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Tidy t-test

Source: R/wrappers.R
t_test.Rd

A tidier version of t.test() for two sample tests.

Usage

t_test(
  x,
  formula,
  response = NULL,
  explanatory = NULL,
  order = NULL,
  alternative = "two-sided",
  mu = 0,
  conf_int = TRUE,
  conf_level = 0.95,
  ...
)

Arguments

x

A data frame that can be coerced into a tibble.

formula

A formula with the response variable on the left and the explanatory on the right. Alternatively, a response and explanatory argument can be supplied.

response

The variable name in x that will serve as the response. This is an alternative to using the formula argument.

explanatory

The variable name in x that will serve as the explanatory variable. This is an alternative to using the formula argument.

order

A string vector of specifying the order in which the levels of the explanatory variable should be ordered for subtraction, where order = c("first", "second") means ("first" - "second").

alternative

Character string giving the direction of the alternative hypothesis. Options are "two-sided" (default), "greater", or "less".

mu

A numeric value giving the hypothesized null mean value for a one sample test and the hypothesized difference for a two sample test.

conf_int

A logical value for whether to include the confidence interval or not. TRUE by default.

conf_level

A numeric value between 0 and 1. Default value is 0.95.

...

For passing in other arguments to t.test().

See also

Other wrapper functions: chisq_stat(), chisq_test(), observe(), prop_test(), t_stat()

Examples

library(tidyr)

# t test for number of hours worked per week
# by college degree status
gss %>%
   tidyr::drop_na(college) %>%
   t_test(formula = hours ~ college,
      order = c("degree", "no degree"),
      alternative = "two-sided")
#> # A tibble: 1 × 7
#>   statistic  t_df p_value alternative estimate lower_ci upper_ci
#>       <dbl> <dbl>   <dbl> <chr>          <dbl>    <dbl>    <dbl>
#> 1      1.12  366.   0.264 two.sided       1.54    -1.16     4.24

# see vignette("infer") for more explanation of the
# intuition behind the infer package, and vignette("t_test")
# for more examples of t-tests using infer