Table of Useful R commands

Command Purpose
help() Obtain documentation for a given R command
example() View some examples on the use of a command
Command Purpose
c(), scan() Enter data manually to a vector in R
plot() Produces a scatterplot
seq() Make arithmetic progression vector
xyplot() Lattice command for producing a scatterplot
rep() Make vector of repeated values
lm() Determine the least-squares regression line
data() Load (often into a data.frame) built-in dataset
anova() Analysis of variance (can use on results of lm())
View() View dataset in a spreadsheet-type format
predict() Obtain predicted values from linear model
str() Display internal structure of an R object
nls() estimate parameters of a nonlinear model
read.csv(), read.table() Load into a data.frame an existing data file
residuals() gives (observed - predicted) for a model fit to data
library(), require() Make available an R add-on package
sample() take a sample from a vector of data
dim() See dimensions (# of rows/cols) of data.frame
replicate() repeat some process a set number of times
length() Give length of a vector
cumsum() produce running total of values for input vector
ls() Lists memory contents
ecdf() builds empirical cumulative distribution function
rm() Removes an item from memory
dbinom(), etc. tools for binomial distributions
names() Lists names of variables in a data.frame
dpois(), etc. tools for Poisson distributions
hist() Command for producing a histogram
pnorm(), etc. tools for normal distributions
histogram() Lattice command for producing a histogram
qt(), etc. tools for student t distributions
stem() Make a stem plot
pchisq(), etc. tools for chi-square distributions
table() List all values of a variable with frequencies
binom.test() hypothesis test and confidence interval for 1 proportion
xtabs() Cross-tabulation tables using formulas
prop.test() inference for 1 proportion using normal approx.
mosaicplot() Make a mosaic plot
chisq.test() carries out a chi-square test
cut() Groups values of a variable into larger bins
fisher.test() Fisher test for contingency table
mean(), median() Identify “center” of distribution
t.test() student t test for inference on population mean
by() apply function to a column split by factors
qqnorm(), qqline() tools for checking normality
summary() Display 5-number summary and mean
addmargins() adds marginal sums to an existing table
var(), sd() Find variance, sd of values in vector
prop.table() compute proportions from a contingency table
sum() Add up all values in a vector
par() query and edit graphical settings
quantile() Find the position of a quantile in a dataset
power.t.test() power calculations for 1- and 2-sample t
barplot() Produces a bar graph
anova() compute analysis of variance table for fitted model
barchart() Lattice command for producing bar graphs
boxplot() Produces a boxplot
bwplot() Lattice command for producing boxplots
R Commands for MATH 143 Examples of usage

Examples of usage
help()

help(mean)

example()
require(lattice)
example(histogram)

c(), rep() seq()

> x = c(8, 6, 7, 5, 3, 0, 9)
> x

[1] 8 6 7 5 3 0 9

> names = c("Owen", "Luke", "Anakin", "Leia", "Jacen", "Jaina")

> names

[1] "Owen" "Luke" "Anakin" "Leia" "Jacen" "Jaina"

> heartDeck = c(rep(1, 13), rep(0, 39))

> heartDeck

[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[49] 0 0 0 0

> y = seq(7, 41, 1.5)

> y

[1] 7.0 8.5 10.0 11.5 13.0 14.5 16.0 17.5 19.0 20.5 22.0 23.5 25.0 26.5 28.0 29.5 31.0 32.5 34.0
[20] 35.5 37.0 38.5 40.0

data(), dim(), names(), View(), str()

> data(iris)
> names(iris)

[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"

> dim(iris)

[1] 150 5

> str(iris)

'data.frame': 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

> View(iris)

2
R Commands for MATH 143 Examples of usage

ls(), rm()
> data(iris)
> data(faithful)
> data(Puromycin)
> data(LakeHuron)
> ls()

[1] "faithful" "heartDeck" "iris" "LakeHuron" "names" "Puromycin" "x" "y"

> newVector = 1:12

> ls()

[1] "faithful" "heartDeck" "iris" "LakeHuron" "names" "newVector" "Puromycin" "x"

[9] "y"

> rm(faithful)
> ls()

[1] "heartDeck" "iris" "LakeHuron" "names" "newVector" "Puromycin" "x" "y"

hist()
data(faithful)
hist(faithful$eruptions)
hist(faithful$eruptions, n=15)
hist(faithful$eruptions, breaks=seq(1.5,5.25,.25), col="red")
hist(faithful$eruptions, freq=F, n=15, main="Histogram of Old Faithful Eruption Times", xlab="Duration (mins)")

