In ready4-dev/ready4use: Ingest, Label, Visualise and Share Health Economic Model Datasets

knitr::opts_chunk$set(echo = TRUE)

Note: This vignette is illustrated with fake data. The dataset explored in this example should not be used to inform decision-making.

library(ready4)
library(ready4use)

ready4use includes a number of tools for visualising health economic model data and forms part of the ready4 framework. Details of how to find compatible datasets are provided in another article. The ready4use visualisation tools illustrated in this vignette provide an interface to functions from the ggpubr library. To work as intended, some (but not most) of these tools also require the ggpubr library to be loaded.

library(ggpubr)

Ingest and pre-process data

We begin by ingesting the data from online repository we require into a Ready4useDyad, as illustrated in another vignette.

objects_ls <- Ready4useRepos(dv_nm_1L_chr = "fakes",
                             dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/HJXYKQ",
                             dv_server_1L_chr = "dataverse.harvard.edu") %>%
  ingest(fls_to_ingest_chr = c("ymh_clinical_tb","ymh_clinical_dict_r3"),
         metadata_1L_lgl = F)
X <- Ready4useDyad(ds_tb = objects_ls$ymh_clinical_tb,
                   dictionary_r3 = objects_ls$ymh_clinical_dict_r3) %>% 
  renew(type_1L_chr = "case")

This dataset is a synthetic ("fake") microdata representation of clinic patients with data for each patient reported for up to two data collection rounds (baseline and follow-up). We also need to transform this dataset into other formats to use with some of the plot types we illustrate in this vignette. We first make a dataset with baseline values only.

X1 <- renewSlot(X, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::filter(round=="Baseline"))

Next, we create summary datasets with mean variable values for the entire sample at baseline.

X2 <- renewSlot(X1, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::group_by(d_studying_working) %>%
                  dplyr::summarise(dplyr::across(dplyr::where(is.numeric), 
                                                 function(x){ mean(x, na.rm = TRUE)})))
X3 <- renewSlot(X1, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::group_by(d_studying_working, d_sex_birth_s) %>%
                  dplyr::summarise(dplyr::across(dplyr::where(is.numeric), 
                                                 function(x){ mean(x, na.rm = TRUE)}), .groups = 'drop'))
X4 <- renewSlot(X1, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::group_by(d_sex_birth_s, d_country_bir_s, 
                                                            d_studying_working, c_p_diag_s,c_clinical_staging_s) %>%
                  dplyr::summarise(dplyr::across(dplyr::where(is.numeric), 
                                                 function(x){ mean(x, na.rm = TRUE)}), .groups = 'drop'))

We also create datasets with summaries of mean variable values at both timepoints.

X5 <- renewSlot(X, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::group_by(round) %>%
                  dplyr::summarise(dplyr::across(dplyr::where(is.numeric), function(x){ mean(x, na.rm = TRUE)})))
X6 <- renewSlot(X, "ds_tb",
                procureSlot(X, "ds_tb") %>% dplyr::group_by(round, d_sex_birth_s) %>%
                  dplyr::summarise(dplyr::across(dplyr::where(is.numeric), function(x){ mean(x, na.rm = TRUE)})))

We make a dataset that is restricted to 50 randomly selected cases for which data is available at two timepoints.

X7 <- renewSlot(X, "ds_tb", 
                procureSlot(X, "ds_tb") %>% 
                  dplyr::filter(fkClientID %in% (intersect(procureSlot(X, "ds_tb") %>% dplyr::filter(round=="Follow-up") %>%
                                                             dplyr::pull(fkClientID), X1@ds_tb$fkClientID) %>% sample(50))))

We next make dataset summaries of the counts at baseline of sample sub-groups.

X8 <- renewSlot(X1, "ds_tb", 
                procureSlot(X1, "ds_tb") %>% dplyr::filter(!is.na(d_studying_working)) %>% 
                  dplyr::group_by(d_studying_working) %>% dplyr::summarise(Count = dplyr::n()))
X9 <- renewSlot(X1, "ds_tb", 
                table(procureSlot(X1, "ds_tb") %>% dplyr::select(d_studying_working, d_sex_birth_s)) %>% tibble::as_tibble())
X10 <- renewSlot(X1, "ds_tb", 
                 table(procureSlot(X1, "ds_tb") %>%
                         dplyr::select(d_studying_working, d_sex_birth_s, c_p_diag_s, c_clinical_staging_s)) %>% tibble::as_tibble())

