**Authors:** Willem Sleegers, Arnoud Plantinga

**License:** MIT

`tidystats`

is a package to easily create a text file containing the output of statistical models. The goal of this package is to help researchers accompany their manuscript with an organized data file of statistical results in order to greatly improve the reliability of meta-research and to reduce statistical reporting errors.

To make this possible, `tidystats`

relies on tidy data principles to combine the output of statistical analyses such as *t*-tests, correlations, ANOVAs, and regression.

Besides enabling you to create an organized data file of statistical results, the `tidystats`

package also contains functions to help you report statistics in APA style. Results can be reported using R Markdown or using a new built-in Shiny app. Additionally, development has started on a Google Docs plugin that uses a tidystats data file to report statistics.

Please see below for instructions on how to install and use this package. **Do note that the package is currently in development. This means the package may contain bugs and is subject to significant changes.** If you find any bugs or if you have any feedback, please let me know by creating an issue here on Github (it’s really easy to do!).

`tidystats`

can be installed from CRAN and the latest version can be installed from Github using devtools.

Load the package and start by creating an empty list to store the results of statistical models in.

The main function is `add_stats()`

. The function has 2 necessary arguments:

`results`

: The list you want to add the statistical output to.`output`

: The output of a statistical test you want to add to the list (e.g., the output of`t.test()`

or`lm()`

)

Optionally you can also add an identifier, type, whether the analysis was confirmatory or exploratory, and additional notes using the `identifier`

, `type`

, `confirmatory`

, and `notes`

arguments, respectively.

The `identifier`

is used to identify the model (e.g., ‘weight_height_correlation’). If you do not provide one, one is automatically created for you.

The `type`

argument is used to indicate whether the statistical test is a hypothesis test, manipulation check, contrast analysis, or other kind of analysis such as descriptives. This can be used to distinguish the vital statistical tests from those less relevant.

The `confirmatory`

argument is used to indicate whether the test was confirmatory or exploratory. It can also be ommitted.

The `notes`

argument is used to add additional information which you may find fruitful. Some statistical tests have default `notes`

output (e.g., t-tests), which will be overwritten when a `notes`

argument is supplied to the `add_stats()`

function.

**Package:** stats

`t.test()`

`cor.test()`

`lm()`

`glm()`

`aov()`

`chisq.test()`

`wilcox.test()`

`fisher.test()`

**Package:** psych

`alpha()`

`corr.test()`

`ICC()`

**Package:** lme4 and lmerTest

`lmer()`

In the following example we perform several statistical tests on a data set, add the output of these results to a list, and save the results to a file.

The data set is called `cox`

and contains the data of a replication attempt of C.R. Cox, J. Arndt, T. Pyszczynski, J. Greenberg, A. Abdollahi, and S. Solomon (2008, JPSP, 94(4), Exp. 6) by Wissink et al. The replication study was part of the Reproducibility Project (see https://osf.io/ezcuj/). The data set is part of the `tidystats`

package.

```
# Perform analyses
M1_condition <- t.test(call_parent ~ condition, data = cox, paired = TRUE)
M2_parent_siblings <- cor.test(cox$call_parent, cox$call_siblings,
alternative = "greater")
M3_condition_anxiety <- lm(call_parent ~ condition * anxiety , data = cox)
M4_condition_sex <- aov(call_parent ~ condition * sex, data = cox)
# Add results
results <- results %>%
add_stats(M1_condition) %>%
add_stats(M2_parent_siblings) %>%
add_stats(M3_condition_anxiety) %>%
add_stats(M4_condition_sex)
```

To write the results to a file, use `write_stats()`

with the results list as the first argument.

To see how the data was actually tidied, you can open the .csv file or you can convert the tidystats results list to a table, as shown below.

```
library(dplyr)
library(knitr)
options(knitr.kable.NA = '-')
results %>%
stats_list_to_df() %>%
select(-notes) %>%
kable()
```

identifier | group | term_nr | term | statistic | value | method |
---|---|---|---|---|---|---|

M1_condition | - | - | - | mean of the differences | -2.7700000 | Paired t-test |

M1_condition | - | - | - | t | -1.2614135 | Paired t-test |

M1_condition | - | - | - | df | 99.0000000 | Paired t-test |

M1_condition | - | - | - | p | 0.2101241 | Paired t-test |

M1_condition | - | - | - | 95% CI lower | -7.1272396 | Paired t-test |

M1_condition | - | - | - | 95% CI upper | 1.5872396 | Paired t-test |

M1_condition | - | - | - | null value | 0.0000000 | Paired t-test |

M2_parent_siblings | - | - | - | r | -0.0268794 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | t | -0.3783637 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | df | 198.0000000 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | p | 0.6472171 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | 95% CI lower | -0.1430882 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | 95% CI upper | 1.0000000 | Pearson’s product-moment correlation |

