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Line Chart

Source: R/line_chart.R
line_chart.Rd

This function generates a line chart from a data frame using specified x and y variables. Optionally, the plot can be rendered as an interactive Plotly object. The function also allows grouping of data based on a specified grouping variable.

Usage

line_chart(
  dynamic = FALSE,
  base = NULL,
  params = list(df, x, y, ci = NULL, lower = NULL, upper = NULL, error_colour =
    c("#f2c75c"), group_var, line_colour = c("blue"), line_type = "solid", width = 1,
    title = NULL, x_label = NULL, x_label_angle = NULL, y_label = NULL, y_label_angle =
    NULL, y_percent = FALSE, st_theme = NULL, add_points = FALSE, show_gridlines = FALSE,
    show_axislines = TRUE, legend_title = NULL, legend_position = NULL, hline = NULL,
    hline_colour = "red", hline_label = NULL),
  ...
)

Arguments

dynamic

A logical value. If TRUE, the line chart will be rendered as a Plotly object for interactivity. If FALSE, a static ggplot2 object will be returned.

base

A base plotly or ggplot2 object to add the line chart to. Default is NULL.

params

A list containing the following elements:

  • df A data frame containing the data to be plotted.

  • x A character string specifying the name of the column in df to be used for the x-axis.

  • y A character string specifying the name of the column in df to be used for the y-axis.

  • group_var A character string specifying the name of the column in df to be used for grouping the data.

  • ci Optional. A character string specifying the column in df for confidence intervals.

  • lower Optional. A character string specifying the column in df for lower bounds of confidence intervals.

  • upper Optional. A character string specifying the column in df for upper bounds of confidence intervals.

  • error_colour The color for error bars. Default is #f2c75c.

  • line_colour List of colours for lines. Default is blue.

  • line_type Line type for single graph, or list of line types Permissable values: "solid", "dotted", "dashed", "longdash", "dotdash"

  • width A numeric value specifying the width of the lines.

  • title Optional. A character string specifying the title of the plot.

  • x_label Optional. A character string specifying the label for the x-axis.

  • x_label_angle Optional. A numeric value specifying the rotation angle for the x-axis labels.

  • y_label Optional. A character string specifying the label for the y-axis.

  • y_label_angle Optional. A numeric value specifying the rotation angle for the y-axis labels.

  • y_percent Optional. A logical value. If TRUE, the y-axis will be scaled to percentages.

  • st_theme Optional. A ggplot2 theme object to customize the style of the plot.

  • add_points Optional. A logical value. If TRUE, points will be added to the line chart.

...

Additional arguments passed to geom_line for static (ggplot2) plots or to plot_ly/add_trace for dynamic (Plotly) plots, allowing custom styling of the lines (e.g., alpha, size, marker, etc.).

Value

A plotly or ggplot2 object representing the line chart.

Examples

library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.5.1
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(epiviz)

# Import df lab_data from epiviz and do some manipulation before passing for the test
test_df <- epiviz::lab_data

# Manipulating date within df
test_df$specimen_date <- as.Date(test_df$specimen_date)

# Setting start date and end date for aggregation
start_date <- as.Date("2023-01-01")
end_date <- as.Date("2023-12-31")

# Summarization
summarised_df <- test_df |>
  group_by(organism_species_name, specimen_date) |>
  summarize(count = n(), .groups = 'drop') |>
  ungroup() |>
  filter(specimen_date >= start_date & specimen_date <= end_date)

# Ensure that summarised_df is a data frame
summarised_df <- as.data.frame(summarised_df)

 # Create params list
 params <- list(
   df = summarised_df,  # Ensure this is correctly referencing the data frame
   x = "specimen_date", # Ensure this matches the column name exactly
   y = "count",         # Ensure this matches the column name exactly
   group_var = "organism_species_name",  # Ensure this matches the column name exactly
   line_colour = c("blue","green","orange"),
   line_type = c("solid", "dotted", "dashed")
 )
 # Generate the line chart
line_chart(params = params, dynamic = FALSE)


# Generate the line chart
result <- epiviz::line_chart(params = params, dynamic = FALSE)

 # Import df lab_data from epiviz and do some manipulation before passing for the test
 test_df <- epiviz::lab_data

 # Manipulating date within df
 test_df$specimen_date <- as.Date(test_df$specimen_date)

 # Setting start date and end date for aggregation
 start_date <- as.Date("2023-01-01")
 end_date <- as.Date("2023-12-31")

 # Summarization
 summarised_df <- test_df |>
   group_by(organism_species_name, specimen_date) |>
   summarize(count = n(), .groups = 'drop') |>
   ungroup() |>
   filter(specimen_date >= start_date & specimen_date <= end_date)

 # Ensure that summarised_df is a data frame
 summarised_df <- as.data.frame(summarised_df)

 # Create params list
 params <- list(
   df = summarised_df,  # Ensure this is correctly referencing the data frame
   x = "specimen_date", # Ensure this matches the column name exactly
   y = "count",         # Ensure this matches the column name exactly
   group_var = "organism_species_name",  # Ensure this matches the column name exactly
   line_colour = c("blue","green","orange"),
   line_type = c("solid", "dotted", "dashed")
 )

 # Generate the line chart
 epiviz::line_chart(params = params, dynamic = TRUE)