Plotting with a <summarised_result>
For this vignette, we will use the penguins dataset from
the palmerpenguins package. This dataset will be
summarised using the PatientProfiles::summariseResult()
function, which aggregates the data into the
<summarised_result> format:
library(PatientProfiles)
library(palmerpenguins)
library(dplyr)
summariseIsland <- function(island) {
penguins |>
filter(.data$island == .env$island) |>
summariseResult(
group = "species",
includeOverallGroup = TRUE,
strata = list("year", "sex", c("year", "sex")),
variables = c(
"bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g",
"sex"),
estimates = c(
"median", "q25", "q75", "min", "max", "count_missing", "count",
"percentage", "density")
) |>
suppressMessages() |>
mutate(cdm_name = island)
}
penguinsSummary <- bind(
summariseIsland("Torgersen"),
summariseIsland("Biscoe"),
summariseIsland("Dream")
)
penguinsSummary |> glimpse()
#> Rows: 429,296
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "Torgersen", "Torgersen", "Torgersen", "Torgersen", "…
#> $ group_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ group_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "number records", "bill_length_mm", "bill_length_mm",…
#> $ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, "density_001", "density_0…
#> $ estimate_name <chr> "count", "median", "q25", "q75", "min", "max", "count…
#> $ estimate_type <chr> "integer", "numeric", "numeric", "numeric", "numeric"…
#> $ estimate_value <chr> "52", "38.9", "36.65", "41.1", "33.5", "46", "1", "29…
#> $ additional_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…Plotting principles for <summarised_result>
objects
1) Tidy Format
When working with <summarised_result> objects, the
data is internally converted into the tidy format before plotting. This
is an important distinction because columns such as
strata_name and strata_level from the original
<summarised_result> cannot be used directly with the
plotting functions. Instead, tidy columns should be referenced.
For more information about the tidy format, refer to the
omopgenerics package vignette on
<summarised_result> here.
To identify the available tidy columns, use the
tidyColumns() function:
tidyColumns(penguinsSummary)
#> [1] "cdm_name" "species" "year" "sex"
#> [5] "variable_name" "variable_level" "count" "median"
#> [9] "q25" "q75" "min" "max"
#> [13] "count_missing" "density_x" "density_y" "percentage"2) Subsetting Variables
Before calling the plotting functions, always subset the
<summarised_result> object to the variable of
interest. Avoid combining results from unrelated variables, as this may
lead to NA values in the tidy format, which can affect your plots.
Scatter plot
We can create simple scatter plots using the
plotScatter() let’s see some examples:
penguinsSummary |>
filter(variable_name == "bill_depth_mm") |>
filterStrata(year != "overall", sex == "overall") |>
scatterPlot(
x = "year",
y = "median",
line = TRUE,
point = TRUE,
ribbon = FALSE,
facet = "cdm_name",
colour = "species"
)
We can use the argument style to update the theme of the
plot to either the visOmopResults default style, or our
darwin style. For darwin style, if we want to use the
official “Calibri” font, we will need first the packafe
extrafont. We can install and load it as follows before the
plot:
requireExtrafont()
penguinsSummary |>
filter(variable_name %in% c("bill_length_mm", "bill_depth_mm"))|>
filterStrata(year == "overall", sex == "overall") |>
filterGroup(species != "overall") |>
scatterPlot(
x = "density_x",
y = "density_y",
line = TRUE,
point = FALSE,
ribbon = FALSE,
facet = cdm_name ~ variable_name,
colour = "species",
style = "darwin"
) 
Otherwise, the style can be applied afterwards with the function
themeVisOmop. Not only that, but the returned plot can be
futher customised with the usual ggplot2 functionalities:
penguinsSummary |>
filter(variable_name == "flipper_length_mm") |>
filterStrata(year != "overall", sex %in% c("female", "male")) |>
scatterPlot(
x = c("year", "sex"),
y = "median",
ymin = "q25",
ymax = "q75",
line = FALSE,
point = TRUE,
ribbon = FALSE,
facet = cdm_name ~ species,
colour = "sex",
group = c("year", "sex")
) +
themeVisOmop(fontsizeRef = 13) +
ggplot2::coord_flip() +
ggplot2::labs(y = "Flipper length (mm)") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1))
penguinsSummary |>
filter(
variable_name %in% c("flipper_length_mm", "bill_length_mm", "bill_depth_mm")
) |>
filterStrata(sex == "overall") |>
scatterPlot(
x = "year",
y = "median",
ymin = "min",
ymax = "max",
line = FALSE,
point = TRUE,
ribbon = TRUE,
facet = cdm_name ~ species,
colour = "variable_name",
group = c("variable_name")
) +
themeVisOmop(style = "darwin", fontsizeRef = 12) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1))
Bar plot
Let’s create a bar plots:
penguinsSummary |>
filter(variable_name == "number records") |>
filterGroup(species != "overall") |>
filterStrata(sex != "overall", year != "overall") |>
barPlot(
x = "year",
y = "count",
colour = "sex",
facet = cdm_name ~ species
) +
themeVisOmop(fontsizeRef = 12)
Box plot
Let’s create some box plots of their body mass:
penguinsSummary |>
filter(variable_name == "body_mass_g") |>
boxPlot(x = "year", facet = species ~ cdm_name, colour = "sex", style = "default")
penguinsSummary |>
filter(variable_name == "body_mass_g") |>
filterGroup(species != "overall") |>
filterStrata(sex %in% c("female", "male"), year != "overall") |>
boxPlot(x = "cdm_name", facet = c("sex", "species"), colour = "year") +
themeVisOmop(fontsizeRef = 11)
Note that as we didnt specify x there is no levels in the x axis, but box plots are produced anyway.
