R/popTime.R
In sahirbhatnagar/casebase: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression
Defines functions
panelBorder
theme_cb
popTime
Documented in
popTime
#' Population Time Plot Data
#'
#' Create a data frame for population time plots to give a visual representation
#' of incidence density
#'
#' @param data a \code{data.frame} or \code{data.table} containing the source
#' dataset.
#' @param time a character string giving the name of the time variable. See
#' Details.
#' @param event a character string giving the name of the event variable
#' contained in \code{data}. See Details. If \code{event} is a numeric
#' variable, then 0 needs to represent a censored observation, 1 needs to be
#' the event of interest. Integers 2, 3, ... and so on are treated as
#' competing events. If event is a \code{factor} or \code{character} and
#' \code{censored.indicator} is not specified, this function will assume the
#' reference level is the censored indicator
#' @param censored.indicator a character string of length 1 indicating which
#' value in \code{event} is the censored. This function will use
#' \code{\link[stats]{relevel}} to set \code{censored.indicator} as the
#' reference level. This argument is ignored if the \code{event} variable is a
#' numeric
#' @param exposure a character string of length 1 giving the name of the
#' exposure variable which must be contained in \code{data}. Default is
#' \code{NULL}. This is used to produced exposure stratified plots. If an
#' \code{exposure} is specified, \code{popTime} returns an `exposure`
#' attribute which contains the name of the exposure variable in the dataset.
#' The plot method for objects of class `popTime` will use this exposure
#' attribute to create exposure stratified population time plots.
#' @param percentile_number Default=0.5. Give a value between 0-1. if the
#' percentile number of available subjects at any given point is less than 10,
#' then sample regardless of case status. Depending on distribution of
#' survival times and events event points may not be evenly distributed with
#' default value.
#'
#'
#' @details It is assumed that \code{data} contains the two columns
#' corresponding to the supplied time and event variables. If either the
#' \code{time} or \code{event} argument is missing, the function looks for
#' columns that contain the words \code{"time"}, \code{"event"}, or
#' \code{"status"} in them (case insensitive). The function first looks for
#' the time variable, then it looks for the event variable. This order of
#' operation is important if for example the time variable is named
#' \code{"event time"} and the event variable is named \code{"event
#' indicator"}. This function will first (automatically) find the time
#' variable and remove this as a possibility from subsequent searches of the
#' event variable. The following regular expressions are used for the time and
#' event variables: \describe{ \item{time}{\code{"[\\s\\W_]+time|^time\\b"}}
#' \item{event}{\code{"[\\s\\W_]+event|^event\\b|[\\s\\W_]+status|^status\\b"}}
#' } This allows for \code{"time"} to be preceded or followed by one or more
#' white space characters, one or more non-word characters or one or more
#' underscores. For example, the following column names would be recognized by
#' the function as the \code{"time"} variable: \code{"time of death",
#' "death_time", "Time", "time", "diagnosis_time", "time.diag", "diag__time"}.
