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Last active Oct 13, 2015
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Treemap of Aalto courses by school and department
####################################################
#
# Treemap of Aalto courses by school and department.
# The color palette indicates the average ECTS
# of the courses of the department.
#
# Data from Linked Open Aalto Data Service
# http://data.aalto.fi/
#
# Tuija Sonkkila 27.11.2012, 18.5.2013
#
# Note that ECON courses do not have
# any granularity
#
# The official school acronyms of today are
# TaiK -> ARTS
# ECON -> BIZ
# CHEM
# ELEC
# ENG
# SCI
#
####################################################
library(SPARQL)
endpoint <- "http://data.aalto.fi/sparql"
q <- '
SELECT ?courseCode ?ectsValue ?deptCode ?schoolCode
WHERE
{
GRAPH <http://data.aalto.fi/id/courses/noppa/>
{
?course <http://purl.org/vocab/aiiso/schema#code> ?courseCode ;
<http://linkedscience.org/teach/ns#ects> ?ects .
BIND (IF (regex(?ects, "-"),
REPLACE(?ects, "^.*-", ""),
?ects) AS ?ectsValue)
?dept <http://purl.org/vocab/aiiso/schema#teaches> ?course ;
<http://purl.org/vocab/aiiso/schema#code> ?deptCode ;
<http://purl.org/vocab/aiiso/schema#part_of> ?school .
?school <http://purl.org/vocab/aiiso/schema#organization> ?dept ;
<http://purl.org/vocab/aiiso/schema#code> ?schoolCode .
}
}
ORDER BY ?schoolCode ?deptCode ?courseCode'
res <- SPARQL(url=endpoint, query=q)$results
# Take a copy of the res dataframe
coursedata <- res
# Change the datatype of ects
coursedata$ectsValue <- as.numeric(coursedata$ectsValue)
# EDIT 18.5.2013 Rename ECON -> BIZ, TaiK -> ARTS, Eri -> OU (Open University)
coursedata$schoolCode <- sub('ECON', 'BIZ', coursedata$schoolCode)
coursedata$schoolCode <- sub('TaiK', 'ARTS', coursedata$schoolCode)
coursedata$schoolCode <- sub('ERI', 'OU', coursedata$schoolCode)
# Factorize dept names
#
# 29.11.2012 don't do this, results in a malplotted map
# see https://blogs.aalto.fi/suoritin/2012/11/28/course-data-up-on-a-treemap/
# coursedata$deptCode <- factor(coursedata$deptCode)
# Count number of courses per department
deptsCourses <- aggregate(courseCode ~ deptCode,
data = coursedata,
FUN = "length")
# Calculate average ects by department with aggregating by department and school
depts <- aggregate(x = coursedata$ectsValue,
by = list(dept = coursedata$deptCode,
school = coursedata$schoolCode),
FUN = mean)
# Merge the two dataframes by the department code
coursedata.merged <- merge(x = deptsCourses,
y = depts,
by.x = "deptCode",
by.y = "dept")
# Clean up variable names
colnames(coursedata.merged) <- c("dept", "courses", "school", "ECTS average")
# Plot a treemap with the treemap library and with a certain Brewer palette
library(treemap)
library(RColorBrewer)
png("aalto.courses.treemap.png", width = 1024, height = 768, res = 72)
tmPlot(coursedata.merged,
index = c("school", "dept"),
vSize = "courses",
vColor = "ECTS average",
type = "value",
palette = "YlOrBr",
range = c(0, 11),
title = "Treemap of Aalto courses by School and department",
subtitle = "Average ECTS by department")
dev.off()
# A treemap with the portfolio library. The color is hard-coded though
#
# library(portfolio)
#
# png("aalto.courses.portfolio.treemap.png", width = 1024, height = 768, res = 72)
# map.market(id = coursedata.merged$dept,
# area = coursedata.merged$courses,
# group = coursedata.merged$school,
# color = coursedata.merged$'ECTS average',
# lab = c(TRUE, TRUE),
# main="Aalto courses by School")
# dev.off()
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