Histogram of Old Faithful Eruption Times

0.6
Density

0.4
0.2
0.0

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Duration (mins)

3
R Commands for MATH 143 Examples of usage

library(), require()
> library(abd)
> require(lattice)

histogram()
require(lattice)
data(iris)
histogram(iris$Sepal.Length, breaks=seq(4,8,.25))
histogram(~ Sepal.Length, data=iris, main="Iris Sepals", xlab="Length")
histogram(~ Sepal.Length | Species, data=iris, col="red")
histogram(~ Sepal.Length | Species, data=iris, n=15, layout=c(1,3))

read.csv()

> As.in.H2O = read.csv("http://www.calvin.edu/~scofield/data/comma/arsenicInWater.csv")

read.table()

> senate = read.table("http://www.calvin.edu/~scofield/data/tab/rc/senate99.dat", sep="\t", header=T)

mean(), median(), summary(), var(), sd(), quantile(),

> counties=read.csv("http://www.calvin.edu/~stob/data/counties.csv")
> names(counties)

[1] "County" "State" "Population" "HousingUnits" "TotalArea"

[6] "WaterArea" "LandArea" "DensityPop" "DensityHousing"

> x = counties$LandArea
> mean(x, na.rm = T)

[1] 1126.214

> median(x, na.rm = T)

[1] 616.48

> summary(x)

Min. 1st Qu. Median Mean 3rd Qu. Max.

1.99 431.70 616.50 1126.00 923.20 145900.00

> sd(x, na.rm = T)

[1] 3622.453

> var(x, na.rm = T)

[1] 13122165

> quantile(x, probs=seq(0, 1, .2), na.rm=T)

0% 20% 40% 60% 80% 100%

1.99 403.29 554.36 717.94 1043.82 145899.69

4
R Commands for MATH 143 Examples of usage

sum()
> firstTwentyIntegers = 1:20
> sum(firstTwentyIntegers)

[1] 210

> die = 1:6

> manyRolls = sample(die, 100, replace=T)
> sixFreq = sum(manyRolls == 6)
> sixFreq / 100

[1] 0.14

stem()
> monarchs = read.csv("http://www.calvin.edu/~scofield/data/comma/monarchReigns.csv")
> stem(monarchs$years)

The decimal point is 1 digit(s) to the right of the |

0 | 0123566799
1 | 0023333579
2 | 012224455
3 | 355589
4 | 4
5 | 069
6 | 3

table(), table(), mosaicplot(), cut()

> pol = read.csv("http://www.calvin.edu/~stob/data/csbv.csv")
> table(pol$sex)

Female Male
133 88

> table(pol$sex, pol$Political04)

Conservative Far Right Liberal Middle-of-the-road

Female 67 0 14 48
Male 47 7 6 28

> xtabs(~sex, data=pol)

sex
Female Male
133 88

> xtabs(~Political04 + Political07, data=pol)

Political07
Political04 Conservative Far Left Far Right Liberal Middle-of-the-road
Conservative 58 0 2 13 39
Far Right 4 0 3 0 0
Liberal 0 1 1 14 4
Middle-of-the-road 20 0 0 22 32

> mosaicplot(~Political04 + sex, data=pol)

5
R Commands for MATH 143 Examples of usage

pol

Conservative Far RightLiberal Middle−of−the−road

Female
sex

Male

Political04

> monarchs = read.csv("http://www.calvin.edu/~scofield/data/comma/monarchReigns.csv")

> table(monarchs$years)

0 1 2 3 5 6 7 9 10 12 13 15 17 19 20 21 22 24 25 33 35 38 39 44 50 56 59 63
1 1 1 1 1 2 1 2 2 1 4 1 1 1 1 1 3 2 2 1 3 1 1 1 1 1 1 1

> xtabs(~years, data=monarchs)

years
0 1 2 3 5 6 7 9 10 12 13 15 17 19 20 21 22 24 25 33 35 38 39 44 50 56 59 63
1 1 1 1 1 2 1 2 2 1 4 1 1 1 1 1 3 2 2 1 3 1 1 1 1 1 1 1

> cut(monarchs$years, breaks=seq(0,65,5))

