http://www.meetup.com/The-Chicago-Data-Visualization-Group/events/97690792/
Date: January 28 Location: 1871
These are some example visualizations and data handling methods for discussion at the meetup.
opts_chunk$set(tidy = FALSE)source('00 Initialize.R')rawdat = read.table(
file = 'Database 2013-01-21 (8zQ4cW7T).csv', sep=',', quote='"',
flush=FALSE, header=TRUE, nrows=-1, fill=FALSE, stringsAsFactors=FALSE,
na.strings=c('None', ''))
str(rawdat)## 'data.frame': 19207 obs. of 11 variables:
## $ charges_citation : chr "720 ILCS 5 12-3.4(a)(2) [16145" "625 ILCS 5 6-101 [12935]" "720 ILCS 5 12-3(a)(1) [10529]" "720 ILCS 550 5(c) [5020200]" ...
## $ race : chr "WH" "LW" "BK" "BK" ...
## $ age_at_booking : int 26 37 18 32 49 26 41 56 40 20 ...
## $ gender : chr "M" "M" "M" "F" ...
## $ booking_date : chr "2013-01-20T00:00:00" "2013-01-20T00:00:00" "2013-01-20T00:00:00" "2013-01-20T00:00:00" ...
## $ jail_id : chr "2013-0120171" "2013-0120170" "2013-0120169" "2013-0120167" ...
## $ bail_status : chr NA NA NA NA ...
## $ housing_location : chr "05-" "05-" "05-L-2-2-1" "17-WR-N-A-2" ...
## $ charges : chr NA NA NA NA ...
## $ bail_amount : int 5000 10000 5000 50000 5000 5000 25000 5000 25000 10000 ...
## $ discharge_date_earliest: chr NA NA NA NA ...
dat = as.data.table(rawdat)
str(dat)## Classes 'data.table' and 'data.frame': 19207 obs. of 11 variables:
## $ charges_citation : chr "720 ILCS 5 12-3.4(a)(2) [16145" "625 ILCS 5 6-101 [12935]" "720 ILCS 5 12-3(a)(1) [10529]" "720 ILCS 550 5(c) [5020200]" ...
## $ race : chr "WH" "LW" "BK" "BK" ...
## $ age_at_booking : int 26 37 18 32 49 26 41 56 40 20 ...
## $ gender : chr "M" "M" "M" "F" ...
## $ booking_date : chr "2013-01-20T00:00:00" "2013-01-20T00:00:00" "2013-01-20T00:00:00" "2013-01-20T00:00:00" ...
## $ jail_id : chr "2013-0120171" "2013-0120170" "2013-0120169" "2013-0120167" ...
## $ bail_status : chr NA NA NA NA ...
## $ housing_location : chr "05-" "05-" "05-L-2-2-1" "17-WR-N-A-2" ...
## $ charges : chr NA NA NA NA ...
## $ bail_amount : int 5000 10000 5000 50000 5000 5000 25000 5000 25000 10000 ...
## $ discharge_date_earliest: chr NA NA NA NA ...
## - attr(*, ".internal.selfref")=<externalptr>
## Convert booking and discharge dates to date time objects
## EXAMPLE FORMAT: 2012-12-30T20:57:19.616186
dat$booking_date = ExtractIsoTime(dat$booking_date)
dat$discharge_date_earliest = ExtractIsoTime(dat$discharge_date_earliest)
table(dat$race, useNA='ifany')##
## AS B BK IN LB LT LW W WH
## 117 29 13879 13 68 1803 1239 44 2015
## I'm going to guess that W and WH are both "White"
dat$race = sub('^W$', 'WH', dat$race)
## I'm going to guess that B and BK are both "White"
dat$race = sub('^B$', 'BK', dat$race)
