Type: | Package |
Title: | Data and Functions to Accompany the Book "Learning R" |
Version: | 0.29.1 |
Date: | 2013-10-20 |
Author: | Richie Cotton |
Maintainer: | Richie Cotton <richierocks@gmail.com> |
Description: | Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases. |
Imports: | plyr |
License: | Unlimited |
LazyData: | TRUE |
Collate: | 'learningr-package.R' 'buggy_count.R' 'hypotenuse.R' 'fix_filenames.R' |
Packaged: | 2019-10-02 15:23:28 UTC; hornik |
NeedsCompilation: | no |
Repository: | CRAN |
Date/Publication: | 2019-10-02 15:27:55 UTC |
Depends: | R (≥ 2.10) |
Datasets to accompany the book Learning R
Description
learningr
contains datasets that are used in
examples in the book “Learning R”.
Author(s)
Richard Cotton richierocks@gmail.com
References
Cotton, R (2013) Learning R O Reilly. ISBN 978-1-4493-5710-8.
Alpe d'Huez
Description
Fastest times for the Alpe d'Huez stage of the Tour de France cycle race, plus some contextual information on year and drug use allegations.
Format
A data frame with the following columns.
- Time
Character time of ride in the form M' S".
- NumericTime
Numeric time of ride in minutes.
- Name
Name of rider.
- Year
Year of race.
- Nationality
Nationality of rider.
- DrugUse
Have allegations of drug use been made against the rider? In
alpe_d_huez
the values are "Y" or "N"; inalpe_d_huez2
this is a logical vector.
Note
The data was kindly compiled by William Hogan.
The dataset is not guaranteed to be error free. Please double check the data if you want to use it for something serious.
References
William Hogan william.hogan@amd.com compiled the data from http://en.wikipedia.org/wiki/Alpe_d%27Huez. Richard Cotton richierocks@gmail.com made some modifications while importing it into R.
A buggy version of plyr's count function
Description
An old version of the plyr
package's count
function that fails when you pass it a factor
input.
Usage
buggy_count(df, vars = NULL, wt_var = NULL)
Arguments
df |
A data frame or an atomic input. |
vars |
Variables in |
wt_var |
Optional variable to weight by. See
|
Value
A data frame with label and freq columns.
Note
In case the “buggy” part of the name didn't give it away, this is not suitable for real world usage!
See Also
Examples
## Not run:
buggy_count(factor()) #oops!
## End(Not run)
Crab tag
Description
Depth and temperature data from a sensor tag attached to an edible crab (Cancer Pangurus) in the North Sea in 2008 and 2009.
Format
A list with 5 elements, as follows.
id_block
is a list with 2 elements
- Firmware Version No
Version of the firmware used in the crab tag.
- Firmware Build Level
Build level of the firmware used in the crab tag.
tag_notebook
is a list with 5 elements.
- Mission Day
Number of days of data.
- Last Deployment Date
Date and time that the tag was released into the sea.
- Deployed by Host Version
UNKNOWN
- Downloaded by Host Version
UNKNOWN
- Last Clock Set Date
UNKNOWN
lifetime_notebook
is a list with 3 elements.
- Tag ID
The unique ID of the tag.
- Pressure Range
UNKNOWN
- No of sensors
The number of sensors on the tag.
deployment_notebook
is a data frame with X columns.
- Start
Start date and time of recording.
- Stop
Stop date and time of recording.
- Logging Rate
UNKNOWN
- Pointer
UNKNOWN
- PA inc
UNKNOWN
- sensors
UNKNOWN
- Flags
UNKNOWN
- Resolution
UNKNOWN
- Fast Rate
UNKNOWN
- V1
UNKNOWN
- V2
UNKNOWN
daylog
is a data frame with X columns.
- Mission.Day
Integer number of days since the start of recording.
- Date
Date of record.
- Max.Temp
Maximum temperature (Celcius) recorded that day.
- Min.Temp
Minimum temperature (Celcius) recorded that day.
- Max.Depth
Maximum depth (m) recorded that day.
- Min.Depth
Minimum depth (m) recorded that day.
- Batt.Volts
Voltage of tag battery.
Note
This data was kindly supplied by Ewan Hunter of CEFAS. It is part of a larger dataset consisting of many crabs.
References
Ewan Hunter ewan.hunter@cefas.gov.uk ran the project where the data was collected. A full analysis is in Hunter, E, Eaton, D, Stewart, C, Lawler, A & Smith, M. 2013. Edible crabs "go west": migrations and incubation cycle revealed by electronic tags. https://www.ncbi.nlm.nih.gov/pubmed/23734180. Richard Cotton richierocks@gmail.com made some modifications while importing it into R.