[1] (20,25] (10,15] (30,35] (15,20] (30,35] (5,10] (15,20] (55,60] (30,35] (15,20] (45,50] (20,25]
[13] (10,15] (5,10] (35,40] (20,25] <NA> (0,5] (20,25] (35,40] (5,10] (0,5] (40,45] (20,25]
[25] (20,25] (20,25] (0,5] (10,15] (5,10] (10,15] (10,15] (30,35] (55,60] (5,10] (5,10] (60,65]
[37] (5,10] (20,25] (0,5] (10,15]
13 Levels: (0,5] (5,10] (10,15] (15,20] (20,25] (25,30] (30,35] (35,40] (40,45] (45,50] ... (60,65]

> table(cut(monarchs$years, breaks=seq(0,65,5)))

(0,5] (5,10] (10,15] (15,20] (20,25] (25,30] (30,35] (35,40] (40,45] (45,50] (50,55] (55,60]
4 7 6 3 8 0 4 2 1 1 0 2
(60,65]
1

> fiveYrLevels = cut(monarchs$years, breaks=seq(0,65,5))

> xtabs(~fiveYrLevels)

fiveYrLevels
(0,5] (5,10] (10,15] (15,20] (20,25] (25,30] (30,35] (35,40] (40,45] (45,50] (50,55] (55,60]
4 7 6 3 8 0 4 2 1 1 0 2
(60,65]
1

6
R Commands for MATH 143 Examples of usage

barplot()
pol = read.csv("http://www.calvin.edu/~stob/data/csbv.csv")
barplot(table(pol$Political04), main="Political Leanings, Calvin Freshman 2004")
barplot(table(pol$Political04), horiz=T)
barplot(table(pol$Political04),col=c("red","green","blue","orange"))
barplot(table(pol$Political04),col=c("red","green","blue","orange"),
names=c("Conservative","Far Right","Liberal","Centrist"))
80
40
0

Conservative Liberal Centrist

barplot(xtabs(~sex + Political04, data=pol), legend=c("Female","Male"), beside=T)

60

Female
Male
50
40
30
20
10
0

Conservative Far Right Liberal Middle−of−the−road

7
R Commands for MATH 143 Examples of usage

boxplot()
data(iris)
boxplot(iris$Sepal.Length)
boxplot(iris$Sepal.Length, col="yellow")
boxplot(Sepal.Length ~ Species, data=iris)
boxplot(Sepal.Length ~ Species, data=iris, col="yellow", ylab="Sepal length",main="Iris Sepal Length by Species")

Iris Sepal Length by Species

7.5
Sepal length

6.5
5.5

●
4.5

setosa versicolor virginica

plot()
data(faithful)
plot(waiting~eruptions,data=faithful)
plot(waiting~eruptions,data=faithful,cex=.5)
plot(waiting~eruptions,data=faithful,pch=6)
plot(waiting~eruptions,data=faithful,pch=19)
plot(waiting~eruptions,data=faithful,cex=.5,pch=19,col="blue")
plot(waiting~eruptions, data=faithful, cex=.5, pch=19, col="blue", main="Old Faithful Eruptions",
ylab="Wait time between eruptions", xlab="Duration of eruption")

Old Faithful Eruptions

Time between eruptions

●
●
90

● ● ●
● ● ●● ● ● ● ●
● ● ●● ● ● ● ●
● ● ●● ● ● ●
● ● ● ●●
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● ● ● ● ● ● ●● ● ●● ●●●●●● ● ● ● ●
● ●
● ● ●● ● ● ● ● ●● ●● ●●
● ● ●
● ● ● ●● ● ●● ●● ●● ●●● ● ●● ● ●●●●●●●●
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●
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●
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70

● ● ●
● ● ● ●
● ●● ●●
● ● ●
● ● ●
● ● ● ● ● ● ●
● ● ● ● ● ● ●
●● ● ●● ●●● ● ●●
●● ●
●●● ●● ● ● ● ●
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●● ● ● ●●● ● ●● ● ●●
50

● ●● ●● ●● ●● ● ● ●
●
● ●● ● ● ● ● ● ●
● ●
● ● ●
●●●● ● ●●
●

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Duration of eruption

8
R Commands for MATH 143 Examples of usage

sample()
> sample(c("Heads","Tails"), size=1)

[1] "Heads"

> sample(c("Heads","Tails"), size=10, replace=T)

[1] "Heads" "Heads" "Heads" "Tails" "Tails" "Tails" "Tails" "Tails" "Tails" "Heads"

