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Faceting daily rides

We noticed some interesting behavior when we looked at daily ride counts faceted by day-of-week. Let's investigate whether faceting on additional variables yields any new insights. Here we will see if there are different day-of-week patterns when also looking at the payment types of cash or credit card.

tx is available for you in your workspace.

Bu egzersiz

Visualizing Big Data with Trelliscope in R

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • After filtering to just cash and credit transactions, create a summary by day of week and payment type count using dplyr, grouping by the pickup_date, pickup_dow, payment_type.
  • Inside summarise(), count the number of rides and assign the result to a new variable n_rides.
  • Plot the result using the daily_count summary dataset as an input to ggplot() and using geom_point(), with pickup_date on the x-axis and n_rides on the y-axis.
  • Use facet_grid() to facet with payment_type as rows and day of week pickup_dow as columns.
  • Note that the coord_fixed() code constrains the aspect ratio of the resulting plot to help highlight patterns visually.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

library(dplyr)
library(ggplot2)

# Summarize taxi rides count by payment type, pickup date, pickup day of week
daily_count <- tx %>%
  filter(payment_type %in% c("Card", "Cash")) %>%
  group_by(___, ___, ___) %>%
  summarise(___)

# Plot the data
ggplot(___, aes(___, ___)) +
  ___ +
  facet_grid(___ ~ ___) +
  coord_fixed(ratio = 0.4)
Kodu Düzenle ve Çalıştır