Package 'headliner'

Description

Append a/an to word

Usage

add_article(x)

Arguments

Details

This function uses crude logic to append 'a' or 'an' to numbers and phrases.

• words that start with aeiou

• negative numbers always start with 'a', ex: 'a -3' or 'a -8'

• decimals always start with 'a' ex: 0.4 is usually pronounced 'a zero point four' or 'a point four'

• numbers starting with 8 are always 'an'

• if the integer that comes after thousand or million is 11 or 18 then 'an'

  • 18,000 becomes 18 and that becomes 'an 18'

• if the integer that comes after thousand or million is in 1, 10, 12, 13, 14, 15, 16, 17, 19 then 'a'

  • 15,500 becomes 15 and that becomes 'a 15'

• otherwise 'a'

Value

Returns a vector the same length as the input.

Examples

add_article("increase")

add_article("decrease")

add_article(c(1, 8, 10, 11, 18, 20, 80))

add_article(18123)

stats::setNames(
  add_article(1.8 * 10^(1:7)),
  prettyNum(1.8 * 10^(1:7), big.mark = ",")
)

Description

Using a reference date (defaults to current date), columns are appended to the data set describing the number of days, weeks, months, quarters, calendar years and fiscal years since the reference date. If the new columns share names with an existing column, the function will show a warning.

Usage

add_date_columns(
  df,
  date_col,
  ref_date = Sys.Date(),
  fiscal_year_offset = 6,
  week_start = 1,
  drop = FALSE
)

Arguments

Value

Returns a data frame with columns appended to describe date distances from a reference date.

Examples

demo_data() |>
  add_date_columns(date_col = date)

# if columns overlap, you will see a warning
demo_data() |>
  dplyr::mutate(week = 1) |>
  add_date_columns(date_col = date)

# to drop the old column and keep the new column use `drop = TRUE`
demo_data() |>
  dplyr::mutate(week = 1) |>
  add_date_columns(date_col = date, drop = TRUE)

Description

This works similar to headline() but acts on and returns a data frame.

Usage

add_headline_column(
  df,
  x,
  y,
  headline = "{trend} of {delta} ({orig_values})",
  ...,
  .name = "headline",
  if_match = "There was no difference",
  trend_phrases = headliner::trend_terms(),
  plural_phrases = NULL,
  orig_values = "{x} vs. {y}",
  n_decimal = 1,
  round_all = TRUE,
  multiplier = 1,
  return_cols = .name
)

Arguments

Details

What is nice about this function is you can return some of the "talking points" used in the headline calculation. For example, if you want to find the most extreme headlines, you can use add_headline_column(..., return_cols = delta) This will bring back a headline column as well as the delta talking point (the absolute difference between x and y). With this result, you can sort in descending order and filter for the biggest difference.

Value

Returns the original data frame with columns appended.

Examples

# You can use 'add_headline_column()' to reference values in an existing data set.
# Here is an example comparing the box office sales of different Pixar films
head(pixar_films) |>
  dplyr::select(film, bo_domestic, bo_intl) |>
  add_headline_column(
    x = bo_domestic,
    y = bo_intl,
    headline = "{film} was ${delta}M higher {trend} (${x}M vs ${y}M)",
    trend_phrases = trend_terms(more = "domestically", less = "internationally")
  ) |>
  knitr::kable("pandoc")

# You can also use 'return_cols' to return any and all "talking points".
# You can use tidyselect helpers like 'starts_with("delta")' or
# 'everything()'. In this example, I returned the 'raw_delta' & 'trend' columns
# and then identified the records at the extremes
pixar_films |>
  dplyr::select(film, bo_domestic, bo_intl) |>
  add_headline_column(
    x = bo_domestic,
    y = bo_intl,
    headline = "${delta}M {trend} (${x}M vs ${y}M)",
    trend_phrases = trend_terms(more = "higher", less = "lower"),
    return_cols = c(raw_delta, trend)
  ) |>
  dplyr::filter(raw_delta %in% range(raw_delta)) |>
  knitr::kable("pandoc")

