I'm using a modified version of the Cereals dataset from Chapter 28 the textbook as an example. The original dataset is available here: datasets from the text 

Cereals <- read.csv("~/Ch28_Cereals.csv")

Here's an idea of what the data looks like:

head(Cereals)
##                        name     brand IsKellogs calories sugars carbo protein
## 1                 100%_Bran   Nabisco        no       70      6   5.0       4
## 2         100%_Natural_Bran    Quaker        no      120      8   8.0       3
## 3                  All-Bran   Kellogs       yes       70      5   7.0       4
## 4 All-Bran_with_Extra_Fiber   Kellogs       yes       50      0   8.0       4
## 5            Almond_Delight   Ralston        no      110      8  14.0       2
## 6   Apple_Cinnamon_Cheerios Gen-Mills        no      110     10  10.5       2
##   fat sodium fiber potass shelf
## 1   1    130  10.0    280 Lower
## 2   5     15   2.0    135 Lower
## 3   1    260   9.0    320 Lower
## 4   0    140  14.0    330 Lower
## 5   2    200   1.0     -1 Lower
## 6   2    180   1.5     70 Upper

One-proportion z-test and confidence interval with summarized data

Input the number of successes followed by the sample size.
WARNING: Results for all one-proportion and two-proportion inference are computed using Wilson's score interval, and won't match results by hand.

prop.test(20,100, conf.level = 0.95, correct=FALSE)
## 
##  1-sample proportions test without continuity correction
## 
## data:  20 out of 100, null probability 0.5
## X-squared = 36, df = 1, p-value = 1.973e-09
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
##  0.1333669 0.2888292
## sample estimates:
##   p 
## 0.2

One-proportion z-test with values in a column 

prop.test(table(Cereals$shelf), p=.2, correct=FALSE)
## 
##  1-sample proportions test without continuity correction
## 
## data:  table(Cereals$shelf), null probability 0.2
## X-squared = 136.89, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is not equal to 0.2
## 95 percent confidence interval:
##  0.6281876 0.8227060
## sample estimates:
##         p 
## 0.7368421

Singling out one of the categories as "success":

prop.test(sum(Cereals$brand=="Kellogs", na.rm=TRUE), sum(complete.cases(Cereals$brand)), p=.2, correct=FALSE)
## 
##  1-sample proportions test without continuity correction
## 
## data:  sum(Cereals$brand == "Kellogs", na.rm = TRUE) out of sum(complete.cases(Cereals$brand)), null probability 0.2
## X-squared = 5.0033, df = 1, p-value = 0.0253
## alternative hypothesis: true p is not equal to 0.2
## 95 percent confidence interval:
##  0.2109133 0.4133420
## sample estimates:
##         p 
## 0.3026316

Two-proportion z-test with summarized data 

prop.test(c(20,30), c(120,130), conf.level = 0.95, correct=FALSE)
## 
##  2-sample test for equality of proportions without continuity
##  correction
## 
## data:  c(20, 30) out of c(120, 130)
## X-squared = 1.6026, df = 1, p-value = 0.2055
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.16254869  0.03434356
## sample estimates:
##    prop 1    prop 2 
## 0.1666667 0.2307692

Two-proportion z-test with samples in two columns 

prop.test(table(Cereals$IsKellogs, Cereals$shelf), correct=FALSE)
## 
##  2-sample test for equality of proportions without continuity
##  correction
## 
## data:  table(Cereals$IsKellogs, Cereals$shelf)
## X-squared = 1.3547, df = 1, p-value = 0.2445
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.32614192  0.07019442
## sample estimates:
##    prop 1    prop 2 
## 0.6981132 0.8260870

One-sample t-test and confidence interval with summarized data

Input the mean of the sample, then the standard deviation, then the sample size.
You first need to load the BSDA package.
install.packages("BSDA") 

library("BSDA")
## Loading required package: lattice
## 
## Attaching package: 'BSDA'
## The following object is masked from 'package:datasets':
## 
##     Orange
tsum.test(10.5, 3.5, 30, alternative = "greater", mu = 8)
## Warning in tsum.test(10.5, 3.5, 30, alternative = "greater", mu = 8): argument
## 'var.equal' ignored for one-sample test.
## 
##  One-sample t-Test
## 
## data:  Summarized x
## t = 3.9123, df = 29, p-value = 0.0002537
## alternative hypothesis: true mean is greater than 8
## 95 percent confidence interval:
##  9.414241       NA
## sample estimates:
## mean of x 
##      10.5

One-sample t-test with values in a column 

t.test(Cereals$calories, mu=100)
## 
##  One Sample t-test
## 
## data:  Cereals$calories
## t = 3.1022, df = 75, p-value = 0.002707
## alternative hypothesis: true mean is not equal to 100
## 95 percent confidence interval:
##  102.4955 111.4519
## sample estimates:
## mean of x 
##  106.9737

For one-sided alternatives, use the options alternative="less" or alternative="greater".