> sample(c(0, 1), 10, replace=T)

[1] 1 0 0 1 1 0 0 1 0 0

> sum(sample(1:6, 2, replace=T))

[1] 10

> sample(c(0, 1), prob=c(.25,.75), size=10, replace=T)

[1] 1 1 1 0 1 1 1 1 1 1

> sample(c(rep(1,13),rep(0,39)), size=5, replace=F)

[1] 0 0 0 0 0

replicate()
> sample(c("Heads","Tails"), 2, replace=T)

[1] "Tails" "Heads"

> replicate(5, sample(c("Heads","Tails"), 2, replace=T))

[,1] [,2] [,3] [,4] [,5]

[1,] "Heads" "Tails" "Heads" "Tails" "Heads"
[2,] "Heads" "Tails" "Heads" "Heads" "Heads"

> ftCount = replicate(100000, sum(sample(c(0, 1), 10, rep=T, prob=c(.6, .4))))

> hist(ftCount, freq=F, breaks=-0.5:10.5, xlab="Free throws made out of 10 attempts",
+ main="Simulated Sampling Dist. for 40% FT Shooter", col="green")

Simulated Sampling Dist. for 40% FT Shooter

Density

0.15
0.00

0 2 4 6 8 10

Free throws made out of 10 attempts

9
R Commands for MATH 143 Examples of usage

dbinom(), pbinom(), qbinom(), rbinom(), binom.test(), prop.test()

> dbinom(0, 5, .5) # probability of 0 heads in 5 flips

[1] 0.03125

> dbinom(0:5, 5, .5) # full probability dist. for 5 flips

[1] 0.03125 0.15625 0.31250 0.31250 0.15625 0.03125

> sum(dbinom(0:2, 5, .5)) # probability of 2 or fewer heads in 5 flips

[1] 0.5

> pbinom(2, 5, .5) # same as last line

[1] 0.5

> flip5 = replicate(10000, sum(sample(c("H","T"), 5, rep=T)=="H"))

> table(flip5) / 10000 # distribution (simulated) of count of heads in 5 flips

flip5
0 1 2 3 4 5
0.0310 0.1545 0.3117 0.3166 0.1566 0.0296

> table(rbinom(10000, 5, .5)) / 10000 # shorter version of previous 2 lines

0 1 2 3 4 5
0.0304 0.1587 0.3087 0.3075 0.1634 0.0313

> qbinom(seq(0,1,.2), 50, .2) # approx. 0/.2/.4/.6/.8/1-quantiles in Binom(50,.2) distribution

[1] 0 8 9 11 12 50

> binom.test(29, 200, .21) # inference on sample with 29 successes in 200 trials

Exact binomial test

data: 29 and 200

number of successes = 29, number of trials = 200, p-value = 0.02374
alternative hypothesis: true probability of success is not equal to 0.21
95 percent confidence interval:
0.09930862 0.20156150
sample estimates:
probability of success
0.145

> prop.test(29, 200, .21) # inference on same sample, using normal approx. to binomial

1-sample proportions test with continuity correction

data: 29 out of 200, null probability 0.21

X-squared = 4.7092, df = 1, p-value = 0.03
alternative hypothesis: true p is not equal to 0.21
95 percent confidence interval:
0.1007793 0.2032735
sample estimates:
p
0.145

10
R Commands for MATH 143 Examples of usage

pchisq(), qchisq(), chisq.test()

> 1 - pchisq(3.1309, 5) # gives P-value associated with X-squared stat 3.1309 when df=5

[1] 0.679813

> pchisq(3.1309, df=5, lower.tail=F) # same as above

[1] 0.679813

> qchisq(c(.001,.005,.01,.025,.05,.95,.975,.99,.995,.999), 2) # gives critical values like Table A

[1] 0.002001001 0.010025084 0.020100672 0.050635616 0.102586589 5.991464547 7.377758908

[8] 9.210340372 10.596634733 13.815510558

> qchisq(c(.999,.995,.99,.975,.95,.05,.025,.01,.005,.001), 2, lower.tail=F) # same as above

[1] 0.002001001 0.010025084 0.020100672 0.050635616 0.102586589 5.991464547 7.377758908

[8] 9.210340372 10.596634733 13.815510558

> observedCounts = c(35, 27, 33, 40, 47, 51)

