xgx theme
The xgx_theme() function includes the xGx recommended
plot settings. It sets the background to white with light grey lines for
the major and minor breaks. This minimizes chart ink as recommended by
Edward Tufte. You can add xgx_theme() to an existing
ggplot object, or you can call xgx_plot() in
place of ggplot() for all of your plot initiations.
xgx_plot(mtcars, aes(x = cyl, y = mpg)) + geom_point()
You may wish to set the theme to xgx_theme for your R
session, as we do below.
theme_set(xgx_theme())
## Alternative, equivalent function:
xgx_theme_set()
# time <- rep(seq(1,10),5)
# id <- sort(rep(seq(1,5), 10))
# conc <- exp(-time)*sort(rep(rlnorm(5),10))
#
# data <- data.frame(time = time, concentration = conc, id = factor(id))
# xgx_plot() + xgx_geom_spaghetti(data = data, mapping = aes(x = time, y = concentration, group = id, color = id))
#
# xgx_spaghetti(data = data, mapping = aes(x = time, y = concentration, group = id, color = id))
Confidence intervals
The code for confidence intervals is a bit complex and hard to
remember. Rather than copy-pasting this code we provide the function
xgx_stat_ci for calculating and plotting default confidence
intervals and also xgx_geom_ci for percentile intervals.
xgx_stat_ci allows the definition of multiple
geom options in one function call, defined through a list.
The default is geom = list("point","line","errorbar").
Additional ggplot options can be fed through the ggplot
object call, or the xgx_stat_ci layer. (Note that
xgx_stat_ci and xgx_geom_ci are equivalent).
xgx_stat_pi and xgx_geom_pi work in a similar
fashion but for percentile intervals.
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
y = rep(c(1, 2, 3), each = 20) + stats::rnorm(60),
group = rep(1:3, 20))
xgx_plot(data,aes(x = x, y = y)) +
xgx_stat_ci(conf_level = .95)
xgx_plot(data,aes(x = x, y = y)) +
xgx_stat_pi(percent = .95)
xgx_plot(data,aes(x = x, y = y)) +
xgx_stat_ci(conf_level = .95, geom = list("pointrange","line"))
xgx_plot(data,aes(x = x, y = y)) +
xgx_stat_ci(conf_level = .95, geom = list("ribbon","line"))
xgx_plot(data,aes(x = x, y = y, group = group, color = factor(group))) +
xgx_stat_ci(conf_level = .95, alpha = 0.5,
position = position_dodge(width = 0.5))
The default settings calculate the confidence interval based on the
Student t Distribution (assuming normally distributed data). You can
also specify “lognormal”“,”binomial”” or “multinomial”” for the
distribution. The first will perform the confidence
interval operation on the log-scaled data, the second uses the binomial
exact confidence interval calculation from the binom
package, and the third uses MultinomCI from the
DescTools package. The “multinomial”” option is used for
ordinal response or categorical data.
Note: you DO NOT need to use both
distribution = "lognormal" and
scale_y_log10(), choose only one of these.
# plotting lognormally distributed data
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
y = 10^(rep(c(1, 2, 3), each = 20) + stats::rnorm(60)),
group = rep(1:3, 20))
xgx_plot(data, aes(x = x, y = y)) +
xgx_stat_ci(conf_level = 0.95, distribution = "lognormal")
# note: you DO NOT need to use both distribution = "lognormal" and scale_y_log10()
xgx_plot(data,aes(x = x, y = y)) +
xgx_stat_ci(conf_level = 0.95) + xgx_scale_y_log10()
# plotting binomial data
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
y = rbinom(60, 1, rep(c(0.2, 0.6, 0.8), each = 20)),
group = rep(1:3, 20))
xgx_plot(data, aes(x = x, y = y)) +
xgx_stat_ci(conf_level = 0.95, distribution = "binomial")
# Example plotting the percent of subjects in a categorical covariate group by treatment.
set.seed(12345)
data = data.frame(x = 120*exp(rnorm(100,0,1)),
response = sample(c("Trt1", "Trt2", "Trt3"), 100, replace = TRUE),
covariate = factor(sample(c("White","Black","Asian","Other"), 100, replace = TRUE),
levels = c("White", "Black", "Asian", "Other")))
xgx_plot(data = data) +
xgx_stat_ci(mapping = aes(x = response, response = covariate),
distribution = "ordinal") +
xgx_stat_ci(mapping = aes(x = 1, response = covariate), geom = "hline",
distribution = "ordinal") +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~covariate) +
xlab("Treatment group") + ylab("Percent of subjects by category")
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> Warning: Unknown or uninitialised column: `flipped_aes`.