Description

Using logic that filter can interpret, compare_conditions() will summarize the data aggregating condition x and condition y

Usage

compare_conditions(df, x, y, .cols = everything(), .fns = lst(mean))

Arguments

Details

compare_conditions() passes its arguments to across. The .cols and .fns work the same. For clarity, it is helpful to use the lst function for the .fns parameter. Using compare_conditions(..., .cols = my_var, .fns = lst(mean, sd)) will return the values mean_my_var_x, mean_my_var_y, sd_my_var_x and sd_my_var_x

Value

Returns a data frame that is either 1 row, or if grouped, 1 row per group.

Examples

# compare_conditions works similar to dplyr::across()
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = rotten_tomatoes
  )


# because data frames are just fancy lists, you pass the result to headline_list()
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = rotten_tomatoes
  ) |>
 headline_list("a difference of {delta} points")


 # you can return multiple objects to compare
 # 'view_List()' is a helper to see list objects in a compact way
 pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = c(rotten_tomatoes, metacritic),
    .fns = dplyr::lst(mean, sd)
  ) |>
  view_list()


# you can use any of the `tidyselect` helpers
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = dplyr::starts_with("bo_")
  )


# if you want to compare x to the overall average, use y = TRUE
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = TRUE,
    .cols = rotten_tomatoes
  )


# to get the # of observations use length() instead of n()
# note: don't pass the parentheses
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = rotten_tomatoes, # can put anything here really
    .fns = list(n = length)
  )


# you can also use purrr-style lambdas
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = rotten_tomatoes,
    .fns = list(avg = ~ sum(.x) / length(.x))
  )

# you can compare categorical data with functions like dplyr::n_distinct()
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    .cols = film,
    .fns = list(distinct = dplyr::n_distinct)
  )

Description

A function to create "talking points" that describes the difference between two values.

Usage

compare_values(
  x,
  y,
  trend_phrases = headliner::trend_terms(),
  orig_values = "{x} vs. {y}",
  plural_phrases = NULL,
  n_decimal = 1,
  round_all = TRUE,
  multiplier = 1,
  check_rounding = TRUE
)

Arguments

Details

Given compare_values(x = 8, y = 10) the following items will be returned in the list:

Value

compare_values() returns a list object that can be used with glue syntax

See Also

headline(), trend_terms(), plural_phrasing() and view_list()

Examples

# the values can be manually entered

compare_values(10, 8) |> head(2)
# percent difference (10-8)/8
compare_values(10, 8)$delta_p

# trend_phrases returns an object called trend if nothing is passed
compare_values(10, 8)$trend

# or if one argument is passed using trend_terms()
compare_values(10, 8, trend_phrases = trend_terms(more = "higher"))$trend

# if a named list is used, the objects are called by their names
compare_values(
  10, 8,
  trend_phrases = list(
    more = trend_terms(),
    higher = trend_terms("higher", "lower")
  )
)$higher

# a phrase about the comparison can be edited by providing glue syntax
# 'c' = the 'compare' value, 'r' = 'reference'
compare_values(10, 8, orig_values = "{x} to {y} people")$orig_values

# you can also adjust the rounding, although the default is 1
compare_values(0.1234, 0.4321)$orig_values
compare_values(0.1234, 0.4321, n_decimal = 3)$orig_values
# or add a multiplier
compare_values(0.1234, 0.4321, multiplier = 100)$orig_values

Description

Small data set referencing the current date

Usage

demo_data(n = 10, by = "-2 month")

Arguments

Value

Returns a data frame of size n.

Examples

demo_data()

demo_data(n = 8, by = "1 day")

Description

Given two values, headline() will use glue syntax to string together "talking points". For example headline(8, 10) will describe a difference of 2 and can be expressed as headline(8, 10, headline = "changed by {delta} ({raw_delta_p}%)"). This returns "changed by 2 (-20%)".

