# R notes for Activity 22-2: Hypothetical Commuting Times

This is one of those fun times in which our data set combines a number of essentially independent columns into a single data frame. Since R pads the empty cells in the data frame with `NA` values, our analyses may be slightly more complicated.

Let's start by loading the data. There's little enough data that we can look at all of it.

```CommuteTimes = read.csv("/home/rebelsky/Stats115/Data/HypoCommute.csv")
CommuteTimes
```

The columns are named `A1` (for Alex's Route 1), `A2` (for Alex's Route 2), B1 (for Barb's Route 1), and so on and so forth.

## 22-2 c. Computing Alex's route statistics

You should be able to read the sample size from the table. To get the sample mean and standard deviation, we can use `mean` and `sd`, but need to tell the functions to ignore the `NA` values. (Having to tell the functions to deal with the NA values differently is one of the disadvantages of combining the columns.

```mean(CommuteTimes\$A1, na.rm=T)
sd(CommuteTimes\$A1, na.rm=T)
```

## 22-2 d. Conducting the significance test

R makes two-sample t-tests very easy to compute. Just call `t.test` with the two samples.

```t.test(CommuteTimes\$A1,CommuteTimes\$A2)
```

## 22-2 f. Confidence intervals

We repeat the t-test, telling it to use a different confidence level.

```t.test(CommuteTimes\$A1,CommuteTimes\$A2, conf.level=.90)
```

## 22-2 k. More Computations

You should be able to figure out how to do these computations by revisiting the Alex examples from above.

Samuel A. Rebelsky, rebelsky@grinnell.edu

Copyright (c) 2007-8 Samuel A. Rebelsky.

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