qwraps2: Data Sets

In qwraps2: Quick Wraps 2

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:

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.

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.

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.

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.

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.

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.

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.

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.

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).

# 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

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:

system.file("spambase", package = "qwraps2")

The data set r backtick(spambase) was generated thusly:

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)

n_perc(spambase$spam) # count and percent of spam messages

References

Session Info

sessionInfo()

qwraps2 documentation built on Nov. 10, 2023, 1:06 a.m.