21 T

21.1 tabular data

Data in a rectangular table format, where each row has an entry for each column.

Tabluar data in R are usually in a data frame or tibble.

year sex name n rank nation
1996 F SOPHIE 7087 1 England & Wales
1996 F CHLOE 6824 2 England & Wales
1996 F JESSICA 6711 3 England & Wales
1996 F EMILY 6415 4 England & Wales
1996 F LAUREN 6299 5 England & Wales
1996 F HANNAH 5916 6 England & Wales

21.2 tag

A way to mark the start and end of HTML elements.

In HTML, each element is delineated by tags that describe it. Tags usually start with the element name and optional attribute-value pairs surrounded by angled brackets, and ending with a forward slash and the name surrounded by angled brackets.

<name attribute="value">Element contents</name>

For example, a paragraph in HTML is marked by "p" tags like this:

<p>Paragraph text...</p>

In shiny, tags can be created with the tags() functions.

paragraph <- tags$p("Paragraph text")

unordered_list <- tags$ul(
  tags$li("First list item"),
  tags$li("Second list item")
)

21.3 theme

A consistent style for a report or plots.

Plots in ggplot can set the theme globally with code like this:

# set the default theme to black-and-white
theme_set(theme_bw())
More...

You can set the theme for a html document in the YAML header. View and download different themes.

---
title: "My Document"
subtitle: "It's Just a Demo"
author: "Me"
date: "2024-09-05"
output:
  html_document:
    theme: spacelab
    highlight: tango
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
    toc_depth: 3
    number_sections: false
---

21.4 tibble

A container for tabular data with some different properties to a data frame

Tibbles are almost exactly like the base R data.frame container type, but has some special printing functions, does not coerce character columns to factors, and does not typically use row names.

# display the mtcars data frame
mtcars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# convert the mtcars data frame to a tibble
tibble::as_tibble(mtcars, rownames = "name")
name mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

21.5 tidy data

A format for data that maps the meaning onto the structure.

Tidy data has three rules:

21.6 tidyverse

A set of R packages that help you create and work with tidy data

The tidyverse can be loaded with library(tidyverse) and loads the following packages:

  • {ggplot2} for data visualisation
  • {tibble} for data tables
  • {tidyr} for tidy data
  • {readr} for reading in data
  • {purrr} for iteration
  • {dplyr} for data wrangling
  • {stringr} for string manipulation
  • {forcats} for factors

Compare with base R

21.7 tied ranks

When two or more values/observations within a variable are identical and assume the same rank order value.

This is the default for the rank() function (ties.method = "average"). You can also set ties to go in the order they are in the dataset (first), in the opposite order (last), a random order (random), all ties have the maximum value (max) or the minimum value (min).

scores <- tibble(
  score = c(5, 5, 5, 15, 15)
) %>%
  mutate(
    rank = rank(score),
    first = rank(score, ties.method = "first"),
    last = rank(score, ties.method = "last"),
    random = rank(score, ties.method = "random"),
    max = rank(score, ties.method = "max"),
    min = rank(score, ties.method = "min")
  )

21.8 transparency

The degree to which all the steps and decisions in a study have been documented and made available for verification.

21.9 treatment code

A coding scheme for categorical variables that creates (n_levels -1) dichotomous variables where each level of the categorical variable is contrasted to a reference level.

Also referred to as dummy-coding.

21.10 true negative

When a test concludes there is no effect when there is really is no effect

See also true positive, false positive/type I error, false negative/type II error.

21.11 true positive

When a test concludes there is an effect when there is really is an effect

See also true negative, false positive/type I error, false negative/type II error.

21.12 two-tailed

A statistical test for which the critical region consists of all values of the test statistic greater than a given value plus all values less than another given value.

See p-value for a comparison of one-tailed and two-tailed tests.

21.13 Type I Error

A false positive; When a test concludes there is an effect when there really is no effect

See also true positive, true negative, and false negative/type II error.

21.14 Type II Error

A false negative; When a test concludes there is no effect when there really is an effect

See also true positive, true negative, and false positive/type I error.