#' But the following will not be recognized: \code{"diagtime","eventtime",
#' "Timediag"}
#' @return An object of class \code{popTime} (or \code{popTimeExposure} if
#' exposure is specified), \code{data.table} and \code{data.frame} in this
#' order! The output of this function is to be used with the plot method for
#' objects of class \code{popTime} or of class \code{popTimeExposure}, which
#' will produce population time plots. This dataset augments the original data
#' with the following columns: \describe{\item{original.time}{value of the
#' time variable in the original dataset - the one specified by the
#' \code{time} user argument to this function}\item{original.event}{value of
#' the event variable in the original dataset - the one specified by the
#' \code{event} user argument to this function}\item{time}{renames the user
#' specified time column to time}\item{event}{renames the user specified event
#' argument to event}}
#' @seealso \code{\link{plot.popTime}}
#' @examples
#' data("bmtcrr")
#' popTimeData <- popTime(data = bmtcrr, time = "ftime")
#' class(popTimeData)
#' popTimeData <- popTime(data = bmtcrr, time = "ftime", exposure = "D")
#' attr(popTimeData, "exposure")
#' @export
#' @importFrom data.table as.data.table rbindlist := setnames .N
#' @importFrom stats quantile
popTime <- function(data, time, event, censored.indicator,
exposure, percentile_number) {
varNames <- checkArgsTimeEvent(data = data, time = time, event = event)
ycoord <- yc <- n_available <- NULL
DT <- data.table::as.data.table(data)
if (missing(percentile_number)) {
percentile_number <- 0.5
}
if (missing(censored.indicator)) {
censored.indicator <- NULL
}
if (missing(exposure)) {
nobs <- nrow(DT)
DT[, "original.time" := get(varNames$time)]
DT[, "original.event" := get(varNames$event)]
if (varNames$time != "time") setnames(DT, varNames$time, "time")
if (varNames$event != "event") setnames(DT, varNames$event, "event")
modifiedEvent <- checkArgsEventIndicator(
data = data, event = varNames$event,
censored.indicator = censored.indicator
)
DT[, event := modifiedEvent$event.numeric]
DT[, "event status" := modifiedEvent$event.factored]
# people with
# short values of t at the top
DT[DT[, order(time)], ycoord := (nobs:1)]
# sample y coordinates for each event, so that we can see the incidence
# density on population-time plots. Sampling from people who have an
# observed time t greater than that of a fixed individual who had the event
# need to
# if there are only two levels, then find out how many controls
# are left to sample from. if there are three levels,
# check to see if there are enough 0's and 2's to sample from (this
# implicitly assumes event=1 is the event of interest)
# we only plot events==1 (i.e. the event of interest)
DT[, yc := 0L]
DT[, n_available := 0L]
DT[event == 1, n_available := sapply(
time,
function(i) DT[time >= i & event != 1, .N]
)]
# if the 50th percentile number of available subjects at any given
# point is less than 10, then sample regardless of case status
### NEED TO MAKE THIS LESS STRINGENT##############??????
if (DT[, stats::quantile(n_available, probs = percentile_number)] < 15) {
DT[
event == 1,
n_available := sapply(
time,
function(i) DT[time >= i, .N]
)
]
DT[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(DT[time >= i, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
DT[event == 1 & n_available == 0, yc := ycoord]
} else {
DT[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(DT[time >= i & event != 1, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
DT[event == 1 & n_available == 0, yc := ycoord]
}
class(DT) <- c("popTime", class(DT))
attr(DT, "exposure") <- NULL
attr(DT, "call") <- match.call()
return(DT)
} else {
DT[, "original.time" := get(varNames$time)]
DT[, "original.event" := get(varNames$event)]
if (varNames$time != "time") setnames(DT, varNames$time, "time")
if (varNames$event != "event") setnames(DT, varNames$event, "event")
l <- split(DT, DT[[exposure]])
l <- lapply(
l,
function(i) {
transform(i,
event = checkArgsEventIndicator(
data = i, event = "event",
