data/generate_data.R
In dustinfife/flexplot: Graphically Based Data Analysis Using 'flexplot'
### attractiveness data
# set.seed(2323)
# n = 207
# stats_skills = rnorm(n)
# group = sample(c("control", "mo money", "wealth building"), size=n, replace=T)
# gender = sample(c("college graduate", "hs graduate"), size=n, replace=T)
# exercise = rnorm(n)
# attractiveness = rnorm(n)
# matrix_mod = model.matrix(attractiveness~stats_skills + group + gender + exercise)
# head(matrix_mod)
# coefs = c(0, .5, .8, 1.5, -1.2, .6)
# attractiveness = t(t(coefs)%*%t(matrix_mod)) + attractiveness
# income = data.frame(
# stats_skills = round(fifer::rescale(stats_skills, 60, 15)),
# group = group,
# graduate = gender,
# hours = exercise %>% fifer::rescale(., new.mean=30, new.sd=15) %>% round,
# income_before = round(fifer::rescale(attractiveness, 60, 15)),
# income_after = round(fifer::rescale(attractiveness, 60, 15) + rnorm(n, 5, 1))
#
# )
# head(income)
#
# write.csv(income, "data/income.csv", row.names=F)
# usethis::use_data(income, overwrite = TRUE)
#' Simulated Dataset About People's Income
#'
#' A dataset containing 207 observations, containing the following information
#'
#' @format A data frame with 207 rows and 6 variables: \describe{
#' \item{stats_skills}{performance on a statistics know-how test}
#' \item{group}{Treatment group (control, mo money, wealth building)}
#' \item{graduate}{hs graduate vs. college graduate}
#' \item{hours}{hours they work a week}
#' \item{income_before}{Income before starting training}
#' \item{income_after}{Income after starting training}
#'}
"income"
# ### paranormal dataset
# n = 394
# #### belief in paranormal dataset
#
# #### conviction about paranormal beliefs - bimodal
# conviction = c(rnorm(n/2, 30, 10), rnorm(n/2, 70, 10))
#
# ### fear.of.aliens = positive skew
# fear = c(rnorm(n, 0, 2)^2)
#
# ## gender (redundant labels)
# gender = sample(c("male", "female", "women"), size=n, replace=T, prob=c(.4, .5, .1))
#
# ## unknown labels
# political = sample(c("republican", "democrat", "independent"), size=n, replace=T, prob=c(.4, .5, .1))
# political[133] = "aliens are REALLLLLLL"
#
# ### time spent investigating paranormal paranormal outliers
# time = rnorm(n, 100, 40)
# time[231] = 6000
# ### 999
#
# ### kidnapped by aliens
# kidnapped = sample(c("yes", "no"), size=n, replace=T, prob=c(.98, .02))
# flexplot(kidnapped~1, data=data.frame(kidnapped=kidnapped))
#
# ### paranormal experiences mixed up coding
# experiences.type = sample(c("eerie feeling", "presence watching", "saw UFO", "saw ghost", "heard voice"), size=n, replace=T, prob=c(5, 1, 3, 6, 2))
# flexplot(experiences.type~1, data=data.frame(experiences.type=experiences.type))
#
# ### NA
# income = sample(c(">100K", "50-75K", "<50K", "75-100K"), size=n, replace=T, prob=c(1, 8, 2, 3))
# income[sample(1:n, 25)] = NA
# table(income)
#
# ### something that doesn't make sense (age)
# age = rnorm(n, 5, 2)^2 + 17
# age[age>70] = runif(1, 18, 75)
# age[age<18] = runif(1, 18, 65)
# age[11] = 123
# age[88] = 2
#
# paranormal = data.frame(conviction = conviction,
# fear=fear, time=time, kidnapped=kidnapped, experiences.type=experiences.type,
# income=income, age=age, gender, political)
# write.csv(paranormal, "data/paranormal.csv", row.names=F)
# usethis::use_data(paranormal, overwrite = TRUE)
#' Simulated Dataset About Experiences with the Paranormal
#'
#' A dataset containing combat attributes of almost 812 fighters in the final
#' Avengers battle. This dataset illustrates several types of problems one might
#' encounter for univariate variables, including bimodality, zero-inflated data,
#' outliers, mixed up labels, etc.
