##p1---------------------------------------------------------

#clear memory
rm(list=ls())

#list defined variabls
ls()

#create/load data
data <- c(1, 2, 3, 4)
data2  <- c(2, 4, 8, 16)

#check data
ls()
data
data2

#plot graph
plot(data, data2, xlab = "something", ylab = "something else")

#find working directory
getwd()

#Set working directory (to access files for R)
setwd("\\\\folder")

#read data file
filename<-read.csv

#help command
help(read.csv)

#access each individual column of a data set using "name"$header
datasetname$trial

#find column names for data set
names(dataset)

#see variables of a data set
attributes(name)

#read csv command to read and name a set of data
setname <- read.csv(file="file name",head=TRUE,sep=",")

#store number as a variable
letter<-number

#initialize a list of numbers the numeric command can be used
a<-numeric(10)

#String: a string is specified by using quotes
a<-"hello"

#find type of string using
typeof(string name)
eg: typeof(a)

#look at file to check several variables
summary(filename$variable)

#creating a dataframe
#here we create several separate vectors
> a <- c(1,2,3,4)
> b <- c(2,4,6,8)
> levels <- factor(c("A","B","A","B"))
#we then create a data frame using these vectors
> bubba <- data.frame(first=a,second=b,f=levels)

#variable types. 2 variables= TRUE and FALSE
a=TRUE
typeof(a)

b=FALSE
typeof(b)

#creating a vector and make it into a table
> a <- factor(c("A","A","B","A","B","B","C","A","C"))
> results <- table(a)
#check it
> results
> attributes(results)
> summary(results)

#creating two way tables, a=x b=y collums (values are examples)
> a <- c("Sometimes","Sometimes","Never","Always","Always","Sometimes","Sometimes","Never")
> b <- c("Maybe","Maybe","Yes","Maybe","Maybe","No","Yes","No")
> results <- table(a,b)
> results

##p2-----------------------------------------------------------------

#summary data set
summary(datasetname)

#boxplot
boxplot(quantileofdata[eg.ppm], col="")

#abline (add a line annotation)
abline(h=value of Y to anotate) eg:(h=12)

#create dashed line
lty=value

#histogram
hist(quantiledata[eg. ppm), col="")

#show data points on graph
rug(quantiledata)

#increase data buckets on graph
graphcommand(breaks="[100]"

#add line to graph
abline(v=Y value intended, lwd[line width]value[2]

#create table from data in r
name <- table(quantile$quantile)

#histogram
hist(quantile/data)

#scatter plot
with(data, plot(quantile,quantile))

#colour quantile data in plot
col=quantile$quantile

#set up plot window for 2 graphs
par(mfrow=c(1,2), mar=c(5,4,2,1))

#create variable
variable name <-subset(data, region=="quantile")

#make q plot
qplot(displ, hwy, data=dataname)

#add gray areas and trndlines
qplot(displ, hwy, data=dataname, geom=c"point", "smooth"))

#allpurpose qplot
qplot=drv,hwy,data=dataname,geom="boxplot")

#assign boxplots by variable
qplot(drv,hwy,data=mpg,geom="boxplot",color=manufacturer)

#histogram display freq for each variable
qplot(hwy, data = dataname, fill =drv)

#scatter plot with 3 facets
qplot(hwy, data = dataname, fill =drv)

#histogram with 3 facets
qplot(hwy, data = mpg, facets = drv ~ ., binwidth = 2)

#lattice to creat object assigned with variable (variable)
variable <- ggplot(mpg, aes(displ,hwy))

#add layer to variable
variable(geom_point()

#add smooth line to table
+geom_smooth()

#change line to linear model(lm)
+geom_smooth(method="lm)

#add colour legend to graph
 g+geom_point(aes(color=drv), size = 4, alpha = 1/2)


#ggplot first layer
ggplot(data=pharynx,aes(x=day, y=pumps))

#gplot 2nd layer
ggplot(pharynx, aes(x=as.factor(day), y=pumps)) + geom_boxplot(fill="red", alpha=0.2) + xlab("day")


#qplot boxplot 1
qplot(x=day, y=pumps,color=treatment,data=pharynx,geom="boxplot")