The kind folks over at @RStudio gave a nod to my recently CRAN-released epidata package in their January data package roundup and I thought it might be useful to give it one more showcase using the recent CRAN update to ggalt and the new hrbrthemes (github-only for now) packages.
Labor force participation rate
The U.S. labor force participation rate (LFPR) is an oft-overlooked and under- or mis-reported economic indicator. I’ll borrow the definition from Investopedia:
Population age distributions and other factors are necessary to honestly interpret this statistic. Parties in power usually dismiss/ignore this statistic outright and their opponents tend to wholly embrace it for criticism (it’s an easy target if you’re naive). “Yay” partisan democracy.
Since the LFPR has nuances when looked at categorically, let’s take a look at it by attained education level to see how that particular view has changed over time (at least since the gov-quants have been tracking it).
We can easily grab this data with epidata::get_labor_force_participation_rate()(and, we’ll setup some library() calls while we’re at it:
library(epidata)
library(hrbrthemes) # devtools::install_github("hrbrmstr/hrbrthemes")
library(ggalt)
library(tidyverse)
library(stringi)
part_rate <- get_labor_force_participation_rate("e")
glimpse(part_rate)
## Observations: 457
## Variables: 7
## $ date <date> 1978-12-01, 1979-01-01, 1979-02-01, 1979-03-01, 1979-04-01, 1979-05-01...
## $ all <dbl> 0.634, 0.634, 0.635, 0.636, 0.636, 0.637, 0.637, 0.637, 0.638, 0.638, 0...
## $ less_than_hs <dbl> 0.474, 0.475, 0.475, 0.475, 0.475, 0.474, 0.474, 0.473, 0.473, 0.473, 0...
## $ high_school <dbl> 0.690, 0.691, 0.692, 0.692, 0.693, 0.693, 0.694, 0.694, 0.695, 0.696, 0...
## $ some_college <dbl> 0.709, 0.710, 0.711, 0.712, 0.712, 0.713, 0.712, 0.712, 0.712, 0.712, 0...
## $ bachelor's_degree <dbl> 0.771, 0.772, 0.772, 0.773, 0.772, 0.772, 0.772, 0.772, 0.772, 0.773, 0...
## $ advanced_degree <dbl> 0.847, 0.847, 0.848, 0.848, 0.848, 0.848, 0.847, 0.847, 0.848, 0.848, 0...One of the easiest things to do is to use ggplot2 to make a faceted line chart by attained education level. But, let’s change the labels so they are a bit easier on the eyes in the facets and switch the facet order from alphabetical to something more useful:
gather(part_rate, category, rate, -date) %>%
mutate(category=stri_replace_all_fixed(category, "_", " "),
category=stri_trans_totitle(category),
category=stri_replace_last_regex(category, "Hs$", "High School"),
category=factor(category, levels=c("Advanced Degree", "Bachelor's Degree", "Some College",
"High School", "Less Than High School", "All"))) -> part_rateNow, we’ll make a simple line chart, tweaking the aesthetics just a bit:
ggplot(part_rate) +
geom_line(aes(date, rate, group=category)) +
scale_y_percent(limits=c(0.3, 0.9)) +
facet_wrap(~category, scales="free") +
labs(x=paste(format(range(part_rate$date), "%Y-%b"), collapse=" to "),
y="Participation rate (%)",
title="U.S. Labor Force Participation Rate",
caption="Source: EPI analysis of basic monthly Current Population Survey microdata.") +
theme_ipsum_rc(grid="XY", axis="XY")
The “All” view is interesting in that the LFPR has held fairly “steady” between 60% & 70%. Those individual and fractional percentage points actually translate to real humans, so the “minor” fluctuations do matter.
It’s also interesting to see the direct contrast between the starting historical rate and current rate (you could also do the same with min/max rates, etc.) We can use a “dumbbell” chart to compare the 1978 value to today’s value, but we’ll need to reshape the data a bit first:
group_by(part_rate, category) %>%
arrange(date) %>%
slice(c(1, n())) %>%
spread(date, rate) %>%
ungroup() %>%
filter(category != "All") %>%
mutate(category=factor(category, levels=rev(levels(category)))) -> rate_range
filter(part_rate, category=="Advanced Degree") %>%
arrange(date) %>%
slice(c(1, n())) %>%
mutate(lab=lubridate::year(date)) -> lab_df(We’ll be using the extra data frame to add labels the chart.)
