Visualize difference from avg. by factor (lollipop)

plot_deltas(df, dv, ..., trunc_length = 100, return_data = FALSE, n_field = 9)

Arguments

df	dataframe to evaluate
dv	dependent variable to use (column name)
...	Arguments passed on to `refactor_columns` `split_on` variable to split data / group by `id_col` field to use as ID `n_cat` for categorical variables, the max number of unique values to keep. This field feeds the `forcats::fct_lump(n = )` argument. `collapse_by` should `n_cat` collapse by the distance to the grand mean `"dv"` leaving the extremes as is and grouping factors closer to the grand mean as "other" or should it use size `"n"` `n_quantile` for numeric/date fields, the number of quantiles used to split the data into a factor. Fields that have less than this amount will not be changed. `n_digits` for numeric fields, the number of digits to keep in the breaks ex: [1.2345 to 2.3456] will be [1.23 to 2.34] if `n_digits = 2` `avg_type` mean or median `ignore_cols` columns to ignore from analysis. Good candidates are fields that have have no duplicate values (primary keys) or fields with a large proportion of null values
trunc_length	number of charcters to print on y-axis
return_data	When TRUE will return data frame instead of a plot.
n_field	How many fields/facets should the plot return.

Examples


# show distances (deltas) from grand mean
plot_deltas(df = ggplot2::mpg, dv = hwy)

# adjust aesthetics with 'trunc_length' and 'n_field'
plot_deltas(
  df = ggplot2::mpg,
  dv = hwy,
  trunc_length = 15,
  n_field = 5
)