knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(knitr) options(knitr.kable.NA = "-")
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 common data
manipulation functions such as filter()
on the data to retrieve the statistics
of interest.
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.
# Load packages library(tidystats) library(dplyr) # Read the .json file containing the statistics and immediately convert it to # a data frame statistics <- read_stats("statistics.json") |> tidy_stats_to_data_frame() # Extract all the p-values p_values <- filter(statistics, statistic_name == "p") p_values
kable(p_values, format = "markdown")
Alternatively, we can can also easily select all significant p-values.
sig_p_values <- filter(statistics, statistic_name == "p" & value < .05)
kable(sig_p_values, format = "markdown")
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.
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.
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
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