Two years ago we bought a greenhouse. It was aluminum framed with plastic panels. Unfortunately, its profile was not as wind-resistant as it needed to be for our campus. So last semester we built three vegetable boxes and salvaged the plastic panels from the greenhouse to build low-profile cold frames. These turned out quite nicely, and the Middle School’s Student-Run-Business’ Gardening Department have been experimenting with different types of produce.
The wood for the raised beds and the frames for the cold frames were purchased using funds from a grant by the Whole Kids Foundation and the pieces cut at the TechShop.
Variable: A placeholder name used to record data for later use. In the first statement above, g, is a variable and it is assigned a value of -9.8. Variable names in python start with a letter or underscore “_”. Variables can hold different types of data, for example:
strings:
x = "hello"
Floating point number (aka. a float):
x = 9.8
Integer:
x = 5
True/False (aka boolean):
x = True
Operations: Statements where some sort of calculation is made:
Add two numbers and assign the result to a variable called “y”:
y = 3 + 5
Divide the value in the variable “y” by 2 and then assign the value to another variable called “z”:
z = y / 2
Operators: The symbols that tell what operation to do:
+ – * / are used for addition, subtraction, multiplication, and division respectively
** is used for exponents so 5 squared (52) is:
a = 5**2
Loops: Tell the computer to do something over and over again. There are different types:
“For” loops are good for doing things a set number of times
for i in range(5):
print i
results in:
0
1
2
3
4
Logical statements: These test to see if something is True or False and then do different things based on the outcome:
Assign a value to a variable called “x”, check to see if the value is greater than 10, and print out a different sentence based on the result:
x = 12
if (x > 10):
print "x is greater than 10"
else:
print "x is less than 10"
Functions: Chunks of code that someone (maybe even you) wrote that can be referenced via a shortcut:
Create a function to calculate the force of gravity at the Earth’s surface if you give it a mass:
def forceOfGravity(mass):
Fg = mass * -9.8
return Fg
Call the function to find the force of gravity acting on a mass of 100 kg and print out the result:
x = forceOfGravity(100)
print x
the result should be:
-980
Classes: A class is like a function that you can assign to a variable and then have it do a lot more stuff.
In vpython there is a class called “sphere”. It renders a 3d sphere on the screen. Here we’ll create a sphere, assign it to a variable “ball” and then change one of its built-in properties, the color, from the default (white) to red. (if you are using Glowscript.org then don’t use the first line that imports the visual module).
from visual import *
ball = sphere()
ball.color = color.red
One of my students is taking an advanced statistics course–mostly online–and it introduced her to the statistical package R. I’ve been meaning to learn how to use R for a while, so I had her show me how use it. This allowed me to give her a final exam that used some PEW survey data for analysis. (I used the data for the 2013 LGBT survey). These are my notes on getting the PEW data, which is in SPSS format, into R.
Instructions on Getting PEW data into R
Go to the link for the 2013 LGBT survey“>2013 LGBT survey and download the data (you will have to set up an account if you have not used their website before).
There should be two files.
The .sav file contains the data (in SPSS format)
The .docx file contains the metadata (what is metadata?).
Load the data into R.
To load this data type you will need to let R know that you are importing a foreign data type, so execute the command:
> library(foreign)
To get the file’s name and path execute the command:
> file.choose()
The file.choose() command will give you a long string for the file’s path and name: it should look something like “C:\\Users\…” Copy the name and put it in the following command to read the file (Note 1: I’m naming the data “dataset” but you can call it anything you like; Note 2: The string will look different based on which operating system you use. The one you see below is for Windows):
> dataset = read.spss(“C:\\Users\...”)
To see what’s in the dataset you can use the summary command:
> summary(dataset)
To draw a histogram of the data in column “Q39” (which is the age at which the survey respondents realized they were LGBT) use:
> hist(dataset$Q35)
If you would like to export the column of data labeled “Q39” as a comma delimited file (named “helloQ39Data.csv”) to get it into Excel, use:
> write.csv(dataset$Q39, ”helloQ39Data.csv”)
This should be enough to get started with R. One problem we encountered was that the R version on Windows was able to produce the histogram of the dataset, while the Mac version was not. I have not had time to look into why, but my guess is that the Windows version is able to screen out the non-numeric values in the dataset while the Mac version is not. But that’s just a guess.