You can install development version from GitHub with:
# install.packages("remotes")
# remotes::install_github("EvaMaeRey/tidytitanic")
install.packages("tidytitanic", repos = c('https://evamaerey.r-universe.dev', 'https://cloud.r-project.org'))
library(tidytitanic)
tidy_titanic |> head()
#> id class sex age survived
#> 1 1 3rd Male Child No
#> 2 2 3rd Male Child No
#> 3 3 3rd Male Child No
#> 4 4 3rd Male Child No
#> 5 5 3rd Male Child No
#> 6 6 3rd Male Child No
flat_titanic |> head()
#> class sex age survived freq
#> 1 1st Male Child No 0
#> 2 2nd Male Child No 0
#> 3 3rd Male Child No 35
#> 4 Crew Male Child No 0
#> 5 1st Female Child No 0
#> 6 2nd Female Child No 0
passengers |> head()
#> passenger_id survived pclass
#> 1 1 0 3
#> 2 2 1 1
#> 3 3 1 3
#> 4 4 1 1
#> 5 5 0 3
#> 6 6 0 3
#> name sex age sib_sp parch
#> 1 Braund, Mr. Owen Harris male 22 1 0
#> 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
#> 3 Heikkinen, Miss. Laina female 26 0 0
#> 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
#> 5 Allen, Mr. William Henry male 35 0 0
#> 6 Moran, Mr. James male NA 0 0
#> ticket fare cabin embarked
#> 1 A/5 21171 7.2500 S
#> 2 PC 17599 71.2833 C85 C
#> 3 STON/O2. 3101282 7.9250 S
#> 4 113803 53.1000 C123 S
#> 5 373450 8.0500 S
#> 6 330877 8.4583 Q
library(tidytitanic)
head(flat_titanic)
#> class sex age survived freq
#> 1 1st Male Child No 0
#> 2 2nd Male Child No 0
#> 3 3rd Male Child No 35
#> 4 Crew Male Child No 0
#> 5 1st Female Child No 0
#> 6 2nd Female Child No 0
head(tidy_titanic)
#> id class sex age survived
#> 1 1 3rd Male Child No
#> 2 2 3rd Male Child No
#> 3 3 3rd Male Child No
#> 4 4 3rd Male Child No
#> 5 5 3rd Male Child No
#> 6 6 3rd Male Child No
head(passengers)
#> passenger_id survived pclass
#> 1 1 0 3
#> 2 2 1 1
#> 3 3 1 3
#> 4 4 1 1
#> 5 5 0 3
#> 6 6 0 3
#> name sex age sib_sp parch
#> 1 Braund, Mr. Owen Harris male 22 1 0
#> 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
#> 3 Heikkinen, Miss. Laina female 26 0 0
#> 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
#> 5 Allen, Mr. William Henry male 35 0 0
#> 6 Moran, Mr. James male NA 0 0
#> ticket fare cabin embarked
#> 1 A/5 21171 7.2500 S
#> 2 PC 17599 71.2833 C85 C
#> 3 STON/O2. 3101282 7.9250 S
#> 4 113803 53.1000 C123 S
#> 5 373450 8.0500 S
#> 6 330877 8.4583 Q
For this package, preparation happens in the data-raw folder, in a
file called dataset_prep.
flat_titanic <- Titanic |>
data.frame() |>
janitor::clean_names() |>
tibble::tibble()
usethis::use_data(flat_titanic, overwrite = T)
#> ✔ Setting active project to '/Users/evangelinereynolds/Google Drive/r_packages/tidytitanic'
#> ✔ Saving 'flat_titanic' to 'data/flat_titanic.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.2.9000 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
tidy_titanic <- Titanic |>
data.frame() |>
janitor::clean_names()|>
uncount(weights = freq) |>
mutate(id = 1:n(), .before = 1) |>
remove_rownames() |>
tibble()
usethis::use_data(tidy_titanic, overwrite = T)
#> ✔ Saving 'tidy_titanic' to 'data/tidy_titanic.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
passengers <-
dplyr::bind_rows(titanic::titanic_train,
titanic::titanic_test |>
dplyr::left_join(titanic::titanic_gender_model)
) |>
janitor::clean_names() |>
select(name, everything()) |>
mutate(prefered_name = str_match(name, '\\"(.+?)\\"')[,2], .before = 1) |>
mutate(prefered_name = ifelse(prefered_name == ")", NA, prefered_name)) |>
mutate(name = name |> str_replace("\"\\)\"", ")"), .before = 1) |>
mutate(maiden_name = name |>
str_extract("\\(.+") |>
str_remove_all("\\(|\\)"),
.before = 1) |>
mutate(maiden_name = ifelse(str_detect(name, "Mrs. "), maiden_name, NA)) |>
mutate(maiden_name = ifelse(maiden_name == prefered_name & !is.na(prefered_name), NA, maiden_name)) |>
mutate(first_name = name |> str_extract("(?<=\\.\\s)\\w+"), .before = 1) |>
mutate(first_name = maiden_name |> str_extract("\\w+") %>% ifelse(!is.na(maiden_name), ., first_name),
) |>
mutate(last_name = name |>
str_extract(".+?,") |>
str_remove(","), .before = 1) |>
mutate(title = name |> str_match(", (.+?) ") |> _[,2] |> str_replace("the", "Countess."), .before = 1) |>
select(passenger_id, title, last_name, first_name, survived, pclass, sex, age,
sib_sp, parch, fare, cabin, embarked, ticket, maiden_name, name, prefered_name)
#> Joining with `by = join_by(PassengerId)`
usethis::use_data(passengers, overwrite = T)
#> ✔ Saving 'passengers' to 'data/passengers.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
#' Tallied characteristics
#'
#' A dataset tallying frequencies of titanic passenger characteristics based on Titanic data from datasets. Includes Crew
#'
#' @format A data frame with 32 rows and 5 variables:
#' \describe{
#' \item{class}{class (1st, 2nd, 3rd or Crew)}
#' \item{sex}{sex of passenger}
#' \item{age}{child or adult}
#' \item{survived}{survival outcome of passanger}
#' \item{freq}{tally of all in category}
#' ...
