This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment.

Import data

library(tidyverse)
library(lubridate)
library(scales)
theme_set(theme_light())
bike_traffic_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-02/bike_traffic.csv")
bike_traffic <- bike_traffic_raw %>%
  mutate(date = mdy_hms(date)) %>%
  filter(bike_count < 2000) %>%
  select(-ped_count)

Description of the data and definition of variables

This data is showing how many people ride bycicles in Seattle. The variables are date, crossing, direction, bike count, and pedestrian count. Date is when the data was recorded, bike count was the number of bikes counted in an hour window, crossing is the street where the bikers were recorded at, direction are the nautical directions the bikers were going, and pedestrian count are the number of pedestrians counted in an hour window.

Visualize data

Hint: One graph of your choice.

bike_traffic %>%
  ggplot(aes(date, fill = is.na(bike_count))) +
  geom_histogram() +
  facet_grid(crossing ~ direction)

What is the story behind the graph?

Hide the messages, but display the code and its results on the webpage.

Write your name for the author at the top.

Use the correct slug.