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plotting.R
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plotting.R
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# U.S. Department of Health and Human Services
# Office for Civil Rights
# Breach Portal: Notice to the Secretary of HHS Breach of Unsecured Protected Health Information
#* https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf
if (!"ggplot2" %in% installed.packages()) install.packages("ggplot2")
if (!"dplyr" %in% installed.packages()) install.packages("dplyr")
if (!"Lahman" %in% installed.packages()) install.packages("Lahman")
library(dplyr)
library(ggplot2)
library(Lahman)
Breach <- read.csv("MeetingContent/2016-04-15/breach_report.csv",
stringsAsFactors = FALSE) %>%
mutate(Breach.Submission.Date = as.Date(Breach.Submission.Date,
format = "%m/%d/%Y"),
Year = format(Breach.Submission.Date,
format = "%Y")) %>%
filter(Individuals.Affected < 10000)
head(Breach)
(plot <-
ggplot(data = Breach,
mapping = aes(x = State,
y = Individuals.Affected)) )
(plot <- plot +
geom_boxplot() )
(plot <- plot +
scale_y_continuous(limits = c(0, 500000)))
#* Annual breaches
ggplot(data = Breach,
mapping = aes(x = Year)) +
geom_bar()
ggplot(data = Breach,
mapping = aes(x = Year,
fill = State)) +
geom_bar()
ggplot(data = Breach,
mapping = aes(x = Year,
y = Individuals.Affected)) +
geom_boxplot()
ggplot(data = Breach,
mapping = aes(x = Year,
y = Individuals.Affected)) +
geom_boxplot() +
geom_point()
ggplot(data = Breach,
mapping = aes(x = Year,
y = Individuals.Affected)) +
geom_boxplot() +
geom_jitter()
data(Salaries)
SubSalary <- filter(Salaries,
teamID %in% c("ATL", "CLE", "SEA", "BOS") &
yearID >= 2005)
ggplot(data = SubSalary,
aes(x = teamID,
y = salary)) +
geom_boxplot()
ggplot(data = SubSalary,
aes(x = teamID,
y = salary,
colour = factor(yearID))) +
geom_boxplot()
ggplot(data = SubSalary,
aes(x = teamID,
y = salary,
colour = factor(yearID))) +
geom_boxplot() +
facet_grid(~ yearID)
ggplot(data = SubSalary,
aes(x = teamID,
y = salary,
colour = factor(yearID))) +
geom_boxplot() +
facet_grid(~ teamID)
ggplot(data = SubSalary,
aes(x = teamID,
y = salary,
colour = factor(yearID))) +
geom_boxplot() +
facet_grid(~ teamID, scales = "free_x")
Boston <- filter(Salaries, teamID == "BOS")
ggplot(data = Boston,
mapping = aes(x = yearID,
y = salary)) +
geom_point()
ggplot(data = Boston,
mapping = aes(x = yearID,
y = salary)) +
geom_point(alpha = .5) +
geom_line(stat = "smooth",
method = "lm")
ggplot(data = Boston,
mapping = aes(x = yearID,
y = salary)) +
geom_point(alpha = .5) +
geom_line(stat = "smooth",
method = "loess")
#Examples from https://gist.github.com/nutterb/fb19644cc18c4e64d12a
#* ggSurvGraph source code at: https://gist.github.com/nutterb/004ade595ec6932a0c29
source("https://gist.githubusercontent.com/nutterb/004ade595ec6932a0c29/raw/f5a94793ab2793c64e96dbd6ccfdd4e6e9ec3823/ggSurvGraph.R")
library(ggplot2)
library(survival)
install.packages("KMsurv")
data(kidney, package="KMsurv")
fit <- survfit(Surv(time, delta) ~ type, data=kidney)
plot(fit)
ggSurvGraph(fit)
ggSurvGraph(fit, conf.bar=FALSE)
ggSurvGraph(fit, conf.bar=FALSE,
gg_expr=list(geom_rect(aes(xmin=time, xmax=next.time,
ymin=lower, ymax=upper,
fill=strata),
alpha=.25, linetype=0)))
ggSurvGraph(fit, conf.bar=FALSE,
n.risk=TRUE, n.event=TRUE,
gg_expr=list(geom_rect(aes(xmin=time, xmax=next.time,
ymin=lower, ymax=upper,
fill=strata),
alpha=.25, linetype=0)))
ggSurvGraph(fit, conf.bar=FALSE, times=seq(0, 30, by=6),
n.risk=TRUE, n.event=TRUE,
gg_expr=list(geom_rect(aes(xmin=time, xmax=next.time,
ymin=lower, ymax=upper,
fill=strata),
alpha=.25, linetype=0)))
ggSurvGraph(fit, conf.bar=FALSE, times=seq(0, 30, by=6),
n.risk=TRUE, n.event=TRUE, cum.inc=TRUE,
gg_expr=list(geom_rect(aes(xmin=time, xmax=next.time,
ymin=lower, ymax=upper,
fill=strata),
alpha=.25, linetype=0)))
ggSurvGraph(fit, conf.bar=FALSE, times=seq(0, 30, by=6),
n.risk=TRUE, n.event=TRUE,
gg_expr=list(geom_rect(aes(xmin=time, xmax=next.time,
ymin=lower, ymax=upper,
fill=strata),
alpha=.25, linetype=0)))