knitr::opts_chunk$set(collapse = TRUE, comment = "#>") library(escapeR)
escapeR is a small escape-room game for learning R through ecological
statistics. Each room gives you a thread of the whole story, a task, and a lock. You solve the task
with ordinary R commands, then submit the answer to move to the next room.
The bundled quest starts with R foundations and moves through data import, visualisation, data manipulation, simple ecological modelling, distance-sampling ideas, and reproducible workflows.
To start playing you simply need to load the package and call escape():
library(escapeR) escape()
In an interactive R session, escape() asks for your player name. Use a short
name you can remember, because escapeR saves your progress under that name.
You can also provide your player name directly:
escape(player = "ana")
The first room is then printed in the console. It includes:
The task is solved outside the game prompt. Use R as you normally would: create objects, inspect data, calculate values, make plots, or fit models. When you think you have the answer, submit it.
Use submit() with the answer that should open the current lock:
submit(70)
If the answer is correct, escapeR shows the success message and moves you to
the next room. If the answer is not correct, the room remains locked and you can
try again.
While numeric answers are submitted as is, text answers should be submitted as character strings:
submit("negative") submit(".qmd")
For simple text locks, escapeR ignores leading and trailing spaces and is not
case-sensitive. Numeric answers are checked with a small tolerance unless a room
uses a custom checker.
If you get stuck, call hint():
hint()
Some rooms have more than one hint. Repeated calls reveal the hints in order:
hint() hint()
Hints are meant to nudge you toward the R idea rather than simply giving away the answer. In a classroom, it is usually worth trying the task first, asking R what objects you have created, and then requesting a hint if the lock is still not opening.
If the console has filled up with other work, call play():
play()
play() does not restart the game. It simply prints the current room again, so
you can reread the task and learning goal.
Use status() to see where you are:
status()
This tells you the active player, how many rooms have been solved out of those in the game, and which room is current. It is useful during longer activities or when returning to the game after a break.
Progress is saved automatically for each player using
tools::R_user_dir("escapeR", "data"). To resume, load the package and call
escape() again with the same player name:
library(escapeR) escape(player = "ana")
If saved progress exists for that player, the game resumes from the current room. If no saved progress exists, a new quest starts.
To restart the active player's quest from the beginning, call:
reset_game()
You can also reset a named player:
reset_game(player = "ana")
Or start again directly with escape(reset = TRUE):
escape(player = "ana", reset = TRUE)
Use resetting with care in class: it deliberately starts that player's progress again from room 1, so all previous progress is lost.
Several rooms ask you to read or inspect files included with the package. Use
escapeR_file() to find them. As an example, if a data file was called "dataX.csv" you would use
escapeR_file("dataX.csv")
For example, a room might ask you to read a CSV file and then inspect it. One option would then be
d <- read.csv(escapeR_file("dataX.csv")) head(d)
The package also provides a separate, small survey data set. survey_counts()
returns this data frame directly; it does not read or modify dados1.csv. Store
the returned data frame in an object before working with it:
survey <- survey_counts() names(survey) head(survey) sum(is.na(survey))
It has five named columns (site, habitat, count, distance_m, and
detected). Two values in count are NA, included deliberately for the data
quality exercise. By contrast, dados1.csv has four columns and no missing
values. The survey data set appears in several rooms about data quality,
summaries, modelling, and distance sampling.
Use list_rooms() to inspect the bundled room sequence:
list_rooms()
The id column is useful when building a shorter custom quest, where you can provide a list of rooms to be played as a separate quest. See next section and the corresponding dedicated vignette for details on how to create new rooms and quests.
Instructors can build a quest from selected room IDs, as an example here, a mini 3-room quest:
short_quest <- build_escape(c("console", "vector", "plotwin")) escape(player = "demo_short", reset = TRUE, escape = short_quest)
The same escape() command starts the custom sequence. The only difference is
that the escape argument receives an escape sequence created with
build_escape().
If room packs have been registered, list_escapes() shows named sequences:
list_escapes()
Those named sequences can also be passed to build_escape().
Here is the core command set while playing:
escape() # start or resume a quest play() # show the current room again hint() # request the next hint submit(70) # submit an answer status() # check progress reset_game() # restart the active quest list_rooms() # inspect available rooms
The most important habit is to solve the room in ordinary R first. The game is the lock; R is the key. Can you get out?
Remove a saved profile with delete_progress("ana") when it is no longer
needed. delete_progress() removes the active profile and closes that game.
Set options(escapeR.progress_dir = "path") to select a different directory.
For demonstrations, use temporary storage and clean it up afterwards. Profiles
are case-insensitive; punctuation is replaced by underscores in filenames.
Names that map to another player's saved file are rejected. An unreadable save
can be removed or deliberately restarted with escape(player, reset = TRUE).
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