censored.indicator = censored.indicator
)$event.numeric,
`event status` = checkArgsEventIndicator(
data = i, event = "event",
censored.indicator = censored.indicator
)$event.factor
)
}
)
lapply(l, function(i) {
nobs <- nrow(i)
i[i[, order(time)], ycoord := (nobs:1)]
})
# sample y coordinates for each event, so that we can see the incidence
# density on population-time plots. Sampling from people who have an
# observed time t greater than that of a fixed individual who had the event
# if there are only two levels, then find out how many controls
# are left to sample from. if there are three levels,
# check to see if there are enough 0's and 2's to sample from (this
# implicitly assumes event=1 is the event of interest)
# we only plot events==1 (i.e. the event of interest)
lapply(l, function(K) {
K[, yc := 0L]
K[, n_available := 0L]
K[event == 1, n_available := sapply(
time,
function(i) K[time >= i & event != 1, .N]
)]
# if the 50th percentile number of available subjects at any given
# point is less than 10, then sample regardless of case status
if (K[, quantile(n_available, probs = percentile_number)] < 10) {
K[
event == 1,
n_available := sapply(
time,
function(i) K[time >= i, .N]
)
]
K[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(K[time >= i, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
K[event == 1 & n_available == 0, yc := ycoord]
} else {
K[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(K[time >= i & event != 1, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
K[event == 1 & n_available == 0, yc := ycoord]
}
})
lk <- data.table::rbindlist(l)
attr(lk, "exposure") <- exposure
class(lk) <- c("popTime", class(lk))
attr(lk, "call") <- match.call()
return(lk)
}
}
# taken verbatim from cowplot::theme_cowplot()
#' @importFrom stats quantile
#' @importFrom grid unit
#' @importFrom ggplot2 theme_grey theme element_line element_rect element_text
#' @importFrom ggplot2 margin element_blank rel %+replace%
theme_cb <- function(font_size = 14, font_family = "", line_size = 0.5,
rel_small = 12/14, rel_tiny = 11/14, rel_large = 16/14) {
half_line <- 0.5 * font_size
small_size <- rel_small * font_size
ggplot2::theme_grey(base_size = font_size, base_family = font_family) %+replace%
theme(line = element_line(color = "black", linewidth = line_size,
linetype = 1, lineend = "butt"),
rect = element_rect(fill = NA, color = NA, linewidth = line_size,
linetype = 1),
text = element_text(family = font_family,
face = "plain", color = "black", size = font_size,
hjust = 0.5, vjust = 0.5, angle = 0, lineheight = 0.9,
margin = margin(), debug = FALSE),
axis.line = element_line(color = "black",
linewidth = line_size,
lineend = "square"),
axis.line.x = NULL,
axis.line.y = NULL,
axis.text = element_text(color = "black", size = small_size),
axis.text.x = element_text(margin = margin(t = 0.25 * small_size),
vjust = 1),
axis.text.x.top = element_text(margin = margin(b = 0.25 * small_size),
vjust = 0),
axis.text.y = element_text(margin = margin(r = 0.25 * small_size),
hjust = 1),
axis.text.y.right = element_text(margin = margin(l = 0.25 * small_size),
hjust = 0),
axis.ticks = element_line(color = "black", linewidth = line_size),
axis.ticks.length = unit(0.5 * half_line, "pt"),
axis.title.x = element_text(margin = margin(t = 0.5 * half_line),
vjust = 1),
axis.title.x.top = element_text(margin = margin(b = 0.5 * half_line),
vjust = 0),
axis.title.y = element_text(angle = 90,
margin = margin(r = 0.5 * half_line),
vjust = 1),
axis.title.y.right = element_text(angle = -90, margin = margin(l = 0.5 * half_line),
vjust = 0), legend.background = element_blank(),
legend.spacing = unit(font_size, "pt"), legend.spacing.x = NULL,
legend.spacing.y = NULL,
legend.margin = margin(0, 0, 0, 0),
legend.key = element_blank(),
legend.key.size = unit(1.1 * font_size, "pt"), legend.key.height = NULL,
legend.key.width = NULL, legend.text = element_text(size = rel(rel_small)),
legend.text.align = NULL, legend.title = element_text(hjust = 0),
legend.title.align = NULL, legend.position = "right",
legend.direction = NULL,