Some of the visualisations that we will be generating in this vignette will require labels that are derived from variable values to be modified (e.g. for brevity). We therefore next make look-up tables for recoding these values.

x <- renew(ready4show::ready4show_correspondences(),
      old_nms_chr = c("Not studying or working", "Studying only", "Studying and working", "Working only", "Female", "Male"),
      new_nms_chr = c("NEITHER", "EDUCATION", "BOTH", "EMPLOYMENT", "FEMALE", "MALE"))
y <- renew(ready4show::ready4show_correspondences(),
           old_nms_chr = c("Not studying or working", "Studying only", "Studying and working", "Working only", 
                           "Depression and Anxiety"),
           new_nms_chr = c("Neither", "Education", "Both", "Employment", "Both"))

Options for using the Depict method illustrated with the default plot type (barplot)

Data contained in a Ready4useDyad can be visualised using the Depict method.

Single plots

Plot results for one variable

Basic plot

depict(X1, x_vars_chr = "d_studying_working")

Drop missing values

depict(X1, x_vars_chr = "d_studying_working", drop_missing_1L_lgl = T)

Drop tick marks and tick lables

depict(X1, x_vars_chr = "d_studying_working", drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T)

Use variable description instead of variable name (via lookup to data dictionary) for X axis and legend labels and remove label from Y axis

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, y_labels_chr = "", 
                     z_labels_chr = NA_character_, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T)

Specify custom X axis and legend labels

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
                     z_labels_chr = "", drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T)

Change Y axis from counts to percentage

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
                     z_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T)

Recode the legend value labels

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
       z_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T,
       recode_lup_r3 = x)

Stratify results by a second (categorical) variable - in this case, sex

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
       z_vars_chr = "d_sex_birth_s", z_labels_chr = "SEX", as_percent_1L_lgl = T, drop_missing_1L_lgl = T,
       recode_lup_r3 = x)

Stack bars

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
       z_vars_chr = "d_sex_birth_s", z_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T,
       position_xx = ggplot2::position_stack(), recode_lup_r3 = y)

Plot results for two variables - one categorical (x axis) and the other numeric (y axis)

Generate title using variable labels

depict(X2, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, y_vars_chr = "k6_total",
       y_labels_chr = NA_character_, z_labels_chr = "", drop_missing_1L_lgl = T, 
       drop_ticks_1L_lgl = T, recode_lup_r3 = x, titles_chr = NA_character_)

Stratify results by a third (categorical) variable - in this case, sex

depict(X3, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_vars_chr = "k6_total",
                     y_labels_chr = "K6", z_vars_chr = "d_sex_birth_s", z_labels_chr = "SEX", drop_missing_1L_lgl = T,
                     titles_chr = "PSYCHOLOGICAL DISTRESS BY EDUCATION, EMPLOYMENT AND SEX")

Modify palettes

The palette options from the "ggsci" package that are compatible with the ready4use Depict method can be retrieved using the following function call.

get_styles("ggsci")

Use Lancet journal palette

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
                     z_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T, style_1L_chr = "lancet")

The palette options from "the viridis" package that are compatible with the ready4use Depict method can be retrieved using the following function call.

get_styles("viridis")

Use the Viridis Rocket palette

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
       z_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T, 
       style_1L_chr = "rocket", type_1L_chr = "viridis")

Use a custom palette

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", y_labels_chr = "",
       z_labels_chr = "", as_percent_1L_lgl = T, colours_chr = c("#130d9c","#9c0d39"), drop_missing_1L_lgl = T,
       drop_ticks_1L_lgl = T, type_1L_chr = "manual")

Single colour plots

Use just the first colour from the selected (in this case, default) palette

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x, 
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS")

Single colour plot using AAAS journal palette

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x, style_1L_chr = "aaas", 
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS")

Single colour plot using custom colour (black)

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       colours_chr = "black",  drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x,
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS", type_1L_chr = "manual")

Supply additional ggpubr arguments

Add label to bars

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x, 
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS", label = T, lab.pos = "in", lab.col = "white")

Change orientation of bars

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x, 
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS", orientation = "horiz")

Change order of bars

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", y_labels_chr = "", as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, fill_single_1L_lgl = T, recode_lup_r3 = x, 
       titles_chr = "EDUCATION AND EMPLOYMENT STATUS", order = c("EDUCATION", "EMPLOYMENT","BOTH", "NEITHER"))