> claimedProbabilities = c(.13, .13, .14, .16, .24, .20)
> chisq.test(observedCounts, p=claimedProbabilities) # goodness-of-fit test, assumes df = n-1

Chi-squared test for given probabilities

data: observedCounts
X-squared = 3.1309, df = 5, p-value = 0.6798

addmargins()
> blood = read.csv("http://www.calvin.edu/~scofield/data/comma/blood.csv")
> t = table(blood$Rh, blood$type)
> addmargins(t) # to add both row/column totals

A AB B O Sum
Neg 6 1 2 7 16
Pos 34 3 9 38 84
Sum 40 4 11 45 100

> addmargins(t, 1) # to add only column totals

A AB B O
Neg 6 1 2 7
Pos 34 3 9 38
Sum 40 4 11 45

> addmargins(t, 2) # to add only row totals

A AB B O Sum
Neg 6 1 2 7 16
Pos 34 3 9 38 84

11
R Commands for MATH 143 Examples of usage

prop.table()

> smoke = matrix(c(51,43,22,92,28,21,68,22,9),ncol=3,byrow=TRUE)

> colnames(smoke) = c("High","Low","Middle")
> rownames(smoke) = c("current","former","never")
> smoke = as.table(smoke)
> smoke

High Low Middle

current 51 43 22
former 92 28 21
never 68 22 9

> summary(smoke)

Number of cases in table: 356

Number of factors: 2
Test for independence of all factors:
Chisq = 18.51, df = 4, p-value = 0.0009808

> prop.table(smoke)

High Low Middle

current 0.14325843 0.12078652 0.06179775
former 0.25842697 0.07865169 0.05898876
never 0.19101124 0.06179775 0.02528090

> prop.table(smoke, 1)

High Low Middle

current 0.4396552 0.3706897 0.1896552
former 0.6524823 0.1985816 0.1489362
never 0.6868687 0.2222222 0.0909091

> barplot(smoke,legend=T,beside=T,main='Smoking Status by SES')

Smoking Status by SES

20 40 60 80

current
former
never
0

High Low Middle

12
R Commands for MATH 143 Examples of usage

par()

> par(mfrow = c(1,2)) # set figure so next two plots appear side-by-side
> poisSamp = rpois(50, 3) # Draw sample of size 50 from Pois(3)
> maxX = max(poisSamp) # will help in setting horizontal plotting region
> hist(poisSamp, freq=F, breaks=-.5:(maxX+.5), col="green", xlab="Sampled values")
> plot(0:maxX, dpois(0:maxX, 3), type="h", ylim=c(0,.25), col="blue", main="Probabilities for Pois(3)")

Histogram of poisSamp Probabilities for Pois(3)

0.30

dpois(0:maxX, 3)

0.20
Density

0.15

0.10
0.00

0.00
0 2 4 6 0 1 2 3 4 5 6 7

Sampled values 0:maxX

fisher.test()
> blood = read.csv("http://www.calvin.edu/~scofield/data/comma/blood.csv")
> tblood = xtabs(~Rh + type, data=blood)
> tblood # contingency table for blood type and Rh factor

type
Rh A AB B O
Neg 6 1 2 7
Pos 34 3 9 38

> chisq.test(tblood)

Pearson's Chi-squared test

data: tblood
X-squared = 0.3164, df = 3, p-value = 0.957

> fisher.test(tblood)

Fisher's Exact Test for Count Data

data: tblood
p-value = 0.8702
alternative hypothesis: two.sided

13
R Commands for MATH 143 Examples of usage

dpois(), ppois()
> dpois(2:7, 4.2) # probabilities of 2, 3, 4, 5, 6 or 7 successes in Pois(4.211)

[1] 0.13226099 0.18516538 0.19442365 0.16331587 0.11432111 0.06859266

> ppois(1, 4.2) # probability of 1 or fewer successes in Pois(4.2); same as sum(dpois(0:1, 4.2))

[1] 0.077977

> 1 - ppois(7, 4.2) # probability of 8 or more successes in Pois(4.2)

[1] 0.06394334

pnorm() qnorm(), rnorm(), dnorm()

> pnorm(17, 19, 3) # gives Prob[X < 17], when X ~ Norm(19, 3)

