Prepare EDA data for a single variable

Classifies y_col using eda_classify_var, pre-processes categorical levels (adding an explicit "(Missing)" level), and returns an hv_eda object. Call plot.hv_eda on the result to obtain a bare ggplot2 barplot or scatter plot that you can decorate with colour scales and hv_theme.

Usage

hv_eda(
  data,
  x_col = "year",
  y_col = "ef",
  y_label = NULL,
  unique_limit = 6L,
  show_percent = FALSE
)

Arguments

data

Data frame; one row per observation.

x_col

Name of the reference (time/grouping) column. Used as the x-axis for both scatter and bar plots. Default "year".

y_col

Name of the variable to plot. Default "ef".

y_label

Optional human-readable label for the variable, used as the plot title, y-axis label (continuous), and fill-legend name (categorical). When NULL (default), y_col is used.

unique_limit

Integer threshold passed to eda_classify_var to distinguish categorical from continuous numeric columns. Default 6.

show_percent

Logical; for categorical plots, use proportions (position = "fill") instead of counts (position = "stack")? Default FALSE.

Value

An object of class c("hv_eda", "hv_data"):

$data

Pre-processed data frame ready for plotting. For continuous variables: two columns x and y. For categorical variables: columns x (factor) and fill (factor with "(Missing)" as explicit level).

$meta

Named list: x_col, y_col, y_label, var_type ("Cont", "Cat_Num", or "Cat_Char"), show_percent, n_obs.

$tables

For continuous variables: rug_data — rows where y_col is NA, used for the rug layer. Empty list for categorical variables.

Details

Iterate over variables with lapply() after selecting columns with eda_select_vars.

References

R templates: tp.dp.EDA_barplots_scatterplots.R, tp.dp.EDA_barplots_scatterplots_varnames.R; helper: Barplot_Scatterplot_Function.R (Function_DataPlotting(), Order_Variables()).

See also

plot.hv_eda, sample_eda_data, eda_classify_var, eda_select_vars

Examples

dta <- sample_eda_data(n = 300, seed = 42)

# 1. Build data object (binary categorical)
ed <- hv_eda(dta, x_col = "year", y_col = "male", y_label = "Sex")
ed  # prints var_type and observation count
#> <hv_eda>
#>   Variable    : male  [Cat_Num]
#>   Label       : Sex
#>   x col       : year
#>   N obs       : 300

# 2. Bare plot -- undecorated ggplot returned by plot.hv_eda
p <- plot(ed)

# 3. Decorate: fill palette, x-axis breaks, labels, theme
p +
  ggplot2::scale_fill_manual(
    values = c("0" = "steelblue", "1" = "firebrick", "(Missing)" = "grey80"),
    labels = c("0" = "Female", "1" = "Male", "(Missing)" = "Missing"),
    name   = NULL
  ) +
  ggplot2::scale_x_discrete(breaks = seq(2005, 2020, 5)) +
  ggplot2::labs(x = "Surgery Year", y = "Count") +
  hv_theme("poster")


# Continuous variable -- same 3-step pattern
ed2 <- hv_eda(dta, x_col = "op_years", y_col = "ef",
                y_label = "Ejection Fraction (%)")
plot(ed2) +
  ggplot2::scale_colour_manual(values = c("firebrick"), guide = "none") +
  ggplot2::scale_x_continuous(breaks = seq(0, 15, 5)) +
  ggplot2::labs(x = "Years from First Surgery Year") +
  hv_theme("poster")


# Variable selection + lapply (varnames template pattern)
cont_vars <- c(ef = "Ejection Fraction (%)",
               lv_mass = "LV Mass Index (g/m\u00b2)",
               peak_grad = "Peak Gradient (mmHg)")
sub_cont <- eda_select_vars(dta, c("op_years", names(cont_vars)))
p_cont <- lapply(names(cont_vars), function(cn) {
  plot(hv_eda(sub_cont, x_col = "op_years", y_col = cn,
               y_label = cont_vars[[cn]])) +
    ggplot2::scale_colour_manual(values = c("steelblue"), guide = "none") +
    ggplot2::scale_x_continuous(breaks = seq(0, 15, 5)) +
    ggplot2::labs(x = "Years from First Surgery Year") +
    hv_theme("poster")
})
p_cont[[1]]