Create multiple plots

Create a list of plots

plot_ls <- depict(X1, x_vars_chr = c("d_sex_birth_s","d_studying_working", "d_country_bir_s"), 
                  x_labels_chr = NA_character_, y_labels_chr = "" , z_labels_chr = "", 
                  as_percent_1L_lgl = T, drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T)

plot_ls$d_sex_birth_s

plot_ls$d_studying_working

plot_ls$d_country_bir_s

Create a composite plot, with a common legend

depict(X4, x_vars_chr = c("d_country_bir_s", "d_studying_working", "c_p_diag_s", "c_clinical_staging_s"),
       x_labels_chr = NA_character_, y_vars_chr = "k6_total", y_labels_chr = "K6", 
       z_vars_chr = "d_sex_birth_s", z_labels_chr = "SEX", arrange_1L_lgl = T,  
       arrange_args_ls = list(ncol = 2, nrow = 2, common.legend = T), 
       drop_missing_1L_lgl = T, recode_lup_r3 = y)

Other plot types

Plot one continuous variable

Density plot

Basic density plot

depict(X1, x_vars_chr = "k6_total", what_1L_chr = "density")

Stratify results by a second (categorical) variable, report percentages rather than probability and look-up labels from dictionary

depict(X1, x_vars_chr = "k6_total", x_labels_chr = NA_character_, y_labels_chr = "Percentatge", 
       z_vars_chr = "d_studying_working", z_labels_chr = "",  as_percent_1L_lgl = T,
       drop_missing_1L_lgl = T, what_1L_chr = "density") 

Empirical cumulative density function (ECDF)

Basic ECDF plot

depict(X1, x_vars_chr = "k6_total", what_1L_chr = "ecdf")

Stratify results by a second (categorical) variable and customise labels

depict(X1,x_vars_chr = "k6_total", x_labels_chr = NA_character_, y_labels_chr = "", 
       z_vars_chr = "d_studying_working", z_labels_chr = "", drop_missing_1L_lgl = T,  
       what_1L_chr = "ecdf")

Histogram

Basic histogram

depict(X1, x_vars_chr = "k6_total", what_1L_chr = "histogram")

Specify the number of bins, report percentages rather than counts and customise labels

depict(X1, x_vars_chr = "k6_total", x_labels_chr = "K6", y_labels_chr = "", as_percent_1L_lgl = T,
                     what_1L_chr = "histogram", bins = 10)

Stratify results by a second (categorical) variable and look-up labels from dictionary

depict(X1, x_vars_chr = "k6_total", x_labels_chr = NA_character_, z_vars_chr = "d_sex_birth_s",
       z_labels_chr = NA_character_, as_percent_1L_lgl = T, drop_missing_1L_lgl = T, position_xx = "dodge",
       what_1L_chr = "histogram", bins=10)

Add custom ggpubr arguments to add density plot and mean lines

depict(X1, x_vars_chr = "k6_total", x_labels_chr = "K6", y_labels_chr = "", 
       z_vars_chr = "d_sex_birth_s", z_labels_chr = "Sex", as_percent_1L_lgl = F, 
       drop_missing_1L_lgl = T, what_1L_chr = "histogram", add = "mean", add_density = TRUE)

QQ Plot

Basic QQ plot

depict(X1, x_vars_chr = "k6_total", what_1L_chr = "qqplot")

Stratify results by a second (categorical) variable and customise labels

depict(X1, x_vars_chr = "k6_total",  y_labels_chr = "K6", drop_missing_1L_lgl = T,
       z_vars_chr = "d_studying_working", z_labels_chr = "", what_1L_chr = "qqplot")

Plot two variables (one continuous, one discrete)

Box plot

Basic box plot

depict(X, x_vars_chr = "round", y_vars_chr = "k6_total", what_1L_chr = "boxplot")

Customise labels and remove x-axis tick-marks

depict(X, x_vars_chr = "round", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = "K6", z_labels_chr = "", drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T, 
       what_1L_chr = "boxplot")

Fill boxes

depict(X, x_vars_chr = "round", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = NA_character_, z_labels_chr = "", drop_missing_1L_lgl = T, drop_ticks_1L_lgl = T, 
       what_1L_chr = "boxplot", fill = "round", fill_single_1L_lgl = F)

Stratify results by a third (categorical) variable

depict(X, x_vars_chr = "round", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = NA_character_, drop_missing_1L_lgl = T, z_vars_chr = "d_sex_birth_s", 
       z_labels_chr = NA_character_, what_1L_chr = "boxplot", fill = "d_sex_birth_s", fill_single_1L_lgl = F)

