monkeymike.co.uk

"C:\Program Files\R\R-2.10.0\bin\Rgui.exe" --save

##load the required bits
library(RODBC)
library(lattice)
##set up the connection
channel<-odbcConnect("mike_db", uid="mike")
##sqlTables(channel)
##a<-sqlFetch(channel,"AnE")
##b<-sqlFetch(channel,"vwAnE")
##b<-sqlQuery(channel, paste("select * from vwAnE order by loc, month_val"))
##xyplot(N~month_val|LOC,b, type="l")

##c<-sqlQuery(channel, paste("select CM,sum(numerator)"
## ,"from EP_2010_2011_ENHANCED"#
## , "WHERE not parnt like 'z_%' "
## ," group by CM"
## ))

##execute a query for the data
e<-sqlQuery(channel, paste("select CM,aggregate_02,spect,Numerator,denominator,value,target, year_month+'-01' as year_month"
,"from EP_2010_2011_ENHANCED"
,"where 1=1 and CM like '%e_01%'and TIME_TYPE='per-month'"
,"and parnt like 'z_t%'order by aggregate_02,spect,year_month"))
##convert the year_month value to a date

e$year_month<-as.Date(e$year_month)

##plot the lattice

xyplot(value~year_month|spect,e, type="l", cex=3)

xyplot(value+target~year_month|spect,e, type="l", size=5)

library(RODBC)
channel<-odbcConnect("mike_db", uid="mike")
sqlTables(channel)
a<-sqlFetch(channel,"AnE")
b<-sqlFetch(channel,"vwAnE")
b<-sqlQuery(channel, paste("select * from vwAnE order by loc, month_val"))
xyplot(N~month_val|LOC,b, type="l")

library(lattice)
library(Hmisc)
bwplot(LOS~DT|CL,act, horiz=FALSE, cex=.5,pch="|")

bwplot(LOS~DT|CL,act, horiz=FALSE, cex=.5)

panel.mean <- function(x, y, ...) {
tmp <- tapply(x, y, FUN = mean)
panel.points(tmp, seq(tmp), pch = 20, ...)
}

bpplot(LOS~DT|CL,act, horiz=FALSE, cex=.3)

bwplot(LOS~DT|CL,act, horiz=FALSE, panel=panel.bwplot, cex=1.3)

bwplot(LOS~DT|CL,act, horiz=FALSE, cex=.5,pch="|",panel.mean <- function(LOS, DT,data=act ...) {
tmp <- tapply(LOS, DT,data=act, FUN = mean)
panel.points(tmp, seq(tmp), pch = 20, ...)
})

############################################try this
a=0.002; b=31.7; c=0.51
sds=rep(c(0,3,5,10,20,50,200), each=3)
y1=c(0,0,0.16, 0, 0.33,0.5, 0.16, 0.83, 1.16, 0.67, 0.5, 1.16, 0.83,
2.33, 3.6, 5.5, 4.33, 1.16, 22, 13, 12)
lo=y1-0.1*y1
hi=y1+0.1*y1
##########################################
# Figure 1
xYplot(
Cbind(y1, lo, hi)~jitter(sds, amount=1),
method="bars",ylim=c(0,max(hi)+1),
ylab="Y", xlab="X"
)

# Figure 2
xYplot(Cbind(y1, lo, hi)~jitter(sds, amount=1),
method="bars",ylim=c(0,max(hi)+1),
ylab="Y", xlab="X",
panel=function(...){
panel.xYplot(x,y,...)
panel.number=panel.number()
panel.curve(curve(a*(x+c)/1+a*b*(x+c), from=0, type="l", lwd=2))
})

#########################################################

###################################################################################################
bwplot(LOS~DT|CL,act, horiz=FALSE,cex=0.6,pch="|",
panel=function(x,y,...)
{
panel.bwplot(x,y,horiz=FALSE,...)
# panel.mean(y,x,col="red",...)
bwplot(DT~LOS|CL,act,horiz=TRUE,panel=panel.mean )
# panel.mean(y, x,col="red", horiz=TRUE,...)
# panel.points(mean(x), y, col="red", ...)
}
)
#####################################
#########################
bwplot(LOS~DT|CL,act,horiz=FALSE,cex=0.5)
################