legend.justification = c("left", "center"),
legend.box = NULL,
legend.box.margin = margin(0, 0, 0, 0), legend.box.background = element_blank(),
legend.box.spacing = unit(font_size, "pt"), panel.background = element_blank(),
panel.border = element_blank(), panel.grid = element_blank(),
panel.grid.major = NULL, panel.grid.minor = NULL,
panel.grid.major.x = NULL, panel.grid.major.y = NULL,
panel.grid.minor.x = NULL, panel.grid.minor.y = NULL,
panel.spacing = unit(half_line, "pt"), panel.spacing.x = NULL,
panel.spacing.y = NULL, panel.ontop = FALSE,
strip.background = element_rect(fill = "grey80"),
strip.text = element_text(size = rel(rel_small),
margin = margin(0.5 * half_line, 0.5 * half_line, 0.5 * half_line,
0.5 * half_line)),
strip.text.x = NULL, strip.text.y = element_text(angle = -90),
strip.placement = "inside", strip.placement.x = NULL,
strip.placement.y = NULL,
strip.switch.pad.grid = unit(0.5 * half_line, "pt"),
strip.switch.pad.wrap = unit(0.5 * half_line, "pt"),
plot.background = element_blank(),
plot.title = element_text(face = "bold", size = rel(rel_large), hjust = 0, vjust = 1, margin = margin(b = half_line)),
plot.subtitle = element_text(size = rel(rel_small), hjust = 0, vjust = 1, margin = margin(b = half_line)),
plot.caption = element_text(size = rel(rel_tiny),
hjust = 1, vjust = 1, margin = margin(t = half_line)),
plot.tag = element_text(face = "bold", hjust = 0,
vjust = 0.7), plot.tag.position = c(0, 1),
plot.margin = margin(half_line, half_line, half_line, half_line),
complete = TRUE)
}
# taken verbatim from cowplot::panel_border
#' @importFrom ggplot2 theme element_blank element_rect
panelBorder <- function(color = "grey85", size = 1, linetype = 1,
remove = FALSE, colour) {
if (!missing(colour)) {
color <- colour
}
if (remove) {
return(theme(panel.border = element_blank()))
}
theme(panel.border = element_rect(
color = color, fill = NA,
linetype = linetype,
linewidth = size
))
}
sahirbhatnagar/casebase documentation built on April 10, 2024, 6:01 a.m.
R Package Documentation
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Defines functions panelBorder theme_cb popTime
Documented in popTime
#' Population Time Plot Data
#'
#' Create a data frame for population time plots to give a visual representation
#' of incidence density
#'
#' @param data a \code{data.frame} or \code{data.table} containing the source
#' dataset.
#' @param time a character string giving the name of the time variable. See
#' Details.
#' @param event a character string giving the name of the event variable
#' contained in \code{data}. See Details. If \code{event} is a numeric
#' variable, then 0 needs to represent a censored observation, 1 needs to be
#' the event of interest. Integers 2, 3, ... and so on are treated as
#' competing events. If event is a \code{factor} or \code{character} and
#' \code{censored.indicator} is not specified, this function will assume the
#' reference level is the censored indicator
#' @param censored.indicator a character string of length 1 indicating which
#' value in \code{event} is the censored. This function will use
#' \code{\link[stats]{relevel}} to set \code{censored.indicator} as the
#' reference level. This argument is ignored if the \code{event} variable is a
#' numeric
#' @param exposure a character string of length 1 giving the name of the
#' exposure variable which must be contained in \code{data}. Default is
#' \code{NULL}. This is used to produced exposure stratified plots. If an
#' \code{exposure} is specified, \code{popTime} returns an `exposure`
#' attribute which contains the name of the exposure variable in the dataset.
#' The plot method for objects of class `popTime` will use this exposure
#' attribute to create exposure stratified population time plots.
#' @param percentile_number Default=0.5. Give a value between 0-1. if the
#' percentile number of available subjects at any given point is less than 10,
#' then sample regardless of case status. Depending on distribution of
#' survival times and events event points may not be evenly distributed with
#' default value.
