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```{r}
#set UCR codes for Chicago
# 2020 http://gis.chicagopolice.org/website/clearMap_crime_sums/crime_types.html
# 2021 http://directives.chicagopolice.org/forms/CPD-63.451_Table.pdf 
# 2021 above from footer of http://directives.chicagopolice.org/directives/data/a7a57bf0-12d7196c-11f12-d71a-3c76ad6f2c11950a.html
# 2021 above search from 
# 388v01::34:51 which 
# 538, grepl 
# Murder
UCR01 = "0110|0130" 

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Data Understanding (1-5)
1. Industry data belongs to: Ecommerce
2. Find count of observations and variables,(use glimpse): 51290 & 24
3. Know data types (use glimpse)
4. Levels: (use glimpse)
5. See if any negatives (because this is ecommerce data)
```{r}
glimpse(df99)
summary(df99)
```

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z_score <- function(x, m, sd){
  return ((x-m)/sd)
}

x_val <- function(z, m, sd){
  return (z*sd + m)
}

m <- 100
sd <- 15

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library(readxl)
library(tidyverse)
library(psych)
library(scales)
setwd("C:/Users/tradingbills/Documents/_exer/_data/math/wk4/")

# 1 compute the covariance
# COV = Sum( (x_i - x_bar) * (y-i -y_bar)) / N-1
# COVARIANCE
covariance <- function(x,y){

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# lm  -- -- -- -- -- -- -- -- -- --  604 Linear Regression
# from Statistics 101: Linear Regression, Residual Analysis
# youtube.com/watch?v=gLENW2AdJWg

bill <- c( 34,	 108, 64	, 88	, 99	, 51	 )
mean(bill)
tip <-c ( 5, 17, 11, 8, 14, 5 )
mean(tip)

#correlation coefficient = covariance / co-standard-deviation

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Data Understanding (1-6)
#1. Industry data belongs to; 2. Observations and variables count: ; 3. Data types 
#4. Levels: ; 5. Any negatives (when ecommerce data); 6 Find Nulls

#1 Industry data belongs to:
#2 Observations & Variable Count
```{r}
library(skimr); skim(df99)
library(psych); describe(df99)
summary(df99)

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# IUCR codes for Ill. define for ifelse grepl below
# code found at # http://gis.chicagopolice.org/website/clearMap_crime_sums/crime_types.html
UCR01 = "0110|0130"
UCR02 = "0261|0262|0263|0264|0265|0266|0271|0272|0273|0274|0275|0281|0291|1753|1754"
UCR03 = "0312|0313|031A|031B|0320|0325|0326|0330|0331|0334|0337|033A|033B|0340"
UCR04A = "051A|051B|0520|0530|0550|0551|0552|0553|0555|0556|0557|0558"
UCR04B = "041A|041B|0420|0430|0450|0451|0452|0453|0461|0462|0479|0480|0481|0482|0483|0485|0488|0489|0490|0491|0492|0493|0495|0496|0497|0498"

# assert == VC by UCR code & append col GUCR for a global variable holding codes in long form for respective crimes
chicago_ds_2015_gte$GUCR = ifelse(grepl(UCR01, chicago_ds_2015_gte$IUCR), "UCR01", 

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Pytest Introduction

Copyright 2019 - Matt Harrison

@__mharrison__

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// win10/_exer/_playground/queryString/index.js

const queryString = require('query-string');

const ef1059 = "https://graph.facebook.com/oauth/authorize?client_id=<client_id>&scope=read_insights,manage_pages,publish_pages,user_posts,publish_actions,publish_to_groups&redirect_uri=https://ui.benchmarkemail.com/FacebookAuthorize"

const myQs = ef1059.split('?');

console.log(queryString.parse(myQs[1], {arrayFormat: 'comma'}));

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" Automatically populate the g:airline_symbols dictionary with the powerline symbols.
let g:airline_powerline_fonts = 1
" Also for airline, show the buffers in a list of there's only one tab open.
let g:airline#extensions#tabline#enabled = 1
let g:airline_theme="hybrid"
let g:enable_italic_font = 1
let g:hybrid_transparent_background = 1