# ch19: Print examples of chapter 19 of 'R for Dummies'. In rfordummies: Code Examples to Accompany the Book "R for Dummies"

## Description

To print a listing of all examples of a chapter, use `ch19()`. To run all the examples of `ch19()`, use `example(ch19)`.

## Usage

 `1` ```ch19() ```

## See Also

`toc`

Other Chapters: `ch01`, `ch02`, `ch03`, `ch04`, `ch05`, `ch06`, `ch07`, `ch08`, `ch09`, `ch10`, `ch11`, `ch12`, `ch13`, `ch14`, `ch15`, `ch16`, `ch17`, `ch18`, `ch20`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102``` ```# Chapter 19 - Ten Things You Can Do in R That You Would've Done in Microsoft Excel # Adding Row and Column Totals iris.num <- iris[, -5] colSums(iris.num) colMeans(iris.num) apply(iris.num, 2, min) apply(iris.num, 2, max) sapply(iris.num, min) sapply(iris.num, max) # Formatting Numbers format(12345.6789, digits=9, decimal.mark=",", big.mark=" ",small.mark=".", , small.interval=3) x <- colMeans(mtcars[, 1:4]) format(x, digits=2, nsmall=2) x <- seq(0.5, 0.55, 0.01) sprintf("%.1f %%", 100*x) set.seed(1) x <- 1000*runif(5) sprintf("\$ %3.2f", x) stuff <- c("bread", "cookies") price <- c(2.1, 4) sprintf("%s costed \$ %3.2f ", stuff, price) # Sorting Data with(mtcars, mtcars[order(hp), ]) with(mtcars, mtcars[order(hp, decreasing=TRUE), ]) # Making Choices with If mtcars <- within(mtcars, mpgClass <- ifelse(mpg < mean(mpg), "Low", "High")) mtcars[mtcars\$mpgClass == "High", ] # Calculating Conditional Totals with(mtcars, mean(mpg)) with(mtcars, mean(mpg[hp < 150])) with(mtcars, mean(mpg[hp >= 150])) with(mtcars, length(mpg[hp > 150])) # Transposing Columns or Rows x <- matrix(1:12, ncol=3) x t(x) t(mtcars[1:4, ]) # Finding Unique or Duplicated Values unique(mtcars\$cyl) dupes <- duplicated(iris) head(dupes) which(dupes) iris[dupes, ] iris[!dupes, ] nrow(iris[!dupes, ]) # Working with Lookup Tables index <- match("Toyota Corolla", rownames(mtcars)) index mtcars[index, 1:4] # Working with Pivot Tables with(mtcars, tapply(hp, list(cyl, gear), mean)) aggregate(hp~cyl+gear+am, mtcars, mean) # Using the Goal Seek and Solver sales <- function(price) { 100 - 0.5 * price } revenue <- function(price) { price * sales(price) } par(mfrow=c(1, 2)) curve(sales, from=50, to=150, xname="price", ylab="Sales", main="Sales") curve(revenue, from=50, to=150, xname="price", ylab="Revenue", main="Revenue") par(mfrow=c(1, 1)) optimize(revenue, interval=c(50, 150), maximum=TRUE) ```

rfordummies documentation built on May 30, 2017, 4:57 a.m.