Aggregate data by response, confidence, and other columns

Counts number of rows in data with unique combinations values in the columns response, confidence, and any other columns in ....

Usage

aggregate_metad(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  .name = "N",
  K = NULL
)

Arguments

data: The data frame to aggregate
...: Grouping columns in data. These columns will be converted to factors.
.stimulus: The name of "stimulus" column
.response: The name of "response" column
.confidence: The name of "confidence" column
.joint_response: The name of "joint_response" column
.name: The name of the resulting column containing trial counts
K: The number of confidence levels in data. If NULL, this is estimated from data.

Value

A tibble with one row per combination of the variables in ..., and another column named by the value of .response containing trial counts. For $K$ confidence levels, this will be an $N \times K*4$ matrix, such that the columns represent (for stimulus $S$, type 1 response $R$, and type 2 response $C$): $$ [N_{S=0, R=0, C=K}, \ldots, N_{S=0, R=0, C=1}, \\ N_{S=0, R=1, C=1}, \ldots, N_{S=0, R=1, C=K}, \\ N_{S=1, R=0, C=K}, \ldots, N_{S=1, R=0, C=1}, \\ N_{S=1, R=1, C=1}, \ldots, N_{S=1, R=1, C=K}] \\ $$

Details

The data frame data must have one column with the name given by .stimulus. Additionally, it must have either:

Two columns with names given by .response and .confidence
One column with the name given by .joint_response

Finally, it must also have columns for any additional variables in ....

Examples

# aggregate a dataset without grouping factors
d <- sim_metad()
aggregate_metad(d)
#> # A tibble: 1 × 3
#>     N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"] [,"N_0_6"]
#>   <int> <int>       <int>      <int>      <int>      <int>      <int>      <int>
#> 1    50    50           8          5          4         12         10          7
#> # ℹ 1 more variable: N[7:16] <int>

# aggregate a dataset with grouping factors
d2 <- sim_metad_condition()
aggregate_metad(d2, condition)
#> # A tibble: 2 × 4
#>   condition   N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"]
#>   <fct>     <int> <int>       <int>      <int>      <int>      <int>      <int>
#> 1 1            50    50           7          4         10         16          5
#> 2 2            50    50           3          6         10         13          9
#> # ℹ 1 more variable: N[6:16] <int>

# can also aggregate ignoring grouping factors
aggregate_metad(d2)
#> # A tibble: 1 × 3
#>     N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"] [,"N_0_6"]
#>   <int> <int>       <int>      <int>      <int>      <int>      <int>      <int>
#> 1   100   100          10         10         20         29         14          9
#> # ℹ 1 more variable: N[7:16] <int>

# aggregate data with only `joint_response` column
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
d |>
  ungroup() |>
  mutate(joint_response = joint_response(
    response, confidence,
    n_distinct(confidence)
  )) |>
  select(-response, -confidence) |>
  aggregate_metad()
#> # A tibble: 1 × 3
#>     N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"] [,"N_0_6"]
#>   <int> <int>       <int>      <int>      <int>      <int>      <int>      <int>
#> 1    50    50           8          5          4         12         10          7
#> # ℹ 1 more variable: N[7:16] <int>