Date: 2026-06-28

# Converting statistics to a data frame

#### 2026-06-28

Besides using tidystats in combination with a text editor add-in to report statistics, you can also use tidystats to read and use the statistics for other purposes. For example, researchers can extract specific statistics and perform analyses such as meta-analyses or a p-curve analysis on the extracted statistics.

One particular useful function that was created for this purpose is `tidy_stats_to_data_frame()`. This function converts a tidystats list of statistics to a standard data frame. That means you can use standard subsetting functions such as `subset()` on the data to retrieve the statistics of interest.

## An example

Below is an example of how to convert a list of statistics to a data frame and perform several simple operations.

In the example below we read the tidystats list and select all the p-values.

    library(tidystats)

    # Read the .json file containing the statistics and immediately convert it to
    # a data frame
    stats_file <- system.file("extdata", "statistics.json", package = "tidystats")
    if (!nzchar(stats_file)) stats_file <- "../inst/extdata/statistics.json"

    statistics <- read_stats(stats_file) |>
      tidy_stats_to_data_frame()

    # Extract all the p-values
    p_values <- subset(statistics, statistic_name == "p")

    p_values

| identifier | analysis_name | group_name_1 | group_name_2 | statistic_name | symbol | subscript | lower | value | upper | interval | level
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sleep_test.5 | sleep_test | sleep\(extra[sleep\)group == 1] and sleep\(extra[sleep\)group == 2] | - | - | p | - | - | - | 0.002833 | - | - | -
lm_D9.6 | lm_D9 | weight ~ group | Model | - | p | - | - | - | 0.249023 | - | - | -
lm_D9.12 | lm_D9 | weight ~ group | Coefficients | (Intercept) | p | - | - | - | 0.000000 | - | - | -
lm_D9.17 | lm_D9 | weight ~ group | Coefficients | groupTrt | p | - | - | - | 0.249023 | - | - | -
npk_aov.6 | npk_aov | yield ~ block + N * P * K | Terms | block | p | - | - | - | 0.015939 | - | - | -
npk_aov.12 | npk_aov | yield ~ block + N * P * K | Terms | N | p | - | - | - | 0.004372 | - | - | -
npk_aov.18 | npk_aov | yield ~ block + N * P * K | Terms | P | p | - | - | - | 0.474904 | - | - | -
npk_aov.24 | npk_aov | yield ~ block + N * P * K | Terms | K | p | - | - | - | 0.028795 | - | - | -
npk_aov.30 | npk_aov | yield ~ block + N * P * K | Terms | N:P | p | - | - | - | 0.263165 | - | - | -
npk_aov.36 | npk_aov | yield ~ block + N * P * K | Terms | N:K | p | - | - | - | 0.168648 | - | - | -
npk_aov.42 | npk_aov | yield ~ block + N * P * K | Terms | P:K | p | - | - | - | 0.862752 | - | - | -

Alternatively, we can can also easily select all significant p-values.

    sig_p_values <- subset(statistics, statistic_name == "p" & value < .05)

| identifier | analysis_name | group_name_1 | group_name_2 | statistic_name | symbol | subscript | lower | value | upper | interval | level
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sleep_test.5 | sleep_test | sleep\(extra[sleep\)group == 1] and sleep\(extra[sleep\)group == 2] | - | - | p | - | - | - | 0.002833 | - | - | -
lm_D9.12 | lm_D9 | weight ~ group | Coefficients | (Intercept) | p | - | - | - | 0.000000 | - | - | -
npk_aov.6 | npk_aov | yield ~ block + N * P * K | Terms | block | p | - | - | - | 0.015939 | - | - | -
npk_aov.12 | npk_aov | yield ~ block + N * P * K | Terms | N | p | - | - | - | 0.004372 | - | - | -
npk_aov.24 | npk_aov | yield ~ block + N * P * K | Terms | K | p | - | - | - | 0.028795 | - | - | -

This could be useful if you want to conduct a p-curve analysis. Although do note that you should not blindly select all p-values. You should select only the p-values that are relevant to a particular hypothesis. If researchers provide the correct meta-information for each test (e.g., by indicating whether it is a primary analysis), this could help meta-researchers make correct decisions about which statistics to include in their analyses.

## Summary

By importing a tidystats-produced file of statistics, you can convert the statistics to a data frame using the `tidy_stats_to_data_frame` function and apply common data transformation functions to extract specific statistics. These statistics can then be used in analyses such as meta-analyses, p-curve analyses, or other analyses.

## References

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). p-curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9(6), 666-681. https://doi.org/10.1177/1745691614553988