Data Visualisation
Interactive Storytelling
Assignment Instructions
Data visualisation is a great way to support fact-based storytelling. For Assignment 3, your goal will be to pick a current news story (within the previous 12 months) and create an interactive data visualisation to support that story. The choice is yours, but you will need to source the data. That will be the tricky part.
Open or Public Data
Assignment 3 must be based on open or public data. Ensure you include the right attribution.
• Work through Andy Kirk’s Visual Design Process described in Module 1 in order to produce an insightful visualisation of the story and data you selected.
• Your design must include interactive elements.
• You are restricted to a one screen visualisation. This may include multiple plots or a dashboard layout.

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title: "Rain in Australia"
    orientation: columns
    vertical_layout: fill
runtime: shiny

```{r setup, include=FALSE}
dat <- read.csv("weatherAUS.csv")
dat$Location <- as.character(dat$Location)
df.selected <- dat %>% select(Location, RainToday, MinTemp, MaxTemp, Rainfall,
                      Evaporation, Sunshine, WindGustSpeed,
                      WindSpeed9am, WindDir3pm, Humidity9am, Humidity3pm,
                      Pressure9am, Pressure3pm, Cloud9am, Cloud3pm, Temp9am,
                      Temp3pm, RISK_MM)

d_year <- dat
d_year <- d_year %>% mutate(Date = as.Date(as.character(Date), format = "%Y-%m-%d"))
d_year <- d_year %>% mutate(Year = year(Date))
d_year$Year <- as.Date(as.character(d_year$Year), format = "%Y")

d_names <- c("Average.MinTemp", "Average.MaxTemp", "Average.Rainfall",
             "Average.Evaporation", "Average.Sunshine",
             "Average.WindGustSpeed", "Average.WindSpeed9am",
             "Average.WindSpeed3pm", "Average.Humidity9am",
             "Average.Humidity3pm", "Average.Pressure9am...

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