Make facet plot of raw data

This function provides a useful way to quickly plot all data for a specific treatment and sex in one facet plot. This can be helpful if you want to create a display of the different treatments.

Usage

make_facet_plot(
  data,
  plot_category,
  plot_treatment,
  plot_sex,
  pruned = "no",
  current_type = "eEPSC",
  y_variable = "P1",
  x_label = "Time (min)",
  treatment_colour_theme,
  ggplot_theme = patchclampplotteR_facet_theme()
)

Arguments

data

A dataframe containing the raw evoked current data generated from make_normalized_EPSC_data(). If pruned = "yes" you must use the $individual_cells component of a pruned dataset.

plot_category

A numeric value specifying the category, which can be used to differentiate different protocol types. In the sample dataset for this package, plot_category == 2 represents experiments where insulin was applied continuously after a 5-minute baseline period.

plot_treatment

A character value specifying the treatment you would like to plot (e.g. "Control"). plot_treatment represents antagonists that were present on the brain slice, or the animals were fasted, etc.

plot_sex

A character value ("Male" or "Female") corresponding to the sex you would like to plot.

pruned

A character value ("yes" or "no") specifying if the data are pruned. The plot will then present the data as means with error bars. This is only relevant for y variables like mean_P1 for current_type = "eEPSC".

current_type

A character describing the current type. Allowed values are "eEPSC" or "sEPSC".

y_variable

A character value specifying the variable to be plotted on the y-axis. For evoked currents (current_type = "eEPSC"), the available y_variables are "P1", "P1_transformed", "mean_P1" and "PPR". Note: If you select "mean_P1", you must set the pruned argument to "yes". For spontaneous currents (current_type = "sEPSC"), the available y_variables are "amplitude" or "frequency". NOTE: "frequency" is only available if pruned = "yes".

x_label

A character value specifying the x-axis label. Defaults to "Time (min)".

treatment_colour_theme

A dataframe containing treatment names and their associated colours as hex values. See sample_treatment_names_and_colours for an example of what this dataframe should look like.

ggplot_theme

The name of a ggplot theme or your custom theme. This will be added as a layer to a ggplot object. The default is patchclampplotteR_theme(), but other valid entries include theme_bw(), theme_classic() or the name of a custom ggplot theme stored as an object.

Value

A ggplot object, which is a facetted plot of the raw data.

Details

I have found it helpful to use this with knitr's RMarkdown features to create an interactive HTML page with the raw plots from all my cells, an interactive summary table (make_interactive_summary_table()), and summary plots (plot_summary_current_data()) for all treatments. This makes it easier to review cells while preparing presentations.

If you are using make_facet_plot() within an RMarkdown document, you will need to change the fig.height and fig.width options.

(Taken from the documentation for get_fig_height()): Set fig.width = 14 and fig.height = get_fig_height(...). Replace the ... with the correct category, treatment and sex you'd like to plot. Plot only one facet plot per chunk.

Examples

# Raw eEPSC facet plots

make_facet_plot(sample_raw_eEPSC_df,
  plot_category = 2,
  plot_treatment = "Control",
  plot_sex = "Male",
  pruned = "no",
  current_type = "eEPSC",
  y_variable = "P1",
  treatment_colour_theme = sample_treatment_names_and_colours,
  ggplot_theme = patchclampplotteR_facet_theme()
)



# Pruned eEPSC facet plots

make_facet_plot(sample_pruned_eEPSC_df$individual_cells,
  plot_category = 2,
  plot_treatment = "Control",
  plot_sex = "Male",
  pruned = "yes",
  current_type = "eEPSC",
  y_variable = "mean_P1",
  treatment_colour_theme = sample_treatment_names_and_colours,
  ggplot_theme = patchclampplotteR_facet_theme()
)