Besides using `tidystats`

in combination with Microsoft Word to report statistics, you can also use `tidystats`

to convert a list of statistics into a data frame. This enables researchers to then easily extract specific statistics to perform additional analyses with (e.g., meta-analyses). Below is an example of how to convert a list of statistics to a data frame and several simple operations.

```
# Load packages
library(tidystats)
library(dplyr)
# Read in a tidystats-produced .json file
results <- read_stats("results.json")
# Convert the list to a data frame
results_df <- tidy_stats_to_data_frame(results)
# Select the p-values
p_values <- filter(results_df, statistic == "p")
```

With the current example, this results in the following data frame:

identifier | term | statistic | value | method | type | preregistered |
---|---|---|---|---|---|---|

sleep_test | p | 0.003 | Paired t-test | primary | ||

lm_D9 | p | 0.249 | Linear regression | no | ||

lm_D9 | (Intercept) | p | 0.000 | Linear regression | no | |

lm_D9 | groupTrt | p | 0.249 | Linear regression | no | |

npk_aov | block | p | 0.016 | ANOVA | ||

npk_aov | N | p | 0.004 | ANOVA | ||

npk_aov | P | p | 0.475 | ANOVA | ||

npk_aov | K | p | 0.029 | ANOVA | ||

npk_aov | N:P | p | 0.263 | ANOVA | ||

npk_aov | N:K | p | 0.169 | ANOVA | ||

npk_aov | P:K | p | 0.863 | ANOVA |

Alternatively, you can select all the significant p-values:

identifier | term | statistic | value | method | type | preregistered |
---|---|---|---|---|---|---|

sleep_test | p | 0.003 | Paired t-test | primary | ||

lm_D9 | (Intercept) | p | 0.000 | Linear regression | no | |

npk_aov | block | p | 0.016 | ANOVA | ||

npk_aov | N | p | 0.004 | ANOVA | ||

npk_aov | K | p | 0.029 | ANOVA |

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.

In short, by reading 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.