Usage

headline(
  x,
  y,
  headline = "{trend} of {delta} ({orig_values})",
  ...,
  if_match = "There was no difference",
  trend_phrases = headliner::trend_terms(),
  plural_phrases = NULL,
  orig_values = "{x} vs. {y}",
  n_decimal = 1,
  round_all = TRUE,
  multiplier = 1,
  return_data = FALSE
)

headline_list(
  l,
  headline = "{trend} of {delta} ({orig_values})",
  x,
  y,
  ...,
  if_match = "There was no difference.",
  trend_phrases = headliner::trend_terms(),
  plural_phrases = NULL,
  orig_values = "{x} vs. {y}",
  n_decimal = 1,
  round_all = TRUE,
  multiplier = 1,
  return_data = FALSE
)

Arguments

Details

headline() relies heavily on glue_data. Objects can be combined into a headline using the following search path: If given

delta <- 123
headline(1, 3, delta = "abc")

## decrease of abc (1 vs. 3)

delta is one of the "talking points" from compare_values() and would usually return "2" but because we passed the named variable delta = "none", headline() (really glue_data) will look first at the named variables, then at the result of compare_values() then in the global environment. So in the example above, the output will return "decrease of xxxxxx (1 vs. 3)"

Value

Returns a character vector the same length as the input,

See Also

compare_values(), trend_terms(), and add_article()

Examples

# values can be manually entered, some headlines are provided by default
headline(10, 8)
headline(8, 10)
headline(1:3, 3:1)

# most likely you'll edit the headline by hand
headline(
  x = 10,
  y = 8,
  headline = "There was a ${delta} {trend} vs last year"
)

# you can also adjust the phrasing of higher/lower values
headline(
  x = 10,
  y = 8,
  headline = "Group A was {trend} by {delta_p}%.",
  trend_phrases = trend_terms(more = "higher", less = "lower")
 )

# a phrase about the comparion can be edited by providing glue syntax
# 'c' = the 'compare' value, 'r' = 'reference'
headline(10, 8, orig_values = "{x} to {y} people")

# you can also add phrases for when the difference = 1 or not
headline(
  x = 10,
  y = 8,
  plural_phrases = list(
    were = plural_phrasing(single = "was", multi = "were"),
    people = plural_phrasing(single = "person", multi = "people")
  ),
  headline = "there {were} {delta} {people}"
)

# you can also adjust the rounding, the default is 1
headline(0.1234, 0.4321)
headline(0.1234, 0.4321, n_decimal = 3)
# or use a multiplier
headline(0.1234, 0.4321, multiplier = 100)

# there are many components you can assemble
headline(
  x = 16,
  y = 8,
  headline = "there was {article_delta_p}% {trend}, \\
  {add_article(trend)} of {delta} ({orig_values})"
)


# compare_conditions() produces a one-row data frame that can be
# passed to headline_list()
pixar_films |>
  compare_conditions(
    x = (rating == "G"),
    y = (rating == "PG"),
    rotten_tomatoes
  ) |>
  headline_list(
    headline = "On average, G-rated films score {delta} points {trend} than \\
    PG films on Rotten Tomatoes",
    trend_phrases = trend_terms(more = "higher", less = "lower")
  )

# if you have more than 2 list items, you can specify them by name
list(
  x = 1,
  y = 2,
  z = 3
 ) |>
  headline_list(
    x = x,
    y = z
  )

This data comes from pixarfilms package by Eric Leung (2022)

Description

The data has box office sales, audience ratings, and release dates for each Pixar film