Now, we can compare the various ranges, once again tweaking aesthetics a bit:
ggplot(rate_range) +
geom_dumbbell(aes(y=category, x=`1978-12-01`, xend=`2016-12-01`),
size=3, color="#e3e2e1",
colour_x = "#5b8124", colour_xend = "#bad744",
dot_guide=TRUE, dot_guide_size=0.25) +
geom_text(data=lab_df, aes(x=rate, y=5.25, label=lab), vjust=0) +
scale_x_percent(limits=c(0.375, 0.9)) +
labs(x=NULL, y=NULL,
title=sprintf("U.S. Labor Force Participation Rate %s-Present", lab_df$lab[1]),
caption="Source: EPI analysis of basic monthly Current Population Survey microdata.") +
theme_ipsum_rc(grid="X")
Fin
One takeaway from both these charts is that it’s probably important to take education level into account when talking about the labor force participation rate. The get_labor_force_participation_rate() function — along with most other functions in the epidata package — also has options to factor the data by sex, race and age, so you can pull in all those views to get a more nuanced & informed understanding of this economic health indicator.
The ? Resistance
I need to be up-front about something: I’m somewhat partially at fault for ? being elected. While I did not vote for him, I could not in any good conscience vote for his Democratic rival. I wrote in a ticket that had one Democrat and one Republican on it. The “who” doesn’t matter and my district in Maine went abundantly for ?’s opponent, so there was no real impact of my direct choice but I did actively point out the massive flaws in his opponent. Said flaws were many and I believe we’d be in a different bad place, but not equally as bad of a place now with her. But, that’s in the past and we’ve got a new reality to deal with, now.
This is a (hopefully) brief post about finding a way out of this mess we’re in. It’s far from comprehensive, but there’s honest-to-goodness evil afoot that needs to be met head on.
Brand Damage
You’ll note I’m not using either of their names. Branding is extremely important to both of them, but is the almost singular focus of ?. His name is his hotel brand, company brand and global identifier. Using it continues to add it to the history books and can only help inflate the power of that brand. First and foremost, do not use his name in public posts, articles, papers, etc. “POTUS”, “The President”, “The Commander in Chief”, “?” (chosen to match his skin/hair color, complexion and that comb-over tuft) are all sufficient references since there is date-context with virtually anything we post these days. Don’t help build up his brand. Don’t populate historical repositories with his name. Don’t give him what he wants most of all: attention.
Document and Defend with Data
Speaking of the historical record, we need to be blogging and publishing regularly the actual facts based on data. We also need to save data as there’s signs of a deliberate government purge going on. I’m not sure how successful said purge will be in the long run and I suspect that the long-term effects of data purging and corruption by this administration will have lasting unintended consequences.
Join/support @datarefuge to save data & preserve the historical record.
Install the Wayback Machine plugin and take the 2 seconds per site you visit to click it.
Create blog posts, tweets, news articles and papers that counter bad facts with good/accurate/honest ones. Don’t make stuff up (even a little). Validate your posits before publishing. Write said posts in a respectful tone.
Support the Media
When the POTUS’ Chief Strategist says things like “The media should be embarrassed and humiliated and keep its mouth shut and just listen for a while” it’s a deliberate attempt to curtail the Press and eventually there will be more actions to actually suppress Press freedom.
I’m not a liberal (I probably have no convenient definition) and I think the Press gave Obama a free ride during his eight year rule. They are definitely making up for that now, mostly because their very livelihoods are at stake.
The problem with them is that they are continuing to let themselves be manipulated by ?. He’s a master at this manipulation. Creating a story about the size of his hands in a picture delegitimizes you as a purveyor of news, especially when — as you’re watching his hands — he’s separating families, normalizing bigotry and undermining the Constitution. Forget about the hands and even forget about the hotels (for now). There was even a recent story trying to compare email servers (the comparison is very flawed). Stop it.
Encourage reporters to focus on things that actually matter and provide pointers to verifiable data they can use to call out the lack of veracity in ?’s policies. Personal blog posts are fleeting things but an NYT, WSJ (etc) story will live on.
Be Kind
I’ve heard and read some terrible language about rural America from what I can only classify as “liberals” in the week this post was written. Intellectual hubris and actual, visceral disdain for those who don’t think a certain way were two major reasons why ? got elected. The actual reasons he got elected are diverse and very nuanced.
Regardless of political leaning, pick your head up from your glowing rectangles and go out of your way to regularly talk to someone who doesn’t look, dress, think, eat, etc like you. Engage everyone with compassion. Regularly challenge your own beliefs.
There is a wedge that I estimate is about 1/8th of the way into the core of America now. Perpetuating this ideological “us vs them” mindset is only going to fuel the fires that created the conditions we’re in now and drive the wedge in further. The only way out is through compassion.
Remember: all life matters. Your degree, profession, bank balance or faith alignment doesn’t give you the right to believe you are better than anyone else.
FIN (for now)
I’ll probably move most of future opines to a new medium (not uppercase Medium) as you may be getting this drivel when you want recipes or R code (even though there are separate feeds for them).