Add jitter points

depict(X, x_vars_chr = "round", x_labels_chr = "", y_vars_chr = "k6_total", y_labels_chr = "K6", 
       drop_missing_1L_lgl = T, z_vars_chr = "d_sex_birth_s", z_labels_chr = NA_character_, 
       what_1L_chr = "boxplot", add = "jitter")

Donut chart

Basic donut chart

depict(X1, x_vars_chr = "d_studying_working", what_1L_chr = "donutchart")

Change colour of separating line, drop missing values and remove label

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", drop_missing_1L_lgl = T,
       line_1L_chr = "white", what_1L_chr = "donutchart")

Report percentage rather than counts and change location and colouring of segment labels

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, 
                     line_1L_chr = "white", what_1L_chr = "donutchart", lab.pos = "in", lab.font = "white")

The same result can be achieved by supplying a dataset with frequency counts. Note, drop_missing_1L_lgl is ignored if using this method.

depict(X8, x_vars_chr = "Count", z_vars_chr = "d_studying_working", z_labels_chr = "", as_percent_1L_lgl = T, 
       line_1L_chr = "white", what_1L_chr = "donutchart", lab.pos = "in", lab.font = "white")

Remove legend and change segment labels to names rather than values.

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, drop_legend_1L_lgl = T, drop_missing_1L_lgl = T,
                     line_1L_chr = "white", what_1L_chr = "donutchart", label = "d_studying_working")

Dot chart

Basic dot chart

depict(X2, x_vars_chr = "d_studying_working", y_vars_chr = "k6_total", what_1L_chr = "dotchart")

Use a single colour for dots, rotate chart, increase dot-size, customise label orders, recode label values and add and customise segment lines

depict(X2, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT", 
       y_vars_chr = "k6_total", y_labels_chr = "K6", drop_missing_1L_lgl = T, fill_single_1L_lgl = T, 
       recode_lup_r3 = y, what_1L_chr = "dotchart", add = "segment", 
       add.params = list(color = "lightgray", size = 1.5),  rotate = T, size = 3)

Stratify by third (categorical) variable.

depict(X3, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT",
       y_vars_chr = "k6_total", y_labels_chr = "K6", z_vars_chr = "d_sex_birth_s", z_labels_chr = "SEX",
       drop_missing_1L_lgl = T, position_xx = ggplot2::position_dodge(0.3), recode_lup_r3 = x,
       what_1L_chr = "dotchart", add = "segment", add.params = list(color = "lightgray", size = 1.5),
       rotate = T, size = 3)

Dot plot

Basic dot plot

depict(X1, x_vars_chr = "d_studying_working", y_vars_chr = "k6_total", what_1L_chr = "dotplot")

Customise and reorder labels, remove missing values and change dot size

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT" ,
       y_vars_chr = "k6_total",  y_labels_chr = "K6", drop_legend_1L_lgl = T, drop_missing_1L_lgl = T,
       recode_lup_r3 = x, what_1L_chr = "dotplot", 
       order = c("EDUCATION", "EMPLOYMENT","BOTH", "NEITHER"), size = 0.3)

Stratify by a third (categorical) variable

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "EDUCATION AND EMPLOYMENT" ,y_vars_chr = "k6_total",  y_labels_chr = "K6", 
                     z_vars_chr = "d_sex_birth_s", z_labels_chr = "SEX", drop_missing_1L_lgl = T, what_1L_chr = "dotplot", size = 0.35)

Error plot

Basic error plot

depict(X1, x_vars_chr = "d_studying_working", y_vars_chr = "k6_total", what_1L_chr = "errorplot")

Customise labels and the positioning and colouring of error lines

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = "K6", z_vars_chr = "d_sex_birth_s", z_labels_chr = NA_character_,
       drop_missing_1L_lgl = T, what_1L_chr = "errorplot",
       palette = "Paired", error.plot = "pointrange", position = ggplot2::position_dodge(0.5))

Line plot

Basic line plot

depict(X5, x_vars_chr = "round", y_vars_chr = "k6_total", what_1L_chr = "line")

Plot mean values with error bars, stratify results by third (categorical) variable and look-up labels from dictionary

depict(X, x_vars_chr = "round", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = "K6", z_vars_chr = "d_sex_birth_s", z_labels_chr = NA_character_,
       drop_missing_1L_lgl = T, position_xx = ggplot2::position_dodge(0.5), 
       what_1L_chr = "line", add = "mean_se")

Paired plot

Basic paired plot

depict(X7, x_vars_chr = "round", y_vars_chr = "k6_total", what_1L_chr = "paired")

