Log in to my visual studio code to do this Code to use is below **Date** JOU4100

Published by John M. Re

Nov 29, 2021

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Log in to my visual studio code to do this
Code to use is below
**Date**
JOU4100 & Digital Journalism 2
Laura Nelson
**Presented to Jean-Sébastien Marier**
# Project 2: Basic Data Analysis & Visualization
Use one hashtag symbol (`#`) to create a level 1 heading like this one.
## Foreword
I have been assigning different versions of this project to my digital journalism and data storytelling students for a few years now. Its structure was inspired by the main sections/chapters of [*The Data Journalism Handbook*](https://datajournalism.com/read/handbook/one/). This version was further inspired by the [Key Capabilities in Data Science](https://extendedlearning.ubc.ca/programs/key-capabilities-data-science) program offered by the University of British Columbia (UBC).
**Here are some useful resources for this assignment:**
* [GitHub’s *Basic writing and formatting syntax* page](https://docs.github.com/en/github/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)
* [GitHub Guides: Mastering Markdown](https://guides.github.com/features/mastering-markdown/)
* [The template repository for this assignment in case you delete something by mistake](https://github.com/jsmarier/Template-for-the-Basic-Data-Analysis-Visualization-Project)
Did you notice how to create a hyperlink? In Markdown, we put the clickable text between square brackets and the actual URL between parentheses.
And to create an unordered list, we simply put a star (`*`) before each item.
## 1. Introduction
Insert text here.
## 2. Getting Data
Use two hashtag symbols (`##`) to create a level 2 heading like this one.
### 2.1. Importation
Use three hashtag symbols (`###`) to create a level 3 heading like this one. Please follow this template when it comes to levels 1 and 2 headings. However, you can use level 3 headings as you see fit.
To include a screen capture, use the sample code below. Your images should be saved in the same folder as your `.md` file.
![](import-screen-capture.png)
*Figure 1: The “Import file” prompt on Google Sheets.*
**Here are examples of functions and lines of code put in grey boxes:**
1. If you name a function, put it between “angled” quotation marks like this: `IMPORTHTML`.
1. If you want to include the entire line of code, do the same thing, albeit with your entire code: `=IMPORTHTML(“https://en.wikipedia.org/wiki/China”; “table”, 5)`.
1. Alternatively, you can put your code in an independent box using the template below:
“` r
=IMPORTHTML(“https://en.wikipedia.org/wiki/China”; “table”, 5)
“`
This also shows how to create an ordered list. Simply put `1.` before each item.
## 3. Understanding Data
Insert text here.
## 4. Delivering Data
Insert text here.
**This section should include a screen capture of your chart and its public link, like so:**
![](map-screen-capture.png)
*Figure 2: The map created with Datawrapper*
[Interactive version here](https://datawrapper.dwcdn.net/o7Wwp/2/)
## 5. Conclusion
Insert text here.
## 6. References
Include a list of your references here.
The data to use is in the link below
https://open.ottawa.ca/documents/ottawa::construction-demolition-and-pool-enclosure-permits-monthly-2020/about
Rubric
For this assignment, you must extract data from a website or database. You must then clean and analyze the data, find a potential story idea, and create a visualization. Your assignment must clearly detail your process. Your analysis must be written with the Markdown format and published on GitHub. You are expected to write about 1000-1500 words, and to include several screen captures showing the different steps you went through.or this assignment, you must extract data from a website or database. You must then clean and analyze the data, find a potential story idea, and create a visualization. Your assignment must clearly detail your process. Your analysis must be written with the Markdown format and published on GitHub. You are expected to write about 1000-1500 words, and to include several screen captures showing the different steps you went through.
Guidelines
This project is an opportunity for you to demonstrate your mastery of this course’s key concepts, from both technical and theoretical standpoints. Using the .md template provided on GitHub Classroom (link below), you must import a dataset, clean it, analyze it, and present it. Please follow this template when it comes to level 1 and level 2 headings. However, you can use level 3 headings as you see fit. Write in complete sentences as much as possible. That being said, ordered and unordered lists are acceptable here and there, especially when you are listing steps.
Make sure to provide a thorough description of the steps you undertake. Reproducibility is key here. Someone else should be able to take your assignment and achieve the same results by following your instructions. If you use a specific Google Sheets function to extract the dataset, write it out in a grey box (using the proper Markdown syntax). Provide us with a step-by-step guide to clean your dataset with OpenRefine and/or Google Sheets. Include at least one screen capture per section. For example, in 2. Getting Data, include a screen capture of your dataset in Google Sheets, so that readers can follow along while you describe the columns and variables. In other words, see this assignment like a lab report.
