Turn tidy data frames of variables from a Bayesian model back into untidy data
Inverse operations of spread_draws() and gather_draws(), giving
results that look like tidy_draws().
ungather_draws(
data,
...,
variable = ".variable",
value = ".value",
draw_indices = c(".chain", ".iteration", ".draw"),
drop_indices = FALSE
)
unspread_draws(
data,
...,
draw_indices = c(".chain", ".iteration", ".draw"),
drop_indices = FALSE
)data |
A tidy data frame of draws, such as one output by |
... |
Expressions in the form of
|
variable |
The name of the column in |
value |
The name of the column in |
draw_indices |
Character vector of column names in |
drop_indices |
Drop the columns specified by |
These functions take symbolic specifications of variable names and dimensions in the same format as
spread_draws() and gather_draws() and invert the tidy data frame back into
a data frame whose column names are variables with dimensions in them.
A data frame.
Matthew Kay
library(dplyr) data(RankCorr, package = "ggdist") # We can use unspread_draws to allow us to manipulate draws with tidybayes # and then transform the draws into a form we can use with packages like bayesplot. # Here we subset b[i,j] to just values of i in 1:2 and j == 1, then plot with bayesplot RankCorr %>% spread_draws(b[i,j]) %>% filter(i %in% 1:2, j == 1) %>% unspread_draws(b[i,j], drop_indices = TRUE) %>% bayesplot::mcmc_areas() # As another example, we could use compare_levels to plot all pairwise comparisons # of b[1,j] for j in 1:3 RankCorr %>% spread_draws(b[i,j]) %>% filter(i == 1, j %in% 1:3) %>% compare_levels(b, by = j) %>% unspread_draws(b[j], drop_indices = TRUE) %>% bayesplot::mcmc_areas()
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