table(dat$race, useNA='ifany')##
## AS BK IN LB LT LW WH
## 117 13908 13 68 1803 1239 2059
## Summary of how many unique values and missing values exist in the data
NAsummary(dat)## col Count nNA rNA nUnique rUnique
## charges_citation 1 19207 315 0.0164 1435 0.0747
## race 2 19207 0 0.0000 7 0.0003
## age_at_booking 3 19207 0 0.0000 67 0.0034
## gender 4 19207 0 0.0000 2 0.0001
## booking_date 5 19207 151 0.0078 4160 0.2165
## jail_id 6 19207 0 0.0000 19207 1.0000
## bail_status 7 19207 10318 0.5371 5 0.0002
## housing_location 8 19207 5 0.0002 6115 0.3183
## charges 9 19207 3052 0.1589 1075 0.0559
## bail_amount 10 19207 6415 0.3339 119 0.0061
## discharge_date_earliest 11 19207 10627 0.5532 8581 0.4467
## More simple summaries
table(dat$gender, useNA='ifany')##
## F M
## 1742 17465
## Chargs and Charges_citation are too big to print:
# table(dat$charges_citation, useNA='ifany')
# table(dat$charges, useNA='ifany')
## Just the top 100 charges_citation
sort(table(dat$charges_citation, useNA='ifany'), T)[1:10]##
## 720 ILCS 570 402(c) [5101110] 720 ILCS 5 12-3.2(a)(1) [10416
## 1531 623
## 720 ILCS 5/9-1(a)(1) 625 ILCS 5 6-303(a) [13526]
## 559 513
## 720 ILCS 570/402(c) 720 ILCS 5 19-1(a) [1110000]
## 470 390
## <NA> 720 ILCS 5 12-3.2(a)(2) [10418
## 315 306
## 720 ILCS 5 19-3(a) [1120000] 720 ILCS 5 16A-3(a) [1060000]
## 302 272
sort(table(dat$charges, useNA='ifany'), T)[1:10]##
## <NA>
## 3052
## POSS AMT CON SUB EXCEPT(A)/(D)
## 1839
## FIRST DEGREE MURDER:INTENDS DEATH/GREAT HARM
## 619
## RET THEFT/DISP MERCH/
## 466
## DOMESTIC BATTERY/BODILY HARM
## 463
## BURGLARY
## 461
## RESIDENTIAL BURGLARY
## 461
## ARMED ROBBERY ARMED WITH FIREARM
## 308
## ROBBERY
## 283
## VIOLATION OF PROBATION
## 265
## Are the NA values really NA?
table(is.na(dat$charges))##
## FALSE TRUE
## 16155 3052
table(is.na(dat$charges_citation))##
## FALSE TRUE
## 18892 315
## Write Temp File (what did you think it would do?)
# wtf(dat)range(dat$booking_date, na.rm=TRUE)## [1] "1993-01-16 CST" "2013-01-20 CST"
## Checking to see if the dates imported correctly
plot(dat$discharge_date_earliest , 1:nrow(dat))
plot(dat$booking_date , 1:nrow(dat)) ## Usually organzied by booking date?
## Not very informative, but it would let me know if the data wasn't right
plot(dat$booking_date, dat$discharge_date_earliest)
hist(dat$bail_amount, 100)
hist(pmin(dat$bail_amount, 1000000), 100)
hist(dat$age_at_booking, 100)
plot(dat$age_at_booking, dat$bail_amount, main='age by bail amount')
plot(log(bail_amount)~age_at_booking, dat, main='age by log(bail amount)')
boxplot(bail_amount~age_at_booking, dat, main='age by bail amount (much better)')
boxplot(pmin(bail_amount, 5e5)~age_at_booking, dat,
main='age by bail amount\n(changing the limits)')
boxplot(log(bail_amount)~age_at_booking, dat, main='age by log(bail amount)')
library(mgcv)## Loading required package: nlme
## This is mgcv 1.7-29. For overview type 'help("mgcv-package")'.
bailbyage.gam = gam(bail_amount ~ s(age_at_booking), data=dat)
plot(bailbyage.gam, main='fitted age by bail amount')
plot(dat$age_at_booking, dat$bail_amount,
main='fitted age by bail amount\n(another way)')
lines(predict(bailbyage.gam), col='red')
ggplot(dat, aes(x=age_at_booking, fill=factor(race))) +
geom_density(alpha=.5) +
facet_grid(race ~ .)+
xlab("Age at booking") +
ylab("Density by race") +
theme(plot.title = element_text(size = 20)) +
labs(title='Age at booking summary\nby race\n')
ggplot(dat, aes(x=bail_amount, fill=factor(race))) +
geom_density(alpha=.5) +
facet_grid(race ~ .) +
xlab("Age at booking") +
ylab("bail amount by race") +
theme(plot.title = element_text(size = 20)) +
labs(title='Bond by race summary\n')## Warning: Removed 48 rows containing non-finite values (stat_density).
## Warning: Removed 4503 rows containing non-finite values (stat_density).
## Warning: Removed 3 rows containing non-finite values (stat_density).
## Warning: Removed 24 rows containing non-finite values (stat_density).
## Warning: Removed 678 rows containing non-finite values (stat_density).
## Warning: Removed 415 rows containing non-finite values (stat_density).
## Warning: Removed 744 rows containing non-finite values (stat_density).
ggplot(dat, aes(x=age_at_booking, fill=factor(gender))) +
geom_density(alpha=.7) +
facet_grid(gender ~ .)+
xlab("Age at booking") +
ylab("Density by race") +
theme(plot.title = element_text(size = 20)) +
labs(title='Age at booking summary\nby gender\n')
ggplot(dat, aes(x=age_at_booking, fill=factor(gender))) +
geom_density(alpha=.7) +
facet_grid(race ~ .)+
xlab("Age at booking") +
ylab("Density by race") +
theme(plot.title = element_text(size = 20)) +
labs(title='Age at booking summary\nby gender and race\n')
ggplot(dat, aes(x=bail_amount, fill=factor(gender))) +
geom_density() +
facet_grid(gender ~ .)+
xlab("Age at booking") +
ylab("bail amount by race") +
theme(plot.title = element_text(size = 20)) +
labs(title='Bond by race summary\n')## Warning: Removed 581 rows containing non-finite values (stat_density).
## Warning: Removed 5834 rows containing non-finite values (stat_density).
