SOCR ≫ DSPA ≫ Topics ≫

1 Common plots

Use the TBI dataset (CaseStudy11_TBI) to demonstrate some visualization plots - e.g., histograms, density plots, pie charts, heatmaps, barplots, and paired correlation plots.

2 Trees and Graphs

Use the SOCR Resource Hierachical Dataset (JSON) or the DSPA Dynamic Certificate Map (JSON) to generate tree/graph displays of the corresponding structural information contains in the JSON objects.

Example of processing a JSON hierarchy 

<code>
library(jsonlite)
library(RCurl)
library(data.tree)
url <- "http://socr.umich.edu/html/navigators/D3/xml/SOCR_HyperTree.json"
raw_data <- getURL(url)
document <- fromJSON(raw_data)
tree <- Node$new(document$name)
for(i in seq_len(length(document))) {
  tree$AddChild(document$children$name[[i]])
  for(j in seq_len(length(document$children$children[[i]]))) {
    tree$children[[i]]$AddChild(document$children$children[[i]]$name[[j]])
    for(k in seq_len(length(document$children$children[[i]]$children[[j]]))){
      tree$children[[i]]$children[[j]]$AddChild((document$children$children[[i]]$children[[j]]$name[[k]]))
    }
  }
}
suppressMessages(library(igraph))
plot(as.igraph(tree, directed = T, direction = "climb"))

suppressMessages(library(networkD3))
treenetwork <- ToDataFrameNetwork(tree, "name")
simpleNetwork(treenetwork, fontSize = 10)
</code>

3 Data EDA examples

  • Use SOCR_OilGasData to generate three individual bar plots for Fossil Fuels, Nuclear Electric Power and Renewable Energy respectively (Hint: you may use ggplot and facet_grid). Include two lines for Productions and Consumption. The x-axis should be time (you may use year as numeric type directly), draw consumption slightly wider and noticeable (e.g., using magenta color).

  • Use SOCR_OzoneData to generate a correlation plot with the variables “MTH_1”, “MTH_2”, …, “MTH_12”,. (Hint: you need obtain correlation matrix first, then apply corrplot package, try to use as more method as possible. E.g “circle”, “pie”, “mixed” etc.).

  • Use SOCR_ CA_OzoneData to generate a 3D surface plot (using variables Longitude, Latitude and O3).

  • Generate random numbers from Cauchy distribution. Draw a histogram and compare it with the histogram of normal distribution. What do you find? You may try different seeds to re-generate the Cauchy random numbers.

  • Use SOCR_Data_PD_BiomedBigMetadata to generate a heatplot. Set RowSideColors and ColSideColors and use rainbow color.

  • Use SOCR_Data_2011_US_JobsRanking to draw a scatter plot Overall_Score - Average_Income(USD). Specify title, legend, and axes labels. Then try qplot Overall_Score - Average_Income(USD), fill with the Stress_Level and size of points according to Hiring_Potential, label points with Job_Title.

  • Use SOCR_TurkiyeStudentEvalData to generate trees and graphs using cutree(). (use variables Q1 - Q28).

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