Graphical Representations

Bar diagrams

• If the number k of the different realizations is small

- absolute frequency

plot(table(Data), ylab = "Y-Label")

- relative frequency

plot(prop.table(Data), ylab = "Label")

Column charts

• An often even clearer variant (in comparisson to bar diagramms) (also for small k)

- absolute frequency

barplot(table(Data), ylab = "Label", col = colour)

- relative frequency

barplot(prop.table(Data), ylab = "Label", col = colour)

Bar charts

- absolute frequency

par(mar = c(number of lines of margin to be specified on the four sides of the plot)) barplot(table(Data), xlab = "Beschriftung", horiz = TRUE, las = 1, col = blues9)

- relative frequency

par(mar = c(number of lines of margin to be specified on the four sides of the plot)) barplot(prop.table(Data), xlab = "Label", horiz = TRUE, las = 1, col = blues9)

pie charts

• also for small k
  
• Disadvantages:
  
  • no direct reading of relative / absolute frequencies possible
  • optically difficult to detect (eye detects relative lengths more easily than relative areas)

pie(table(Data), col = blues9)

Histograms

for large number of different expressions histogram(Data, breaks = seq(), xlab = "Label", ylab = "Label")

Two-dimensional bar charts

• For small k and m
• Structure: Enter absolute / relative frequencies for each combination of proficiencies
• visualization in r: library(epade) bar3d.ade(table(input), alpha = 0.2) ### Scatter plots
• for two metric characteristics
• in R: plot(characterisicOne, characteristicTwo
• For more than two metric characteristics, you can form pairwise scatterplots and combine them into a scatter plot matrix

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