---
title: "qwraps2: Data Sets"
author: "Peter E. DeWitt"
output:
rmarkdown::html_vignette:
toc: true
number_sections: true
bibliography: references.bib
vignette: >
%\VignetteIndexEntry{qwraps2: Data Sets}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r}
library(qwraps2)
packageVersion("qwraps2")
```
# mtcars2
The base R package datasets provides the mtcars data set. The information in
mtcars is the fuel consumption and automobile characteristics of
`r nrow(mtcars) `
automobiles as reported in the March, April, June and July 1974 issues of
_Motor Trend_ magazine [@hocking1976biometrics].
That dataset is modified and extended to provide support for examples within
the qwraps2 package documentation. This vignette documents the construction
of mtcars2.
# Construction of mtcars2
Starting with the original mtcars:
```{r}
mtcars2 <- mtcars
str(mtcars2)
```
The cyl column provides the number of cylinders for in the engine of the
automobiles. We will use two additional versions of this information, one as
character column and one as a factor. Please note that the order of the
factor levels is intentionally set to be non-sequential. This will help to
illustrate the ordering or results when using a factor or a character vector
as a grouping variable.
```{r}
mtcars2$cyl_character <- paste(mtcars2$cyl, "cylinders")
mtcars2$cyl_factor <- factor(mtcars2$cyl,
levels = c(6, 4, 8),
labels = paste( c(6, 4, 8), "cylinders"))
```
Create other factor variables.
```{r}
mtcars2$gear_factor <-
factor(mtcars2$gear, levels = c(3, 4, 5), labels = paste(c(3, 4, 5), "forward gears"))
```
Engine configuration: the
`r backtick(vs) `
column is an integer vector for indicating
V-shaped or straight. The constructed column engine is a factor the same
information as a labeled factor.
```{r}
mtcars2$engine <-
factor(mtcars2$vs, levels = c(0, 1), labels = c("V-shaped", "straight"))
```
Transmission: the
`r backtick(am) `
column is an integer vector indicating if the transmission is automatic or
manual. We construct a
`r backtick(transmission) `
column to provide the same information as a factor.
```{r}
mtcars2$transmission <-
factor(mtcars2$am, levels = c(0, 1), labels = c("Automatic", "Manual"))
```
The rownames of the mtcars2 data set provide the make and model of the
automobiles. Here we will create columns for make and model and then omit
the rownames.
```{r}
mtcars2$make <- sub("^(\\w+)\\s(.+)", "\\1", rownames(mtcars2))
mtcars2$model <- sub("^(\\w+)\\s(.+)", "\\2", rownames(mtcars2))
rownames(mtcars2) <- NULL
```
To have some dates to use in examples we are going to add an mostly
arbitrary date column to mtcars2. Given that the data came from the March
through July issues of _Motor Trend_ in 1974, we will create a
`r backtick(test_date) `
column starting in January 1974 forward with one to three tests per week
through May 1974. This assumes the data is in chronological order of the data.
```{r}
set.seed(42)
mtcars2$test_date <-
as.POSIXct("1974-01-03", tz = "GMT") +
cumsum(sample(c(2, 3, 4, 7) * 3600 * 24, size = nrow(mtcars2), replace = TRUE))
```
Lastly we will order the columns of mtcars2 so similar columns are next to
each other.
```{r}
mtcars2 <-
mtcars2[, c("make", "model", "mpg", "disp", "hp", "drat", "wt", "qsec",
"cyl", "cyl_character", "cyl_factor",
"vs", "engine",
"am", "transmission",
"gear", "gear_factor",
"carb",
"test_date")]
```
# Summary of mtcars2
mtcars2 is a data frame with
`r nrow(mtcars2) `
observations with
`r ncol(mtcars2) `
variables. Some of the variables tell us the same information, but in
different formats.
