knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

Tidycomm includes four functions for bivariate explorative data analysis:

`crosstab()`

for both categorical independent and dependent variables`t_test()`

for dichotomous categorical independent and continuous dependent variables`unianova()`

for polytomous categorical independent and continuous dependent variables`correlate()`

for both continuous independent and dependent variables

```
library(tidycomm)
```

We will again use sample data from the Worlds of Journalism 2012-16 study for demonstration purposes:

WoJ

`crosstab()`

outputs a contingency table for one independent (column) variable and one or more dependent (row) variables:

WoJ %>% crosstab(reach, employment)

Additional options include `add_total`

(adds a row-wise `Total`

column if set to `TRUE`

) and `percentages`

(outputs column-wise percentages instead of absolute values if set to `TRUE`

):

WoJ %>% crosstab(reach, employment, add_total = TRUE, percentages = TRUE)

Setting `chi_square = TRUE`

computes a $\chi^2$ test including Cramer's $V$ and outputs the results in a console message:

WoJ %>% crosstab(reach, employment, chi_square = TRUE)

Finally, passing multiple row variables will treat all unique value combinations as a single variable for percentage and Chi-square computations:

WoJ %>% crosstab(reach, employment, country, percentages = TRUE)

Use `t_test()`

to quickly compute t-Tests for a group variable and one or more test variables. Output includes test statistics, descriptive statistics and Cohen's $d$ effect size estimates:

WoJ %>% t_test(temp_contract, autonomy_selection, autonomy_emphasis)

Passing no test variables will compute t-Tests for all numerical variables in the data:

WoJ %>% t_test(temp_contract)

If passing a group variable with more than two unique levels, `t_test()`

will produce a `warning`

and default to the first two unique values. You can manually define the levels by setting the `levels`

argument:

WoJ %>% t_test(employment, autonomy_selection, autonomy_emphasis) WoJ %>% t_test(employment, autonomy_selection, autonomy_emphasis, levels = c("Full-time", "Freelancer"))

Additional options include:

`var.equal`

: By default,`t_test()`

will assume equal variances for both groups. Set`var.equal = FALSE`

to compute t-Tests with the Welch approximation to the degrees of freedom.`pooled_sd`

: By default, the pooled variance will be used the compute Cohen's $d$ effect size estimates ($s = \sqrt\frac{(n_1 - 1)s^2_1 + (n_2 - 1)s^2_2}{n_1 + n_2 - 2}$). Set`pooled_sd = FALSE`

to use the simple variance estimation instead ($s = \sqrt\frac{(s^2_1 + s^2_2)}{2}$).`paired`

: Set`paired = TRUE`

to compute a paired t-Test instead. It is advisable to specify the case-identifying variable with`case_var`

when computing paired t-Tests, as this will make sure that data are properly sorted.

`unianova()`

will compute one-way ANOVAs for one group variable and one or more test variables. Output includes test statistics and $\eta^2$ effect size estimates.

WoJ %>% unianova(employment, autonomy_selection, autonomy_emphasis)

Descriptives can be added by setting `descriptives = TRUE`

. If no test variables are passed, all numerical variables in the data will be used:

WoJ %>% unianova(employment, descriptives = TRUE)

You can also compute *Tukey's HSD* post-hoc tests by setting `post_hoc = TRUE`

. Results will be added as a `tibble`

in a list column `post_hoc`

.

WoJ %>% unianova(employment, autonomy_selection, autonomy_emphasis, post_hoc = TRUE)

These can then be unnested with `tidyr::unnest()`

:

WoJ %>% unianova(employment, autonomy_selection, autonomy_emphasis, post_hoc = TRUE) %>% dplyr::select(Var, post_hoc) %>% tidyr::unnest(post_hoc)

`correlate()`

will compute correlations for all combinations of the passed variables:

WoJ %>% correlate(work_experience, autonomy_selection, autonomy_emphasis)

If no variables passed, correlations for all combinations of numerical variables will be computed:

WoJ %>% correlate()

By default, Pearson's product-moment correlations coefficients ($r$) will be computed. Set `method`

to `"kendall"`

to obtain Kendall's $\tau$ or to `"spearman"`

to obtain Spearman's $\rho$ instead.

To obtain a correlation matrix, pass the output of `correlate()`

to `to_correlation_matrix()`

:

WoJ %>% correlate(work_experience, autonomy_selection, autonomy_emphasis) %>% to_correlation_matrix()

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