Jason Ashley tradingbills
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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", |
tradingbills
/ R Data Understanding
Last active
February 18, 2021 20:12
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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) |
tradingbills
/ lm r (correlation coefficient), r-squared(coefficient of determination), SSR, SST, SSE
Created
July 9, 2020 14:58
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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 |
tradingbills
/ covariance|pearson|spearman
Created
July 20, 2020 18:17
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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){ |
tradingbills
/ z_score, x_value, empirical rule
Created
August 2, 2020 17:54
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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 |
tradingbills
/ gist:a936b89a102ae9c3aad2a1a71c21f210
Created
February 14, 2021 02:56
Data Understanding (edureka Tableau)
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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) | |
| ``` |
tradingbills
/ UCR Codes 2021
Created
February 26, 2021 20:25
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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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