Deer Endocranial Volume
Description
The dataset contains the endocranial volume of 33 red deer (Cervus elaphus), using four different methods of measurement: cardiac tomography, filling the skull with glass beads (yes, the skulls are from dead deer), simply measuring the length, width and height and multiplying the numbers, and using Finarelli's equation. "Endocranial volume" is a proxy for brain size.
Format
A data frame with the following columns.
- SkullID
A unique identifier for each red deer.
- VolCT
Endocranial volume calculated by cardiac tomography.
- VolBead
Endocranial volume calculated by glass beads.
- VolLWH
Endocranial volume calculated by length*width*height.
- VolFinarelli
Endocranial volume calculated by Finarelli's equation.
- VolCT2
A second measurement via cardiac tomography.
- VolBead2
A second measurement via glass beads.
- VolLWH2
A second measurement via l*w*h.
Note
The data was kindly provided by Corina Logan. Second measurements are provided for several of the deer. Finarelli's equation is used for estimating the brain volume of non-bovid ruminant Artiodactylid species (say that 10 times fast).
\ln(volume(skull)) = 2.6616 *
\ln(width(skull)) - 6.2722
References
The dataset was collected by Corina Logan itsme@corinalogan.com. It is stored in the Dryad Digital Repository, doi: 10.5061/dryad.4t7h2. A more full analysis is given in the paper Logan CJ, Clutton-Brock TH. 2013. Validating methods for estimating endocranial volume in individual red deer (Cervus elaphus). Behavioural Processes 92:143-146. doi:10.1016/j.beproc.2012.10.015. http://www.sciencedirect.com/science/article/pii/S037663571200232X Richard Cotton richierocks@gmail.com made some modifications while importing it into R.
English Monarchs
Description
Names, dates and houses of English kings and queens from post-Roman rule (the fifth century) until England invaded Ireland in the Early 13th century.
Format
A data frame with the following columns.
- name
Name of monarch(s).
- house
Royal house of monarch(s).
- start.of.reign
Year they rose to power.
- end.of.reign
Year they left power.
- domain
Region of England ruled over.
Note
This dataset is a bit messy and ambiguous in places, because history is like that. In fact, the messy parts of the dataset are in general a good indicator that soemthing interesting was happening at the time. (See, for example, missing or multiple rulers, starts and ends of reigns in the same year, and rulers that appear several times with different territories.) Evan defining a monarch of England is tricky. Most of the monarchs in this dataset were around before England existed (it consisted of seven territories called the heptarchy). The data stops before John I (the bad guy from the Robin Hood stories) because he proclaimed himself King of Ireland, although some people consider monarchs up to Anne, five hundred years later, to be English Monarchs even though they ruled over Ireland, Wales and Scotland to varying degrees.
The heptarchy consisted of East Anglia, Essex, Kent, Mercia, Northumbria, Sussex and Wessex. Northubria was originally divided into Deria and Bernicia. There are also periods of Norse and Danish rule.
The dataset was compiled from Wikipedia and thus is not guaranteed to be error free. Please double check the data if you want to use it for something serious.
References
Richard Cotton richierocks@gmail.com compiled the dataset from various Wikipedia pages. http://en.wikipedia.org/wiki/Kings_of_england http://en.wikipedia.org/wiki/Kings_of_East_Anglia http://en.wikipedia.org/wiki/Kings_of_Essex http://en.wikipedia.org/wiki/Kings_of_Kent http://en.wikipedia.org/wiki/Kings_of_Mercia http://en.wikipedia.org/wiki/Kings_of_Northumbria http://en.wikipedia.org/wiki/Kings_of_Sussex http://en.wikipedia.org/wiki/Kings_of_Wessex
Convert filenames to match those in the book
Description
Some filenames have been altered in order to comply with portability requirements on CRAN. This function converts the filenames between the CRAN forms and the book forms.
Usage
fix_filenames(x = c("tobook", "tocran"),
dir = system.file("extdata", packages = "learningr"))
Arguments
x |
Either “tocran” or “tobook”. |
dir |
Directory containing the files. |
Value
A logical vector of length 4, TRUE
for each file
whose name was changed.
Examples
## Not run:
#To convert the files to the book form, use:
fix_filenames("tobook")
#The files were converted to CRAN form using:
fix_filenames("tocran", "learningr/inst/extdata")
## End(Not run)
Gonorrhoea
Description
Rates of gonorrhoea infection in the US by year, with contextual information about age, ethnicity and gender.
