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Plot prevalence/incidence rates

Source: R/plot_rates.R
plot_rates.Rd

The plot_rates() plots prevalence/incidence rates

Usage

plot_rates(
  data,
  date_col = "date",
  rate_col,
  grouping_var = NULL,
  facet_var = NULL,
  plot_type = c("line", "bar_chart", "lollipop", "jitter"),
  percent = TRUE,
  palette = c("fhi_colors", "viridis", "okabe_ito"),
  single_color = "black",
  annotated_line = NULL,
  CI_lower = NULL,
  CI_upper = NULL,
  plot_title = "",
  x_name = "",
  y_name = "",
  legend_title = "",
  coord_flip = FALSE,
  start_end_points = FALSE,
  interactive = FALSE
)

Arguments

data

A data frame with the prevalence/incidence rates and auxiliary information.

date_col

A character string. Name of the date column in data. Default is "date".

rate_col

A character string. Name of the rate column in data.

grouping_var

A character string. Name of the variable/column in data used to group rates.

facet_var

A character string. Name of the variable/column in data used to facet plots.

plot_type

A character string. Type of plot, options are "line", "bar_chart", "lollipop", "jitter"

percent

Logical. Do you want axis to be in percent? Default set to TRUE.

palette

A character string. Color palette to be used in the plots, options are: "fhi_colors", "viridis", "okabe_ito"

single_color

A character string. Single color applied to all the plot. Default is set to "black"

annotated_line

Character string. Position of annotated line. Default is NULL.

CI_lower

A character string. Name of the column containing the lower confidence interval in data.

CI_upper

A character string. Name of the column containing the upper confidence interval in data.

plot_title

Character string. Title of the plot.

x_name

A character string. Title of the x axis.

y_name

A character string. Title of the y axis.

legend_title

A character string. Title for the legend box.

coord_flip

Logical. Default is set to FALSE

  • For lollipop, bar charts and jitter TRUE it flips the orientation of the plot.

start_end_points

Logical. Want to annotate the start and end points of a line plot? Default is set to FALSE.

  • If TRUE, the start and end point of a line plot are annotated with their corresponding numerical values.

interactive

Logical. Do you want to make the plot interactive with plotly? Default is set to FALSE.

Value

A ggplot object

Examples

log_file <- tempfile()
cat("Example log file", file = log_file)

pop_df <- tidyr::expand_grid(year = 2012:2020,
  sex = as.factor(c(0, 1)),
  innvandringsgrunn = c("ARB", "UKJ", "NRD")) |>
    dplyr::mutate(population = floor(runif(dplyr::n(), min = 3000, max = 4000)))

linked_df <- linked_df |> dplyr::rename("year"= "y_diagnosis_first")

prev_series <- regtools::calculate_prevalence_series(linked_df,
  time_points = c(2012:2020),
  id_col = "id",
  date_col = "year",
  pop_data = pop_df,
  pop_col = "population",
  grouping_vars = c("sex", "innvandringsgrunn"),
  only_counts = FALSE,
  suppression = FALSE,
  CI = TRUE,
  CI_level = 0.95,
  log_path = log_file)
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2012
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2013
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2014
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2015
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2016
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2017
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2018
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2019
#> Computing prevalence rates/counts...
#> ! No suppression. Confidentiality cannot be assured.
#> Joining with `by = join_by(sex, innvandringsgrunn, year)`
#> 
#> ✔ Prevalence rates ready!
#> 
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ℹ Diagnostic and demographic data: linked_data
#> ℹ Population data: pop_data
#> ℹ Grouped by variables: sex and innvandringsgrunn
#> ℹ For time point/period:  2020

plot_rates(prev_series,
 date_col = "year",
 rate_col = "prev_rate",
 plot_type = "line",
 grouping_var = "sex",
 facet_var = "innvandringsgrunn",
 palette = "fhi_colors",
 CI_lower = "ci_results_lower",
 CI_upper = "ci_results_upper",
 plot_title = "Prevalence by sex and reason of immigration",
 x_name = "Year",
 start_end_points = TRUE)