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Construct labels

construct_label_2.Rd

This function creates a new column or label, merging estimates and standard errors with significant estimates represented in bold or as superscript (via label_est), and standard errors in brackets, if needed (via label_se). NAs are converted to empty strings. Main usage is for plotting tables and brace labels.

Usage

construct_label_2(
  dat,
  new_name = "label",
  label_est = NULL,
  label_se = NULL,
  label_sig_bold = NULL,
  label_sig_superscript = NULL,
  label_sig_superscript_extra_column = FALSE,
  round_est = 0,
  round_se = 1,
  plot_settings = plotsettings_tablebarplot()
)

Arguments

dat

Data frame with the columns that should be merged into labels.

new_name

Character string for the new column that is added to dat. Defaults to 'label'.

label_est

Character string for the column with the estimates.

label_se

Character string for the column with the standard errors.

label_sig_bold

Character string for the column with the significant estimates that should determine the bold printing.

label_sig_superscript

Character string for the column with the significant estimates that should determine the superscript.

label_sig_superscript_extra_column

Logical, if set 'FALSE' the superscript for significant values is added directly into the label (necessary for line plots), if set 'TRUE' the superscript for significant values is written into an extra column with the ending '_sig_superscript' (necessary for tables).

round_est

Rounding of label_est.

round_se

Rounding of label_se.

plot_settings

Named list constructed with plotsettings_lineplot(). Defaults to a list with all settings set to 0. There are several predefined lists with optimized settings for different plots. See plotsettings_lineplot() for an overview.

Value

The data frame with an added column for the constructed label.

Examples

# example data frame
dat <- data.frame(
  names = c("Berlin", "Hamburg", "Hessen", "Niedersachsen", "Saarland"),
  estimate = c(400, 650, 380, 500, 600),
  se = c(0.1, 0.45, 1, 0.27, 0.9),
  p_estimate = c(FALSE, FALSE, TRUE, TRUE, FALSE)
)