#> Unknown or uninitialised column: `flipped_aes`.
#> Warning: Unknown or uninitialised column: `width`.
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
xgx_stat_ci can now also cut data by quantiles of
x using the bins option,
e.g. bins = 4 will cut the data by quartiles of
x. You can also supply your own breaks to cut the data.
# plotting
set.seed(12345)
data = data.frame(x = 120*exp(rnorm(100,0,1)),
response = sample(c("Mild","Moderate","Severe"), 100, replace = TRUE),
covariate = sample(c("Male","Female"), 100, replace = TRUE)) %>%
mutate(y = (50 + 20*x/(200 + x))*exp(rnorm(100, 0, 0.3)))
# plotting a lognormally distributed variable by quartiles of x
xgx_plot(data = data) +
xgx_stat_ci(mapping = aes(x = x, y = y, colour = covariate),
distribution = "lognormal", bins = 4)
# plotting ordinal or multinomial data, by quartiles of x
xgx_plot(data = data) +
xgx_stat_ci(mapping = aes(x = x, response = response, colour = covariate),
distribution = "ordinal", bins = 4) +
scale_y_continuous(labels = scales::percent_format()) + facet_wrap(~response)
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: colour
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: colour
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: colour
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> Warning: Unknown or uninitialised column: `flipped_aes`.
#> Unknown or uninitialised column: `flipped_aes`.
#> Warning: Unknown or uninitialised column: `width`.
xgx_plot(data = data) +
xgx_stat_ci(mapping = aes(x = x, response = response, colour = response),
distribution = "ordinal", bins = 4) +
scale_y_continuous(labels = scales::percent_format()) + facet_wrap(~covariate)
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: colour
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: PANEL
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: colour
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: PANEL
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> In xgx_stat_ci:
#> The following aesthetics are identical to response: colour
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_ci:
#> The following aesthetics are different from response: PANEL
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> Warning: Unknown or uninitialised column: `flipped_aes`.
#> Warning: Unknown or uninitialised column: `flipped_aes`.
#> Warning: Unknown or uninitialised column: `width`.
Nonlinear smoothing (e.g. Emax), and ordinal response smoothing
The current ggplot2::geom_smooth does not allow for plotting
confidence bands for method = “nls”, as ggplot2 does not supply a
predictdf for an object of class nls, which
geom_smooth silently calls to calculate the ymin and ymax for the
confidence bands. The xgxr package includes a definition of
predictdf.nls, allowing for confidence bands for method =
“nls”.
set.seed(123456)
Nsubj <- 10
Doses <- c(0, 25, 50, 100, 200)
Ntot <- Nsubj*length(Doses)
times <- c(0,14,30,60,90)
dat1 <- data.frame(ID = 1:(Ntot),
DOSE = rep(Doses, Nsubj),
E0 = 50*rlnorm(Ntot, 0, 0.3),
Emax = 100*rlnorm(Ntot, 0, 0.3),
ED50 = 50*rlnorm(Ntot, 0, 0.3)) %>%
dplyr::mutate(Response = (E0 + Emax*DOSE/(DOSE + ED50))*rlnorm(Ntot, 0, 0.3) ) %>%
merge(data.frame(ID = rep(1:(Ntot), each = length(times)), Time = times), by = "ID")
gg <- xgx_plot(data = dat1, aes(x = DOSE, y = Response))
gg <- gg + geom_point()
gg
gg + geom_smooth(method = "nlsLM",
formula = y ~ E0 + Emax*x/(ED50 + x),
method.args = list(start = list(E0 = 1, ED50 = 1, Emax = 1),
lower = c(-Inf, 0, -Inf)))
xgxr also includes an Emax smooth function called
xgx_geom_smooth_emax which utilizes the “nlsLM” method, and
silently calls the predictdf.nls defined by xgxr.
gg + xgx_geom_smooth_emax()
#> Warning in xgx_geom_smooth_emax(): Formula not specified.
#> Using default formula y ~ E0 + Emax*x/(ED50 + x),
#> initializing E0, Emax, and ED50 to 1,
#> and setting lower bound on ED50 to 0
#> Warning: Ignoring unknown parameters: n_boot
gg +
xgx_geom_smooth_emax(geom = "ribbon", color = "black", fill = NA, linetype = "dashed") +
xgx_geom_smooth_emax(geom = "line", color = "red")
#> Warning in xgx_geom_smooth_emax(geom = "ribbon", color = "black", fill = NA, : Formula not specified.
