radovankavicky

## R setup (https://github.com/hoxo-m/easyRFM)
install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("hoxo-m/easyRFM")
library(easyRFM)
## setwd("~/Desktop")

## Load / View Data
data <- rfm_generate_data(30000, begin="2014-01-01", end="2015-12-31", seed=123)
summary(data$payment)

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consumer_key = 'your-consumer-key'
consumer_secret = 'your-consumer-secret'
access_token = 'your-access-token'
access_secret = 'your-access-secret'

radovankavicky

## load the requisite libraries into R
library("xlsx")
library("choroplethr")
library("choroplethrAdmin1")
library("ggplot2")

indianregions <- get_admin1_regions("india")
## gets dataframe of 2 columns with name of country ("india") throughout column 1
## and name of regions in 2nd column

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var landGrid = [
  {
		"X": "1",
		"Y": "1"
	},
	{
		"X": "1",
		"Y": "2"
	},
	{

radovankavicky

import pandas as pd
import time
from nltk.sentiment import SentimentIntensityAnalyzer

t0 = time.time()

top_100 = pd.read_csv('/Users/brit.cava/Desktop/TabPy/top100.csv')

text = top_100['Word']
sid = SentimentIntensityAnalyzer()

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############################################################################### 
# Batch script for setting up fun Python libraries for Computational Methods in the Civic Sphere 2017
#
# Should be run after installing Anaconda 4.2+/Python 3.5+ via https://www.continuum.io/downloads
#
# Doesn't include libraries that are installed as dependencies (e.g. numpy via pandas) 
##############################################################################

##############################################################################
###### Yes

radovankavicky

#SCRIPT_REAL is a function in Tableau which returns a result from an external service script. It's in this function we pass the python code.

SCRIPT_REAL("from nltk.sentiment import SentimentIntensityAnalyzer

text = _arg1 #you have to use _arg1 to reference the data column you're analyzing, in this case [Word]. It gets word further down after the ,
scores = [] #this is a python list where the scores will get stored
sid = SentimentIntensityAnalyzer() #this is a class from the nltk (Natural Language Toolkit) library. We'll pass our words through this to return the score

for word in text: # this loops through each row in the column you pass via _arg1; in this case [Word]
    ss = sid.polarity_scores(word) #passes the word through the sentiment analyzer to get the score

radovankavicky

library(rvest)
library(magrittr)
library(dplyr)
library(purrr)
library(lubridate)
library(tidyr)
library(ggplot2)
library(scales)

setwd("...working directory...")

radovankavicky

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

radovankavicky

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/