Deliverable For Share Phase
Supporting visualizations and key findings
Embedded Files
# Let's visualize weekday vs number_of_rides
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")+
labs(title="Weekday vs Number Of Rides")+
scale_y_continuous(labels=comma)
Interpretation: The Bar graph shows that casual riders make more rides than the members starting from Sunday to Saturday espescially on Sunday,Tuesday,Thursday and Friday
# Let's create a visualization for weekday vs 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")+
labs(title="Weekday vs Average Duration")+
scale_y_continuous(labels=comma)
Interpretation: Also casual rides ride more duration than regular members on all weekdays, especially on Monday, Tuesday,Thursday and Friday
#Let's create a visualization for Member_ Casual vs Count distribution
all_trips_V2 %>%
group_by(member_casual) %>%
summarize(count=length(ride_id),
percentage_of_total=(length(ride_id)/nrow(all_trips_V2))*100)
ggplot(all_trips_V2, aes(member_casual, fill=member_casual))+
geom_bar()+
labs(title="Member vs Casual distribution")+
scale_y_continuous(labels=comma)
Interpretation: The bar graph shows that members are of more count than casuals on an average
#Let's create a visualization and check how count vary per each week of the day
ggplot(all_trips_V2, aes(day_of_week, fill=member_casual))+
geom_bar()+
labs(title="Day_of_week vs Count distribution")+
scale_y_continuous(labels=comma)
Interpretation: The Bar Graph shows that the members are more of count than casuals during the day of the week starting from Sunday to Saturday where Tuesdays and Fridays are being more riders
#Let's create a visualization and check how rideable_type vary with count
ggplot(all_trips_V2, aes(rideable_type, fill=member_casual))+
geom_bar()+
labs(title="Rideable_type vs Count")
scale_y_continuous(labels=comma)
Interpretation: The Bar chart shows that classic bikes are the most widely used rideable type. Then comes the electric bikes which are more widely used and docked bikes being the least that were used in the rides
#Let's create a visualization and check how member_casual vary with ride_length
ggplot(all_trips_V2, aes(x=member_casual, y=ride_length, fill=member_casual))+
geom_boxplot()
Interpretation
The geom_boxplot shows that casual riders made ride lengths more than members. So casual riders used Cyclistic Bike Share bikes more than the regular members
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