#> Using default formula y ~ E0 + Emax*x/(ED50 + x),
#> initializing E0, Emax, and ED50 to 1,
#> and setting lower bound on ED50 to 0
#> Warning in xgx_geom_smooth_emax(geom = "ribbon", color = "black", fill = NA, : Ignoring unknown parameters: n_boot
#> Warning in xgx_geom_smooth_emax(geom = "line", color = "red"): Formula not specified.
#> Using default formula y ~ E0 + Emax*x/(ED50 + x),
#> initializing E0, Emax, and ED50 to 1,
#> and setting lower bound on ED50 to 0
#> Warning: Ignoring unknown parameters: n_boot
xgxr also modifies the stats method predict.nls for
nls objects in order to include confidence interval
prediction. Upon loading the xgxr package, the predict
method for class nls should be updated to the xgxr version,
and include functionality to supply confidence intervals. In order to
output the confidence intervals, be sure to specify
interval = "confidence". The output will contain a “fit”
data.frame with values for “fit”, “lwr” and “upr” representing the
prediction and lower and upper confidence intervals.
mod <- nlsLM(formula = Response ~ E0 + Emax * DOSE / (ED50 + DOSE),
data = dat1,
start = list(E0 = 1, ED50 = 1, Emax = 1),
lower = c(-Inf, 0, -Inf))
predict(mod,
newdata = data.frame(DOSE = c(0, 25, 50, 100, 200)),
se.fit = TRUE)
#> $fit
#> [1] 44.60368 99.70517 116.14226 128.68292 136.76043
#>
#> $se.fit
#> [1] 5.219335 4.289631 3.012974 2.969559 4.294878
#>
#> $df
#> [1] 247
predict(mod,
newdata = data.frame(DOSE = c(0, 25, 50, 100, 200)),
se.fit = TRUE, interval = "confidence", level = 0.95)
#> $fit
#> fit lwr upr
#> 1 44.60368 34.32360 54.88376
#> 2 99.70517 91.25625 108.15409
#> 3 116.14226 110.20786 122.07666
#> 4 128.68292 122.83404 134.53181
#> 5 136.76043 128.30118 145.21969
#>
#> $se.fit
#> [1] 5.219335 4.289631 3.012974 2.969559 4.294878
#>
#> $df
#> [1] 247
xgxr also includes ordinal response smoothing as an option under the
xgx_stat_smooth function, indicated by
method = "polr". This requires a dataset of x values and
response values, to be defined in the mapping. This method also allows
defining of color, fill, facet, linetype, etc. by the response category,
while preserving the ordinal response fit across these categories.
# example with ordinal data (method = "polr")
set.seed(12345)
data = data.frame(x = 120*exp(stats::rnorm(100,0,1)),
response = sample(c("Mild","Moderate","Severe"), 100, replace = TRUE),
covariate = sample(c("Male","Female"), 100, replace = TRUE)) %>%
dplyr::mutate(y = (50 + 20*x/(200 + x))*exp(stats::rnorm(100, 0, 0.3)))
# example coloring by the response categories
xgx_plot(data = data) +
xgx_stat_smooth(mapping = ggplot2::aes(x = x, response = response,
colour = response, fill = response),
method = "polr") +
ggplot2::scale_y_continuous(labels = scales::percent_format())
#> `geom_smooth()` using formula 'response ~ x'
#> In xgx_stat_smooth:
#> The following aesthetics are identical to response: PANEL, colour, fill
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_smooth:
#> response should be a factor, converting to factor using as.factor(response) with default levels
# example faceting by the response categories, coloring by a different covariate
xgx_plot(data = data) +
xgx_stat_smooth(mapping = ggplot2::aes(x = x, response = response,
colour = covariate, fill = covariate),
method = "polr", level = 0.80) +
ggplot2::facet_wrap(~response) +
ggplot2::scale_y_continuous(labels = scales::percent_format())
#> `geom_smooth()` using formula 'response ~ x'
#> In xgx_stat_smooth:
#> The following aesthetics are identical to response: PANEL
#> These will be used for differentiating response groups in the resulting plot.
#> In xgx_stat_smooth:
#> The following aesthetics are different from response: colour, fill
#> These will be used to divide the data into different groups before calculating summary statistics on the response.
