The wildcard package is a templating mechanism for data frames. Wildcards are placeholders for text, and you can evaluate them to generate new data frames from templates. The functionality is straightforward.
wildcard()library(wildcard)
myths <- data.frame(
myth = c("Bigfoot", "UFO", "Loch Ness Monster"),
claim = c("various", "day", "day"),
note = c("various", "pictures", "reported day"))
myths
## myth claim note
## 1 Bigfoot various various
## 2 UFO day pictures
## 3 Loch Ness Monster day reported day
wildcard(myths, wildcard = "day", values = c("today", "yesterday"))
## myth claim note
## 1 Bigfoot various various
## 2 UFO today pictures
## 3 UFO yesterday pictures
## 4 Loch Ness Monster today reported today
## 5 Loch Ness Monster yesterday reported yesterday
wildcard(myths, wildcard = "day", values = c("today", "yesterday"),
expand = FALSE)
## myth claim note
## 1 Bigfoot various various
## 2 UFO today pictures
## 3 Loch Ness Monster yesterday reported today
wildcard(myths, wildcard = "day", values = c("today", "yesterday"),
include = "claim")
## myth claim note
## 1 Bigfoot various various
## 2 UFO today pictures
## 3 UFO yesterday pictures
## 4 Loch Ness Monster today reported day
## 5 Loch Ness Monster yesterday reported day
wildcard(myths, wildcard = "day", values = c("today", "yesterday"),
exclude = c("claim", "note"))
## myth claim note
## 1 Bigfoot various various
## 2 UFO day pictures
## 3 Loch Ness Monster day reported day
locations <- data.frame(
myth = c("Bigfoot", "UFO", "Loch Ness Monster"),
origin = "where")
rules <- list(
where = c("North America", "various", "Scotland"),
UFO = c("spaceship", "saucer"))
wildcard(locations, rules = rules, expand = c(FALSE, TRUE))
## myth origin
## 1 Bigfoot North America
## 2 spaceship various
## 3 saucer various
## 4 Loch Ness Monster Scotland
numbers <- data.frame(x = 4, y = 3, z = 4444, w = 4.434)
wildcard(numbers, wildcard = 4, values = 7)
## x y z w
## 1 7 3 7777 7.737
expandrows()df <- data.frame(
ID = c("24601", "Javert", "Fantine"),
fate = c("fulfillment", "confusion", "misfortune"))
expandrows(df, n = 2, type = "each")
## ID fate
## 1 24601 fulfillment
## 2 24601 fulfillment
## 3 Javert confusion
## 4 Javert confusion
## 5 Fantine misfortune
## 6 Fantine misfortune
expandrows(df, n = 2, type = "times")
## ID fate
## 1 24601 fulfillment
## 2 Javert confusion
## 3 Fantine misfortune
## 4 24601 fulfillment
## 5 Javert confusion
## 6 Fantine misfortune
nofactors()class(iris$Species)
## [1] "factor"
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
out <- nofactors(iris)
class(out$Species)
## [1] "character"
str(out)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : chr "setosa" "setosa" "setosa" "setosa" ...
You can submit questions, bug reports, and feature requests to the issue tracker. Please take care to search for duplicates first, even among the closed issues.
Be sure that wildcards and are not also replacement values.
df <- data.frame(x = "a", y = "b")
rules <- list(a = letters[1:3], b = LETTERS[1:3])
wildcard(df, rules = rules)
## x y
## 1 a A
## 2 a B
## 3 a C
## 4 A A
## 5 B B
## 6 C C
## 7 c A
## 8 c B
## c C
## Warning message:
## In check_rules(rules) :
## In `rules`, some wildcards are also replacement values.
## The returned data frame may be different than you expect,
## and it may depend on the order of the wildcards in `rules`.