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### 2. Getting Help on a Function


help(func)
?(func)
args(func)
example(func)



### 3. Viewing the Supplied Documentation


help.start()



### 4. Searching the Web for Help


dfrm <- read.table("filename.txt", sep=":" ) print dftm < code>


### 6. Reading from CSV Files




### 7. Creating a Vector


v1 <- c(1,2,3) v2 <- c("a","b","c") mode(c(3.1415, "foo")) < code>

### 8. Computing Basic Statistics


mean(x)
median(x)
sd(x)
var(x)
cor(x,y)
cov(x,y)



### 9. Initializing a Data Frame form Column Data


dfrm <- data.frame(v1, v2, v3, f1, f2) dfrm <- as.data.frame(list.of.vectors) data.frame(pred1, pred2, pred3, resp) data.frame(p1="pred1," p2="pred2," p3="pred3," r="resp)" < code>

### 10. Selecting Data Frame Columns by Position


dfrm[[n]]
dfrm[n]
dfrm[c(n_1, n_2, ..., n_k)]
dfrm[, n]
dfrm[, c(n_1, n_2, ..., n_k)]
dfrm[, vec, drop=FALSE]



### 11. Selecting Data Frame Columns by Name


dfrm[[anme]]
dfrm$name dfrm["name"] dfrm[c("name_1", "name_2", ..., "name_k")] dfrm[, "name"] dfrm[, c("name_1", "name_2", ..., "name_k")]  ### 12. Forming a Confidence Interval for a Mean  x <- rnorm(50, mean="100," sd="50)" t.test(x) t.test(x, conf.level="0.99)" < code> ### 13. Forming a Confidence Interval for a Proportion  prop.test(n, x) prop.test(6, 9) prop.test(n, x,,p, conf.level=0.99) # 99% confidence level  ### 14. Comparing the Means of Two Samples  t.test(x, y) t.test(x, y, paired=TRUE)  ### 15. Testing a Correlation for Significance  cor.test(x, y) cor.test(x, y, method="Spearman")  ### 16. Creating a Scatter Plot  plot(x, y) plot(dfrm)  ### 17. Creating a Bar Chart  barplot(c(height1, height2, height3)) barplot(heights, main="Mean Temp. by Month" names.arg=c("May", "Jun", "July", "Aug", "Sep"), ylab="Temp (deg. F)")  ### 18. Creating a Box Plot  boxplot(x)  ### 19. Creating a Histogram  data(Cars93, package="MASS") hist(Cars93$MPG.city)
hist(Cars93$MPG.city, 20) hist(Cars93$MPG.city, 20, main="City MPG (1993)", xlab="MPG")



### 20. Performing Simple Linear Regression


lm(y ~ x)
lm(y ~ x, data=dfrm) # Take x and y from dfrm



### 21. Performing Multiple Linear Regression


y=c(6.584519, 6.425215, 7.830578, 2.757777, 5.794566, 7.314611, 2.533638, 8.696910, 6.304464, 8.095094)
u=c(0.79939065, -2.31338537, 1.71736899, 1.27652888, 0.39643488, 1.82247760, -1.34186107, 0.75946803, 0.92000133, 1.02341093)
v=c(2.7971413, 2.7836201, 2.7570401, 0.4191765, 2.3785468, 1.8291302, 2.3472593, 3.4028180, 2.0654513, 2.6729252)
w=c(4.366557, 4.515084, 3.865557, 2.547935, 3.265971, 4.518522, 2.570884, 4.442560, 2.835248, 3.868573)
lm(y ~ u + v + w)
lm(y ~ u + v + w, data=dfrm)



### 22. Getting Regression Statistics


y=c(6.584519, 6.425215, 7.830578, 2.757777, 5.794566, 7.314611, 2.533638, 8.696910, 6.304464, 8.095094)
u=c(0.79939065, -2.31338537, 1.71736899, 1.27652888, 0.39643488, 1.82247760, -1.34186107, 0.75946803, 0.92000133, 1.02341093)
v=c(2.7971413, 2.7836201, 2.7570401, 0.4191765, 2.3785468, 1.8291302, 2.3472593, 3.4028180, 2.0654513, 2.6729252)
w=c(4.366557, 4.515084, 3.865557, 2.547935, 3.265971, 4.518522, 2.570884, 4.442560, 2.835248, 3.868573)
m <- lm(y ~ u + v w) summary(m) anova(m) coefficients(m) coef(m) confint(m) deviance(m) effects(m) fitted(m) residuals(m) resid(m) vcov(m) < code>

### 23. Diagnosing a Linear Regression


m <- lm(y ~ x) plot(m) library(car) outlier.test(m) < code>

### 24. Predicting New Values


m <- lm(y ~ u + v w) preds <- data.frame(u="3.1," w="5.5)" predict(m, newdata="preds)" data.frame( 3.1, 3.2, 3.3), 4.0, 4.1, 4.2), 5.5, 5,7, 5.9) ) < code>

### 25. Accessing the Functions in a Package


library(packagename)
library(MASS)
lda(f ~ x + y)
detach(package:MASS)