R/data.R

In shirewoman2/LaurasHelpers: Miscellaneous Helper Functions for Common PK or Analytical Chemistry Tasks

#' Contributions to King County, WA, 2020 political candidates
#'
#' A dataset containing the amounts of money donated to candidates for political
#' offices in King County, Washington, as of August 1, 2020. For a complete list of all
#' variables and their descriptions, please see
#' \url{https://www.pdc.wa.gov/browse/open-data/contributions-candidates-and-political-committees}.
#'
#' @format A data.frame with 37 variables: \describe{
#'   \item{filer_name}{Name of the candidate} \item{office}{The office they're
#'   running for} \item{legislative_district}{The legislative district of that
#'   office} \item{party}{The political party with which they're affiliated}
#'   \item{amount}{The dollar amount of the contribution}
#'   \item{contributor_name}{The name of the person who made the donation} ... }
#' @source \url{https://data.wa.gov/Politics/Contributions-to-Candidates-and-Political-Committe/kv7h-kjye/data}. Accessed on Aug. 1, 2020.
"Candidates"

#' Contributions to candidates for the Washington State Legislature for the 2020
#' election
#'
#' A dataset containing the amounts of money donated to candidates for the
#' Washington State legislature from three counties in Western Washington and
#' three counties in Eastern Washington as of August 1, 2020. For a complete
#' list of all variables and their descriptions, please see
#' \url{https://www.pdc.wa.gov/browse/open-data/contributions-candidates-and-political-committees}.
#'
#' @format A data.frame with 38 variables: \describe{ \item{filer_name}{Name of
#'   the candidate} \item{office}{The office they're running for}
#'   \item{legislative_district}{The legislative district of that office}
#'   \item{party}{The political party with which they're affiliated}
#'   \item{amount}{The dollar amount of the contribution}
#'   \item{contributor_name}{The name of the person who made the donation} ... }
#' @source
#' \url{https://data.wa.gov/Politics/Contributions-to-Candidates-and-Political-Committe/kv7h-kjye/data}.
#' Accessed on Aug. 1, 2020.
"WALeg"

#' Example data for converting data.frames from long to wide format
#'
#' Example mass spec data for a run of the cotinine assay
#'
#' @format A data.frame with 44 rows and 9 variables:
#' \describe{
#'   \item{SampleID}{Sample ID}
#'   \item{File}{MS data file}
#'   \item{DateTime}{Date and time of the acquisition}
#'   \item{Method}{MS method file used}
#'   \item{Position}{Well position}
#'   \item{Analyte}{Analyte measured}
#'   \item{RT}{Retention time (min)}
#'   \item{Area}{Peak area (counts)}
#'   \item{Height}{Peak height (counts)}
#' }
"Cotinine"


#' Example data for data manipulation, graphing
#'
#' Example mass spec data for a run of the metformin assay
#'
#' @format A data.frame with 37 rows and 6 variables:
#' \describe{
#'   \item{SampleID}{Sample ID}
#'   \item{Matrix}{biological matrix sample was in}
#'   \item{Metformin.in.working.solution..ng.uL.}{Concentration of metformin in working solution (ng/uL)}
#'   \item{Volume.working.solution.added..uL.}{Volume of working solution added to sample (uL)}
#'   \item{metformin.peak.area}{Metformin peak area (counts)}
#'   \item{d6.metformin.peak.area}{d6-metformin peak area (counts)}
#' }
"Metformin"