Format
A data frame with the following columns.
- Year
Year that infected people visited the clinic.
- Age.Group
Age group of person infected.
- Ethnicity
Ethnicity of person infected.
- Gender
Gender of person infected.
- Rate
Number of infections per 100000 people.
References
Compiled by Richard Cotton richierocks@gmail.com from http://www.cdc.gov/std/stats11/tables/22b.htm
Hafu
Description
Half-caste manga characters.
Format
Both data frames have the following columns.
- Year
Integer year that the manga was made.
- Series
Name of series.
- Character
Name of character.
- Gender
Gender of character.
- Father
Nationality of character's father.
- Mother
Nationality of character's mother.
- Eyes
Character's eye colour.
- Hair
Character's hair colour.
- Notes
Notes on data collection or ambiguity.
hafu2
has these additional columns.
- FathersNationalityIsUncertain
- MothersNationalityIsUncertain
Note
The dataset was kindly provided by Gwern Branwen.
hafu2
is a lightly cleaned up version of
hafu
.
Gwern's notes: The following list includes manga, light novel, anime, and video game characters (there being little point in keeping the mediums separate). It also includes characters who are not hafu themselves but a quarter-foreign inasmuch as they imply a hafu at some point. Characters are treated separately even if they are in the same work (eg. siblings). Classification is based on in-universe or out-of- universe information, since appearance can be highly misleading in anime (blue eyes may indicate heroic status, rather than being Caucasian; hair color may be chosen for contrast against other characters or signal stereotypes like red hair indicating a fiery personality), and different groups will identify the same anime character as belonging to their own race (Lu 2009), perhaps due to minimalistic drawings intended to save money or enable viewers to project themselves onto a character.
References
The dataset was compiled by Gwern Branwen gwern0@gmail.com. The original is available from http://www.gwern.net/hafu#list.
Dumb hypotenuse function.
Description
Calculate the (Pythagorean) hypotenuse of two numeric vectors using the obvious algorithm.
Usage
hypotenuse(x, y)
Arguments
x |
A numeric vector. |
y |
A numeric vector. |
Value
A numeric vector of the hyptenuse of the inputs.
Note
This algorithm fails when the inputs are very large of very small, making it unsuitable for real-world use.
References
Cleve Moler (MATLAB creator and discoverer of the Moler-Morrison algorithm for calculating hypotenuses) discusses the pro and cons of several algorithms here. http://blogs.mathworks.com/cleve/2012/07/30/pythagorean-addition
See Also
Examples
hypotenuse(5, 12) #okay
hypotenuse(1e-300, 1e-300) #fails
hypotenuse(1e300, 1e300) #fails
Obama vs. McCain
Description
State-by-state voting information in the 2008 US presidential election, along with contextual information on income, unemployment, ethnicity and religion.
Format
A data frame with 52 observations (one for each US state) and the following columns.
- State
The name of the US state.
- Region
The US Federal region.
- Obama
Percentage of voters who voted for Barack Obama in the 2008 presidential election.
- McCain
Percentage of voters who voted for John McCain in the 2008 presidential election.
- Turnout
Percentage of people who voted in the 2008 presidential election.
- Unemployment
Percentage of people who are unemployed.
- Income
Mean annual income in US dollars.
- Population
Number of people living in the state.
- Catholic
Percentage of people identifying as Catholic.
- Protestant
Percentage of people identifying as Protestant.
- Other
Percentage of people identifying as religious, but not Catholic or Protestant.
- Non.religious
Percentage of people identifying as non-religious.
- Black
Percentage of people identifying as black.
- Latino
Percentage of people identifying as Latino.
- Urbanization
Percentage of people living in an urban area.
Note
Religious identification data are not available for Alaska and Hawaii. The totals of these columns is generally less than 100, since some people didn't give an answer. The District of Columbia is included, even though it isn't a state. The dataset is not guaranteed to be error free. Please double check the data if you want to use it for something serious.
References
This dataset was kindly compiled and provided by Edwin Thoen edwinthoen@hotmail.com.
The voting information came from http://www.uselectionatlas.org/, extracted on 2011-12-09.
The ethnicity, income and urbanisation information came from http://quickfacts.census.gov, extracted on 2011-12-09.
The unemployment information came from http://data.bls.gov/timeseries/LNS14000000, extracted 2011-12-09.
The religious information came from Table 12 of the American Religious Identification Survey 2008. http://commons.trincoll.edu/aris/files/2011/08/ARIS_Report_2008.pdf.