#############
bwplot(LOS~DT|CL,act,horiz=FALSE,panel=panel.mean )
############

#####
xYplot(
LOS~DT|CL,act,
panel=function(x,y,...)
{
xYplot(x,y,...)
bwplot(LOS~DT|CL,act,horiz=FALSE,panel=panel.mean )
#panel.mean
}
)
########

#########
xYplot(LOS~DT|CL,act,horiz=FALSE,cex=0.5)
################
library(ggplot2)
p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_boxplot()
str(act)
###############################
###############################

library(ggplot2)
p<-ggplot(act,aes(factor(DT),LOS))

p + geom_boxplot() + geom_hline(aes(yintercept=mean(LOS)),colour="red",linetype=2) + facet_wrap(~CL)+ geom_point(stat = "summary",colour = "red", size = I(3) ,shape = 5, fun.y = "mean", position = position_dodge(width = 0.75))+geom_line(stat = "summary",colour = "red", size = I(3) ,shape = 5, fun.y = "mean", position = position_dodge(width = 0.75))
last_plot() + theme_bw()

###################################################
###################################################
library(doBy)
summaryBy(LOS + DT ~ CL + DT, data = act, FUN = function(x) { c(mean = mean(x), sd = sd(x), var=var(x),sum=sum(x)) } )
###############
summaryBy(LOS ~ CL , data = act, FUN = function(x) { c(mean = mean(x), sd =sd(x), var=var(x),sum=sum(x)) } )
##############

####################################

p+geom_boxplot()+geom_line(aes(x=factor(DT),y=mean(LOS)) )
+ geom_point(stat = "summary",colour = "red", size = I(3) ,shape = 5, fun.y = "mean", position = position_dodge(width = 0.75))

#################################

library(ggplot2)
p<-ggplot(act,aes(factor(CL),LOS))
p + geom_mean()

p<-ggplot(act,aes(factor(DT),LOS))
p + geom_boxplot() +geom_hline(aes(yintercept=mean(LOS)),colour="red")+ facet_wrap(~CL)
###################################
# qplot examples -------------------------------------------------------------

qplot(diamonds$cut, diamonds$carat)
qplot(carat, price, data = diamonds)
qplot(carat, price, data = diamonds, colour=clarity)
qplot(carat, price, data = diamonds, geom=c("point", "smooth"), method=lm)

qplot(carat, data = diamonds,
 geom="histogram")
qplot(carat, data = diamonds,
 geom="histogram", binwidth = 1)
qplot(carat, data = diamonds,
 geom="histogram", binwidth = 0.1)
qplot(carat, data = diamonds,
 geom="histogram", binwidth = 0.01)

# using ggplot() -------------------------------------------------------------
d <- ggplot(diamonds, aes(x=carat, y=price))
d + geom_point()
d + geom_point(aes(colour = carat))
d + geom_point(aes(colour = carat)) + scale_colour_brewer()

ggplot(diamonds) + geom_histogram(aes(x=price))

# Separation of statistcs and geometric elements -----------------------------

p <- ggplot(diamonds, aes(x=price))

p + geom_histogram()
p + stat_bin(geom="area")
p + stat_bin(geom="point")
p + stat_bin(geom="line")

p + geom_histogram(aes(fill = clarity))
p + geom_histogram(aes(y = ..density..))

# Setting vs mapping ---------------------------------------------------------
p <- ggplot(diamonds, aes(x=carat,y=price))
# What will this do?
p + geom_point(aes(colour = "green"))
p + geom_point(colour = "green")
p + geom_point(colour = colour)
##################################

subset(dataframe, County==”CONWY”, c(N,date,hospital))

subsetting using an OR relation.

con2<-subset(ae,(AREA=="CONWY" & HEORG=="Ffestiniog_MIU" | AREA=="GWYNEDD" & HEORG=="Ffestiniog_MIU"), c(AT,AREA,DT_3,HEORG), order=c(HEORG,DT_3))

temp <- read.table('clipboard', header=TRUE)
DataFrameName <-read.csv ("C:/filename.csv", header= TRUE)

When importing from the clipboard make sure you don't have any commas or spaces in the data or the headings as it may fail to import correctly. You'll get errors talking about too many or to few columns if you leave them in.