briandk

briandk

```python
import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np

def make_forest( height, width, density ):
    '''
    This function will take in a width, height, and density specified by the user and use those values
    to generate a forest (2-d numpy array) matching those characteristics. A 0 will represent an empty
    space, a 1 will represent a tree

briandk

# Early in the script
pkgs_pre_script <- try(devtools::loaded_packages(), silent = TRUE)
 
 
 
# Late in the script
pkgs_used_during_script <- try(devtools::loaded_packages(), silent = TRUE)
 
cat("* ")
print(citation(), style="text") # or Bibtex or LaTeX

briandk

sudo rm -rf /Library/Frameworks/R.framework \
            /Applications/R.app \
            /usr/bin/R /usr/bin/Rscript

pkgutil --regexp --forget org\.r-project*

briandk

briandk

%matplotlib inline

try:
   import seaborn as sns
   print('seaborn is already installed')
except:
   try:
       import sys
       sys.path
       sys.path.insert(0,'./packages')

briandk

briandk

import pandas as pd
import seaborn as sns

flint_data = pd.DataFrame.from_csv(
    'https://raw.githubusercontent.com/ComputationalModeling/intro-to-computational-modeling/master/201-spring-2017/day05-flint-water-data-analysis-day1/flint_data_with_simplified_column_names.csv?token=AAUJNLxtLQZh8meM0qSXyn4U1REp-PLAks5Ym3SjwA%3D%3D'
)

sns.distplot(flint_data['zipCode'])

briandk

const parseRecord = function (rawRecord) {
  // Parsing logic goes here
}

const fs = require('fs')
const records = fs.readFileSync('myBibfile.bib')
  .split('@') // @ is the delimiter in bibfiles

// Array.from() takes an iterable argument *and* a mapping function 
// if you want to map elements from the source array

briandk

$$ f(x) = x + 3 $$

y = x + 3

function f turns x into x + 3

we apply function f by saying f(something)

f = function (x) { return(x + 3) }