2-sample t-test

Samples in two columns:

t.test(Cereals$sugars, Cereals$calories)
## 
##  Welch Two Sample t-test
## 
## data:  Cereals$sugars and Cereals$calories
## t = -43.385, df = 82.64, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -104.5710  -95.4027
## sample estimates:
##  mean of x  mean of y 
##   6.986842 106.973684

Samples in 1 column divided by values of a categorical variable in a different column:

t.test(subset(Cereals$calories, Cereals$shelf=="Upper"), subset(Cereals$calories, Cereals$shelf=="Lower"))
## 
##  Welch Two Sample t-test
## 
## data:  subset(Cereals$calories, Cereals$shelf == "Upper") and subset(Cereals$calories, Cereals$shelf == "Lower")
## t = -1.6964, df = 72.308, p-value = 0.09411
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -13.205429   1.062572
## sample estimates:
## mean of x mean of y 
##  102.5000  108.5714

Paired t-test 

t.test(Cereals$calories, Cereals$sugars, paired=TRUE)
## 
##  Paired t-test
## 
## data:  Cereals$calories and Cereals$sugars
## t = 49.84, df = 75, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   95.99034 103.98335
## sample estimates:
## mean of the differences 
##                99.98684

Chi-squared test for independence or homogeneity 

chisq.test(table(Cereals$brand, Cereals$shelf))
## Warning in chisq.test(table(Cereals$brand, Cereals$shelf)): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  table(Cereals$brand, Cereals$shelf)
## X-squared = 5.8704, df = 5, p-value = 0.319

Chi-squared test for independence or homogeneity with summarized data 

chisq.test(matrix(c(30,40,50,100),ncol=2,byrow=TRUE))
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  matrix(c(30, 40, 50, 100), ncol = 2, byrow = TRUE)
## X-squared = 1.4818, df = 1, p-value = 0.2235

Chi-squared test for goodness-of-fit 

chisq.test(table(Cereals$brand), p= c(.25, .25, .2, .1, .1, .1))
## 
##  Chi-squared test for given probabilities
## 
## data:  table(Cereals$brand)
## X-squared = 7.1842, df = 5, p-value = 0.2073

Chi-squared test for goodness-of-fit with summarized data 

chisq.test(matrix(c(30,40,50,100,200)), p= c(.25, .25, .2, .2, .1))
## 
##  Chi-squared test for given probabilities
## 
## data:  matrix(c(30, 40, 50, 100, 200))
## X-squared = 705, df = 4, p-value < 2.2e-16

Regression inference 

summary(lm(Cereals$carbo ~ Cereals$sugars))
## 
## Call:
## lm(formula = Cereals$carbo ~ Cereals$sugars)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.2463  -1.2562  -0.2463   1.8692   6.1766 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    17.78411    0.74684  23.813  < 2e-16 ***
## Cereals$sugars -0.42296    0.09045  -4.676 1.29e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.469 on 74 degrees of freedom
## Multiple R-squared:  0.2281, Adjusted R-squared:  0.2176 
## F-statistic: 21.86 on 1 and 74 DF,  p-value: 1.286e-05

Confidence interval for predicted slope

confint(lm(Cereals$carbo ~ Cereals$sugars), level=.95)
##                     2.5 %     97.5 %
## (Intercept)    16.2960066 19.2722210
## Cereals$sugars -0.6031946 -0.2427287

Confidence intervals for predicted values

tempvariable <- Cereals$carbo
predict(lm(Cereals$sugars~tempvariable), newdata= data.frame(tempvariable = 19), interval="confidence")
##        fit      lwr      upr
## 1 4.737612 3.426047 6.049177
predict(lm(Cereals$sugars~tempvariable), newdata= data.frame(tempvariable = 19), interval="predict")
##        fit       lwr      upr
## 1 4.737612 -3.176984 12.65221

ANOVA (one-way ANOVA)

Comparing a quantitative and categorical variable (stacked data):

anova(lm(Cereals$sugars ~ Cereals$brand))
## Analysis of Variance Table
## 
## Response: Cereals$sugars
##               Df  Sum Sq Mean Sq F value  Pr(>F)  
## Cereals$brand  5  243.24  48.648  2.7737 0.02416 *
## Residuals     70 1227.75  17.539                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(aov(Cereals$sugars ~ Cereals$brand))
##               Df Sum Sq Mean Sq F value Pr(>F)  
## Cereals$brand  5  243.2   48.65   2.774 0.0242 *
## Residuals     70 1227.7   17.54                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Comparing several quantitative variables (unstacked data):

artsurvey <- read.csv("~/artsurvey.csv")
tempvariable<-stack(artsurvey, select=c("artist.1", "artist.2", "artist.3", "artist.4", "artist.5"))
anova(lm(tempvariable))
## Analysis of Variance Table
## 
## Response: values
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## ind        4  72.04 18.0100  4.3558 0.002809 **
## Residuals 95 392.80  4.1347                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Bonferroni pairwise comparisons:

pairwise.t.test(tempvariable$values,tempvariable$ind, p.adjust = "bonferroni")
## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  tempvariable$values and tempvariable$ind 
## 
##          artist.1 artist.2 artist.3 artist.4
## artist.2 1.0000   -        -        -       
## artist.3 0.0152   0.3194   -        -       
## artist.4 0.0071   0.1786   1.0000   -       
## artist.5 0.3194   1.0000   1.0000   1.0000  
## 
## P value adjustment method: bonferroni