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Automatically plot an epi_df or epi_archive

Source: R/autoplot.R
autoplot-epi.Rd

Automatically plot an epi_df or epi_archive

Usage

# S3 method for class 'epi_df'
autoplot(
  object,
  ...,
  .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
  .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
  .base_color = "#3A448F",
  .facet_filter = NULL,
  .max_facets = deprecated(),
  .max_keys = 10,
  .interactive = FALSE,
  .facet_to_dropdown = FALSE
)

# S3 method for class 'epi_archive'
autoplot(
  object,
  ...,
  .base_color = "black",
  .versions = NULL,
  .mark_versions = FALSE,
  .facet_filter = NULL,
  .max_keys = 6,
  .interactive = FALSE,
  .facet_to_dropdown = FALSE
)

# S3 method for class 'epi_df'
plot(x, ...)

# S3 method for class 'epi_archive'
plot(x, ...)

Arguments

object, x

An epi_df or epi_archive

...

<tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

.color_by

Which variables should determine the color(s) used to plot lines. Options include:

  • all_keys - the default uses the interaction of any key variables including the geo_value

  • geo_value - geo_value only

  • other_keys - any available keys that are not geo_value

  • .response - the numeric variables (same as the y-axis)

  • all - uses the interaction of all keys and numeric variables

  • none - no coloring aesthetic is applied

.facet_by

Similar to .color_by except that the default is to display each numeric variable on a separate facet

.base_color

Lines will be shown with this color if .color_by == "none". For example, with a single numeric variable and faceting by geo_value, all locations would share the same color line.

.facet_filter

Select which facets will be displayed. Especially useful for when there are many geo_value's or keys. This is a <rlang> expression along the lines of dplyr::filter(). However, it must be a single expression combined with the & operator. This contrasts to the typical use case which allows multiple comma-separated expressions which are implicitly combined with &. When multiple variables are selected with ..., their names can be filtered in combination with other factors by using .response_name. See the examples below.

.max_facets

[Deprecated]

.max_keys

Maximum number of key combinations to display. If the data contains more key combinations than this limit, a random sample of size .max_keys is displayed, and a warning is issued. Set to Inf to display all keys. Subsampling is not performed if .interactive = TRUE (though a similar limit may be applied to initial legend visibility) when .facet_to_dropdown = FALSE.

.interactive

Logical. If TRUE, returns an interactive plotly::ggplotly() widget instead of a static ggplot2::ggplot() object. This is especially useful for exploring datasets with many keys. Default is FALSE.

.facet_to_dropdown

Logical. If TRUE, and .interactive = TRUE, any facets will be converted into a dropdown menu. This is useful for maximizing screen real estate when there are many facets. Default is FALSE.

.versions

Select which versions will be displayed. By default, a separate line will be shown with the data as it would have appeared on every day in the archive. This can sometimes become overwhelming. For example, daily data would display a line for what the data would have looked like on every single day. To override this, you can select specific dates, by passing a vector of values here. Alternatively, a sequence can be automatically created by passing a string like "2 weeks" or "month". For time types where the time_value is a date object, any string that is interpretable by base::seq.Date() is allowed.

For time_type = "integer", an integer larger than 1 will give a subset of versions.

.mark_versions

Logical. Indicate whether to mark each version with a vertical line. Note that displaying many versions can become busy.

Value

A ggplot2::ggplot object, OR plotly::plotly object if .interactive = TRUE

Examples


# -- Use it on an `epi_df`
autoplot(cases_deaths_subset, case_rate_7d_av, death_rate_7d_av)
#> Warning: Too many key combinations to display clearly. Showing 10 of 12.
#> → To plot all keys, use `autoplot(..., .max_keys = Inf)`.
#> → To explore all keys interactively, use `autoplot(..., .interactive = TRUE)`.
#> → To plot specific keys, use `autoplot(..., .facet_filter = ...)`.


# Launch interactive version in web browser:
autoplot(cases_deaths_subset, case_rate_7d_av, death_rate_7d_av,
  .interactive = TRUE
)



# Use dropdowns instead of facets for interactive plots
autoplot(cases_deaths_subset, case_rate_7d_av, death_rate_7d_av,
  .interactive = TRUE, .facet_to_dropdown = TRUE
)



autoplot(cases_deaths_subset, case_rate_7d_av,
  .color_by = "none",
  .facet_by = "geo_value"
)


autoplot(cases_deaths_subset, case_rate_7d_av,
  .color_by = "none",
  .base_color = "red", .facet_by = "geo_value"
)


# .base_color specification won't have any effect due .color_by default
autoplot(cases_deaths_subset, case_rate_7d_av,
  .base_color = "red", .facet_by = "geo_value"
)


# filter to only some facets, must be explicitly combined
autoplot(cases_deaths_subset, cases, death_rate_7d_av,
  .facet_by = "all",
  .facet_filter = (.response_name == "cases" & geo_value %in% c("tx", "pa")) |
    (.response_name == "death_rate_7d_av" &
      geo_value %in% c("ca", "fl", "ga", "ny"))
)


# Just an alias for convenience
plot(cases_deaths_subset, cases, death_rate_7d_av,
  .facet_by = "all",
  .facet_filter = (.response_name == "cases" & geo_value %in% c("tx", "pa")) |
    (.response_name == "death_rate_7d_av" &
      geo_value %in% c("ca", "fl", "ga", "ny"))
)


# -- Use it on an archive

autoplot(archive_cases_dv_subset, percent_cli, .versions = "week")
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_line()`).

autoplot(archive_cases_dv_subset_all_states, percent_cli,
  .versions = "week",
  .facet_filter = geo_value %in% c("or", "az", "vt", "ms")
)
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_line()`).

autoplot(archive_cases_dv_subset, percent_cli,
  .versions = "month",
  .facet_filter = geo_value == "ca"
)
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_line()`).

autoplot(archive_cases_dv_subset_all_states, percent_cli,
  .versions = "1 month",
  .facet_filter = geo_value %in% c("or", "az", "vt", "ms"),
  .mark_versions = TRUE
)
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_line()`).

# Just an alias for convenience
plot(archive_cases_dv_subset_all_states, percent_cli,
  .versions = "1 month",
  .facet_filter = geo_value %in% c("or", "az", "vt", "ms"),
  .mark_versions = TRUE
)
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_line()`).