M2_parent_siblings | - | - | - | null value | 0.0000000 | Pearson’s product-moment correlation |

M3_condition_anxiety | coefficients | 1 | (Intercept) | b | 29.4466534 | Linear model |

M3_condition_anxiety | coefficients | 1 | (Intercept) | SE | 9.9311192 | Linear model |

M3_condition_anxiety | coefficients | 1 | (Intercept) | t | 2.9650891 | Linear model |

M3_condition_anxiety | coefficients | 1 | (Intercept) | p | 0.0034017 | Linear model |

M3_condition_anxiety | coefficients | 1 | (Intercept) | df | 196.0000000 | Linear model |

M3_condition_anxiety | coefficients | 2 | conditionmortality salience | b | 20.2945974 | Linear model |

M3_condition_anxiety | coefficients | 2 | conditionmortality salience | SE | 14.0193962 | Linear model |

M3_condition_anxiety | coefficients | 2 | conditionmortality salience | t | 1.4476085 | Linear model |

M3_condition_anxiety | coefficients | 2 | conditionmortality salience | p | 0.1493242 | Linear model |

M3_condition_anxiety | coefficients | 2 | conditionmortality salience | df | 196.0000000 | Linear model |

M3_condition_anxiety | coefficients | 3 | anxiety | b | -1.5511207 | Linear model |

M3_condition_anxiety | coefficients | 3 | anxiety | SE | 3.0119376 | Linear model |

M3_condition_anxiety | coefficients | 3 | anxiety | t | -0.5149910 | Linear model |

M3_condition_anxiety | coefficients | 3 | anxiety | p | 0.6071396 | Linear model |

M3_condition_anxiety | coefficients | 3 | anxiety | df | 196.0000000 | Linear model |

M3_condition_anxiety | coefficients | 4 | conditionmortality salience:anxiety | b | -5.5666889 | Linear model |

M3_condition_anxiety | coefficients | 4 | conditionmortality salience:anxiety | SE | 4.3104789 | Linear model |

M3_condition_anxiety | coefficients | 4 | conditionmortality salience:anxiety | t | -1.2914316 | Linear model |

M3_condition_anxiety | coefficients | 4 | conditionmortality salience:anxiety | p | 0.1980750 | Linear model |

M3_condition_anxiety | coefficients | 4 | conditionmortality salience:anxiety | df | 196.0000000 | Linear model |

M3_condition_anxiety | model | - | - | R squared | 0.0360246 | Linear model |

M3_condition_anxiety | model | - | - | adjusted R squared | 0.0212698 | Linear model |

M3_condition_anxiety | model | - | - | F | 2.4415618 | Linear model |

M3_condition_anxiety | model | - | - | numerator df | 3.0000000 | Linear model |

M3_condition_anxiety | model | - | - | denominator df | 196.0000000 | Linear model |

M3_condition_anxiety | model | - | - | p | 0.0655150 | Linear model |

M4_condition_sex | - | 1 | condition | df | 1.0000000 | Factorial ANOVA |

M4_condition_sex | - | 1 | condition | SS | 383.6450000 | Factorial ANOVA |

M4_condition_sex | - | 1 | condition | MS | 383.6450000 | Factorial ANOVA |

M4_condition_sex | - | 1 | condition | F | 1.7299360 | Factorial ANOVA |

M4_condition_sex | - | 1 | condition | p | 0.1899557 | Factorial ANOVA |

M4_condition_sex | - | 2 | sex | df | 1.0000000 | Factorial ANOVA |

M4_condition_sex | - | 2 | sex | SS | 1140.4861329 | Factorial ANOVA |

M4_condition_sex | - | 2 | sex | MS | 1140.4861329 | Factorial ANOVA |

M4_condition_sex | - | 2 | sex | F | 5.1426918 | Factorial ANOVA |

M4_condition_sex | - | 2 | sex | p | 0.0244352 | Factorial ANOVA |

M4_condition_sex | - | 3 | condition:sex | df | 1.0000000 | Factorial ANOVA |

M4_condition_sex | - | 3 | condition:sex | SS | 66.1529617 | Factorial ANOVA |

M4_condition_sex | - | 3 | condition:sex | MS | 66.1529617 | Factorial ANOVA |

M4_condition_sex | - | 3 | condition:sex | F | 0.2982976 | Factorial ANOVA |

M4_condition_sex | - | 3 | condition:sex | p | 0.5855728 | Factorial ANOVA |

M4_condition_sex | - | 4 | Residuals | df | 196.0000000 | Factorial ANOVA |

M4_condition_sex | - | 4 | Residuals | SS | 43466.5909054 | Factorial ANOVA |

M4_condition_sex | - | 4 | Residuals | MS | 221.7683209 | Factorial ANOVA |

There are two ways to report your results using tidystats: Using R Markdown or using a built-in Shiny app. In both cases, you need the tidystats list that contains the tidied output of your statistical tests.