```{r}
str(mtcars2)
```
| Element | Name | Description |
| ------: | :------------ | :--------------------------------------- |
| [, 1] | make | Vehicle Manufacturer |
| [, 2] | model | Vehicle model |
| [, 3] | mpg | Miles/(US) gallon |
| [, 4] | disp | Displacement (cu.in.) |
| [, 5] | hp | Gross horsepower |
| [, 6] | drat | Rear axle ratio |
| [, 7] | wt | Weight (1000 lbs) |
| [, 8] | qsec | 1/4 mile time |
| [, 9] | cyl | Number of cylinders |
| [, 10] | cyl_character | Number of cylinders as a character string |
| [, 11] | cyl_factor | Number of cylinders as a factor |
| [, 12] | vs | Engine (0 = V-shaped, 1 = straight) |
| [, 13] | engine | same info as vs, but as a factor |
| [, 14] | am | Transmission (0 = automatic, 1 = manual) |
| [, 15] | transmission | same info as am as a factor |
| [, 16] | gear | Number of forward gears |
| [, 17] | gear_factor | Number of forward gears as a factor |
| [, 18] | carb | Number of carburetors |
| [, 19] | test_date | arbitrary date - created to approximate when the vehicle would have been assessed. |
# pefr
Peak expiratory flow rate (pefr) data is used for examples within the qwraps2
package. The data has been transcribed from [@bland1986statistical].
> The sample comprised colleagues and family of J.M.B. chosen to give a wide
> range of PEFR but in no way representative of any defined population. Two
> measurements were made with a Wright peak flow meter and two with a mini
> Wright meter, in random order. All measurements were taken by J.M.B., using
> the same two instruments. (These data were collected to demonstrate the
> statistical method and provide no evidence on the comparability of these two
> instruments.) We did not repeat suspect readings and took a single reading as
> our measurement of PEFR. Only the first measurement by each method is used to
> illustrate the comparison of methods, the second measurements being used in
> the study of repeatability.
The units of measure for the pefr are liters per minute (L/min).
```{r}
# copied text from the manuscript
pefr_table <-
read.delim(
header = FALSE,
text = "
1 494 490 512 525
2 395 397 430 415
3 516 512 520 508
4 434 401 428 444
5 476 470 500 500
6 557 611 600 625
7 413 415 364 460
8 442 431 380 390
9 650 638 658 642
10 433 429 445 432
11 417 420 432 420
12 656 633 626 605
13 267 275 260 227
14 478 492 477 467
15 178 165 259 268
16 423 372 350 370
17 427 421 451 443")
```
Build the data set
```{r}
pefr <-
expand.grid(subject = 1:17,
measurement = 1:2,
meter = c("Wright peak flow meter", "Mini Wright peak flow meter"),
KEEP.OUT.ATTRS = FALSE,
stringsAsFactors = FALSE)
pefr$pefr <- do.call(c, pefr_table[, 2:5])
head(pefr)
```
See
`r backtick(vignette("qwraps2-graphics", package = "qwraps2")) `
for examples using this data set, specifically in the construction and use of
Bland-Altman plots via
`r backtick(qblandaltman) %s% "." `
# Spambase
Spambase [@spambase] is a useful data set for example needed a binary outcome
and several possible predictors. The data set and documentation can be found
in this package in the directory on your machine at:
```{r}
system.file("spambase", package = "qwraps2")
```
The data set
`r backtick(spambase) `
was generated thusly:
```{r}
nms <-
scan(system.file("spambase", "spambase.names", package = "qwraps2")
, what = character()
, skip = 33
, sep = "\n"
, quiet = TRUE
)
nms <- sapply(strsplit(nms, split = ":"), getElement, 1)
nms <- c(nms, "spam")
# clean up char_freq names
nms <-
nms |>
sub(";", "semicolon", x = _, fixed = TRUE) |>
sub("(", "parenthesis", x = _, fixed = TRUE) |>
sub("[", "square_bracket", x = _, fixed = TRUE) |>
sub("!", "exclamation_point", x = _, fixed = TRUE) |>
sub("$", "dollar_sign", x = _, fixed = TRUE) |>
sub("#", "pound", x = _, fixed = TRUE)
spambase <- read.csv(
file = system.file("spambase", "spambase.data", package = "qwraps2")
, header = FALSE
, col.names = nms
)
```
There are
`r frmt(nrow(spambase)) `
rows of data with
`r frmt(ncol(spambase) - 1L) `
predictors for the binary outcome
`r backtick(spam) `
```{r}
n_perc(spambase$spam) # count and percent of spam messages
```
# References
<div id="refs"></div>
# Session Info
```{r label = "sessioninfo"}
sessionInfo()
```