add_stats: Add statistical output to a tidy stats list
In WillemSleegers/tidystats-v0.3: Create a Tidy Statistics Output File

Description Usage Arguments Details Examples

add_stats adds output to a tidystats list. It can take either the output of a statistical test as input or a data frame. See Details for more information on adding data frames.

1 2	add_stats(results, output, identifier = NULL, type = NULL, confirmatory = NULL, notes = NULL, class = NULL, args = NULL)

`results`	A tidystats list.
`output`	Output of a statistical test or a data frame. If a data frame is provided, it must already be in a tidy format.
`identifier`	A character string identifying the model. Automatically created if not provided.
`type`	A character string indicating the type of test. One of "hypothesis", "manipulation check", "contrast", "descriptives", or "other". Can be abbreviated.
`confirmatory`	A boolean to indicate whether the statistical test was confirmatory (TRUE) or exploratory (FALSE). Can be NA.
`notes`	A character string to add additional information. Some statistical tests produce notes information, which will be overwritten if notes are provided.
`class`	A character string to indicate which function was used to produce the output. See 'Details' for a list of supported functions.
`args`	An optional list of additional arguments. Can be used to specify how model results should be summarized.

Some statistical functions produce unidentifiable output, which means tidystats cannot figure out how to tidy the data. To add these results, you can provide a class via the class argument or you can manually tidy the results yourself and add the resulting data frame via add_stats().

A list of supported classes are: - confint

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

# Example: t-test
model_t_test <- t.test(extra ~ group, data = sleep)
results <- add_stats(results, model_t_test, identifier = "t_test")

# Example: correlation
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)

model_correlation <- cor.test(x, y)

# Add output to the results list, only storing the correlation and p-value
results <- add_stats(results, model_correlation, identifier = "correlation")

# Example: Regression
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)

model_lm <- lm(weight ~ group)

# Add output to the results list, with notes
results <- add_stats(results, model_lm, identifier = "regression", notes =
"regression example")

# Example: ANOVA
model_aov <- aov(yield ~ block + N * P * K, npk)

results <- add_stats(results, model_aov, identifier = "ANOVA")

# Example: Within-subjects ANOVA
model_aov_within <- aov(extra ~ group + Error(ID/group), data = sleep)

results <- add_stats(results, model_aov_within, identifier = "ANOVA_within")

# Example: Manual chi-squared test of independence
library(tibble)

x_squared_data <- tibble(
  statistic = c("X-squared", "df", "p"),
  value = c(5.4885, 6, 0.4828),
  method = "Chi-squared test of independence"
)

results <- add_stats(results, x_squared_data, identifier = "x_squared")