#'
#' @format A data frame with 1000 rows and 7 variables: \describe{
#' \item{conviction}{Degree of conviction they have about the existence of the
#' paranormal} \item{fear}{How much they fear being kidnapped by aliens}
#' \item{time}{How much time they spend a year researching the paranormal}
#' \item{kidnapped}{Whether they've been kidnapped by aliens}
#' \item{experiences.type}{What type of experiences they have had with the
#' paranormal? Can be "eerie feeling," "presence watching", "saw UFO", "saw ghost", or "heard voice"}
#' \item{income}{How much money they make}
#' \item{age}{Age of respondent}
#' \item{gender}{Gender of respondent}
#' \item{political}{Political Ideology of respondent}
#'}
"paranormal"
#' #'
#' #' ### avengers dataset
#' set.seed(2323)
#' require(tidyverse)
#' require(dplyr)
#' require(flexplot)
#' #'
#' #'
#' # specify predictor variables ---------------------------------------------
#'
#' ### correlation matrix of abilities:
#' # intelligence, agility, speed, strength,
#' # damage resistance, superpower (yes/no), flexibility, willpower
#' # agility/speed and strength need u-shaped relationship
#' ### adding ptsd score + north vs. south
#' rho = matrix(c(
#' 1, 0, 0, 0, 0, 0, 0, .2, -.3, 0, # iq
#' 0, 1, .4, .4,-.2, .6, .5, .1, -.3, 0, # agility
#' 0, .4, 1, .4,-.1, .6, .4, 0, -.1, 0, # speed
#' 0, .4, .4, 1, .6, .8,-.2, .2, -.1, 0, # strength
#' 0, -.2,-.1, .6, 1, .8,-.3, .3, .2, 0, # damage resistance
#' 0, .6, .6, .8, .8, 1, 0, .1, -.5, 0, # superpower
#' 0, .5, .4, -.2,-.3, 0, 1, 0, 0, 0, # flexibility
#' .2, .1, 0, .2, .3, .1, 0, 1,-.35, 0, # willpower
#' -.3,-.3,-.1, -.1, .4,-.9, 0,-.35, 1, .6, # ptsd
#' 0, 0, 0, 0, 0, 0, 0, 0, .3, 1 # north vs. south
#' ), nrow = 10)
#' #iq ag spd str dam sup flex will
#' det(rho)
#' rho = Matrix::nearPD(rho)$mat
#'
#' ### simulate data
#' d = MASS::mvrnorm(n=812, mu = rep(0, times=nrow(rho)), Sigma = rho) %>%
#' data.frame %>%
#' setNames(c("iq", "agility", "speed", "strength", "damage.resistance",
#' "superpower", "flexibility", "willpower", "ptsd", "north_south"))
#' d$speed = d$speed + -.2*d$strength^2
#' d$agility = d$agility + -.25*d$strength^2
#' d$ptsd = d$ptsd + .25*d$damage.resistance^2
#' flexplot::flexplot(ptsd~damage.resistance, data=d)
#' flexplot::flexplot(ptsd~superpower, data=d)
#'
#'
#' # specify outcome variables -----------------------------------------------
#'
#' # died (logistic), kills (negative binomial), injuries (ordered logistic?),
#' # minutes_fighting (gamma), shots taken (normal)
#'
#' rho2 = matrix(c(
#' 1, -.3, .5, -.4, -.2, # died
#' -.3, 1, -.2, .5, .5, # kills
#' .5, -.2, 1, .4, .2, # injuries
#' -.4, .5, .4, 1, .5, # minutes_fighting
#' -.2, .5, .2, .5, 1 # shots taken
#' ), nrow = 5)
#'
#' d2 = MASS::mvrnorm(n=nrow(d), mu = rep(0, times=5), Sigma = rho2) %>%
#' data.frame %>%
#' setNames(c("died", "kills", "injuries", "minutes.fighting", "shots.taken"))
#'
#'
#' # link the two datasets ---------------------------------------------------
#'
#' died_link = matrix(c( .1, .3, .2, .2, .4, .5, .1, .4, .5, .5)*.25, ncol=1)
#' d2$died = d2$died + as.numeric(t(died_link) %*% t(data.matrix(d)))
#'
#' kills_link = matrix(c(.2, .2, .3, .4, .1, .6, .1, .3, .2, .2), ncol=1)
#' d2$kills = d2$kills + as.numeric(t(kills_link) %*% t(data.matrix(d)))
#'
#' injuries_link = matrix(c(.1, .4, -.1, .2, .5, .5, .1, .1, .6, .2), ncol=1)
#' d2$injuries = d2$injuries + as.numeric(t(injuries_link) %*% t(data.matrix(d)))
