For this vignette, we will create and use a synthetic dataset.
library(dplyr) set.seed(54321) N = 40 c1 <- rnorm(N, mean = 100, sd = 25) c2 <- rnorm(N, mean = 100, sd = 50) g1 <- rnorm(N, mean = 120, sd = 25) g2 <- rnorm(N, mean = 80, sd = 50) g3 <- rnorm(N, mean = 100, sd = 12) g4 <- rnorm(N, mean = 100, sd = 50) gender <- c(rep('Male', N/2), rep('Female', N/2)) id <- 1: N wide.data <- tibble::tibble( Control1 = c1, Control2 = c2, Group1 = g1, Group2 = g2, Group3 = g3, Group4 = g4, Gender = gender, ID = id) my.data <- wide.data %>% tidyr::gather(key = Group, value = Measurement, -ID, -Gender) head(my.data)
This dataset is a tidy dataset, where each observation (datapoint) is a row, and each variable (or associated metadata) is a column.
dabestr requires that data be in this form, as do other popular R packages for data visualization and analysis.
dabest function is the main workhorse of the
dabestr package. To create a two-group estimation plot (aka a Gardner-Altman plot), specify:
paired = TRUEor
paired = FALSE,
library(dabestr) two.group.unpaired <- my.data %>% dabest(Group, Measurement, # The idx below passes "Control" as the control group, # and "Group1" as the test group. The mean difference # will be computed as mean(Group1) - mean(Control1). idx = c("Control1", "Group1"), paired = FALSE) # Calling the object automatically prints out a summary. two.group.unpaired
To create a two-group estimation plot (aka a Gardner-Altman plot), simply use
Advanced R users would be interested to learn that
dabest produces an object of class
dabest. There is a generic S3
plot method for
dabest objects that produces the estimation plot.
plot(two.group.unpaired, color.column = Gender)
The key features of the Gardner-Altman estimation plot are:
The estimation plot produced by
dabest differs from the one first introduced by Gardner and Altman in one important aspect.
dabest derives the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.
The 95% CI presented is bias-corrected and accelerated (ie. a BCa bootstrap). You can read more about bootstrap resampling and BCa correction in this vignette.
If you have paired or repeated observations, you must specify the
id.col, a column in the data that indicates the identity of each paired observation. This will produce a Tufte slopegraph instead of a swarmplot.
two.group.paired <- my.data %>% dabest(Group, Measurement, idx = c("Control1", "Group1"), paired = TRUE, id.col = ID) # The summary indicates this is a paired comparison. two.group.paired plot(two.group.paired, color.column = Gender)
To create a multi-two group plot, one will need to specify a list, with each element of the list corresponding to the each two-group comparison.
multi.two.group.unpaired <- my.data %>% dabest(Group, Measurement, idx = list(c("Control1", "Group1"), c("Control2", "Group2")), paired = FALSE ) multi.two.group.unpaired plot(multi.two.group.unpaired, color.column = Gender)
This is a Cumming estimation plot. It is heavily influenced by the plot designs of Geoff Cumming in his 2012 text Understanding the New Statistics. The effect size and 95% CIs are plotted a separate axes that is now positioned below the raw data. In addition, summary measurements are displayed as gapped lines to the right of each group. These vertical lines are identical to conventional mean ± standard deviation error bars. Here, the mean of each group is indicated as a gap in the line, drawing inspiration from Edward Tufte's low data-ink ratio dictum.
dabest plots the mean ± standard deviation of each group as a gapped line beside each group. The
group.summaries = 'median_quartiles' parameter will plot the median and 25th & 75th percentiles of each group is plotted instead. If
group.summaries = NULL, the summaries are not shown.
plot(multi.two.group.unpaired, color.column = Gender, group.summaries = "median_quartiles")
One can also produce a multi-paired plot.
multi.two.group.paired <- my.data %>% dabest(Group, Measurement, idx = list(c("Control1", "Group1"), c("Control2", "Group2")), paired = TRUE, id.col = ID ) multi.two.group.paired plot(multi.two.group.paired, color.column = Gender, slopegraph = TRUE)
If you supply a character vector to
idx with more than 2 groups, a shared control plot will be produced.
shared.control <- my.data %>% dabest(Group, Measurement, idx = c("Control2", "Group2", "Group4"), paired = FALSE ) shared.control plot(shared.control, color.column = Gender, rawplot.type = "swarmplot")
multi.group <- my.data %>% dabest(Group, Measurement, idx = list(c("Control1", "Group1", "Group3"), c("Control2", "Group2", "Group4")), paired = FALSE ) multi.group plot(multi.group, color.column = Gender)
You can control several graphical aspects of the estimation plot.
effsize.ylim parameters to supply custom y-limits for the rawplot and the delta plot, respectively.
plot(multi.group, color.column = Gender, rawplot.ylim = c(-100, 200), effsize.ylim = c(-60, 60) )
You can control the size of the dots used to create the rawplot data with
rawplot.markersize. The default size (in points) is 2.
To obtain an aesthetically-pleasing plot, You should use this option in tandem with the
rawplot.groupwidth option. This sets the maximum amount that each group of datapoints is allowed to spread in the x-direction. The default is 0.3.
plot(multi.group, color.column = Gender, rawplot.markersize = 1, rawplot.groupwidth = 0.4 )
effsize.ylabel parameters control the y-axis titles for the rawplot and the delta plot, respectively.
plot(multi.group, color.column = Gender, rawplot.ylabel = "Rawplot Title?", effsize.ylabel = "My delta plot!" )
palette parameter accepts any
ggplot2 palettes. The default palette applied is "Set2".
plot(multi.group, color.column = Gender, palette = "Dark2" # The default is "Set2". )
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