x
x[2]
x[3]
save.image( "obay.RData" )
x <- 42
x
load( "obay.RData" )
x
x
}
}
return(myVector);
return(arg.1);
}
return(42)
# comment
return(42)
other.function <- function(arg.1, ...) {
kl
}
my.function(42)
my.function(42)
my.function(42)
my.function <- function(argument.1, argument.2, ...) {
# clever manipulations of arguments
return(42)
}
my.function <- function(argument.1, argument.2, ...) {
# clever manipulations of arguments
return(42)
}
my.function(42)
MyMode <- function(arg.1) {
# clever manipulations of arguments
return(arg.1)
}
MyMode( c(1,3,4))
MyMode <- function(arg.1) {
# clever manipulations of arguments
uniqueValues <- unique(myVector)
return(uniqueValues)
}
MyMode <- function(arg.1) {
# clever manipulations of arguments
uniqueValues <- unique(myVector)
return(uniqueValues)
}
MyMode <- function(arg.1) {
# clever manipulations of arguments
uniqueValues <- unique(myVector)
return(uniqueValues)
}
x
x <- c(x,x,4)
x
MyMode(x)
MyMode <- function(myVector) {
# clever manipulations of arguments
uniqueValues <- unique(myVector)
return(uniqueValues)
}
MyMode <- function(myVector) {
# clever manipulations of arguments
uniqueValues <- unique(myVector)
return(uniqueValues)
}
MyMode(x)
?tabulate
tabulate(x)
source('~/.active-rstudio-document', echo=TRUE)
?match
tabulate( c(0,0,1,2))
tabulate( c(0,0,1,2,8,9))
d <- c(1,2,1,2,3,3,3,4,5,4,5,5,5,5,1,1,1)
e <- c(d,9,9,9,9,9,9,9,9)
e
tabulate(d)
d
tabulate(e)
match(e)
match( c(1,2,9), c(9,2,1) )
match( c(1,2,9,2,9), c(9,2,1) )
e
match( e, unique(e) )
tabulate(match( e, unique(e) ) )
install.packages("modeest")
install.packages("modeest")
library(modeest)
e
mfv(e)
mfv
factor(e)
f <- factor(e)
f
tf <- tabulate(f)
tf
tf == max(tf)
levels(f)
?factor
?levels
myFamilyNames <- c("dad", "mom", "sis", "bro","dog")
myFamilyNames
mfAges <- c(43,42,12,8,5)
mfAges
mf <- data.frame( myFamilyNames, mfAges )
View(mf)
mf
mf$myFamilyNames
mf$mfAges
factors(mf$mfAges)
factor(mf$mfAges)
_
f <- factor(mf$mfAges)
levels(f)
as.numeric( levels(f) )
str(mf)
mfGenders = c("Male","Female","Female","Male","Male")
?cbind
m <- cbind(1, 1:7)
M
m
mf
mf <- cbind( mf, mfGenders )
mf
View(mf)
View(mf)
mf$mfGenders
mode(mf)
mode(mf$mfGenders)
str(mf)
summary(mf)
mf
rbind( mf, c("cat","3","Female"))
View(mf)
View(mf)
mf
mf[[1]]
mf[[2]]
mf[1]
mf[2]
?[[]]
seq(5)
iri
iris
names(iris)
ir <- iris[1:2,]
ir
ir#Sepal.Length
ir$Sepal.Length
ir[Sepal.Length]
ir["Sepal.Length"]
ir[1]
ir[1,2]
ir[2,1]
ir[1][2]
ir[1]
ir[1][2]
ir[1][[2]]
t <- ir[1]
t
t[1,1]
t[2,1]
ir
ir[-2]
str(ir)
iris[iris$Species == "setosa"]
iris[iris$Species == "setosa",]
iris[iris$Species == "setosa",]
ir
ir <- iris[1:10,]
ir
ir[ir$Sepal.Length >= 5.0, ]
ir[ir$Sepal.Length >= 5.0 & Sepal.Width<3.8, ]
ir[ir$Sepal.Length >= 5.0 & ir$Sepal.Width<3.8, ]
ir[ir$Sepal.Length >= 5.0 | ir$Sepal.Width<3.8, ]
ir
ir[ c(F,T), ]
ir
ir$Sepal.Length <- c(ir$Sepal.Length,4.2)
ir
View(m)
