 ***
#> stateWI     -1.13071    2.33534  -0.484             0.628534    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 10.77 on 389 degrees of freedom
#> Multiple R-squared:  0.09101,	Adjusted R-squared:  0.084 
#> F-statistic: 12.98 on 3 and 389 DF,  p-value: 0.00000004246
#> 
plot(m$fitted, m$residuals)

boxplot(m$residuals ~ d$sex)

plot(m$residuals ~ d$age)

hist(m$residuals)