#'
#'
#' @details It is assumed that \code{data} contains the two columns
#' corresponding to the supplied time and event variables. If either the
#' \code{time} or \code{event} argument is missing, the function looks for
#' columns that contain the words \code{"time"}, \code{"event"}, or
#' \code{"status"} in them (case insensitive). The function first looks for
#' the time variable, then it looks for the event variable. This order of
#' operation is important if for example the time variable is named
#' \code{"event time"} and the event variable is named \code{"event
#' indicator"}. This function will first (automatically) find the time
#' variable and remove this as a possibility from subsequent searches of the
#' event variable. The following regular expressions are used for the time and
#' event variables: \describe{ \item{time}{\code{"[\\s\\W_]+time|^time\\b"}}
#' \item{event}{\code{"[\\s\\W_]+event|^event\\b|[\\s\\W_]+status|^status\\b"}}
#' } This allows for \code{"time"} to be preceded or followed by one or more
#' white space characters, one or more non-word characters or one or more
#' underscores. For example, the following column names would be recognized by
#' the function as the \code{"time"} variable: \code{"time of death",
#' "death_time", "Time", "time", "diagnosis_time", "time.diag", "diag__time"}.
#' But the following will not be recognized: \code{"diagtime","eventtime",
#' "Timediag"}
#' @return An object of class \code{popTime} (or \code{popTimeExposure} if
#' exposure is specified), \code{data.table} and \code{data.frame} in this
#' order! The output of this function is to be used with the plot method for
#' objects of class \code{popTime} or of class \code{popTimeExposure}, which
#' will produce population time plots. This dataset augments the original data
#' with the following columns: \describe{\item{original.time}{value of the
#' time variable in the original dataset - the one specified by the
#' \code{time} user argument to this function}\item{original.event}{value of
#' the event variable in the original dataset - the one specified by the
#' \code{event} user argument to this function}\item{time}{renames the user
#' specified time column to time}\item{event}{renames the user specified event
#' argument to event}}
#' @seealso \code{\link{plot.popTime}}
#' @examples
#' data("bmtcrr")
#' popTimeData <- popTime(data = bmtcrr, time = "ftime")
#' class(popTimeData)
#' popTimeData <- popTime(data = bmtcrr, time = "ftime", exposure = "D")
#' attr(popTimeData, "exposure")
#' @export
#' @importFrom data.table as.data.table rbindlist := setnames .N
#' @importFrom stats quantile
popTime <- function(data, time, event, censored.indicator,
exposure, percentile_number) {
varNames <- checkArgsTimeEvent(data = data, time = time, event = event)
ycoord <- yc <- n_available <- NULL
DT <- data.table::as.data.table(data)
if (missing(percentile_number)) {
percentile_number <- 0.5
}
if (missing(censored.indicator)) {
censored.indicator <- NULL
}
if (missing(exposure)) {
nobs <- nrow(DT)
DT[, "original.time" := get(varNames$time)]
DT[, "original.event" := get(varNames$event)]
if (varNames$time != "time") setnames(DT, varNames$time, "time")
if (varNames$event != "event") setnames(DT, varNames$event, "event")
modifiedEvent <- checkArgsEventIndicator(
data = data, event = varNames$event,
censored.indicator = censored.indicator
)
DT[, event := modifiedEvent$event.numeric]
DT[, "event status" := modifiedEvent$event.factored]
# people with
# short values of t at the top
DT[DT[, order(time)], ycoord := (nobs:1)]
# sample y coordinates for each event, so that we can see the incidence
# density on population-time plots. Sampling from people who have an
# observed time t greater than that of a fixed individual who had the event
# need to
# if there are only two levels, then find out how many controls
# are left to sample from. if there are three levels,
# check to see if there are enough 0's and 2's to sample from (this
# implicitly assumes event=1 is the event of interest)
# we only plot events==1 (i.e. the event of interest)
DT[, yc := 0L]
DT[, n_available := 0L]
DT[event == 1, n_available := sapply(
time,
function(i) DT[time >= i & event != 1, .N]
)]