View(m)
?del
?rm
rm(m)
ctl
ir[ c(F,T), ]
str(mf)
mf
mfa <- mfAges
mfa
mfa[1] <- 44
mfa
mfAges
mfa <- mfAges
var(mfa)
sd(mfa)
?sqrt
read.DIF("clipboard",transpose=T)
read.DIF("clipboard",transpose=T)
sp <- read.DIF("clipboard",transpose=T)
sp
sp1 <- sp$V1
sp1
1:10
1:10 * 2
sum(1:10)
1:10 * 10:1
1:10 * 2
sp
mode(sp)
sp[ sp$V1 > 1e6]
sp[ sp$V1 > 1000000]
sp[1:4]
sp[ sp$V1 > 1000000,]
sp[ sp$V1 > 1e6,]
sp[ sp$V1 > 1e7,]
sp1
mean(sp1)
median(sp1)
sd(sp1)
hist(sp1)
hist(sp1,breaks=20)
?gnorm
qgeom((1:9)/10, prob = .2)
Ni <- rgeom(20, prob = 1/4); table(factor(Ni, 0:max(Ni)))
rgeom(100,prob=.5)
rgeom(100,prob=.1)
?sample
sp1
sample( sp1, size=16, replace=T)
mean(sp1)
mean(sample( sp1, size=16, replace=T))
mean(sample( sp1, size=16, replace=T))
mean(sample( sp1, size=160, replace=T))
mean(sample( sp1, size=160, replace=T))
mean(sample( sp1, size=160, replace=T))
mean(sample( sp1, size=1600, replace=T))
mean(sample( sp1, size=1600, replace=T))
mean(sample( sp1, size=16, replace=T))
?replicate
replicate(4, mean(sample( sp1, size=16, replace=T)))
mean(replicate(4, mean(sample( sp1, size=16, replace=T))))
mean(replicate(40, mean(sample( sp1, size=16, replace=T))))
mean(replicate(40, mean(sample( sp1, size=16, replace=T))))
mean(replicate(400, mean(sample( sp1, size=16, replace=T))))
mean(replicate(4000, mean(sample( sp1, size=16, replace=T))))
mean(sp1)
hist(replicate(4000, mean(sample( sp1, size=16, replace=T))))
sm <- replicate(10000, mean(sample( sp1, size=16, replace=T)))
length(sm)
str(sm)
summary(sm)
hist(sm)
?quantile
quantile(sm)
quantile(sm,probs=c(0,1,.25))
quantile(sm,probs=seq(0,1,.25))
seq(0,1,.25)
quantile(sm,probs=seq(0,1,.2))
quantile(sm,probs=seq(0,1,.1))
median(sm)
hist(sm)
hist(sm,breaks=30)
hist(sm,breaks=30,col=green)
hist(sm,breaks=30,col="green)
""
"
hist(sm,breaks=30,col="green")
download.file
sqrt
co2
Presige
Prestige
prestige
data()
uspop
Prestige
str(uspop)
plot(uspop)
swiss
sunspots
?aggregate
24
.
x<-24
.
891-742
??csv
getwd()
getwd()
?cd
?setwd
setwd("C:/Users/Owner/Desktop")
getwd()
?read.csv
t <- read.csv("train.csv")
View(t)
summary(t)
predictor <- function(obs) {
return(0)
}
pr <- function(obs) {
return(0)
}
head(t)
t2 <- t[1:6,]
t2
pr(t2[1,])
t2[1,]
?lapply
lapply(t2,pr)
?predict
predict
t2
t2$sex
t2[1,]
pr <- function(obs) {
if (obs$sex == "female") {
return(1)
} else {
return(0)
}
}
t2
pr( t2[1,])
pr( t2[2,])
# full data-frame prediction...
df_pr(FUN,df) {
L <-  length(df)
print(L)
return(42)
}
# full data-frame prediction...
df_pr <- function(FUN,df) {
L <-  length(df)
print(L)
return(42)
}
df_pr(pr,t2)
length(t2)
t2
str(t2)
nobs
nobs(t2)
?nobs
??nobs
?nrows
??rows
length(t2[1])
length(t2[[1]])
?concatenate
cat("hi", 42)
# full data-frame prediction...
df_pr <- function(FUN,df) {
NUM_ROWS <-  length(df[[1]])
cat("NUM_ROWS is", NUM_ROWS)
}
df_pr(t2)
df_pr(pr,t2)
?nrow
nrow(t2)
?vector
vector(mode="numeric",length=3)