#'
#' minutes_link = matrix(c(-.3, .1, .1, .3, .3, .5, .1, .6, .4, .1), ncol=1)
#' d2$minutes.fighting = d2$minutes.fighting + as.numeric(t(minutes_link) %*% t(data.matrix(d)))
#'
#' shots_link = matrix(c(0, .1, -.3, .1, 0, -1.85, 0, 0, -.3, .2), ncol=1)
#' d2$shots.taken = d2$shots.taken + as.numeric(t(shots_link) %*% t(data.matrix(d)))
#'
#'
#'
#'
#' # add interactions --------------------------------------------------------
#'
#' d2$died = d2$died + -.2*d$superpower*d$strength
#' d2$shots.taken = d2$shots.taken + .75*d$superpower*d$speed
#' d2$minutes.fighting = d2$minutes.fighting + -.5*d$superpower*d$damage.resistance
#'
#'
#' # combine data
#' d = cbind(d, d2)
#'
#'
#' # dichotomize variables ---------------------------------------------------
#' d$superpower = cut(d$superpower, breaks=c(-Inf, 2, Inf), labels=c("no", "yes"))
#' d$north_south = cut(d$north_south, breaks=c(-Inf, median(d$north_south), Inf), labels=c("north", "south"))
#' d$died = cut(d$died, breaks=c(-Inf, -1.5, Inf), labels=c("yes", "no"))
#' d$kills = exp(d$kills)
#' d$injuries = as.numeric(cut(-1*d$injuries, breaks=c(-Inf, -4, -3, -1, 0, 1, Inf), labels=c(1:6), ordered=T))
#' d$injuries = d$injuries - 1
#' d$minutes.fighting = d$minutes.fighting + 10 + exp(d$minutes.fighting)
#' d$minutes.fighting[d$minutes.fighting<1] = 5
#'
#' ### add problematic stuff
#' # outliers
#' d[111,c("ptsd", "strength")] = c(7, 7)
#'
#' # invert relationships (where applicable) ---------------------------------
#' # flexplot(died~agility, data=d, method="logistic")
#' # flexplot(died~speed, data=d, method="logistic")
#' # flexplot(died~strength, data=d, method="logistic")
#' # flexplot(died~damage.resistance, data=d, method="logistic")
#' # flexplot(died~flexibility, data=d, method="logistic")
#' # flexplot(died~willpower, data=d, method="logistic")
#' #
#' # flexplot(superpower~agility, data=d, method="logistic")
#' # flexplot(superpower~speed, data=d, method="logistic")
#' # flexplot(superpower~strength, data=d, method="logistic")
#' # flexplot(superpower~damage.resistance, data=d, method="logistic")
#' # flexplot(superpower~flexibility, data=d, method="logistic")
#' # flexplot(superpower~willpower, data=d, method="logistic")
#' #
#' # flexplot(kills~agility, data=d)
#' # flexplot(kills~speed, data=d)
#' # flexplot(kills~strength, data=d)
#' # flexplot(kills~damage.resistance, data=d)
#' # flexplot(kills~flexibility, data=d)
#' # flexplot(kills~willpower, data=d)
#' #
#' # flexplot(agility~injuries, data=d)
#' # flexplot(speed~injuries, data=d)
#' # flexplot(strength~injuries, data=d)
#' # flexplot(damage.resistance~injuries, data=d)
#' # flexplot(flexibility~injuries, data=d)
#' # flexplot(ptsd~superpower, data=d)
#' # flexplot(ptsd~damage.resistance, data=d)
#'
#' # put variables on correct scale ------------------------------------------
#' require(fifer)
#' d_final = d %>% mutate(iq = rescale(iq, 110, 8),
#' agility = rescale(agility, 50, 15),
#' speed = rescale(speed, 5, .17), # meter dash
#' strength = rescale(strength, 500, 150), # bench press mas
#' damage.resistance = rescale(damage.resistance, 2, .21),
#' flexibility = rescale(flexibility, -1, 1), # inches beyond shoes
#' willpower = rescale(willpower, 60, 18),
#' shots.taken = rescale(shots.taken, 130, 30),
#' ptsd = rescale(ptsd, 4, .6)) %>%
#' mutate_at(vars(speed,damage.resistance),
#' funs(round(., 2))) %>%