#' }
#' @source R stats
"flat_titanic"
#> [1] "flat_titanic"
#'
#'
#' A dataset titanic passengers and characteristics based on Titanic data fromdatasets. Includes Crew
#'
#' @format A data frame with 2201 rows and 5 variables:
#' \describe{
#' \item{id}{an id for each passenger}
#' \item{class}{class (1st, 2nd, 3rd or Crew)}
#' \item{sex}{sex of passenger}
#' \item{age}{child or adult}
#' \item{survived}{survival outcome of passanger}
#' ...
#' }
#' @source R stats
"tidy_titanic"
#> [1] "tidy_titanic"
#' Titanic test data.
#'
#' @format Data frame with columns
#' \describe{
#' \item{passenger_id}{Passenger ID}
#' \item{title}{Passenger title}
#' \item{last_name}{Passenger last name}
#' \item{first_name}{Passenger first name}
#' \item{survived}{survival status}
#' \item{pclass}{Passenger Class}
#' \item{sex}{Sex}
#' \item{age}{Age}
#' \item{sib_sp}{Number of Siblings/Spouses Aboard}
#' \item{parch}{Number of Parents/Children Aboard}
#' \item{fare}{Passenger Fare}
#' \item{cabin}{Cabin}
#' \item{embarked}{Port of Embarkation}
#' \item{ticket}{Ticket Number}
#' \item{maiden_name}{Maiden name}
#' \item{name}{Full name}
#' \item{prefered_name}{Prefered name}
#'
#'
#' ...
#' }
#' @source titanic package, https://www.kaggle.com/c/titanic/data
"passengers"
#> [1] "passengers"
titanic is an R package containing data sets providing information on the fate of passengers on the fatal maiden voyage of the ocean liner “Titanic”, with variables such as economic status (class), sex, age and survival. These data sets are often used as an introduction to machine learning on Kaggle. More details about the competition can be found here, and the original data sets can be found here.
library(tidytitanic)
library(tidyverse)
passengers |>
mutate(id = row_number()) |>
sample_n(30) |>
ggplot() +
aes(x = survived,
group = id,
fill = sex) +
geom_bar() +
stat_count(geom = "text",
aes(label = first_name),
vjust = 1)
scale_y_logistic <- function(...){
scale_y_continuous(breaks = 0:5/5, ...)
}
geom_smooth_logistic <- function(formula = y ~ x, ...){
list(geom_smooth(method = "glm",
method.args = list(family = "binomial"),
se = TRUE, ...),
scale_y_logistic(),
theme(panel.grid.major.y = element_line()))
}
geom_jitter_logistic <- function(...){
geom_jitter(width = 0,
height = .1,
alpha = .1, ...)
}
compute_layer_logistic <- function(data, ..., formula = y ~ x){
model <- glm(formula = formula, family = "binomial")
x_as_numeric <- data_x |> as.numeric()
fitted(model, seq(min(x_as_numeric), max(x_as_numeric)))
}
ggchalkboard::theme_chalkboard(base_size = 15) |> theme_set()
library(ggplyr)
tidytitanic::passengers |>
ggplot() +
aes(pclass, survived) +
geom_jitter_logistic() +
facet_wrap(~sex) +
geom_smooth_logistic() + intercept() +
aes(x = fare) + intercept() +
data_filter(fare < 500) + intercept()
#> p1
#> p2
#> p3
#> `geom_smooth()` using formula = 'y ~ x'
NULL
#> NULL
library(patchwork)
#> Warning: package 'patchwork' was built under R version 4.4.1
p1 / p2 / p3
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 1 row containing non-finite outside the scale range
#> (`stat_smooth()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
#> `geom_smooth()` using formula = 'y ~ x'
# ggplot(cars) +
# aes(speed, dist, other = dist > mean(dist), group = 1) +
# geom_polygon()
knitrExtra::chunk_to_dir("data_documentation", dir = "R")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.
devtools::document()
devtools::check()
devtools::install(".", upgrade = "never")
devtools::submit_cran()