# if the 50th percentile number of available subjects at any given
# point is less than 10, then sample regardless of case status
### NEED TO MAKE THIS LESS STRINGENT##############??????
if (DT[, stats::quantile(n_available, probs = percentile_number)] < 15) {
DT[
event == 1,
n_available := sapply(
time,
function(i) DT[time >= i, .N]
)
]
DT[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(DT[time >= i, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
DT[event == 1 & n_available == 0, yc := ycoord]
} else {
DT[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(DT[time >= i & event != 1, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
DT[event == 1 & n_available == 0, yc := ycoord]
}
class(DT) <- c("popTime", class(DT))
attr(DT, "exposure") <- NULL
attr(DT, "call") <- match.call()
return(DT)
} else {
DT[, "original.time" := get(varNames$time)]
DT[, "original.event" := get(varNames$event)]
if (varNames$time != "time") setnames(DT, varNames$time, "time")
if (varNames$event != "event") setnames(DT, varNames$event, "event")
l <- split(DT, DT[[exposure]])
l <- lapply(
l,
function(i) {
transform(i,
event = checkArgsEventIndicator(
data = i, event = "event",
censored.indicator = censored.indicator
)$event.numeric,
`event status` = checkArgsEventIndicator(
data = i, event = "event",
censored.indicator = censored.indicator
)$event.factor
)
}
)
lapply(l, function(i) {
nobs <- nrow(i)
i[i[, order(time)], ycoord := (nobs:1)]
})
# sample y coordinates for each event, so that we can see the incidence
# density on population-time plots. Sampling from people who have an
# observed time t greater than that of a fixed individual who had the event
# if there are only two levels, then find out how many controls
# are left to sample from. if there are three levels,
# check to see if there are enough 0's and 2's to sample from (this
# implicitly assumes event=1 is the event of interest)
# we only plot events==1 (i.e. the event of interest)
lapply(l, function(K) {
K[, yc := 0L]
K[, n_available := 0L]
K[event == 1, n_available := sapply(
time,
function(i) K[time >= i & event != 1, .N]
)]
# if the 50th percentile number of available subjects at any given
# point is less than 10, then sample regardless of case status
if (K[, quantile(n_available, probs = percentile_number)] < 10) {
K[
event == 1,
n_available := sapply(
time,
function(i) K[time >= i, .N]
)
]
K[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(K[time >= i, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
K[event == 1 & n_available == 0, yc := ycoord]
} else {
K[
event == 1 & n_available > 0,
yc := sapply(
time,
function(i) {
sample(K[time >= i & event != 1, ycoord], 1)
}
)
]
# use original coordinate if there is no one left to sample from
K[event == 1 & n_available == 0, yc := ycoord]
}
})
lk <- data.table::rbindlist(l)
attr(lk, "exposure") <- exposure
class(lk) <- c("popTime", class(lk))
attr(lk, "call") <- match.call()
return(lk)
}
}
# taken verbatim from cowplot::theme_cowplot()
#' @importFrom stats quantile
#' @importFrom grid unit
#' @importFrom ggplot2 theme_grey theme element_line element_rect element_text
#' @importFrom ggplot2 margin element_blank rel %+replace%
theme_cb <- function(font_size = 14, font_family = "", line_size = 0.5,
rel_small = 12/14, rel_tiny = 11/14, rel_large = 16/14) {
half_line <- 0.5 * font_size
small_size <- rel_small * font_size
ggplot2::theme_grey(base_size = font_size, base_family = font_family) %+replace%
theme(line = element_line(color = "black", linewidth = line_size,
linetype = 1, lineend = "butt"),
rect = element_rect(fill = NA, color = NA, linewidth = line_size,
