How Many Pieces of Knowledge from Different Places are Hidden in my Code

For a hobby programmer like me, Google is like a bible for fundamentalists. When they don‘t have an answer for something, they go and find it on bible. When I have a programming problem, I go and find it by googling it.

So I was wondering how many websites do I usually have to access for my to program something. I have a feeling that the number has to be quite high, but I did not know how much.

In order to figure that out, I decide to try and figure out how to program a factor analysis in the python. I wanted to get the loadings of variables for different factors. I continued until I had a working code that could print the loadings in the terminal for analysis. I only counted the websites whose solutions ended up in the code. There were more, but they were dead ends so I did not include them.

For the analysis I used the answers to the Big Five personality questionnaire that I found on http://personality-testing.info/_rawdata/.

This is the final code that I ended up with:

```    from sklearn.decomposition import FactorAnalysis
import numpy
from rpy2.robjects.packages import importr
from rpy2.robjects import r, numpy2ri

data = numpy.genfromtxt('data.csv', delimiter='\t')

for i in [6,5,4,3,2,1,0]:
data = numpy.delete(data, i, 1)
data = numpy.delete(data, 0, 0)

numpy2ri.activate()
fit = r.factanal(data, 5, rotation="varimax")
print(results)
```

I used 7 different websites to code this 12 lines of code. Which means that I needed to check one webpage for ever 1.7 line of code.

Here is the code with websites used above the piece of code that they were used for:

```    from sklearn.decomposition import FactorAnalysis
import numpy

data = numpy.genfromtxt('data.csv', delimiter='\t')

#http://stackoverflow.com/questions/24898754/delete-dimension-of-array
#http://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html
for i in [6,5,4,3,2,1,0]:
data = numpy.delete(data, i, 1)
data = numpy.delete(data, 0, 0)

#http://stackoverflow.com/questions/25036588/extract-correlation-matrix-from-rs-factanal-via-rpy
from rpy2.robjects import r, numpy2ri
numpy2ri.activate()

#http://blog.yhat.com/posts/rpy2-combing-the-power-of-r-and-python.html
from rpy2.robjects.packages import importr