Imaging Scientist Avatar Scoring

imaging_scientist_scores.txt

domain	total_points	earned_points
domain expertise, source	4	2
domain expertise,value	4	2
domain expertise, impact	4	1
programming, breadth	5	4
programming, depth	5	4
programming, software	7	5
methods, implementation	5	3
methods,understanding	7	4
communication, verbal	6	4
communication, written	9	6
communication, visual	8	5

imaging_scientist_scoring.R

# Read in data file with domain categories, total points, and earned points
data = read.csv("imaging_scientist_scores.txt",sep="\t",head=TRUE)

# We want the categories to be the rows
rownames(data) = data$domain
data = data[,-1]

# Now we want to summarize as percents! Let's normalize each to be
# a percentage between 0 and 100.  For fun, let's write a function that we
# can apply to the matrix to return the matrix to plot!

unecessary_function_for_score = function(row) {
  possible = row[1]
  earned = row[2]
  total = possible + earned
  possible_perc = possible / total
  earned_perc = earned / total
  return(c(earned_perc,possible_perc))
}

# Apply it to our matrix!
score = apply(data,1,unecessary_function_for_score)

# Oops, let's transpose it so it's more easy to read
score = as.data.frame(t(score))

# We want to sort the rows from least to greatest achievment
score = score[with(score, order(-earned_points)), ]

# We need to make our margins a little bigger for the text
par(mar=c(12,4,4,2))

# Get some nice "colours"!
colors = sample(colours(),2)

# One more flip, and as a matrix for the barplot function...
barplot(as.matrix(t(score)), col = colors, main = "Vanessa Graduate Student Avatar Progress!",ylab="progress",xlab="",width=2,las=2)
legend(20,.8, inset = c(-0.25, 0), fill = colors, legend = c( "accomplished","to go"),bg="white")