#' Example concentration-time data
#'
#' Made up concentration-time data for a crossover study design in which
#' subjects received drugs A and B both IV and subcutaneously. For convenience,
#' fitted parameters and some metadata are included in the same data.frame,
#' although I don't like how replicative this data.frame is.
#'
#' @format A data.frame with 1800 rows and 13 variables: \describe{
#'   \item{Drug}{Drug administered}
#'
#'   \item{DoseRoute}{Route of dose administation: IV or SC}
#'
#'   \item{SubjectID}{Unique identifier for the subejct}
#'
#'   \item{k_meanpop}{Mean estimate of the terminal elimination rate constant k
#'   for the population}
#'
#'   \item{Vd_meanpop}{Mean estimate of the volume of distribution for the
#'   population}
#'
#'   \item{k}{Estimate of the terminal elimination rate constant k for this
#'   subject}
#'
#'   \item{Vd}{Estimate of the volume of distribution for this subject}
#'
#'   \item{Sex}{Sex of the subject}
#'
#'   \item{Age}{Subject's age}
#'
#'   \item{Occupation}{Subject's occupation}
#'
#'   \item{TimeHr}{Time (hr) since dose}
#'
#'   \item{Dose}{Dose of the drug (mg)}
#'
#'   \item{Concentration}{Plasma drug concentration (ng/mL)}
#'
#'   }
"ConcTime"


#' Example data for data manipulation, graphing
#'
#' Example oxycodone concentration-time data
#'
#' @format A data.frame with 12 rows and 3 variables:
#' \describe{
#'   \item{Time}{Time sample drawn post-dose (min)}
#'   \item{Conc1}{Concentration for 1st subject (ng/mL)}
#'   \item{Conc2}{Concentration for 2nd subject (ng/mL)}
#' }
"Oxycodone"


#' Example data for joining data.frames
#'
#' Students' name and the number of pets they have for joining with 'Students'
#' data.frame.
#'
#' @format A data.frame with 29 rows and 2 variables:
#' \describe{
#'   \item{Name}{Student's name}
#'   \item{Number.of.pets}{Number of pets that student has}
#' }
"Pets"


#' Example data for using stdCurve function
#'
#' Example mass spec data for a run of the metformin assay
#'
#' @format A data.frame with 37 rows and 6 variables: \describe{
#'   \item{SampleID}{Sample ID} \item{MET.nominalmass}{Nominal mass of metformin
#'   in the sample (ng)} \item{MET.area}{Metformin peak area (counts)}
#'   \item{d6MET.area}{d6-metformin peak area (counts)}
#'   \item{MET.peakarearatios}{Peak area ratio of MET/d6-MET}
#'   \item{Weight.1overx}{Weights to use for a 1-over-x weighting scheme (1/MET
#'   nominal mass)} }
"ExStdCurve"




#' Example data for data manipulation, data checking, etc.
#'
#' Students in the UW Medicinal Chemistry or Pharmaceutics Departments and
#' some made-up data on their imagined habits.
#'
#' @format A data.frame with 37 rows and 6 variables:
#' \describe{
#'   \item{Name}{First name}
#'   \item{Department}{Department}
#'   \item{Gender}{Gender}
#'   \item{Cookie}{Favorite type of cookie}
#'   \item{VampTV.hr}{Amount of time spent watching trashy vampire TV last week (hr)}
#'   \item{Sleep.hr}{Amount of sleep last night (hr)}
#' }
"Students"



#' Salaries of University of Washington employees from 2014-2018 (public record)
#'
#' The pre-tax salaries of all UW employees from 2014 to 2018 in US dollars.
#' (NB: Not all employees worked a full year or worked a full year in a given
#' position.) Why am I including this in this package? Because it's a great data
#' set and also because 1. you have a right to know as a taxpayer and 2. if
#' you're a UW employee (and most of the people who will use this package are),
#' you should know that this information is part of the public record. Same
#' thing for your UW email, fyi. If you're interested in a titillating tale, ask
#' me about a *former* Spokane County Public Works Department supervisor who had
#' an affair on company time and wrote emails to his paramour, a supervisee,
#' using his Spokane County email... and then had his emails requested as part of
#' a public record search by the local newspaper.
#' @source \url{http://fiscal.wa.gov/Salaries.aspx}
#' @format A data.frame with 9 variables: \describe{ \item{Agy}{Agency ID
#'   number} \item{AgyTitle}{Agency title} \item{Name}{Employee name}
#'   \item{JobTitle}{Job title} \item{Sal2014}{Salary in 2014}
#'   \item{Sal2015}{Salary in 2015} \item{Sal2016}{Salary in 2016}
#'   \item{Sal2017}{Salary in 2017} \item{Sal2018}{Salary in 2018}}
"UWsalaries"