linetype = 1),
text = element_text(family = font_family,
face = "plain", color = "black", size = font_size,
hjust = 0.5, vjust = 0.5, angle = 0, lineheight = 0.9,
margin = margin(), debug = FALSE),
axis.line = element_line(color = "black",
linewidth = line_size,
lineend = "square"),
axis.line.x = NULL,
axis.line.y = NULL,
axis.text = element_text(color = "black", size = small_size),
axis.text.x = element_text(margin = margin(t = 0.25 * small_size),
vjust = 1),
axis.text.x.top = element_text(margin = margin(b = 0.25 * small_size),
vjust = 0),
axis.text.y = element_text(margin = margin(r = 0.25 * small_size),
hjust = 1),
axis.text.y.right = element_text(margin = margin(l = 0.25 * small_size),
hjust = 0),
axis.ticks = element_line(color = "black", linewidth = line_size),
axis.ticks.length = unit(0.5 * half_line, "pt"),
axis.title.x = element_text(margin = margin(t = 0.5 * half_line),
vjust = 1),
axis.title.x.top = element_text(margin = margin(b = 0.5 * half_line),
vjust = 0),
axis.title.y = element_text(angle = 90,
margin = margin(r = 0.5 * half_line),
vjust = 1),
axis.title.y.right = element_text(angle = -90, margin = margin(l = 0.5 * half_line),
vjust = 0), legend.background = element_blank(),
legend.spacing = unit(font_size, "pt"), legend.spacing.x = NULL,
legend.spacing.y = NULL,
legend.margin = margin(0, 0, 0, 0),
legend.key = element_blank(),
legend.key.size = unit(1.1 * font_size, "pt"), legend.key.height = NULL,
legend.key.width = NULL, legend.text = element_text(size = rel(rel_small)),
legend.text.align = NULL, legend.title = element_text(hjust = 0),
legend.title.align = NULL, legend.position = "right",
legend.direction = NULL,
legend.justification = c("left", "center"),
legend.box = NULL,
legend.box.margin = margin(0, 0, 0, 0), legend.box.background = element_blank(),
legend.box.spacing = unit(font_size, "pt"), panel.background = element_blank(),
panel.border = element_blank(), panel.grid = element_blank(),
panel.grid.major = NULL, panel.grid.minor = NULL,
panel.grid.major.x = NULL, panel.grid.major.y = NULL,
panel.grid.minor.x = NULL, panel.grid.minor.y = NULL,
panel.spacing = unit(half_line, "pt"), panel.spacing.x = NULL,
panel.spacing.y = NULL, panel.ontop = FALSE,
strip.background = element_rect(fill = "grey80"),
strip.text = element_text(size = rel(rel_small),
margin = margin(0.5 * half_line, 0.5 * half_line, 0.5 * half_line,
0.5 * half_line)),
strip.text.x = NULL, strip.text.y = element_text(angle = -90),
strip.placement = "inside", strip.placement.x = NULL,
strip.placement.y = NULL,
strip.switch.pad.grid = unit(0.5 * half_line, "pt"),
strip.switch.pad.wrap = unit(0.5 * half_line, "pt"),
plot.background = element_blank(),
plot.title = element_text(face = "bold", size = rel(rel_large), hjust = 0, vjust = 1, margin = margin(b = half_line)),
plot.subtitle = element_text(size = rel(rel_small), hjust = 0, vjust = 1, margin = margin(b = half_line)),
plot.caption = element_text(size = rel(rel_tiny),
hjust = 1, vjust = 1, margin = margin(t = half_line)),
plot.tag = element_text(face = "bold", hjust = 0,
vjust = 0.7), plot.tag.position = c(0, 1),
plot.margin = margin(half_line, half_line, half_line, half_line),
complete = TRUE)
}
# taken verbatim from cowplot::panel_border
#' @importFrom ggplot2 theme element_blank element_rect
panelBorder <- function(color = "grey85", size = 1, linetype = 1,
remove = FALSE, colour) {
if (!missing(colour)) {
color <- colour
}
if (remove) {
return(theme(panel.border = element_blank()))
}
theme(panel.border = element_rect(
color = color, fill = NA,
linetype = linetype,
linewidth = size
))
}
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