Data Analysis.R

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# STEP 4: CONDUCT DESCRIPTIVE ANALYSIS
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# Descriptive analysis on ride_length (all figures in seconds)
mean(all_trips_v2$ride_length) #straight average (total ride length / rides)
median(all_trips_v2$ride_length) #midpoint number in the ascending array of ride lengths
max(all_trips_v2$ride_length) #longest ride
min(all_trips_v2$ride_length) #shortest ride

# You can condense the four lines above to one line using summary() on the specific attribute
summary(all_trips_v2$ride_length)

# Compare members and casual users
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)

# See the average ride time by each day for members vs casual users
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

# Notice that the days of the week are out of order. Let's fix that.
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

# Now, let's run the average ride time by each day for members vs casual users
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

# analyze ridership data by type and weekday
all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>%  #creates weekday field using wday()
  group_by(member_casual, weekday) %>%  #groups by usertype and weekday
  summarise(number_of_rides = n()							#calculates the number of rides and average duration 
  ,average_duration = mean(ride_length)) %>% 		# calculates the average duration
  arrange(member_casual, weekday)								# sorts

# Let's visualize the number of rides by rider type
all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
  geom_col(position = "dodge")

# Let's create a visualization for average duration
all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
  geom_col(position = "dodge")

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# STEP 5: EXPORT SUMMARY FILE FOR FURTHER ANALYSIS
#=================================================
# Create a csv file that we will visualize in Excel, Tableau, or my presentation software
# N.B.: This file location is for a Mac. If you are working on a PC, change the file location accordingly (most likely "C:\Users\YOUR_USERNAME\Desktop\...") to export the data. You can read more here: https://datatofish.com/export-dataframe-to-csv-in-r/
counts = aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)
write.csv(counts, "output.csv", row.names=FALSE)

# saving final all_rides_v2 file as .csv
write.csv(all_trips_v2, "all_rides_final.csv")

#You're done! Congratulations!