Customise labels, remove x-axis ticks and labels and change line colour

depict(X7, x_vars_chr = "round", x_labels_chr = "", y_vars_chr = "k6_total", 
       y_labels_chr = "K6", z_labels_chr = NA_character_, 
       drop_ticks_1L_lgl = T, line_1L_chr = "lightgray", what_1L_chr = "paired")

Pie chart

Basic pie chart

depict(X1, x_vars_chr = "d_studying_working", what_1L_chr = "pie")

Report percentage rather than counts, change location and colouring of segment labels and remove labels

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = "", as_percent_1L_lgl = T, drop_missing_1L_lgl = T, 
       line_1L_chr = "white", what_1L_chr = "pie", lab.pos = "in", lab.font = "white")

The same result can be achieved by supplying a dataset with frequency counts. Note, the drop_missing_1L_lgl is ignored if using this method.

depict(X8, x_vars_chr = "Count", z_vars_chr = "d_studying_working", z_labels_chr = "",
       as_percent_1L_lgl = T, drop_missing_1L_lgl = T, line_1L_chr = "white", what_1L_chr = "pie", 
       lab.pos = "in", lab.font = "white")

Strip chart

Basic strip chart

depict(X1, x_vars_chr = "d_studying_working", y_vars_chr = "k6_total", what_1L_chr = "strip")

Look-up labels, drop legend and add mean and standard deviation lines

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
                     y_labels_chr = "K6", drop_legend_1L_lgl = T, drop_missing_1L_lgl = T, what_1L_chr = "strip", add = "mean_sd")

Violin plot

Basic violin plot

depict(X1, x_vars_chr = "d_studying_working", y_vars_chr = "k6_total", what_1L_chr = "violin")

Customise labels, drop legend and add mean and jitter dots

depict(X1, x_vars_chr = "d_studying_working", x_labels_chr = NA_character_, y_vars_chr = "k6_total",
       y_labels_chr = "", drop_legend_1L_lgl = T, drop_missing_1L_lgl = T, 
       what_1L_chr = "violin", add = "jitter")

For plotting two variables (both continuous)

Scatter plot

Basic scatter plot

depict(X1, x_vars_chr = "phq9_total", y_vars_chr = "k6_total", what_1L_chr = "scatter")

Add regression line, change dot sizes and and customise labels.

depict(X1, x_vars_chr = "phq9_total", x_labels_chr = NA_character_, y_vars_chr = "k6_total", 
       y_labels_chr = "K6", drop_missing_1L_lgl = T, what_1L_chr = "scatter", 
       add = "reg.line", size = 0.5, conf.int = TRUE, cor.coef = TRUE, 
       cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"))

Scatter plot with histograms

Basic scatter plot with histograms

depict(X1, x_vars_chr = "phq9_total", y_vars_chr = "k6_total", what_1L_chr = "scatterhist")

Customise labels (except for z_labels_chr which currently does not work with this type of plot) and stratify by third categorical variable

depict(X, x_vars_chr = "phq9_total", x_labels_chr = NA_character_,  y_vars_chr = "k6_total", 
       y_labels_chr = "K6", z_vars_chr = "round", drop_missing_1L_lgl = T, what_1L_chr = "scatterhist")

Contingency table

Balloon plot

Basic balloon plot

depict(X9, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", 
       what_1L_chr = "balloonplot")

Single colour balloon plot

depict(X9, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", what_1L_chr = "balloonplot",  fill_single_1L_lgl = T)

Fized size, labelled balloons

depict(X9, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", 
       what_1L_chr = "balloonplot", size = 10, show.label=T)

Change pallete (from which 3 colours will be selected)

depict(X9, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", 
       style_1L_chr = "C", type_1L_chr = "viridis", what_1L_chr = "balloonplot", size = 10, show.label=T)

Change pallete (to specify a number of colours other than 3)

depict(X9, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", 
       what_1L_chr = "balloonplot", palette = c("blue",  "red"), size = 10, show.label=T)

Facet by up to two additional variables

depict(X10, x_vars_chr = "d_studying_working", y_vars_chr = "d_sex_birth_s", z_vars_chr = "n", 
       what_1L_chr = "balloonplot", 
       facet.by = c("c_p_diag_s", "c_clinical_staging_s"), 
       ggtheme = ggplot2::theme_bw(), palette = c("blue",  "red"))

ready4-dev/ready4use documentation built on June 1, 2025, 2:06 p.m.