#' mutate_at(vars(flexibility, minutes.fighting, ptsd), round, digits=1) %>%
#' mutate_at(vars(iq, agility, strength, willpower, kills, shots.taken), round, digits=0)
#' avengers = d_final
#' head(avengers)
#' usethis::use_data(avengers, overwrite = TRUE)
#' write.csv(avengers, "data/avengers.csv", row.names=F)
#' Simulated Statistics on the Final Avengers Battle
#'
#' A dataset containing combat attributes of almost 812
#' fighters in the final Avengers battle
#'
#' @format A data frame with 812 rows and 13 variables:
#' \describe{
#' \item{iq}{Intelligence}
#' \item{agility}{weighted scores on an obstacle course}
#' \item{speed}{Speed in running the 40 meter dash}
#' \item{strength}{Pounds lifted in a benchpress}
#' \item{damage.resistance}{Amount of skin deflection (in mm) when slapped with a frozen fish}
#' \item{flexibility}{Number of inches past their toes they can reach}
#' \item{willpower}{Length of time they wait at a DMV for a driver's license}
#' \item{ptsd}{Score on a Likert-scale PTSD questionnaire}
#' \item{north_south}{whether the individual was assigned to fight on the north or south battlefield}
#' \item{died}{Whether the person died at the final battle}
#' \item{kills}{Number of enemies killed in the final battle}
#' \item{injuries}{Number of injuries sustained. Anything more than 5 is marked as a 5}
#' \item{minutes.fighting}{Number of minutes spent fighting before dying or giving up}
#' \item{shots.taken}{Number of shots (with a gun) or punches thrown at an enemy}
#' }
"avengers"
#'
#'
#'
#'
#'
#'
#'
#'
#' #
dustinfife/flexplot documentation built on Sept. 23, 2024, 9:01 p.m.
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### attractiveness data
# set.seed(2323)
# n = 207
# stats_skills = rnorm(n)
# group = sample(c("control", "mo money", "wealth building"), size=n, replace=T)
# gender = sample(c("college graduate", "hs graduate"), size=n, replace=T)
# exercise = rnorm(n)
# attractiveness = rnorm(n)
# matrix_mod = model.matrix(attractiveness~stats_skills + group + gender + exercise)
# head(matrix_mod)
# coefs = c(0, .5, .8, 1.5, -1.2, .6)
# attractiveness = t(t(coefs)%*%t(matrix_mod)) + attractiveness
# income = data.frame(
# stats_skills = round(fifer::rescale(stats_skills, 60, 15)),
# group = group,
# graduate = gender,
# hours = exercise %>% fifer::rescale(., new.mean=30, new.sd=15) %>% round,
# income_before = round(fifer::rescale(attractiveness, 60, 15)),
# income_after = round(fifer::rescale(attractiveness, 60, 15) + rnorm(n, 5, 1))
#
# )
# head(income)
#
# write.csv(income, "data/income.csv", row.names=F)
# usethis::use_data(income, overwrite = TRUE)
#' Simulated Dataset About People's Income
#'
#' A dataset containing 207 observations, containing the following information
#'
#' @format A data frame with 207 rows and 6 variables: \describe{
#' \item{stats_skills}{performance on a statistics know-how test}
#' \item{group}{Treatment group (control, mo money, wealth building)}
#' \item{graduate}{hs graduate vs. college graduate}
#' \item{hours}{hours they work a week}
#' \item{income_before}{Income before starting training}
#' \item{income_after}{Income after starting training}
#'}
"income"
