Package 'violinplotter'

Dummy dataset

Description

A dummy dataset

Usage

dummy_data

Format

A dataframe with 1,039 rows and 5 variables

STRATUM

sampling strata

TREATMENT

participant names

RESPONSE_1

numeric response variable 1

RESPONSE_2

numeric response variable 2

DATE

date of measurements with YYYYMMDD format

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Plotting and Comparing Means with Violin Plots

Description

Plotting and Comparing Means with Violin Plots

Usage

violinplotter(formula, 
                     data=NULL, 
                     TITLE="", 
                     XLAB="", 
                     YLAB="", 
                     VIOLIN_COLOURS=c("#e0f3db","#a8ddb5","#7bccc4","#2b8cbe"), 
                     PLOT_BARS=TRUE, 
                     ERROR_BAR_COLOURS=c("#636363","#1c9099","#de2d26"), 
                     SHOW_SAMPLE_SIZE=FALSE, 
                     SHOW_MEANS=TRUE, 
                     CATEGORICAL=TRUE, 
                     LOGX=FALSE, 
                     LOGX_BASE=10, 
                     MANN_WHITNEY=TRUE, 
                     HSD=FALSE, 
                     ALPHA=0.05, 
                     REGRESS=FALSE)

Arguments

formula	R's compact symbolic form to represent linear models with fixed additive and interaction effects (See ?formula for more information) [mandatory]
data	data.frame containing the response and explanatory variables which forms the formula above [default=NULL]
TITLE	string or vector of strings corresponding to violin plot title/s [default: combinations of the "response variable name X explanatory variable" from the dataframe column names]
XLAB	string or vector of strings specifying the x-axis labels [default: column names of the explanatory variables (and their combinations) from data]
YLAB	string or vector of strings specifying the y-axis labels [default: column names of the response variable from data]
VIOLIN_COLOURS	vector or list of vectors of colors of the violin plots which are repeated if the length is less than the number of explanatory factor levels or less than the number of explanatory factors in the case of a list [default=c("#e0f3db", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe")]
PLOT_BARS	logical (i.e. TRUE or FALSE) to plot all or none of the bars; or vector strings which bars to plot (e.g. "stdev", "sterr", "ci") [default=TRUE=c("stdev", "sterr", "ci")]
ERROR_BAR_COLOURS	vector of colors of standard deviation, standard error and 95 percent confidence interval error bars (error bar selection via leaving one of the three colors empty) [default=c("#636363", "#1c9099", "#de2d26")]
SHOW_SAMPLE_SIZE	logical referring to whether or not to show the sample sizes for each category [default=FALSE]
SHOW_MEANS	logical referring to whether or not to show the means [default=TRUE]
CATEGORICAL	logical or vector of logicals referring to whether the explanatory variable/s is/are strictly categorical [default=TRUE]
LOGX	logical or vector of logicals referring to whether to transform the explanatory variable/s into the logarithm scale [default=FALSE]
LOGX_BASE	numeric or vector of numerics referring to the logarithm base to transform the explanatory variable/s with [default=1]
MANN_WHITNEY	logical or vector of logicals referring to whether to perform Mann-Whitney Grouping [default=TRUE]
HSD	logical or vector of logicals referring to whether to perform Tukey's Honest Significance Grouping [default=FALSE]
ALPHA	numeric significance level for the analysis of variance F-test and Tukey's mean comparison [default=0.05]
REGRESS	logical or vector of logicals referring to whether to regress the response variable against the explanatory variable/s [default=FALSE]

Value

Violin plot/s with optional error bars, mean comparison grouping/s, and regression line/s

Mean comparison grouping/s based on Tukey's Hones significant difference and regression line statistics, if applicable

Examples

x1 = rep(rep(rep(c(1:5), each=5), times=5), times=5)
x2 = rep(rep(letters[6:10], each=5*5), times=5)
x3 = rep(letters[11:15], each=5*5*5)
y = rep(1:5, each=5*5*5) + rnorm(rep(1:5, each=5), length(x1))
formula = log(y) ~ exp(x1) + x2 + x3 + (x2:x3)
test1 = violinplotter(formula=formula)
test2 = violinplotter(formula=formula, PLOT_BARS=c("ci", "stdev"))

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