# full data-frame prediction...
df_pr <- function(FUN,df) {
NUM_ROWS <-  nrow(df)
cat("NUM_ROWS is", NUM_ROWS)
predictions = vector(mode="numeric",length=NUM_ROWS)
for (row in 1:NUM_ROWS) {
next_prediction = pr(df[row,])
predictions[row] = next_prediction
}
return(predictions)
}
df_pr(pr,t2)
# full data-frame prediction...
df_pr <- function(FUN,df) {
NUM_ROWS <-  nrow(df)
#cat("NUM_ROWS is", NUM_ROWS)
predictions = vector(mode="numeric",length=NUM_ROWS)
for (row in 1:NUM_ROWS) {
next_prediction = pr(df[row,])
predictions[row] = next_prediction
}
return(predictions)
}
x <- df_pr(pr,t2)
x
t2
x
t2[1]
t2[[1]]
x - t2[[1]]
sum(as.numeric(x == t2[[1]]))
sum(x == t2[[1]])
y <- x
y[2]
y[2] <-  0
y
x
t2[t2$survived!=y]
t2
t2$survived
t2$survived!=y
t2[t2$survived!=y,]
t <- read.csv("train742.csv")
View(t)
View(t)
?scan
mhall <- function() {
cat("Enter a number:")
user = scan(n=1)
cat("You entered",user)
}
mhall()
?runif
?sample
sample(seq(3),1)
replicate( 10, sample(seq(3),1))
save.image("C:/Users/Owner/Desktop/wksp1.Rwk.RData")
sample(seq(3),1)
replicate( 10, sample(seq(3),1))
}
source('C:/Users/Owner/Desktop/titanic.R', echo=TRUE)
source('C:/Users/Owner/Desktop/titanic.R', echo=TRUE)
pr0(42)
t2
source('C:/Users/Owner/Desktop/titanic.R', echo=TRUE)
t2
pr_df(pr0,t2)
sum(t2[t2$survived == pr_df(pr0,t2)])
t2$survived
t2$survived == pr_df(pr0,t2)
sum(t2$survived == pr_df(pr0,t2))
print("Hi")
x <- 42
print("Hi", x)
cat("Hi",x)
?cat
cat("Hi",x)
View(sp)
sp
?read.csv
s <- read.csv("states.csv")
View(s)
View(sp)
View(s)
summary(s)
hist(sp)
sp
sp <- s$pop
mode(sp)
hist(sp)
sp <- as.numeric(sp)
hist(sp)
sp <- s$Pop
sp
mode(sp)
hist(sp)
hist(sp,breaks=20)
?quantile
quantile( sp, c(.5,.95))
quantile( sp, c(.05,.95))
mean(sp)
sd(sp)
1:10
sample( 1:10, 3 )
sample( sp, 16 )
samp <- sample( sp, 16 )
mean(samp)
man(sp)
mean(sp)
samp <- sample( sp, 16 ) ; mean(samp)
samp <- sample( sp, 16 ) ; mean(samp)
samp <- sample( sp, 16 ) ; mean(samp)
samp <- sample( sp, 16 ) ; mean(samp)
samp <- sample( sp, 16 ) ; mean(samp)
42 ; 43
sampdist <- replicate( 1000 , mean(sample( sp, 16 )) )
length(sampdist)
summary(sampdist)
mean(sp)
hist(sampdist)
sampdist <- replicate( 10000 , mean(sample( sp, 16 )) )
hist(sampdist)
unknown_mean = 4000000
quantiles( sampdist, c(.05,.95))
quantile( sampdist, c(.05,.95))
mean(sp)
median(sp)
sort(sp)
sorted <- sort(sp)
sorted[26]
?1which
?1which
?which
which(s$Pop==sorted[26])
s[18]
s[18,]
s[s$Pop == sorted[26]]
s[s$Pop == sorted[26],]
s[s$Pop == sort(sp)[length(sp)/2],]
length(sp)/2
sort(sp)[25.5]
sort(sp)[25]
s[s$Pop == sort(sp)[(length(sp)+1)/2],]
which( c(1,2,3,2)==2 )
which( c(7,8,9,8)==2 )
which( c(7,8,9,8)==8 )
s[which( c(7,8,9,8)==8 ),]
savehistory("C:/Users/Owner/Desktop/median_example.txt")