Introduction to R 

#################
#1. Entering data
#################

#(a) using scan('filename')

> data<-matrix(scan('A:/remisNH.txt'),byrow=T,ncol=5)
Read 105 items
> data
      [,1] [,2] [,3] [,4] [,5]
 [1,]    1    1    1   10    1
 [2,]    2    2   22    7    1
...
[21,]   21    2    8   10    0

#With this approach, you have to define all the variables by yourself. 
#For example, 

> pair<-data[,1]
> pair
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
> rem<-data[,2]
> rem
 [1] 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2

#(b) using read.table

> data<-read.table('A:/remisWH.txt', header=T)
> attach(data)
> names(data)
 [1] "pair"    "rem"     "timep"   "timet"   "statust"
> rem
 [1] 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2

#The advantage is that you do not have to name the columns if the data file
#comes with a header line. 

#(c) entering from the keyboard

> pair<-1:21
> pair
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
> rem<-c(1,2,2,2,2,1,2,2,2,2,2,1,2,2,2,1,1,2,2,2,2)
> rem
 [1] 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2

##################################
#2. Data checking and manipulation
##################################

#(a) subsets of data

> data[1,]
  pair rem timep timet statust
1    1   1     1    10       1
> data[,2:3]
   rem timep
1    1     1
2    2    22
...
21   2     8
> data[1:5,2:3]
  rem timep
1   1     1
2   2    22
...
5   2     8

#you can change some part of data
> x<-1:5
> x
[1] 1 2 3 4 5
> x[3]<-6
> x
[1] 1 2 6 4 5

#(b) some simple functions

> dim(data)
[1] 21  5
> length(rem)
[1] 21

> table(rem)
rem
 1  2 
 5 16 
> min(timet)
[1] 6
# also try max, mean, median, var

> sd(timet)
[1] 9.999524

3#############################################
#3. Arithmetric, matrix and logical operations
##############################################

#arithmetric operations: + - * / ^
> 2+3
[1] 5
> 2^3
[1] 8

#matrix operations: t, %*%
> A<-matrix(1:6,ncol=2)
> A
     [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> t(A)  #transpose
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6

> B<-cbind(rep(1,3),c(1,0,0))
> B
     [,1] [,2]
[1,]    1    1
[2,]    1    0
[3,]    1    0
> t(A) %*% B
     [,1] [,2]
[1,]    6    1
[2,]   15    4

#logical operations: >,<,==,!=,<=,>=,!,  &, |
> (7>5)
[1] TRUE
> !(7>5)
[1] FALSE
> (1:10>=5)
 [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
> sum(1:10>=5)
[1] 6
> sum(c(1:3,2:5)==3)
[1] 2
> x<-1:10
> (x>=5 & x<7)
 [1] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
> (x>=5 | x==2)
 [1] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

############################################################################
#4. cumulative probability, percentile and random variates for distributions
############################################################################

> qnorm(.975)
[1] 1.959964
> pnorm(1)
[1] 0.8413447
> rnorm(10)
 [1]  0.9594821 -1.0000935  0.1733313 -0.2323405 -0.1728959  0.4997507
 [7]  0.2924879 -0.1303746  0.7994302  0.4397668

#p,q and r can be added to such distributions as unif, exp, t, chisq, f, gamma,
# weibull, etc. If you are not sure about the usage, check the help files

#######################
#5. making figures in R 
#######################

#This is arguably the best feature of Splus/R compared to other stat packages

plot(x,y)
hist(x)
abline(0,1)

#####################################
#Appendix: some very useful functions
#####################################

#rep
> rep(3,10)
 [1] 3 3 3 3 3 3 3 3 3 3
> rep(1:2,5)
 [1] 1 2 1 2 1 2 1 2 1 2
> rep(1:2,c(3,4))
[1] 1 1 1 2 2 2 2

#cbind, rbind, apply, ifelse
#these are very useful for avoiding loops

> x<-1:4
> y<-11:14
> cbind(x,y)
     x  y
[1,] 1 11
[2,] 2 12
[3,] 3 13
[4,] 4 14
> rbind(x,y)
  [,1] [,2] [,3] [,4]
x    1    2    3    4
y   11   12   13   14
> A<-rbind(x,y)
> apply(A,1,sum)  # 1 for row operations
 x  y 
10 50 
> apply(A,2,sum)  # 2 for column operations
[1] 12 14 16 18

> x<-c(1,5,2,99,4,6,3,99,2)
> x<-ifelse(x!=99,x,NA)
> x
[1]  1  5  2 NA  4  6  3 NA  2

#floor,ceiling,round

> floor(3.4)
[1] 3
> ceiling(3.4)
[1] 4
> round(3.14159,2)
[1] 3.14

#unique,sort,order,duplicated

> x<-c(1,3,5,3,2,1,6)
> x
[1] 1 3 5 3 2 1 6
> unique(x)
[1] 1 3 5 2 6
> sort(x)
[1] 1 1 2 3 3 5 6
> duplicated(x)
[1] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
> order(x)
[1] 1 6 5 2 4 3 7

#using order to sort a matrix according to one of the columns
> A<-cbind(x,c(7:10,1:3))
> A
     x   
[1,] 1  7
[2,] 3  8
[3,] 5  9
[4,] 3 10
[5,] 2  1
[6,] 1  2
[7,] 6  3
> ord<-order(x)
> A[ord,]
     x   
[1,] 1  7
[2,] 1  2
[3,] 2  1
[4,] 3  8
[5,] 3 10
[6,] 5  9
[7,] 6  3