Now reached 3,500 members, one of largest groups in world!
Slack bit.ly/Slack-CRUG
Twitter ChicagoRusers
GitHub @Chicago-R-User-Group
Meetup ChicagoRUG
Website Chicago-r-user-group.github.io
From the inaugural conference in 2009, the annual R/Finance conference in Chicago has become the primary meeting for academics and practitioners interested in using R in Finance.
Next Meetup:
For more information find them:
Spring Workshop on Causal Inference
The workshop will be appropriate for analysts who are familiar with classical statistical methods and ideas such as regression and confidence intervals. Participants interested in implementing some of the statistical methods during the workshop should bring a laptop that includes R software.
https://s01.123signup.com/servlet/SignUpMember?PG=1531573182300&P=15315731911433509500&Info
https://www.meetup.com/Chicago-Women-in-Big-Data
WeWork provides shared workspaces for technology startup, and services for entrepreneurs, freelancers, startups, small businesses and large enterprises.
IBM for sponsoring food and beverages!
Agenda
Crime in Chicago’s Community Areas
https://troyhernandez.com/2019/03/12/crime-in-chicagos-community-areas/
library(leaflet)
library(sp)
bikeracks <- read.csv("data/Bike_Racks.csv")
head(bikeracks)
## RackID Address Ward Community.Area Community.Name TotInstall
## 1 651 1 E Jackson Blvd 2 32 Loop 1
## 2 1409 1 E 51st St 3 40 Washington Park 1
## 3 1989 1 E Adams St 42 32 Loop 3
## 4 6379 1 E Erie St 42 8 Near North Side 1
## 5 3058 1 E Jackson Blvd 2 32 Loop 2
## 6 5019 1 E Jackson Blvd 2 32 Loop 2
## Latitude Longitude Historical F12 F13 LOCATION
## 1 41.87810 -87.62749 0 41.87810 -87.62749 (41.878100, -87.627495)
## 2 41.80190 -87.62581 1 41.80190 -87.62581 (41.801900, -87.625808)
## 3 41.87938 -87.62752 1 41.87938 -87.62752 (41.879382, -87.627517)
## 4 41.89397 -87.62794 1 41.89397 -87.62794 (41.893974, -87.627945)
## 5 41.87810 -87.62749 1 41.87810 -87.62749 (41.878100, -87.627495)
## 6 41.87810 -87.62749 1 41.87810 -87.62749 (41.878100, -87.627495)
leaflet() %>%
addTiles() %>%
addCircles(lng = bikeracks$Longitude, lat = bikeracks$Latitude,
radius = .5, opacity = 1, col = "blue")
https://gis.stackexchange.com/questions/168886/r-how-to-build-heatmap-with-the-leaflet-package
How do we use this and other data to make biking, and the rest of our city, better?