Code

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(gganimate)
library(gifski)
df <- read.csv("Transport_over_time.csv")
df$Type <- as.factor(df$Type)
df$Time <- as.POSIXct(df$Time, format = "%Y-%m-%d %H:%M:%OS")

p <- ggplot(df, aes(x=Time)) +
     geom_line(aes(y=total_car, color="total_car")) + 
     geom_line(aes(y=total_walking, color="total_walking")) + 
     geom_line(aes(y=total_train, color="total_train")) + 
     geom_line(aes(y=total_tram, color="total_tram")) + 
     geom_line(aes(y=total_bus, color="total_bus")) +
     labs(y="Count of Transportation Method Usage", title="My Commutes Since I Moved To Melbourne") +
     #theme(axis.title.y = element_text(size=10)) +
     theme(plot.title = element_text(size=15))
p <- p + 
     transition_reveal(Time) + 
     geom_point(aes(y=total_car, color="total_car")) +
     geom_point(aes(y=total_walking, color="total_walking")) +
     geom_point(aes(y=total_train, color="total_train")) +
     geom_point(aes(y=total_tram, color="total_tram")) +
     geom_point(aes(y=total_bus, color="total_bus"))

VISUALISATION

Sources

  • Google Maps, 2022. [online] Sourced through “Your data in Maps” [Accessed 30th May 2022].

The data was originally sourced through my own Google account, under the “Your data in Maps” feature which is available to all users who have allowed certain permissions. I have extracted the original json files into a clean CSV that was used for this visualisation. For privacy reasons, it also does not include any location data.

The dataset used for this visualization is open and accessible at: https://github.com/oliver-southon/CommuteMethods

It will be in the file named “Transportation_over_time.csv”.