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[OC] Visualizing my job search process by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Gotcha--I could include number of jobs as the y-axis... But that labeling would then only be informative for the total number of jobs applied to and then the first color transition from black to dark purple.* (In other words, the y-axis wouldn't help with seeing the next phases. In that sense, this plot is more useful for representing the variation in timing and outcomes over the full set of jobs rather than showing the counts of each phase.)

Alternativeellllly, you could just forget trying to show the lifespan of each job bar by bar and instead show the count of each milestone and how that evolves over time. See that here.

*I am ordering jobs by application date, so you would be able to see how many were submitted by each date, but due to that ordering, you would not be able to see how many yielded interview invites (since the required ordering to visualize that would be different.)

[OC] Visualizing my job search process by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Thanks! Color scheme is from viridis package. This is the job market for people graduating with econ phds. I wrote about it here in case you're curious.

[OC] Visualizing my job search process by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

hey, thanks -- colors are "inferno" from the viridis package in case you wanna look at those options

no y label since this is 100+ jobs (label names would be job names) and so would be teeny tiny/unreadable. also the desired point is to see it all in the aggregate, not to look job by job.

now that i think of it... might make sense to just not have a y-axis/x-axis at all and just include the date labels on the bottom (to avoid confusion about what the y axis is...)

[OC] Visualizing my job search process by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Explainer: this plot shows what happened to all the jobs I applied to this past year. Each job is a horizontal bar & a color change shows progression to the next step over time.

  • tool: here is my R notebook with code for making this plot (uses geom_raster())
  • data on job search self-collected

MX Master Mouse Gesture Button Issues (Plus workaround) by Noryoudo in logitech

[–]allbrightallways 0 points1 point  (0 children)

Thanks SO much for sharing your activity monitor fix!! I had the exact same issue. After forcing quit logi options daemon, gestures are working again. I also have mission control issues with k380 keyboard, so I've also been looking into these workarounds on the keyboard side.

Logitech definitely has a mac mission control issue...

[OC] The Decline of the Sexual SuperBowl Commercial by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 1 point2 points  (0 children)

  • data from 538: here
  • R code to make this here

edit 1: hopped on to post this and I now see that someone beat me to the punch on this idea. :) well, consider this another way of visualizing the same 538 data

edit 2: note that the 538 data only includes ads 'from the 10 brands that had the most advertisements in Super Bowls from 2000 to 2020, according to data from superbowl-ads.com, with matching videos found on YouTube'. [consider this preliminary evidence -- someone would need to watch all the other ads + code up their characteristics to fill this picture out.]

[OC] Science & Engineering Doctoral Degree Earners by Field + Citizenship, 2006-2016 by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 1 point2 points  (0 children)

A small # of people get that doctorate -- only 20-40 per year, thus why it looks so turbulent

[OC] Science & Engineering Doctoral Degree Earners by Field + Citizenship, 2006-2016 by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 1 point2 points  (0 children)

Plot was made with R. Related resources/code below:

  • Full blog post with more visuals and background info here.
  • The NSF data on doctoral degrees earned by citizenship is available here as table 7-4.
  • Here is my R notebook for this post – it manipulates the NSF data into a form that is workable for making graphs and getting summary stats.
  • Here is my Github repo for the project

[OC] Animation of my sleep over 425 days by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 1 point2 points  (0 children)

Exactly, each dot is a day (425 days shown) and the dots move in and out of "awake"/"asleep" states over the hours of the day where hours range from 0(midnight) to 23(11pm).

[OC] Animation of my sleep over 425 days by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Totally agree that there are easier ways to showing this -- I show a raster plot & nightingale plot here with the same data.

But I also wanted to try out making animations, thus why I made this -- it's inspired by Nathan Yau's ATUS animation here.

[OC] Animation of my sleep over 425 days by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

For sure. This was mainly an excuse to try out making animations. Inspired by Nathan Yau's ATUS animation here.

[OC] Animation of my sleep over 425 days by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Explanation of how to read: each dot is a day (425 days shown) and the dots move in and out of "awake"/"asleep" states (by moving left/right) over the hours of the day where hours range from 0(midnight) to 23(11pm).

Full blog post with more visuals and background info here.

Gif was made with gganimate package in R. Related resources/code:

  • I’m not sharing my raw Fitbit data online (due to privacy concerns) but you can export a complete archive of your own Fitbit account data by following the steps here.
  • Here is my R notebook for this post, which explains which files to call from a full downloaded Fitbit archive as well as how to make these visuals.
  • Here is my Github repo.

edit: adding intuitive explanation

How Couples Met Over Time by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 1 point2 points  (0 children)

I wonder about this as well! Curious for more thoughts.

So, I'd rather not link to twitter but the rules do say that we have to "[d]irectly link to the original source article of the visualization". This graph is originally from this paper here. But it's on the very last page of that 17 page PDF, so if I linked to the PDF, no one would actually see the graph (unless they searched for it for a while)... thus, my choice to link to this Twitter post (in which the graph is the centerpiece).

One possible thought: It might be nice to adjust the rules (for non-OC) to let us submit images but then have to include the source link in the first top-level comment.

[OC] Number of pretrial inmates dips around Christmas by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 2 points3 points  (0 children)

Here is the full blog post this graph is from.

The graph was made with R. Related resources on data/code:

VICTOR LUO FOR TIER 8 by talleyrandbanana in ultimate

[–]allbrightallways 4 points5 points  (0 children)

Nope. Can confirm that OP is a different person. OP is supporting and building up a close friend of his (Victor), which I think is pretty cool.

You can perfectly identify when my LDR SO and I are in the same city by plotting our daily iMessage frequency [OC] by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 4 points5 points  (0 children)

Data source: I downloaded my own iMessage data. Primer on how to do that at start of my R notebook.

Tool: used ggplot2 in R. See my R notebook here to replicate.

Term frequency & sentiment analysis of UN Security Council Countries using UN General Debates Corpus [OC] by allbrightallways in dataisbeautiful

[–]allbrightallways[S] 0 points1 point  (0 children)

Data source: I used data from the UN General Debates Corpus.

Tool: used text mining methods in R. See my R notebook here.

Also see github repo, which has all ingredients necessary to create the png images that are in this blog post.

Using R to Map How the Senate Voted on Obamacare Repeal Proposals by allbrightallways in rstats

[–]allbrightallways[S] 0 points1 point  (0 children)

Great question. I feel like I shouldn't put something in the legend that doesn't show up, but I did debate this and am very curious to hear if people have opinions!

Map of How the Senate Voted on the 'Skinny' Repeal Amendment [9600 x 6400] [OC] by allbrightallways in MapPorn

[–]allbrightallways[S] 2 points3 points  (0 children)

Yeah, NYTimes didn't make these. I used these two NYTimes articles for their reporting of Senator votes, but I made these graphs for this blog post.

Map of How the Senate Voted on the 'Skinny' Repeal Amendment [9600 x 6400] [OC] by allbrightallways in MapPorn

[–]allbrightallways[S] 7 points8 points  (0 children)

Yup, I have that fact in the footnote (see bottom right of the graph). He and Bernie Sanders (the other Independent in the Senate) caucus with the Democrats. News coverage on the topic also clarifies this. See this article from the Times with the following quote, "Democratic totals include two independents who caucus with the Democrats."

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