# ### paranormal dataset
# n = 394
# #### belief in paranormal dataset
#
# #### conviction about paranormal beliefs - bimodal
# conviction = c(rnorm(n/2, 30, 10), rnorm(n/2, 70, 10))
#
# ### fear.of.aliens = positive skew
# fear = c(rnorm(n, 0, 2)^2)
#
# ## gender (redundant labels)
# gender = sample(c("male", "female", "women"), size=n, replace=T, prob=c(.4, .5, .1))
#
# ## unknown labels
# political = sample(c("republican", "democrat", "independent"), size=n, replace=T, prob=c(.4, .5, .1))
# political[133] = "aliens are REALLLLLLL"
#
# ### time spent investigating paranormal paranormal outliers
# time = rnorm(n, 100, 40)
# time[231] = 6000
# ### 999
#
# ### kidnapped by aliens
# kidnapped = sample(c("yes", "no"), size=n, replace=T, prob=c(.98, .02))
# flexplot(kidnapped~1, data=data.frame(kidnapped=kidnapped))
#
# ### paranormal experiences mixed up coding
# experiences.type = sample(c("eerie feeling", "presence watching", "saw UFO", "saw ghost", "heard voice"), size=n, replace=T, prob=c(5, 1, 3, 6, 2))
# flexplot(experiences.type~1, data=data.frame(experiences.type=experiences.type))
#
# ### NA
# income = sample(c(">100K", "50-75K", "<50K", "75-100K"), size=n, replace=T, prob=c(1, 8, 2, 3))
# income[sample(1:n, 25)] = NA
# table(income)
#
# ### something that doesn't make sense (age)
# age = rnorm(n, 5, 2)^2 + 17
# age[age>70] = runif(1, 18, 75)
# age[age<18] = runif(1, 18, 65)
# age[11] = 123
# age[88] = 2
#
# paranormal = data.frame(conviction = conviction,
# fear=fear, time=time, kidnapped=kidnapped, experiences.type=experiences.type,
# income=income, age=age, gender, political)
# write.csv(paranormal, "data/paranormal.csv", row.names=F)
# usethis::use_data(paranormal, overwrite = TRUE)
#' Simulated Dataset About Experiences with the Paranormal
#'
#' A dataset containing combat attributes of almost 812 fighters in the final
#' Avengers battle. This dataset illustrates several types of problems one might
#' encounter for univariate variables, including bimodality, zero-inflated data,
#' outliers, mixed up labels, etc.
#'
#' @format A data frame with 1000 rows and 7 variables: \describe{
#' \item{conviction}{Degree of conviction they have about the existence of the
#' paranormal} \item{fear}{How much they fear being kidnapped by aliens}
#' \item{time}{How much time they spend a year researching the paranormal}
#' \item{kidnapped}{Whether they've been kidnapped by aliens}
#' \item{experiences.type}{What type of experiences they have had with the
#' paranormal? Can be "eerie feeling," "presence watching", "saw UFO", "saw ghost", or "heard voice"}
#' \item{income}{How much money they make}
#' \item{age}{Age of respondent}
#' \item{gender}{Gender of respondent}
#' \item{political}{Political Ideology of respondent}
#'}
"paranormal"
#' #'
#' #' ### avengers dataset
#' set.seed(2323)
#' require(tidyverse)
#' require(dplyr)
#' require(flexplot)
#' #'
#' #'
#' # specify predictor variables ---------------------------------------------
#'
#' ### correlation matrix of abilities:
#' # intelligence, agility, speed, strength,
#' # damage resistance, superpower (yes/no), flexibility, willpower
#' # agility/speed and strength need u-shaped relationship
#' ### adding ptsd score + north vs. south
#' rho = matrix(c(
#' 1, 0, 0, 0, 0, 0, 0, .2, -.3, 0, # iq
#' 0, 1, .4, .4,-.2, .6, .5, .1, -.3, 0, # agility
#' 0, .4, 1, .4,-.1, .6, .4, 0, -.1, 0, # speed
#' 0, .4, .4, 1, .6, .8,-.2, .2, -.1, 0, # strength
#' 0, -.2,-.1, .6, 1, .8,-.3, .3, .2, 0, # damage resistance
#' 0, .6, .6, .8, .8, 1, 0, .1, -.5, 0, # superpower
#' 0, .5, .4, -.2,-.3, 0, 1, 0, 0, 0, # flexibility
#' .2, .1, 0, .2, .3, .1, 0, 1,-.35, 0, # willpower