If you have previously created a tidystats file, you can read in this file to re-create the tidystats list, using the `read_stats()`

function.

If you do not want to use R Markdown, you can use the built-in Shiny app to interactively produce APA-output and copy it to your manuscript. To start the app, run the `inspect()`

function.

The `inspect()`

function takes the tidystats list as its first argument, optionally followed by one or more identifiers. If no identifiers are provided, all models will be displayed. The results of each model will be displayed in a table and you can click on a row to produce APA output. This APA output will appear in a textbox at the bottom, next to a copy button that can be pressed to copy the results into your clipboard. See below for an example.

You can use the `report()`

function to report your results via R Markdown. This function requires at minimum the tidystats list and an identifier identifying the exact test you want to report. It may also be necessary to provide additional information, such as a term in a regression, for the `report()`

function to figure out what you want to report.

To reduce repetition, you can use `options()`

to set the default tidystats list to use. This way the `report()`

function requires one fewer argument. You set the default tidystats list by running the following code:

To figure out how to report the output in APA style, the `report()`

function uses the **method** information stored in the tidied model. For example, the model with identifier ‘M1’ is a paired t-test. `report()`

will parse this, see that it is part of the t-test family, and produce results accordingly.

Below is a list of common report examples:

code | output |
---|---|

`report("M1_condition")` |
t(99) = -1.26, p = .21, 95% CI [-7.13, 1.59] |

`report("M1_condition", statistic = "t")` |
-1.26 |

`report("M2_parent_siblings")` |
r(198) = -.027, p = .65 |

`report("M3_condition_anxiety", term = "conditionmortality salience")` |
b = 20.29, SE = 14.02, t(196) = 1.45, p = .15 |

`report("M3_condition_anxiety", term_nr = 2)` |
b = 20.29, SE = 14.02, t(196) = 1.45, p = .15 |

`report("M3_condition_anxiety", term = "(Model)")` |
adjusted R^{2} = .0035, F(1, 198) = 1.70, p = .19 |

`report("M4_condition_sex", term = "condition:sex")` |
F(1, 196) = 0.30, p = .59 |

As you can see in the examples above, you can use `report()`

to produce a full line of output. You can also retrieve a single statistic by using the `statistic`

argument. Additionally, you can refer to terms using either the term label or the term number (and in some cases, using a group). Although it may be less descriptive to use a term number, it reduces the amount of code clutter in your Markdown document. Our philosophy is, in line with Markdown’s general writing philosophy, that the code should not distract from writing. To illustrate, writing part of a results section with `tidystats`

will look like this:

We found no significant difference between the mortality salience condition and the dental pain condition on the number of minutes allocated to calling one’s parents,

`r report(“M1_condition”)`

.

To execute the code, the code segment should be surrounded by backward ticks (see http://rmarkdown.rstudio.com/lesson-4.html), which results in:

We found no significant difference between the mortality salience condition and the dental pain condition on the number of minutes allocated to calling one’s parents,

t(99) = -1.26,p= .21, 95% CI [-7.13, 1.59].

Since it’s common to also report descriptives in addition to the statistical results, we have added a hopefully useful `describe_data()`

and `count_data()`

function to calculate common descriptive statistics that can be tidied and added to a results data frame. Several examples follow using the `cox`

data.

```
## # A tibble: 1 x 13
## var missing n M SD SE min max range median mode
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 anxi… 0 200 3.22 0.492 0.0348 1.38 4.38 3 3.25 3.5
## # ... with 2 more variables: skew <dbl>, kurtosis <dbl>
```

```
## # A tibble: 2 x 14
## # Groups: condition [2]
## var condition missing n M SD SE min max range median
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 anxi… dental p… 0 100 3.26 0.497 0.0497 1.62 4.38 2.75 3.38
## 2 anxi… mortalit… 0 100 3.17 0.485 0.0485 1.38 4.38 3 3.25
## # ... with 3 more variables: mode <dbl>, skew <dbl>, kurtosis <dbl>
```

```
## # A tibble: 2 x 4
## var group n pct
## <chr> <chr> <int> <dbl>
## 1 condition dental pain 100 50
## 2 condition mortality salience 100 50
```

If you use the `describe_data()`

and `count_data()`

function from the `tidystats`

package to get the descriptives, you can use the `tidy_describe_data()`

and `tidy_count_data()`

function to tidy the output, and consequently add it to a results list.

(Note: This will soon be improved)

```
anxiety_tidy <- cox %>%
describe_data(anxiety) %>%
tidy_describe_data()
results <- results %>%
add_stats(anxiety_tidy, type = "d", notes = "Anxious attachment style")
```

```
## Warning in add_stats.data.frame(., anxiety_tidy, type = "d", notes =
## "Anxious attachment style"): You added a data.frame to your results list.
## Please make sure it is properly tidied.
```