[1] 0.2524925

> qnorm(c(.95, .975, .995)) # obtain z* critical values for 90, 95, 99% CIs

[1] 1.644854 1.959964 2.575829

> nSamp = rnorm(10000, 7, 1.5) # draw random sample from Norm(7, 1.5)
> hist(nSamp, freq=F, col="green", main="Sampled values and population density curve")
> xs = seq(2, 12, .05)
> lines(xs, dnorm(xs, 7, 1.5), lwd=2, col="blue")

Sampled values and population density curve

0.20
Density

0.10
0.00

2 4 6 8 10 12

nSamp

14
R Commands for MATH 143 Examples of usage

qt(), pt(), rt(), dt()

> qt(c(.95, .975, .995), df=9) # critical values for 90, 95, 99% CIs for means

[1] 1.833113 2.262157 3.249836

> pt(-2.1, 11) # gives Prob[T < -2.1] when df = 11

[1] 0.02980016

> tSamp = rt(50, 11) # takes random sample of size 50 from t-dist with 11 dfs
> # code for comparing several t distributions to standard normal distribution
> xs = seq(-5,5,.01)
> plot(xs, dnorm(xs), type="l", lwd=2, col="black", ylab="pdf values",
+ main="Some t dists alongside standard normal curve")
> lines(xs, dt(xs, 1), lwd=2, col="blue")
> lines(xs, dt(xs, 4), lwd=2, col="red")
> lines(xs, dt(xs, 10), lwd=2, col="green")
> legend("topright",col=c("black","blue","red","green"),
+ legend=c("std. normal","t, df=1","t, df=4","t, df=10"), lty=1)

Some t dists alongside standard normal curve

0.4

std. normal
pdf values

t, df=1
t, df=4
0.2

t, df=10
0.0

−4 −2 0 2 4

xs

by()
> data(warpbreaks)
> by(warpbreaks$breaks, warpbreaks$tension, mean)

warpbreaks$tension: L
[1] 36.38889
---------------------------------------------------------------------------
warpbreaks$tension: M
[1] 26.38889
---------------------------------------------------------------------------
warpbreaks$tension: H
[1] 21.66667

15
R Commands for MATH 143 Examples of usage

t.test()
> data(sleep)
> t.test(extra ~ group, data=sleep) # 2-sample t with group id column

Welch Two Sample t-test

data: extra by group

t = -1.8608, df = 17.776, p-value = 0.0794
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.3654832 0.2054832
sample estimates:
mean in group 1 mean in group 2
0.75 2.33

> sleepGrp1 = sleep$extra[sleep$group==1]

> sleepGrp2 = sleep$extra[sleep$group==2]
> t.test(sleepGrp1, sleepGrp2, conf.level=.99) # 2-sample t, data in separate vectors

Welch Two Sample t-test

data: sleepGrp1 and sleepGrp2

t = -1.8608, df = 17.776, p-value = 0.0794
alternative hypothesis: true difference in means is not equal to 0
99 percent confidence interval:
-4.027633 0.867633
sample estimates:
mean of x mean of y
0.75 2.33

qqnorm(), qqline()

> qqnorm(precip, ylab = "Precipitation [in/yr] for 70 US cities", pch=19, cex=.6)

> qqline(precip) # Is this line helpful? Is it the one you would eyeball?
Precipitation [in/yr] for 70 US cities

Normal Q−Q Plot

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Theoretical Quantiles

16
R Commands for MATH 143 Examples of usage

power.t.test()
> power.t.test(n=20, delta=.1, sd=.4, sig.level=.05) # tells how much power at these settings

Two-sample t test power calculation

n = 20
delta = 0.1
sd = 0.4
sig.level = 0.05
power = 0.1171781
alternative = two.sided

NOTE: n is number in *each* group

> power.t.test(delta=.1, sd=.4, sig.level=.05, power=.8) # tells sample size needed for desired power

Two-sample t test power calculation

n = 252.1281
delta = 0.1
sd = 0.4
sig.level = 0.05
power = 0.8
alternative = two.sided

NOTE: n is number in *each* group

anova()
require(lattice)
require(abd)
data(JetLagKnees)
xyplot(shift ~ treatment, JetLagKnees, type=c('p','a'), col="navy", pch=19, cex=.5)
anova( lm( shift ~ treatment, JetLagKnees ) )

Analysis of Variance Table

Response: shift
Df Sum Sq Mean Sq F value Pr(>F)
treatment 2 7.2245 3.6122 7.2894 0.004472 **
Residuals 19 9.4153 0.4955
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

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control eyes knee

treatment

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