tanykim/coursera-exdata_plotting1 — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2015-03-08
Follow along to learn how to load and clean a real household energy dataset in R.
Reuse the plotting scripts as a template for turning time-series data into charts.
Study a worked example of filtering data to a specific date range before visualizing it.
| tanykim/coursera-exdata_plotting1 | jacobjameson/tte_cc | hadley/logger | |
|---|---|---|---|
| Stars | — | 0 | 1 |
| Language | R | R | R |
| Last pushed | 2015-03-08 | — | 2024-10-16 |
| Maintenance | Dormant | — | Stale |
| Setup difficulty | easy | moderate | easy |
| Complexity | 1/5 | 4/5 | 2/5 |
| Audience | general | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires downloading the electricity meter dataset separately before running the scripts.
This repository contains homework solutions for a college course on exploratory data analysis. The assignment asks students to download real electricity meter data from a household and create four specific charts that show how power consumption changed over a two-day period in February 2007. The core task is straightforward: take a large dataset of electrical measurements (including things like active power usage, voltage, and sub-metering readings from different parts of the house) and visualize it in ways that reveal patterns. The README provides the dataset, explains what each measurement means, and shows example images of what the four finished plots should look like. Students need to write R code that loads the data, filters it to just those two February days, and then generates charts saved as PNG images. The repository serves as a student's submission, they fork the starter repository, write code to reproduce the four required plots, and push their solutions back to GitHub. Each plot needs both a standalone R script file (so anyone can re-run the code and recreate the chart) and the PNG image file itself. The assignment emphasizes practical skills: handling a moderately large dataset, working with date and time formats, and using R's base plotting system to turn raw numbers into visual insights. This is a typical introductory data analysis assignment you'd find in an online course or statistics class. It assumes the student knows basic R but wants them to practice real-world workflows, downloading data, cleaning it, exploring it visually, and sharing their work via version control. The README doesn't explain what the "base plotting system" is, so prior familiarity with R is probably expected.
A student's homework submission that turns two days of household electricity meter data into four R-generated charts for a data analysis course.
Mainly R. The stack also includes R.
Dormant — no commits in 2+ years (last push 2015-03-08).
No license information is mentioned in the explanation.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.