#> In xgx_stat_smooth:
#> response should be a factor, converting to factor using as.factor(response) with default levels
Nice scale for percent change data
This transform is useful for plotting percent change from baseline
data. Percent change data can range from -100% to +Inf%, and depending
on the range of the data, a linear scale can lose the desired
resolution. This transform plots percent change data on a scale of
log10(PCHG + 100%), similar to a log scale of ratio to baseline.
Nsubj <- 10
Doses <- c(0, 25, 50, 100, 200)
Ntot <- Nsubj*length(Doses)
times <- c(0,14,30,60,90)
dat1 <- data.frame(ID = 1:(Ntot),
DOSE = rep(Doses, Nsubj),
PD0 = rlnorm(Ntot, log(100), 1),
Kout = exp(rnorm(Ntot,-2, 0.3)),
Imax = 1,
ED50 = 25) %>%
dplyr::mutate(PDSS = PD0*(1 - Imax*DOSE/(DOSE + ED50))*exp(rnorm(Ntot, 0.05, 0.3)) ) %>%
merge(data.frame(ID = rep(1:(Ntot), each = length(times)), Time = times), by = "ID") %>%
dplyr::mutate(PD = ((PD0 - PDSS)*(exp(-Kout*Time)) + PDSS),
PCHG = (PD - PD0)/PD0)
ggplot2::ggplot(dat1 %>% subset(Time == 90),
ggplot2::aes(x = DOSE, y = PCHG, group = DOSE)) +
ggplot2::geom_boxplot() +
xgx_theme() +
xgx_scale_y_percentchangelog10() +
ylab("Percent Change from Baseline") +
xlab("Dose (mg)")
ggplot2::ggplot(dat1,
ggplot2::aes(x = Time, y = PCHG, group = ID, color = factor(DOSE))) +
ggplot2::geom_line() +
xgx_theme() +
xgx_scale_y_percentchangelog10() +
guides(color = guide_legend(title = "Dose (mg)")) +
ylab("Percent Change from Baseline")
dat2 <- data.frame(ID = 1:(Ntot),
DOSE = rep(Doses, Nsubj),
PD0 = rlnorm(Ntot, log(100), 1),
Kout = exp(rnorm(Ntot,-2, 0.3)),
Emax = 50*rlnorm(Ntot, 0, 0.3),
ED50 = 300) %>%
dplyr::mutate(PDSS = PD0*(1 + Emax*DOSE/(DOSE + ED50))*exp(rnorm(Ntot, -1, 0.3)) ) %>%
merge(data.frame(ID = rep(1:(Ntot), each = length(times)), Time = times), by = "ID") %>%
dplyr::mutate(PD = ((PD0 - PDSS)*(exp(-Kout*Time)) + PDSS),
PCHG = (PD - PD0)/PD0)
ggplot2::ggplot(dat2, ggplot2::aes(x = DOSE, y = PCHG, group = DOSE)) +
ggplot2::geom_boxplot() +
xgx_theme() +
xgx_scale_y_percentchangelog10() +
ylab("Percent Change from Baseline") +
xlab("Dose (mg)")
ggplot2::ggplot(dat2,
ggplot2::aes(x = Time, y = PCHG, group = ID, color = factor(DOSE))) +
ggplot2::geom_line() +
xgx_theme() +
xgx_scale_y_percentchangelog10() +
guides(color = guide_legend(title = "Dose (mg)")) +
ylab("Percent Change from Baseline")
Scaling x-axis as a time scale
For time, it’s often good for the x ticks to be spaced in a
particular way. For instance, for hours, subdividing in increments by
24, 12, 6, and 3 hours can make more sense than by 10 or 100. Similarly
for days, increments of 7 or 28 days are preferred over 5 or 10 days.
xgx_scale_x_time_units allows for this, where it is the
input and output units.
data <- data.frame(x = 1:1000, y = stats::rnorm(1000))
g <- xgx_plot(data = data, aes(x = x, y = y)) +
geom_point()
g1 <- g + xgx_scale_x_time_units(units_dataset = "hours", units_plot = "hours")
g2 <- g + xgx_scale_x_time_units(units_dataset = "hours", units_plot = "days")
g3 <- g + xgx_scale_x_time_units(units_dataset = "hours", units_plot = "weeks")
g4 <- g + xgx_scale_x_time_units(units_dataset = "hours", units_plot = "months")
gridExtra::grid.arrange(g1, g2, g3, g4, nrow = 2)
