Away from work for a week to study data journalism at Boston University, I had to confront a difficult fact: I am terrible at math and science.
Coming to terms with this meant figuring out why I was so bad at crunching numbers. Were the microaggressions from my male colleagues too distracting for me? That could be it. Maybe I was the victim of some sort of stigma. Were my instructors not inclusive enough?
The federal government spends billions every year on Science, Technology, Engineering, and Math programs, or "STEM." Increasingly, the government’s focus is how to get more women and girls to like STEM. The Obama administration started an initiative based on the idea that girls are "not being encouraged" enough to become scientists or engineers.
A quick search of taxpayer-funded projects finds plenty of examples. The Department of Agriculture has a program to engage rural girls in STEM learning by making bracelets with LED lights. The State Department created a time traveling STEM game for teenagers with the goal of "empowering women." The government is spending $548,459 trying to figure out if microaggressions are to blame for fewer female engineering undergrads.
The National Science Foundation has given over $1 million to give women a greater "sense of belonging" in STEM.
So this must be it. Clearly, last week I was a victim of the gender gap. After all, researchers funded by the National Science Foundation say a sense of belonging is "critical" to succeeding in STEM. But women "report feeling a lower sense of belonging in STEM than do men" causing "gender disparities."
The reasons given for why women don’t feel like they belong in STEM have to do with external factors—not enough instructor support, the "salience of gender stereotypes"—rather than internal ones.
Here’s a simpler explanation: my lady brain just doesn’t work that way.
There was a lot of personal acceptance throughout my journey of trying to understand "R programming," but none of it had to do with feeling accepted as a woman in a STEM environment. I had to accept a fact I learned long ago: I’m terrible at math and science—which is why I’m a staff writer at the Washington Free Beacon and not crushing it at Goldman Sachs. The only disparity here was between the left and right sides of my brain.
The programming language is spectacular if you can learn to use it. You can take datasets ranging in the millions, clean the stuff up, analyze what’s in it, and apparently find exactly what you are looking for. The problem was that it combines html-like coding and statistical analysis, not exactly my fortes.
You can do anything in R. Want a shopping list? It’s simple:
fruits <- c("Apples", "Bananas", "Oranges")
fruit_num <- c(2, 3, 8)
vegetables <- c("Carrots", "Onions")
veggie_num <- c(4, 9)
shopping_list <- list(Fruit = fruits,
+ Fruit_Quant = fruit_num,
+ Veggies = vegetables, Veggie_Quant = veggie_num)
shopping_list
$Fruit
[1] "Apples" "Bananas" "Oranges"$Fruit_Quant
[1] 2 3 8
Sweet. I have two apples, three bananas, and eight oranges. But let’s move on to the complicated stuff. You can find the standard deviation of the price of diamonds from a substantial dataset. But, oh no—I forgot to install the GGally extension to ggplot2. Now my faceted grids with ggplot aren’t loading. I see the bold red error message often throughout the week.
Let’s try the faceted grid again.
> ggplot(diamonds, aes(x = price)) + geom_histogram() + facet_grid(cut)
Error in facet_grid(cut) : object 'rows' not found
My bad, I forgot the tilde before "cut."
Now I have this lovely chart. But please don’t ask me what it means beyond there are some nice diamonds in this data.
This is the basic stuff. We haven’t even gotten into regression yet, when you have to load not only the tree, ISLR, and MASS libraries, but the leaps, too.
I’m feeling pretty lost. I could try to blame my failure on a lack of faculty inclusiveness, but my instructors were top notch.
Luckily, I look around the room and see most of my fellow students are checking Facebook. Sense of belonging achieved.