Monash University
FIT5147 Data Exploration and Visualisation
Semester 1, 2022

Programming Exercise 2: R (5%)
Please carefully review all the requirements below to ensure you have a good
understanding of what is required for your assessment.
1. Due Date
2. Instructions & Brief
3. Assessment Resources
4. Assessment Criteria
1. Grading Rubric
2. Word Count (& Penalties)
5. How to Submit

1. Due Date
Friday Week 5, 1 April 2022, 11:55 PM

2. Instructions & Brief
In this assignment you are required to create an interactive visualisation
using R. The visualisation will show both spatial and tabular data. It is an
individual assignment and worth 5% of your total mark for FIT5147.
Relevant learning outcomes for FIT5147:
1. Perform exploratory data analysis using a range of visualisation tools;
6. Implement interactive data visualisations using R and other tools.




Details of task:
The data set for this assignment is based on an Our City's Little Gems
study which observed butterfly biodiversity and flower-butterfly interactions
in the City of Melbourne between January - March 2017
(https://data.melbourne.vic.gov.au/Environment/Butterfly-biodiversity-
survey-2017/kmtd-nvqr). In the original study, the researcher recorded what
butterflies they saw, if any, when they walked through various areas of
Melbourne. A single site may have different plants and locations in which
the study occurred and the researcher visited these sites multiple times
during the study. This enabled them to see which types of butterflies they
could find, where, when and under which weather conditions. This
assignment will only include a modified subset of the original data.
The task is to use R Shiny, ggplot2, and Leaflet to create a data
visualisation using the provided dataset. The Shiny application that you
create should be based on the following template layout:


SURVEY OF BUTTERFLIES IN MELBOURNE 2017

[Brief description of the context of the data and project]

Location of the survey [RANGE SLIDER]

[MAP]

[Relevant description
of MAP]















Top Sites for
Butterflies
[VIS 1] [VIS 2]

[Relevant
description of
VIS 1 & VIS 2]








There is one (1) dataset used in this assignment:
• Butterfly_biodiversity_survey_2017_PE2.csv contains records of the
observations within 15 sites around Melbourne during 2017.
You are expected to:
1. Load the data set into RStudio and transform the data into the
appropriate format(s) for you to carry out Steps 2-5.
2. Create a visualisation using ggplot2 that shows only the top 5 sites
in the data based on the total number of butterflies observed in 2017
(VIS 1). The visualisation should display these totals for each of the 5
sites.
3. Create a visualisation using ggplot2 that shows the total number of
butterflies observed each day at the same 5 sites in Step 2 over the
course of 2017 (VIS 2).
4. Create an interactive proportional symbol map using Leaflet that
shows the spatial positions of all 15 sites in the dataset (MAP). This
map should:
a. Use the average of the provided longitude and latitude values
within each site in the data to position the symbols.
b. Encode the total number of butterflies observed at the site to its
symbol’s radius.
c. Provide a range selection slider to filter the visible symbols
according to the total number of butterflies that had been
observed at the site in 2017. You must allow a minimum and
maximum value to be set by the slider.
d. When a symbol is clicked, show a tooltip that displays the
name of the site and the total number of butterflies observed
over 2017 at that site.
5. Compose VIS1, VIS2, MAP, and your descriptions of these graphs
into a nice looking layout using Shiny (preferably using a fixed
layout, instead of fluid layout). This should resemble the above
template, but does not need to follow it precisely. The descriptions
must refer to the relevant visualisations, their data, their design, their
use and any important interpretations. A very brief description of the
context of the project must also be provided on the layout.
NOTES:
1. The Butterfly_biodiversity_survey_2017_PE2.csv is a different
version of the survey’s dataset to that used in PE1. Please make
sure you are using the correct dataset for this assignment.
2. No data checking or cleaning is required, but you will need to perform
data transformations and some minor calculations in order to create
the required visualisations (see Step 1). You can use an R package
such as dplyr (https://dplyr.tidyverse.org/) for this purpose.
3. Including a legend that describes the proportional symbol map's size
is not required. However you should include one if you decide to
encode data using colour (which is optional).
4. There are no requirements on the use of colour palettes, but color
brewer (https://ggplot2.tidyverse.org/reference/scale_brewer.html) is
recommended should you wish to use it.
5. No collusion between students is allowed and any R code that is
largely based on third party code must cite the original source in
comments within the R scripts(s), including webpages or social
media messages.
3. Assessment Resources
See the Assessments section on Moodle for the data.

4. Assessment Criteria
The following outlines the criteria which you will be assessed against.
• Demonstrate the ability to read in and transform data using R [1%]
• Demonstrate the ability to create static visualisations in R using
ggplot2 [1%]
• Demonstrate the ability to create a data map in R with Leaflet [1%]
• Demonstrate the ability to create an interactive visualisation in R with
Shiny [2%]
As part of the grading process, mandatory interviews to discuss your
submission will occur during your tutorial in Week 7.

5. How to Submit
Submit a zip file containing all files required to run your work. Name the
zip file in this format:
PE2_[LAST NAME]_[STUDENT ID].zip.
Before submitting your assignment, please double check that your Shiny
application runs correctly. To do so, clear objects from the workspace by
clicking on the “Broomstick” icon on the top-right section of RStudio.
Afterwards, make sure your application is still working by clicking the “Run
App” button on RStudio.
The files that you need to include in your submission are:
• The one dataset supplied for this assignment
• R script(s) for the final Shiny application (you can use a single R
script, or two scripts for UI and Server)
o Have all required "library(xxx)" or "require(xxx)" statements at
the beginning of your R files (you do not need the code to
install the packages)
o Use relative paths when reading your dataset (do not use
absolute paths)
6. Late penalty
See the late penalty guidelines in the Assessments section on Moodle.