Open ksliney opened 6 years ago
You should post some legit matplotlib graphics with your pitch, even if they're terrible or useless, just so you can confirm that your data is actually manageable and solid (I know your collected it yourself, but it's good practice!)
UPDATE: UGLY charts to prove this topic is semi-possible (ignore design and colors at this point)
Not yet.
Ideal scenario: (1) building a scraper for Linkedin Recruiter (2) figuring out who came up as a lawyer, vs. military, vs. PMF. (3) Figure out length of time served. (4)Figuring out when served (many came on during lame duck period) Problems: Verifying the total number of staffers that served on Obama's NSC (can't find an official source to compare against who I've found on LinkedIn)
Hi! I'm a little robot, checking in on your project.
You need some feedback, let me summon @zle2105, @cfelke, @collleenwang for you
It looks like we need to fix up your your update a little bit! Edit it by clicking the pencil in the top right-hand corner. It requires:
Maybe you just didn't use the template? If not, edit your comment, cut and paste the template in, and then fill it out.
I like your choice of topic; looking at your previous works I really love that you're addressing the female angle/representation in various fields. You go, woman!
Regarding your first graph, it would be more helpful, if you display the bars in descending order, so I a as a reader can see for myself what's going on at first glance. Moreover, I'd move the title further to the center and show some x grid lines in the background for orientation, since you're displaying a couple of bars/values.
Regarding your second graph, I'd ditch the grid lines and display the values in or next to the end of your bars, you only have two. And I know everyone associates women with pink and men with blue, but people can adapt and certainly be challenged. I personally would prefer a change of colour. Speaking of, I'd suggest a play with colours overall, white and grey are plain/boring, especially when you're doing such a cool project.
I recommend fixing up your update with the template so we don't make the robot work so hard.
ax.set_axisbelow(True)
df.school.replace("The Johns Hopkins University blah blah long title", "Johns Hopkins", inplace=True
to make things fit a bit nicer. Even if there's a technical name for something, the common name is probably better to make things fit more nicely!If you still had long long school names, you could also align the titles on top of the bars (not sure how to do this in matplotlib offhand, you'd probably want to do this manually in illustrator by moving all of the labels at the same time).
I like your topic, though it's simple but clear. I think the most important thing for displaying charts is to show people insights or findings.
For the second graph, I would add the values into the bar graph since you have the space.
For the first graph, you might be able to save space by putting the names of the schools in the bars, instead of on the X-Axis. The graph might also look much more streamlined.
also you can use \n to break the university into two lines. Is this project finished? Would be interesting to see the final graphs.
Please complete all of the following sections, or the ghost of Joseph Pulitzer will spookily dance around your issue! A completed version of this template can be found at https://github.com/jsoma/data-studio-projects/issues/1
Pitch
Summary
Critics argue Obama relied too heavily on the National Security Council for operational control of foreign policy at the expense of State and Department of Defense participation. Others argue this isn't a critique of the Obama administration so much as it is a response shared by multiple administrations to a need for flexibility to respond to a complex global security environment.
So, if the NSC is growing in both size and influence, who staffs the NSC? What is their background and expertise? What are some of the ways that these people get selected for these jobs?
For more context: https://www.defenseone.com/ideas/2016/04/whats-wrong-obamas-national-security-council/127802/ https://www.washingtonpost.com/world/national-security/how-the-obama-white-house-runs-foreign-policy/2015/08/04/2befb960-2fd7-11e5-8353-1215475949f4_story.html
Details
Possible headline(s): I reverse engineered Obama's national security team. Here's what the data shows.
Data set(s): *I've compiled gender and education data on 417 officials using Linkedin. Using a Selenium-based scraper, I will continue to dig for details on their path to the NSC.
Code repository: https://github.com/ksliney/data-studio/tree/master/code/02-Obama_NSC
Possible problems/fears/questions: -TIME
Work so far
I realize this is still ugly, but...here's a basic graph:
Checklist
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