This function provides a flexible way to group data, compute summary statistics, and reshape results. It works similar to `dplyr::summarise()` but with added capabilities for pivoting results wider.
Arguments
- .data
A data frame to summarise, or NULL to create a reusable summary function
- ...
Summary expressions using dplyr-style syntax. Named arguments become column names in the output (e.g., `mean_rt = mean(rt)`).
- .by
Character vector of grouping column names. Default is "condition_idx".
- .wider_by
Character vector of columns to keep as identifiers when pivoting. Default is "condition_idx". Must be a subset of `.by`. When `.wider_by` differs from `.by`, the extra columns in `.by` will be spread across as column suffixes.
Value
- If `.data` is provided: A data frame with summarised results - If `.data` is NULL: A function that can be applied to data later
Details
You can use `summarise_by()` in two ways: 1. **Direct use**: Pass your data directly and get results immediately 2. **Build-then-apply**: Create reusable summary functions, combine them with `+`, then apply to your data later
The build-then-apply approach is useful when you want to compute different types of summaries (e.g., RT statistics and accuracy statistics) and automatically join them together.
Usage with ABC workflows
If you plan to use build_abc_input for ABC analysis, you must use
summarise_by() to generate summary statistics (or manually handle the arrow
output format). This function typically works together with map_by_condition
to process simulation results. See map_by_condition for workflow examples.
Examples
# Example 1: Direct use - pass data and get results immediately
trial_data <- data.frame(
condition_idx = rep(1:2, each = 4),
item_idx = rep(1:2, 4),
rt = c(0.5, 0.6, 0.7, 0.8, 0.55, 0.65, 0.75, 0.85),
accuracy = c(1, 1, 0, 1, 1, 0, 1, 1)
)
# Compute mean RT and accuracy by condition and item
result <- summarise_by(
trial_data,
mean_rt = mean(rt),
mean_acc = mean(accuracy),
.by = c("condition_idx", "item_idx"),
.wider_by = "condition_idx"
)
# Result has columns: condition_idx, mean_rt_item_idx_1, mean_rt_item_idx_2, etc.
result
#> # A tibble: 2 × 5
#> condition_idx mean_rt_item_idx_1 mean_rt_item_idx_2 mean_acc_item_idx_1
#> <int> <dbl> <dbl> <dbl>
#> 1 1 0.6 0.7 0.5
#> 2 2 0.65 0.75 1
#> # ℹ 1 more variable: mean_acc_item_idx_2 <dbl>
# Example 2: Build-then-apply - create reusable summary functions
# Build separate summary functions for different statistics
rt_summary_pipe <- summarise_by(
mean_rt = mean(rt),
sd_rt = stats::sd(rt),
.by = c("condition_idx", "item_idx"),
.wider_by = "condition_idx"
)
acc_summary_pipe <- summarise_by(
mean_acc = mean(accuracy),
n_trials = length(accuracy),
.by = c("condition_idx", "item_idx"),
.wider_by = "condition_idx"
)
# Combine with + and apply to data
combined_summary_pipe <- rt_summary_pipe + acc_summary_pipe
result <- combined_summary_pipe(trial_data)
# Result has all summaries joined by condition_idx
result
#> # A tibble: 2 × 9
#> condition_idx mean_rt_item_idx_1 mean_rt_item_idx_2 sd_rt_item_idx_1
#> <int> <dbl> <dbl> <dbl>
#> 1 1 0.6 0.7 0.141
#> 2 2 0.65 0.75 0.141
#> # ℹ 5 more variables: sd_rt_item_idx_2 <dbl>, mean_acc_item_idx_1 <dbl>,
#> # mean_acc_item_idx_2 <dbl>, n_trials_item_idx_1 <int>,
#> # n_trials_item_idx_2 <int>