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title: "Introduction to tidystats"
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## Why use tidystats?
The tidystats package is designed to address two problems common in
scientific research: incomplete and incorrect statistics reporting. The
problem of incomplete statistics reporting likely stems from a fundamental
trade-off between comprehensively reporting all statistics on the one hand and
providing a clear, easy-to-read, text on the other. Word limits don't help
either. The problem of incorrect statistics reporting is likely caused by
manually copy-pasting statistical output from the output window into a text
editor. tidystats addresses these two problems by enabling researchers to
combine the statistics from their statistical analyses into a single
file, which can be shared with others and used to report statistics using a
text editor add-in.
## How to use tidystats?
tidystats is designed to easily fit in your data analysis workflow. In fact,
tidystats can simply be tacked on at the end of a data analysis session,
assuming you have stored the results from each statistical function into a
variable. For example, when you create a regression model using the `lm()`
function, you often store the result of that analysis in a variable:
```{r}
model <- lm(extra ~ group, data = sleep)
```
By storing the output of statistical functions in a variable, you can use the
`add_stats()` function from tidystats to extract the statistics and add them to
a list. Once all the statistics are gathered together, you save them to a .json
file using the `write_stats()` function. This .json file can then be read by a
text editor add-in to report the statistics, or shared with others and read into
R to extract statistics (e.g., for meta-analyses).
## An example
Below follows an example of several analyses conducted on the `quote_source`
data contained within the tidystats package. The data is from a large-scaled
replication of Lorge & Curtiss (1936). More details can be found in the paper
of the replication effort (Klein et al., 2014). In short, participants saw the
following quote:
> "I have sworn to only live free, even if I find bitter the taste of death."
The quote was attributed to either George Washington, a liked individual, or
Osama Bin Laden, a disliked individual. Participants were asked to what extent
they agree with the quote, on a 9-point Likert scale ranging from 1
(disagreement) to 9 (agreement).
We start with a bit of setup.
```{r setup, message = FALSE}
library(tidystats)
data <- quote_source
```
The main hypothesis is that people will like the quote more when it is
attributed to George Washington compared to Osama Bin Laden. We test this
hypothesis by conducting a *t*-test.
```{r t-test}
t_test <- t.test(response ~ source, data = data)
t_test
```
Participants appear to rate the quote a bit more positively when it is
attributed to George Washington.
We can also perform some additional tests. For instance, does it matter if the
participant is from the US? And does age matter? To answer these questions, we
can perform interaction tests using `lm()`.
Let's start with the interaction between the source and whether the participant
is from the U.S. or not.
```{r regression1}
lm_us_or_not <- lm(response ~ source * us_or_international, data = data)
summary(lm_us_or_not)
```
The interaction is significant, so it appears to matter whether the participant
is from the U.S. or not. In fact, participants from the U.S. show a stronger
effect than those from outside the U.S.
What about the interaction between source and the participant's age?
```{r regression2}
lm_age <- lm(response ~ source * age, data = data)
summary(lm_age)
```
No significant interaction effect, so we do not have evidence for age changing
the size of the effect.
Let's say these are the analyses we want to save the output of and report later.
This is where tidystats comes in. The steps to perform are to first create an
empty list and then to use the `add_stats()` function to add statistics to the
list. This is why we stored each analysis into a variable. The `add_stats()`
function takes an analysis, extracts the statistics, and adds the result to the
list. Optionally, you can add additional information about each analysis, such
as whether it was preregistered, whether it was a primary, secondary, or
exploratory analysis, or simply add some notes.
```{r tidystats-example}
# Create an empty list to store the analyses in
statistics <- list()
# Add the analyses
statistics <- statistics |>
add_stats(
t_test,
preregistered = TRUE, type = "primary",
notes = "A t-test comparing the effect of source on the quote rating."
) |>
add_stats(
lm_us_or_not,
preregistered = FALSE, type = "exploratory",
notes = "Interaction effect with being from the U.S. or not."
) |>
add_stats(lm_age)
```
You can see that I added quite some information to the first and second
analysis. I did this to add some documentation about why each analysis was
conducted. This might be particularly helpful when others will import the file
and use it for meta-research (e.g., performing a meta-analysis or p-curve
analysis).
To save these analyses to a file, use the `write_stats()` function.
```{r saving, eval = FALSE}
write_stats(statistics, "lorge-curtiss-1936-replication.json")
```
Note the file extension: .json. These types of files are simply text files, but
in a format that is machine-readable (unfortunately, not very human-readable).
This file can be used to share with others so that they can read it back into R
and extract statistics (e.g., for meta-analyses) or by you to report the
statistics in a text editor.
## References
Lorge, I., & Curtiss, C. C. (1936). Prestige, suggestion, and attitudes.
*The Journal of Social Psychology*, *7*, 386-402.
https://doi.org/10.1080/00224545.1936.9919891
Klein, R.A. et al. (2014) Investigating Variation in Replicability: A "Many
Labs" Replication Project. *Social Psychology*, *45*(3), 142-152.
https://dx.doi.org/10.1027/1864-9335/a000178