Function reference • grafify

All functions
`data_1w_death`	In vitro experiments measuring percentage cell death in three genotypes of cells.
`data_2w_Festing`	Data from two-way ANOVA with randomised block design of treatments of strains of mice.
`data_2w_Tdeath`	In vitro measurement of percentage cell death - two-way ANOVA design with repeated measures, and randomised blocks.
`data_cholesterol`	Hierarchical data from 25 subjects either treated or not at 5 hospitals - two-way ANOVA design with repeated measures.
`data_doubling_time`	Doubling time of E.coli measured by 10 students three independent times.
`data_t_pdiff`	Matched data from two groups where difference between them is consistent.
`data_t_pratio`	Matched data from two groups where ratio between them is consistent.
`data_zooplankton`	Time-series data on zooplankton in lake Menon.
`ga_anova()`	ANOVA table from a generalised additive model (`gam`)
`ga_model()`	Fit a generalised additive model (`gam`)
`get_graf_colours()`	Get graf internal
`graf_colours`	List of hexcodes of colours in grafify palettes
`graf_col_palette()`	Call `grafify` palettes for scale & fill functions
`graf_col_palette_default()`	Call `grafify` palettes for scale & fill functions
`graf_palettes`	List of palettes available in grafify package
`make_1way_data()`	Make one-way or two-way independent group or randomised block design data.
`make_1way_rb_data()`	Make one-way or two-way independent group or randomised block design data.
`make_2way_data()`	Make one-way or two-way independent group or randomised block design data.
`make_2way_rb_data()`	Make one-way or two-way independent group or randomised block design data.
`mixed_anova()`	ANOVA table from linear mixed effects analysis.
`mixed_anova_slopes()`	ANOVA table from linear mixed effects analysis.
`mixed_model()`	Model from a linear mixed effects model
`mixed_model_slopes()`	Model from a linear mixed effects model with varying slopes
`plot_3d_point_sd()`	Plot of mean & error bars for 1-way ANOVAs with matched shapes mapped to blocking factor.
`plot_3d_scatterbar()`	Plot a bar graph for 1-way ANOVAs with matched shapes mapped to blocking factor.
`plot_3d_scatterbox()`	Plot a scatter and box plot for 1-way ANOVAs with matched shapes mapped to blocking factor.
`plot_3d_scatterviolin()`	Plot a scatter with violin & box plot for 1-way ANOVAs with matched shapes mapped to blocking factor.
`plot_4d_point_sd()`	Plot mean & error bars for 2-way ANOVAs with or without a blocking factor.
`plot_4d_scatterbar()`	Plot scatter plot with bar & error bars for 2-way ANOVAs with or without a blocking factor.
`plot_4d_scatterbox()`	Plot scatter, box & whiskers for 2-way ANOVAs with or without a blocking factor.
`plot_4d_scatterviolin()`	Plot scatter, box & violin for 2-way ANOVAs with or without a blocking factor.
`plot_befafter_box()`	Before-after style graph with a boxplot
`plot_befafter_colours()` `plot_befafter_colors()`	Plot a before-after plot with lines joining colour-matched symbols.
`plot_befafter_shapes()`	Plot a before-after plot with lines joining shape-matched symbols.
`plot_density()`	Plot density distribution of data.
`plot_dotbar_sd()`	Plot a dotplot on a bar graph with SD error bars with two variables.
`plot_dotbox()`	Plot a dotplot on a boxplot with two variables.
`plot_dotviolin()`	Plot a dotplot on a violin plot with two variables.
`plot_gam_predict()`	Plot prediction of `gam` model
`plot_grafify_palette()`	See grafify colour palettes
`plot_histogram()`	Plot data distribution as histograms.
`plot_lm_predict()`	Plot data and predictions from linear model
`plot_logscale()`	Add log transformations to graphs
`plot_point_sd()`	Plot a point as mean with SD error bars using two variables.
`plot_qqline()`	Plot quantile-quantile (QQ) graphs from data.
`plot_qqmodel()`	Plot quantile-quantile (QQ) graphs from residuals of linear models.
`plot_qq_gam()`	Plot model diagnostics for generalised additive models
`plot_scatterbar_sd()`	Plot scatter dots on a bar graph with SD error bars with two variables.
`plot_scatterbox()`	Plot a scatter plot on a boxplot with two variables.
`plot_scatterviolin()`	Plot a scatter plot on a violin plot with two variables.
`plot_xy_CatGroup()`	Plot points on a quantitative X - Y plot & a categorical grouping variable.
`plot_xy_NumGroup()`	Plot points on a quantitative X - Y plot & a numeric grouping variable.
`posthoc_Levelwise()`	Level-wise post-hoc comparisons from a linear or linear mixed effects model.
`posthoc_Pairwise()`	Pairwise post-hoc comparisons from a linear or linear mixed effects model.
`posthoc_Trends_Levelwise()`	Use emtrends to get level-wise comparison of slopes from a linear model.
`posthoc_Trends_Pairwise()`	Use emtrends to get pairwise comparison of slopes from a linear model.
`posthoc_Trends_vsRef()`	Use emtrends to get level-wise comparison of slopes from a linear model.
`posthoc_vsRef()`	Post-hoc comparisons to a control or reference group.
`scale_colour_grafify()` `scale_color_grafify()`	`scale_colour_` and `scale_fill_` functions
`scale_fill_grafify()`	`scale_colour_` and `scale_fill_` functions
`simple_anova()`	ANOVA table from a linear model fit to data.
`simple_model()`	Model from a linear model fit to data.
`table_summary()`	Get numeric summary grouped by factors
`table_x_reorder()`	Reordering groups along X-axis
`theme_grafify()`	A modified `theme_classic()` for `grafify`-like graphs.

Reference

All functions