Usage

pixar_films

Format

A tibble with 22 rows and 10 columns:

order

order of release

film

name of film

release_date

date film premiered

year

the year the film premiered

run_time

film length in minutes

film_rating

rating based on Motion Picture Association (MPA) film rating system

rotten_tomatoes

score from the American review-aggregation website Rotten Tomatoes; scored out of 100

metacritic

score from Metacritic where scores are weighted average of reviews; scored out of 100

bo_domestic

box office gross amount in U.S. dollars (millions) for U.S. and Canada

bo_intl

box office gross amount in U.S. dollars (millions) for other territories

Examples

pixar_films

library(ggplot2)

headline(
  x = min(pixar_films$run_time),
  y = max(pixar_films$run_time),
  headline =
    "The shortest film was {delta} minutes less than the longest film ({orig_values} minutes)"
)

ggplot(pixar_films, aes(bo_intl, rating)) +
  geom_boxplot() +
  xlim(0, NA) +
  labs(title = "International Box Office by MPA Rating")


ggplot(pixar_films, aes(release_date, run_time)) +
  geom_line() +
  geom_point() +
  ylim(0, NA) +
  labs(title = "Film runtimes by release date")


ggplot(pixar_films, aes(y = reorder(film, rotten_tomatoes))) +
  geom_linerange(aes(xmin = rotten_tomatoes, xmax = metacritic), size = 2, color = "grey85") +
  geom_point(aes(x = rotten_tomatoes, color = "rotten_tomatoes")) +
  geom_point(aes(x = metacritic, color = "metacritic")) +
  scale_color_manual(values = c("steelblue1", "coral2")) +
  theme_minimal(base_size = 9) +
  labs(
    title = "Rotten Tomatoes vs Metacritic by film",
    color = NULL,
    y = NULL,
    x = "Audience Score"
  )

Description

plural_phrasing() returns a list object describing the value to use when display when x - y is 1 (single) or not one (multiple or fraction). This helps write "1 person" vs "2 people"

Usage

plural_phrasing(single, multi)

Arguments

Details

plural_phrasing() will primarily be used in headline() and passed along to compare_conditions(). Similar to trend_terms(). Plural phrases can be passed in a list. See examples below.

Value

Returns a list object.

Examples

plural_phrasing(single = "person", multi = "people")

headline(
  x = 1:2,
  y = 0,
  headline = "a difference of {delta} {people}",
  plural_phrases = list(people = plural_phrasing("person", "people"))
)


# a complex example passing multiple trends and plural phrases
headline(
  35, 30,
  headline =
    "We had {an_increase} of {delta} {people}.
    That is {delta} {more} {employees} \\
    than the same time last year ({orig_values}).",
  trend_phrases = list(
    an_increase = trend_terms("an increase", "a decrease"),
    more = trend_terms("more", "less")
  ),
  plural_phrases =
    list(
      people = plural_phrasing("person", "people"),
      employees = plural_phrasing("employee", "employees")
    )
)

Description

trend_terms() returns a list object describing the values to display when x is greater than y or x is less than y.

Usage

trend_terms(more = "increase", less = "decrease")

Arguments

Details

trend_terms() will primarily be used in headline() and passed along to compare_conditions(). Similar to plural_phrasing() Trend terms can be passed in a list. See examples below.

Value

Returns a list object.

See Also

compare_values()

Examples

headline(
  x = c(9, 11),
  y = 10,
  headline = "{trend} by {delta_p}%",
  trend_phrases = trend_terms("higher", "lower")
)

# a complex example passing multiple trends and plural phrases
headline(
  35, 30,
  headline =
    "We had {an_increase} of {delta} {people}.
    That is {delta} {more} {employees} \\
    than the same time last year ({orig_values}).",
  trend_phrases = list(
    an_increase = trend_terms("an increase", "a decrease"),
    more = trend_terms("more", "less")
  ),
  plural_phrases =
    list(
      people = plural_phrasing("person", "people"),
      employees = plural_phrasing("employee", "employees")
    )
)

Description

Compact view of list values

Usage

view_list(x)

Arguments

Value

Returns a data frame to display a list or vector vertically.

See Also

compare_values()

Examples

compare_values(10, 8) |>
  view_list()

add_article(c(1,8,10, 11, 18)) |>
  view_list()