Plot proportions of factor levels (y axis) by bins (x axis)

Bin a numerical column for the categorical x axis. Calculate the proportion of the rows that are present for each level of an additional factor column, like sex, by x-axis bins.

Usage

plot_proportion(
  data,
  column_factor,
  column_bin,
  digits = 0,
  bar_width = c("n", "equal"),
  ...
)

Arguments

data

A data frame.

column_factor

<data-masking> Variable in data for the grouping structure. Should be a variable in data that contains a factor but it can also be a variable in data that stores characters. See the examples for ideas.

column_bin

<data-masking> Variable in data for the binning structure. If this is not already a factor or character, then ggplot2::cut_width() will be used to create bins. The bins are saved in bin in the returned ggplot2 object.

digits

A numeric value passed to the digits argument of round(). Positive values pertain to rounding the right side of the decimal place and negative values pertain to rounding the left side of the decimal place, i.e., to the tens or hundreds with -1 and -2. The default is to round to the nearest integer using digits = 0.

bar_width

A string of "n" or "equal", where the former leads to bar widths based on the number of observations contained in that group and the latter leads to equally-sized bars for all bars with data. For groups without any data, the width of the placeholder on the x axis will be smaller than the width of the bars for groups with data regardless of which option you choose. The default is to have bars of variable widths, i.e., "n".

...

Additional arguments that users want to pass to ggplot2::cut_width(). If data[[column_bin]] is not a factor, then at least width needs to be passed to create bins. But, any argument accepted by ggplot2::cut_width() can be passed, where boundary = 0 is common so the calculation of bins will start at zero rather than the minimum value present, preventing bins like (35.4, 36.4] when you really want (35, 36].

Value

A ggplot object created with geom_mosaic features.

Details

Create a plot object using ggplot for categorical data that can be modified upon return.

See also

• ggplot2::cut_width()

• ggmosaic::geom_mosaic()

• factor()

Other plot_: PlotSexRatio.fn(), PlotVarLengthAtAge.fn(), plot_age_length_sampling(), plot_bio_patterns(), plot_comps(), plot_cpue(), plot_cpue_map(), plot_index(), plot_length_age(), plot_sex_ratio(), plot_sex_ratio_strata(), plot_var_length_at_age(), plot_weight_length(), plot_westcoast()

Author

Ian G. Taylor, Chantel R. Wetzel, Kelli F. Johnson

Examples

if (FALSE) { # \dontrun{
# Add presence/absence factor to data
catch_data <- pull_catch(
  common_name = "petrale sole"
)
temp <- catch_data |>
  dplyr::mutate(new = factor(
    cpue_kg_km2 <= 0,
    levels = c(FALSE, TRUE),
    labels = c("Present", "Absent")
  ))

# Plot depth bins (50 m) by presence/absence with default colors
plot_proportion(
  data = temp,
  column_factor = new,
  column_bin = Depth_m,
  width = 50,
  boundary = 0
)
# Plot latitude bins (1 decimal degree) by presence/absence
# with custom colors
plot_proportion(
  data = temp,
  column_factor = new,
  column_bin = Latitude_dd,
  width = 1,
  boundary = 0
) +
  ggplot2::scale_fill_manual(values = c(
    "darkorchid3",
    grDevices::gray(0.7)
  ))
# Plot depth bins (25 m) by sex (F, M, U)
plot_proportion(
  data = bio_nwfsc_combo |>
    dplyr::mutate(Sex = codify_sex(Sex)),
  column_factor = Sex,
  column_bin = Depth_m,
  width = 25,
  boundary = 0
)
# Change to equal sized bars
plot_proportion(
  data = bio_nwfsc_combo |>
    dplyr::mutate(Sex = codify_sex(Sex)),
  column_factor = Sex,
  column_bin = Depth_m,
  width = 25,
  boundary = 0,
  bar_width = "equal"
)
} # }