#' -.3,-.3,-.1, -.1, .4,-.9, 0,-.35, 1, .6, # ptsd
#' 0, 0, 0, 0, 0, 0, 0, 0, .3, 1 # north vs. south
#' ), nrow = 10)
#' #iq ag spd str dam sup flex will
#' det(rho)
#' rho = Matrix::nearPD(rho)$mat
#'
#' ### simulate data
#' d = MASS::mvrnorm(n=812, mu = rep(0, times=nrow(rho)), Sigma = rho) %>%
#' data.frame %>%
#' setNames(c("iq", "agility", "speed", "strength", "damage.resistance",
#' "superpower", "flexibility", "willpower", "ptsd", "north_south"))
#' d$speed = d$speed + -.2*d$strength^2
#' d$agility = d$agility + -.25*d$strength^2
#' d$ptsd = d$ptsd + .25*d$damage.resistance^2
#' flexplot::flexplot(ptsd~damage.resistance, data=d)
#' flexplot::flexplot(ptsd~superpower, data=d)
#'
#'
#' # specify outcome variables -----------------------------------------------
#'
#' # died (logistic), kills (negative binomial), injuries (ordered logistic?),
#' # minutes_fighting (gamma), shots taken (normal)
#'
#' rho2 = matrix(c(
#' 1, -.3, .5, -.4, -.2, # died
#' -.3, 1, -.2, .5, .5, # kills
#' .5, -.2, 1, .4, .2, # injuries
#' -.4, .5, .4, 1, .5, # minutes_fighting
#' -.2, .5, .2, .5, 1 # shots taken
#' ), nrow = 5)
#'
#' d2 = MASS::mvrnorm(n=nrow(d), mu = rep(0, times=5), Sigma = rho2) %>%
#' data.frame %>%
#' setNames(c("died", "kills", "injuries", "minutes.fighting", "shots.taken"))
#'
#'
#' # link the two datasets ---------------------------------------------------
#'
#' died_link = matrix(c( .1, .3, .2, .2, .4, .5, .1, .4, .5, .5)*.25, ncol=1)
#' d2$died = d2$died + as.numeric(t(died_link) %*% t(data.matrix(d)))
#'
#' kills_link = matrix(c(.2, .2, .3, .4, .1, .6, .1, .3, .2, .2), ncol=1)
#' d2$kills = d2$kills + as.numeric(t(kills_link) %*% t(data.matrix(d)))
#'
#' injuries_link = matrix(c(.1, .4, -.1, .2, .5, .5, .1, .1, .6, .2), ncol=1)
#' d2$injuries = d2$injuries + as.numeric(t(injuries_link) %*% t(data.matrix(d)))
#'
#' minutes_link = matrix(c(-.3, .1, .1, .3, .3, .5, .1, .6, .4, .1), ncol=1)
#' d2$minutes.fighting = d2$minutes.fighting + as.numeric(t(minutes_link) %*% t(data.matrix(d)))
#'
#' shots_link = matrix(c(0, .1, -.3, .1, 0, -1.85, 0, 0, -.3, .2), ncol=1)
#' d2$shots.taken = d2$shots.taken + as.numeric(t(shots_link) %*% t(data.matrix(d)))
#'
#'
#'
#'
#' # add interactions --------------------------------------------------------
#'
#' d2$died = d2$died + -.2*d$superpower*d$strength
#' d2$shots.taken = d2$shots.taken + .75*d$superpower*d$speed
#' d2$minutes.fighting = d2$minutes.fighting + -.5*d$superpower*d$damage.resistance
#'
#'
#' # combine data
#' d = cbind(d, d2)
#'
#'
#' # dichotomize variables ---------------------------------------------------
#' d$superpower = cut(d$superpower, breaks=c(-Inf, 2, Inf), labels=c("no", "yes"))
#' d$north_south = cut(d$north_south, breaks=c(-Inf, median(d$north_south), Inf), labels=c("north", "south"))
#' d$died = cut(d$died, breaks=c(-Inf, -1.5, Inf), labels=c("yes", "no"))
#' d$kills = exp(d$kills)
#' d$injuries = as.numeric(cut(-1*d$injuries, breaks=c(-Inf, -4, -3, -1, 0, 1, Inf), labels=c(1:6), ordered=T))
#' d$injuries = d$injuries - 1
#' d$minutes.fighting = d$minutes.fighting + 10 + exp(d$minutes.fighting)
#' d$minutes.fighting[d$minutes.fighting<1] = 5
#'
#' ### add problematic stuff
#' # outliers
#' d[111,c("ptsd", "strength")] = c(7, 7)
#'
#' # invert relationships (where applicable) ---------------------------------
#' # flexplot(died~agility, data=d, method="logistic")
#' # flexplot(died~speed, data=d, method="logistic")
