You may use any data of your choosing in the following problems, but I would suggest you choose a data set you find interesting or would give an interesting graph (so, don’t use something like the old iris data set). You will get more out of the project in the end, and it will look better to those in the future you are showing it to. If the data set comes from an R package then reference this. If the data set is from elsewhere, then upload a copy to blackboard (.csv format).
Create a plotly graph of your choosing that represents at least two variables, one of which must be a categorical variable.
This plot can be a scatter plot, overlayed density plots (graphing variable is continuous, separate densities grouped by categorical variable), etc. choropleth maps could also be on the list…you have to admit they look kinda cool.
The graph must include:
customized hover text that is informative to the graphing elements in the plot
separate color to represent groups
labeled axes and appropriate title
Include at least a 1-paragraph discussion about the graph. Discuss what is being plotted and what information is being displayed in the graph. Discuss any information that the reader may gain from hovering the cursor over graphing elements. Discuss any issues/chalenges you had (if any) while making the plot, and you you dealt with or overcame them.
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## intersect, setdiff, setequal, uniondata("storms")
storms %>%
plot_ly(x = ~long, y = ~lat,
hoverinfo = "text",
text = ~name) %>%
add_text(x = -60, y = 10, text = ~year, frame = ~year,
textfont = list(size = 80, color = toRGB("grey70"))) %>%
add_markers(frame = ~year,
ids = ~name,
size = ~wind, color = ~status,
marker = list(sizemode = "diameter",
sizeref = 2)) %>%
layout(title = "Storms Animation with Year Labels",
xaxis = list(title = "Longitude"),
yaxis = list(title = "Latitude")) %>%
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## Returning the palette you asked for with that many colors
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## Returning the palette you asked for with that many colors## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement length# This animation shows tropical storms and hurricanes from the storms dataset, plotting their longitude and latitude to display movement over time. Bubble color represents storm status, and size reflects wind speed, so viewers can quickly see both location of the storm and the intensity of the storm. Hovering over points reveals storm details like name, wind speed, and pressure, adding depth to the visualization. A challenge I faced was scaling bubble sizes since wind speeds vary widely; adjusting the sizeref parameter solved this and kept the plot readable. Overall, the graph provides both a broad view of storm activity and detailed insights through interactivity.Create an animated plotly graph with a data set of your choosing. This can be, but does not have to be a scatter plot. Also, the animation does not have to take place over time. As mentioned in the notes, the frame can be set to a categorical variable. However, the categories the frames cycle through should be organized (if needs be) such that the progression through them shows some pattern or trend.
This graph should include:
Aside from the graphing variable, a separate categorical variable. For example, in our animated scatter plot we color grouped the points by continent.
Appropriate axis labels and a title
Augment the frame label to make it more visible. This can include changing the font size and color to make it stand out more, and/or moving the frame label to a new location in the plotting region. Note, if you do this, make sure it is still clearly visible and does not obstruct the view of your plot.
Include at least a 1-paragraph discussion about the plot. Discuss what you are plotting and what trends can be seen throughout the animation. Discuss any issues, if any, you ran into in making the plot and how you overcame them.
library(plotly)
library(ggplot2)
library(dplyr)
data("economics")
economics <- economics %>%
mutate(decade = paste0(floor(as.numeric(format(date, "%Y"))/10)*10, "s"))
economics %>%
plot_ly(x = ~unemploy, y = ~psavert,
hoverinfo = "text",
text = ~paste("Date:", date,
"<br>Unemployed:", unemploy,
"<br>Savings Rate:", psavert, "%")) %>%
add_markers(frame = ~decade,
color = ~decade, # categorical variable for color
marker = list(sizemode = "diameter",
sizeref = 2)) %>%
add_text(x = 15000, y = 20, text = ~decade, frame = ~decade,
textfont = list(size = 40, color = toRGB("grey50"))) %>%
layout(title = "Unemployment vs Savings Rate by Decade",
xaxis = list(title = "Number Unemployed (thousands)"),
yaxis = list(title = "Personal Savings Rate (%)")) %>%
animation_slider(hide = TRUE)# This animated Plotly graph uses the economics dataset to plot the relationship between unemployment and the personal savings rate, with the frames cycling through different decades. By organizing the frames this way, the animation highlights how economic conditions shift over longer periods rather than just individual years. The plot shows that unemployment spikes during certain decades, such as the 1980s and 2000s, while savings rates generally trend downward in more recent decades compared to earlier ones. One issue I ran into was that the dataset only had continuous dates, so I had to create a categorical variable for decades using mutate(). Overall, the animation makes long-term economic trends easier to see and compare, while still allowing detailed exploration of individual data points through hover text.What to turn in:
knit your final assignment to an html document and publish it to an RPubs page.
submit (1) the rmd file and (2) the link to this page in Blackboard (this can be in a word document or some other form to submit the link).