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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:

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.


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.

t_test <- t.test(response ~ source, data = data)
##  Welch Two Sample t-test
## data:  response by source
## t = -12.801, df = 6322.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Bin Laden and group Washington is not equal to 0
## 95 percent confidence interval:
##  -0.8017441 -0.5888010
## sample estimates:
##  mean in group Bin Laden mean in group Washington 
##                 5.232241                 5.927514

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.

lm_us_or_not <- lm(response ~ source * us_or_international, data = data)
## Call:
## lm(formula = response ~ source * us_or_international, data = data)
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0047 -1.2278 -0.2278  1.7722  3.7722 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             5.24884    0.08489  61.832  < 2e-16 ***
## sourceWashington                        0.40522    0.11720   3.457 0.000549 ***
## us_or_internationalUS                  -0.02101    0.09550  -0.220 0.825891    
## sourceWashington:us_or_internationalUS  0.37169    0.13227   2.810 0.004966 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 2.159 on 6321 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.02748,    Adjusted R-squared:  0.02701 
## F-statistic: 59.53 on 3 and 6321 DF,  p-value: < 2.2e-16

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?

lm_age <- lm(response ~ source * age, data = data)
## Call:
## lm(formula = response ~ source * age, data = data)
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7433 -1.2018 -0.1516  1.7928  3.8828 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          4.933702   0.095891  51.451  < 2e-16 ***
## sourceWashington     0.557365   0.135579   4.111 3.99e-05 ***
## age                  0.011470   0.003361   3.412 0.000648 ***
## sourceWashington:age 0.005453   0.004783   1.140 0.254328    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 2.156 on 6308 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.03085,    Adjusted R-squared:  0.03038 
## F-statistic: 66.92 on 3 and 6308 DF,  p-value: < 2.2e-16

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.

# Create an empty list to store the analyses in
statistics <- list()

# Add the analyses
statistics <- statistics |>
    preregistered = TRUE, type = "primary",
    notes = "A t-test comparing the effect of source on the quote rating."
  ) |>
    preregistered = FALSE, type = "exploratory",
    notes = "Interaction effect with being from the U.S. or not."
  ) |>

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.

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.


Lorge, I., & Curtiss, C. C. (1936). Prestige, suggestion, and attitudes. The Journal of Social Psychology, 7, 386-402.

Klein, R.A. et al. (2014) Investigating Variation in Replicability: A “Many Labs” Replication Project. Social Psychology, 45(3), 142-152.