Data distributions

QQ, Density, and Histogram plots

Data format

Three functions are available to plot data distributions. All `plot_` functions require a long-format data table as the first argument. Unlike other `plot_..` functions, these three require the quantitative variable next (`ycol`) and then a categorical variable (`Group`). Repeat the name of the quantitative variable if there is no grouping variable or to plot distribution of all data.

Saving graphs

See Saving graphs for tips on how to save plots for making figures.

`plot_qqline`

This function allows quick visualisation of distribution by plotting a QQ (quantile-quantile) graph. It needs a data table, Y values and a grouping factor (if available).

``````plot_qqline(data = data_t_pratio,
ycol = Cytokine,
group = Genotype)+
labs(title = "QQ plot",
subtitle = "(`all_grafify` palette)")``````
``````
plot_qqline(data_t_pratio,
log(Cytokine),       #log transformed-data
Genotype)+
labs(title = "QQ plot with log-transformation",
subtitle = "(`all_grafify` palette)")``````
``````
plot_qqline(data = data_t_pdiff,
ycol = Mass)+       #entire dataset
labs(title = "QQ plot of all response",
subtitle = "(no subgrouping)")``````

Data format

See the data help page and ensure data table is in the long-format.

`plot_density`

This function plots smooth density distributions using `geom_density` geometry. The example below uses the `chicwts` dataset in R.

``````plot_density(data = chickwts,
ycol = weight,
group = feed,
fontsize = 16) ``````
``````
plot_density(data = chickwts,
ycol = weight,
group = feed,
TextXAngle = 45,
fontsize = 16,
facet = feed) #with faceting``````

`plot_histogram`

This function plots histograms using `geom_histogram` geometry. The example below uses the `diamonds` dataset in `dplyr`. Change bin size on X-axis using the `BinSize` argument (default is 30)

``````plot_histogram(data = diamonds,
ycol = carat,
group = cut,
fontsize = 16) ``````
``````
plot_histogram(data = diamonds,
ycol = carat,
group = cut,
TextXAngle = 45,
fontsize = 16,
facet = cut)+ #faceting
scale_x_log10()  #log x axis``````

`plot_qqmodel`

New since v1.5.0: this function is for one-step plotting of residuals from linear models. It generates a Q-Q plot from model residuals for as a quick model diagnostic.

``````#generate a linear model
mod1 <- simple_model(data_2w_Festing,
"GST",
c("Treatment", "Strain"))

#get Q-Q plot of model residuals
plot_qqmodel(mod1)``````

This also works for mixed effects models.

``````#generate a mixed effects linear model
mod2 <- mixed_model(data_2w_Festing,
"GST",
c("Treatment", "Strain"),
"Block")

#get Q-Q plot of model residuals
plot_qqmodel(mod2)``````