#' # flexplot(died~strength, data=d, method="logistic")
#' # flexplot(died~damage.resistance, data=d, method="logistic")
#' # flexplot(died~flexibility, data=d, method="logistic")
#' # flexplot(died~willpower, data=d, method="logistic")
#' #
#' # flexplot(superpower~agility, data=d, method="logistic")
#' # flexplot(superpower~speed, data=d, method="logistic")
#' # flexplot(superpower~strength, data=d, method="logistic")
#' # flexplot(superpower~damage.resistance, data=d, method="logistic")
#' # flexplot(superpower~flexibility, data=d, method="logistic")
#' # flexplot(superpower~willpower, data=d, method="logistic")
#' #
#' # flexplot(kills~agility, data=d)
#' # flexplot(kills~speed, data=d)
#' # flexplot(kills~strength, data=d)
#' # flexplot(kills~damage.resistance, data=d)
#' # flexplot(kills~flexibility, data=d)
#' # flexplot(kills~willpower, data=d)
#' #
#' # flexplot(agility~injuries, data=d)
#' # flexplot(speed~injuries, data=d)
#' # flexplot(strength~injuries, data=d)
#' # flexplot(damage.resistance~injuries, data=d)
#' # flexplot(flexibility~injuries, data=d)
#' # flexplot(ptsd~superpower, data=d)
#' # flexplot(ptsd~damage.resistance, data=d)
#'
#' # put variables on correct scale ------------------------------------------
#' require(fifer)
#' d_final = d %>% mutate(iq = rescale(iq, 110, 8),
#' agility = rescale(agility, 50, 15),
#' speed = rescale(speed, 5, .17), # meter dash
#' strength = rescale(strength, 500, 150), # bench press mas
#' damage.resistance = rescale(damage.resistance, 2, .21),
#' flexibility = rescale(flexibility, -1, 1), # inches beyond shoes
#' willpower = rescale(willpower, 60, 18),
#' shots.taken = rescale(shots.taken, 130, 30),
#' ptsd = rescale(ptsd, 4, .6)) %>%
#' mutate_at(vars(speed,damage.resistance),
#' funs(round(., 2))) %>%
#' mutate_at(vars(flexibility, minutes.fighting, ptsd), round, digits=1) %>%
#' mutate_at(vars(iq, agility, strength, willpower, kills, shots.taken), round, digits=0)
#' avengers = d_final
#' head(avengers)
#' usethis::use_data(avengers, overwrite = TRUE)
#' write.csv(avengers, "data/avengers.csv", row.names=F)
#' Simulated Statistics on the Final Avengers Battle
#'
#' A dataset containing combat attributes of almost 812
#' fighters in the final Avengers battle
#'
#' @format A data frame with 812 rows and 13 variables:
#' \describe{
#' \item{iq}{Intelligence}
#' \item{agility}{weighted scores on an obstacle course}
#' \item{speed}{Speed in running the 40 meter dash}
#' \item{strength}{Pounds lifted in a benchpress}
#' \item{damage.resistance}{Amount of skin deflection (in mm) when slapped with a frozen fish}
#' \item{flexibility}{Number of inches past their toes they can reach}
#' \item{willpower}{Length of time they wait at a DMV for a driver's license}
#' \item{ptsd}{Score on a Likert-scale PTSD questionnaire}
#' \item{north_south}{whether the individual was assigned to fight on the north or south battlefield}
#' \item{died}{Whether the person died at the final battle}
#' \item{kills}{Number of enemies killed in the final battle}
#' \item{injuries}{Number of injuries sustained. Anything more than 5 is marked as a 5}
#' \item{minutes.fighting}{Number of minutes spent fighting before dying or giving up}
#' \item{shots.taken}{Number of shots (with a gun) or punches thrown at an enemy}
#' }
"avengers"
#'
#'
#'
#'
#'
#'
#'
#'
#' #
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