In PSU’s Data Science class for August 2018, we were supplied a dataset of daily bicycle counts on select bridges across the Willamette River in Portland, Oregon.
## # A tibble: 3 x 2
## name avg_daily_counts
## <chr> <dbl>
## 1 Hawthorne 3904.
## 2 Steel 2240.
## 3 Tilikum 1725.
The counts were distributed as viewed in this graph.
We proved in class that bicycle use is highest when a day’s minimum temperature is between freezing and 65F.
I started to test whether there’s also a relationship between bike counts and a day’s minutes of sunlight. I plan to complete this test outside of class. Here’s a snapshot of the data.
## # A tibble: 10 x 3
## Day month hours
## <dbl> <chr> <time>
## 1 1 Jan. 8.783333 hours
## 2 2 Jan. 8.800000 hours
## 3 3 Jan. 8.816667 hours
## 4 4 Jan. 8.833333 hours
## 5 5 Jan. 8.850000 hours
## 6 6 Jan. 8.883333 hours
## 7 7 Jan. 8.900000 hours
## 8 8 Jan. 8.916667 hours
## 9 9 Jan. 8.950000 hours
## 10 10 Jan. 8.966667 hours