Intro to R

intro.r

# reading and writing files
# press ctrl+enter to send 
iris
fix(iris)

write.csv(iris,"c:/temp/iris.csv")
iris2 = read.csv("c:/temp/iris.csv")
nrow(iris2)

# get help
?write.csv
# www.rdocumentations.org

# set row names
rownames(iris2) = paste0(letters[seq(1,26,length.out=nrow(iris2))],1:300)

# extract by rownames 
iris2["a1","Sepal.Length"]
iris2[c("a1","a6"),"Sepal.Length"]

# install packages --l need internet
install.packages("Hmisc")

# sequences
x = 1:100
y = matrix(x,ncol = 10)

x[10]
y[1,9]

length(x)
sum(x)
mean(x)
cumsum(x)

#
d = rnorm(1000)*1000 + 500
pd = pretty(d)
cpd = cut(d,pd)
levels(cpd)
ff = c("a","b","c")
ff.f = as.factor(ff)

# data coersion
a = c("1.1","2")
a.nu = as.numeric(a)
a.c = as.character(a.nu)

# arrays
a = c(a,3)
print(a)
a = a[-1]
print(a) 
a = a[-c(1,2)]
print(a)

# TRUE FALSE indexing
xx = c(1,2,3)
print(xx)
x.tf = x[c(TRUE,FALSE,FALSE)]
x1 = x[!(x>90)]

# data.frame
iris2 = rbind(iris, iris) # same as SAS append
rn = data.frame(random = rnorm(nrow(iris)), another.var = 1:nrow(iris))
iris2.r = cbind(iris2, rn )

# sql
install.packages("sqldf")
require(sqldf) #y or library(sqldf)
merged.data = sqldf("select * from dataframe1, datamframe2")

# lists
ll = list(a=1,b=rnorm(1000),c =iris)
str(ll)

# simple r things
summary(iris)
str(iris) # structure of iris
names(iris) # column names
hist(iris$Sepal.Length)
hist(iris$Sepal.Width)
boxplot(iris$Sepal.Length)
plot(iris$Petal.Length)
lines(lowess(1:nrow(iris),iris$Petal.Length))

# subsetting
## select only 2 columns of iris
iris_2cols <- iris[c("Sepal.Length","Sepal.width")]


# freqs 
table(iris$Species)
# similar to by statement in SAS
mean.by = by(iris$Petal.Length,iris$Species,mean)
by(iris$Petal.Length,iris$Species,max)

first.one = function(x) {
	x[1]
}

by(iris$Petal.Length,iris$Species,first.one)

iris2$mean.by = as.vector(mean.by[iris$Species])

first.3.and.last.3 = function(x) {
	sort(x)[c(1:3,(length(x)-2):length(x))]
}

by(iris$Petal.Length,iris$Species,first.3.and.last.3)
by(iris$Petal.Length,iris$Species,first.3.and.last.3)