xgxr (version 1.0.0)

xgx_stat_ci: Plot data with mean and confidence intervals

Description

xgx_stat_ci returns a ggplot layer plotting mean +/- confidence intervals

Usage

xgx_stat_ci(mapping = NULL, data = NULL, conf_level = 0.95,
  distribution = "normal", geom = list("point", "line", "errorbar"),
  position = "identity", ..., fun.args = list(), na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE)

Arguments

mapping

Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot.

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

conf_level

The percentile for the confidence interval (should fall between 0 and 1). The default is 0.95, which corresponds to a 95 percent confidence interval.

distribution

The distribution which the data follow, used for calculating confidence intervals. The options are "normal", "lognormal", and "binomial". The "normal" option will use the Student t Distribution to calculate confidence intervals, the "lognormal" option will transform data to the log space first. The "binomial" option will use the binom.exact function to calculate the confidence intervals. Note: binomial data must be numeric and contain only 1's and 0's.

geom

Use to override the default geom. Can be a list of multiple geoms, e.g. list("point","line","errorbar"), which is the default.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

other arguments passed on to layer. These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.

fun.args

Optional additional arguments passed on to the functions.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.

Value

ggplot2 plot layer

Details

This function can be used to generate mean +/- confidence interval plots for different distributions, and multiple geoms with a single function call.

Examples

# NOT RUN {
# default settings for normally distributed data, 95% confidence interval,  
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, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95)

# try different geom 
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, geom = list("ribbon", "point", "line"))
 
# 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, ggplot2::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, ggplot2::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 = stats::rbinom(60, 1, rep(c(0.2, 0.6, 0.8),
                   each = 20)),
                   group = rep(1:3, 20))
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, distribution = "binomial")
 
# including multiple groups in same plot
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, distribution = "binomial", 
              ggplot2::aes(color = factor(group)),
              position = ggplot2::position_dodge(width = 0.5))
 
# }