- Weight: 20%
- Type: Remote
- Grade: Rubric
- Due: 04-10 12:00 (ID 1), 06-10 15:20 (ID 2), 04-10 15:20 (ID 3)
- Results
- Grades
In assessment 1 you’ll make a basic visualisation from given data.
You may use other data but that probably means you’ll have to clean it yourself. Cleaning data is hard and we’ll cover that in class 3. If you want to clean data, you must do so with code and hand in this code too.
We’ll check how you apply d3, whether you understand what’s happening, how well data is represented, and quality of your code and documentation.
For this assessment you’ll build a project in a fork of
cmda-tt/fe3-assessment-1 (not our course repo) and
host the project through GitHub Pages. First, fork the repo. Then,
work on your project and upload the final results to your fork either using
Git or the GitHub interface as covered in class 1. You do not need
to create branches for this assessment. You should upload files to the root
directory.
We will download your code when it’s due and check your assessment on GitHub. You do not need to create a pull request.
Assessment 1 tests that you’ve attained the following knowledge:
- SVG and canvas (subgoal 1)
- Load external data (subgoal 2)
- Read and write docs (subgoal 3 and subgoal 4)
- Debug and refactor code (subgoal 5 and subgoal 6)
- Use scales (subgoal 7)
Pick one data set. You may use other data but that probably means you’ll have to clean it yourself. It’s simplest to pick any of the cleaned data sets:
Feel free to use other data sets. For example, from:
data.amsterdam.nl— Amsterdam city datadata.overheid.nl— Dutch national datacbs.nl— Data of Statistics Netherlandsprojects.knmi.nl— Weather data in the Netherlandsdata.europa.eu— European Union open datadata.un.org— United Nations datadata.oecd.org— Organisation for Economic Co-operation and Development datafivethirtyeight/data— Data and code behind stories and interactives at FiveThirtyEight
Homogenised monthly temperature time series of De Bilt (1901-present). Cleaned
from knmi.nl.
Download assessment-1/temperature.csv.
Comma-separated values (CSV) with 1400 rows and two columns:
date— Date inYYYYMMDDtemp— Homogenised monthly temperature in degrees celsius
date,temp
19010131,-0.424
19010228,-0.761
19010331,3.369
19010430,8.782
19010531,12.181
...
20170430,8.44
20170531,14.854
20170630,17.926
20170731,17.828
20170831,17.131
Languages listed as having 50 million or more speakers. Crawled from
wikipedia.org.
Download assessment-1/languages.tsv.
Tab-separated values (TSV) with 26 rows and two columns:
language— Name of languagespeakers— Estimated number of total speakers
language speakers
Mandarin Chinese 1090000000
English 983000000
Hindustani 544000000
Spanish 527000000
Arabic 422000000
...
Marathi 74000000
Yue Chinese 72000000
Turkish 71000000
Vietnamese 68000000
Italian 66000000Percentage of the population living in a dwelling without indoor flushing toilet
for the sole use of the household. Cleaned from
data.europa.eu.
Download assessment-1/toilets.json.
JavaScript Object Notation (JSON) with 34 rows and 2+ columns:
code— ISO 3166-1 country codecountry— Human readable country name2005…2016— Percentage of population without a toilet if known andnullotherwise
[
{
"2005": 1.5,
"2006": 0.9,
"2007": 1.5,
"2008": 1.5,
"2009": 1.3,
"2010": 1.2,
"2011": 1.2,
"2012": 1,
"2013": 1,
"2014": 1,
"2015": 1,
"2016": 0.9,
"code": "AT",
"country": "Austria"
},
// ...
{
"2005": 1,
"2006": 0.8,
"2007": 0.8,
"2008": 0.6,
"2009": 0.5,
"2010": 0.5,
"2011": 0.1,
"2012": 0.3,
"2013": 0.2,
"2014": 0.4,
"2015": 0.4,
"2016": 0.4,
"code": "GB",
"country": "United Kingdom"
}
]💁 We don’t like plagiarism and report it to our assessment committee (examencommissie in Dutch). See ¶ 6.1.2 of Teaching and Examination Regulations (TER) for a full definition, but here are a few cases that count as plagiarism:
a. using or copying someone else’s texts, data or ideas without a full and correct acknowledgement of sources; b. presenting the structure or central ideas developed by someone else as your own work or ideas, even when a reference to other authors has been included;
[…]
e. copying (parts of) media files or other sources, software source codes, models and other diagrams of other people without acknowledgement and allowing it to be held as your own work;
[…]
g. copying the work of fellow-students and allowing it to be held as your own work;
[…]
| 1-2 | 3-4 | 5-6 | 7-8 | 9-10 | |
|---|---|---|---|---|---|
| Representation | There is no data, no visualisation, or data is barely used in the visualisation (if at all) | Data and a visualisation exist but interpreting the visualisation is harder than interpreting the data itself | Data is displayed in a visualisation based on an example | The visualisation goes beyond an example; There are demonstrable additions and the student can name them | 🎓 Several of the data’s dimensions are beautifully visualised |
| Application of subject matter | d3 is either not referenced or not used | d3 is either used to load data or to make a dynamic visualisation | d3 is used to load data and to make a dynamic visualisation | The visualisation contains a well-chosen scale, axes, or other useful additions | 😱 The way the student applies d3 is more advanced than what they were taught in class; let’s switch places |
| Understanding | There is either no substantial code or the student cannot explain the code that exists | The student cannot explain parts of the code | The student can explain every part of the code | The student can explain every part of the code and describe why it’s used instead of alternatives | 🤓 The student understands JavaScript and d3’s programming principles |
| Quality | The project is handed in late, broken, undocumented, or otherwise not proper | Code style is inconsistent or code is partially documented | Code adheres to standards and docs cover what the project is and does | Code quality is consistently good and docs are professional | 📚 Code and docs both read like great books |