I also want to understand your critical thought process. Support your main observations and decisions by citing relevant class materials. Why did you decide to leave certain columns out of your analysis? Why did you decide to use this type of visualization instead of another one? You do not need to follow APA per se, but be consistent. If you decide to use parenthetical citations, use them throughout your assignment. You can also use Wikipedia-style footnotes. The Markdown code for footnote references is here.
* As you will likely notice, this assignment is based on the hands-on activities for modules 6-9. If you have not already done so, I therefore encourage you to complete them before undertaking this project.
Files to Choose From
For this assignment, you must analyze a dataset titled “Construction, demolition, and pool enclosure permits monthly – 2020.” It features a list of permits for construction, demolition, and pool enclosure projects issued by the City of Ottawa in 2020. You can find more information about the data on the City of Ottawa’s open data portal.
The original dataset includes monthly summaries, which I removed. Thus, please use one of the following versions:
The CSV file in my GitHub repository. The file is too big for IMPORTDATA. Thus, you will need to download it and import it into Google Sheets manually. Hint: The separator is a semicolon.
A public Google Sheets spreadsheet. You can either make a copy or, if you truly want to impress me, use IMPORTRANGE. Hint: The second argument is city-ottawa-building-permits-2020!1:12552.
Required Elements for the Assignment
Here are the main sections required, with a suggested word count for each section:
Top Section
The date, the course code and course name, your name, and my name in the top left corner.
A level 1 heading with the title of the assignment.
A short foreword is optional.
1. Introduction (about 100-150 words)
Briefly explain the context for this assignment.
Summarize the dataset you are using, mention its source, and explain how the data was collected.
Mention the main sections of your assignment.
Include a link to the dataset.
2. Getting Data (about 200-250 words)
Explain how to import the data into Google Sheets. Write down the function you used.
Include a screen capture of your dataset in Google Sheets. It’s fine if we don’t see all of the rows, but make sure we see the headings (first row). (And don’t forget to put the image in the same folder as your .md file.)
Make general observations regarding the dataset:
Who created it? When?
When was it last updated?
How many columns and rows are there?
What types of variables are we dealing with? Be specific. For example: “Column A features nominal variables with the names of all participants to the study. Column B includes the age of each participant as discrete variables.”
What are things that caught your attention right away? Are there empty cells? Does the overall formatting look OK?
Etc.
3. Understanding Data (about 500-600 words)
Explain what you did to make sure that your data is clean:
Did you use OpenRefine? If so, what did you do exactly? Why?
Did you use Google Sheets’ data-cleaning tools (whitespace, duplicates, find and replace)? If so, which ones? Why?
Did you freeze the first row, applied filters?
Did you change the format of your columns based on the variables (date, currency, etc.)? (Hint: Amongst other things, the “VALUE” column needs some attention.)
Did you use formulas and functions to reorganize the data (SPLIT, CONCATENATE, etc.)?
Did you erase some information?
Did you conduct a VIMO analysis?
Etc.
Include a screen capture of your dataset after the cleaning process.
At this stage, do you have reasons to question the validity and reliability of the data? Cite relevant course materials to support your observations.
Analyze the data using at least the following methods:
Two measures of central tendency (mean, median, mode)
One other function or formula
One pivot table
One basic Google Sheets chart (exploratory chart)
Include the following elements in your report:
Your functions in grey Markdown boxes and their results (the numbers you got)
A screen capture of your pivot table
A copy of your exploratory chart (you can download a PNG copy by clicking on the three little dots)
Explain your main findings:
Does a particular number or statistic stand out?
What did you learn? What’s the story here?
Which variables and numbers do you want to showcase in your main visualization (next step)?
Include a public link to the final version of your final Google Sheets spreadsheet.
4. Delivering Data (about 200-250 words)
Explain how you prepared the data for visualization.
List the main steps you undertook to create your chart with Datawrapper.
Make sure to include the following elements under the “Annotation” tab:
A meaningful title
The source of your data
A link to your data source (ideally, the original dataset on the City of Ottawa’s open data portal)
Your byline
An alternative description for screen readers
The description and notes are optional
Explain your editorial and creative decisions:
Why did you choose this type of chart?
Why this particular colour scheme?
Etc.
Include a PNG copy of your chart.
Include a public link to the online version of your chart.
5. Conclusion (about 100-150 words)
Summarize your main findings.
Offer some critical final thoughts.
6. References
Include a list of the relevant course materials.
Include a list of other sources you used.

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