#########################################################################
#                           BASIC OPERATIONS
#########################################################################

#data vectors
	a<-1
	a
	a<-c(1,2,3)
	a
	b<-rep(1,10)
	b	
	b<-rep(a,4)
	b
	b<-rep(a,each=4)
	b
	a<-seq(1,10)
	a
	c<-1:10
	a
	c
	a<-seq(1,10,by=2)
	a	
	a<-seq(10,1)
	a
	seq(1:10)
	a
	b<-c(a,100)
	b




#########################################################################
#              DATA IMPORT AND BASIC MANIPULATION WITH DATA SETS
#########################################################################

#import data file into R

	data<-read.table("http://user.mendelu.cz/drapela/Forest_Biometry/Data/plots.txt",sep='\t',header=TRUE)




#preview of first rows of the data table

	head(data)


#column names

	names(data)


#function summary - basic statististical properties of data frame (different fot quantitative and qualitative variables)

	summary(data)


#excel-like table suitable for quick editing of data

	fix(data)


## MANIPULATION WITH DATA

#data in row 63  

	data[63,]


#data in 4th column

	data[,4]


#the same operation with column name

	data$d13


#value in row 63 and column 8

	data[63,8]


#values in rows 63 and 70

	data[c(63,70),]



#values in rows 63 and 70 and colums 4 and 5

	data[c(63,70),c(4,5)]


#values in rows 60-70 and colums 4-8

	data[60:70,4:8]


# data type of dataframe "data" and variables

	class(data)
	class(data$d13)
	class(data$species)


#fitering data only for pine

	data[data$species=='PINE',]




#fitering data only for pine on plot 1424

	data[data$species=='PINE'&data$id_plot=='1424', ]



#saving values of age into new variable x

	x <- data$age 
	x


#length of vector x

	length(x)



#variables in workspace

	ls()


#to make the variables accessible by name within the R session

	attach(data)




# now we can access to variables directly without name of dataframe

	age
	h
	d13



#detaching of variables

	detach(data)



#variables cannot be called directly

	age
	h
	d13



## BASIC STATISTICAL FUNCTIONS

	mean(data$d13)




# mean for d13 and age

	mean_d_age<-apply(data[,c(4,8)],2,mean)
	mean_d_age



#mean of d13  only for spruce

	mean(data[data$species=='SPRUCE',]$d13)





#########################################################################
#                            BASIC GRAPHS
#########################################################################


#barplot
	x<-seq(1,20,by=2) # generating data
	labels<-letters[1:10] #generating labels
	barplot(x,names.arg=labels, main="Bar plot", col="red")
	windows()
	barplot(x,names.arg=labels, main="Bar plot", col=sample(colors(),10))
	


#1000 random values from standardized normal distribution in two variables "x" and "y" (1000 values for each variable)
	x<-rnorm(1000)
	y<-rnorm(1000)


#scatter plot 
	plot(x,y, main="Scatter plot", xlab="x", ylab="y", xlim=c(-4,4), ylim=c(-4,9))


#add red line with intercept 1 and slope 0
	abline(1,0, col="red")


#add blue vertical line at point x=1
	abline(v = 1, col="blue")


#add green line with intercept 1 and slope 2
	abline(1, 2, col="green")


#add additional points to graph

	x1<-seq(-3, 3, by=0.1)
	y1<-x1^2 - 3
	points(x1,y1, pch=21, bg="blueviolet")


#histogram for x

	hist(x, main = "Histogram - variable x" , xlab="x ", probability=TRUE, col="lightblue")


#add empirical density for x

lines(density(x), col="red")


# histogram for x and y in one plot

	par(mfrow=c(1,2))
	hist(x, main = "Histogram  x", xlab="x ", probability=TRUE, col="lightblue")
	lines(density(x), col="red")
	hist(y, main = "Histogram  y", xlab="y ", probability=TRUE, col="lightgreen")
	lines(density(x), col="red")




#saving graphs to file with extension *.png

	png("histogram_xy.png",width=800, height=600)