In hilaryparker/explainr: Explains R in plain English

## Convert power.htest to something usable (later use broom?)
## z <- as.data.frame(unclass(x)) 
z <- x
single <- nrow(z) == 1L  ## let's assume single run for now
onesample <- grepl("^One-sample", z$method)  ## otherwise two-sample
if(onesample) { 
    method <- "one-sample *t* test of a normal mean"
} else {
    method <- "two-sample *t* test of normal means"
}

txt1 <- paste0(
    "A total of ", ceiling(z$n) + ceiling(z$n) %% 2, 
    " subjects will be enrolled in this study. Using a ", 
    method, " we have ", trunc(z$power * 100), 
    "% power to detect a treatment effect at a two-sided ", z$sig.level, 
    " significance level, if the true effect is ", 
    z$delta, " ", 
    ifelse(z$delta == 1, "unit", "units"), " different from zero. This is based on the assumption that the standard deviation of the response variable is ", 
    z$sd, "."
)

txt2 <- paste0(
    "A total of ", ceiling(z$n) + ceiling(z$n) %% 2, 
    " subjects will be enrolled in this study. Using a ", 
    method, " we have ", trunc(z$power * 100), 
    "% power to detect a treatment difference at a two-sided ", z$sig.level, 
    " significance level, if the true difference in the response between treatment groups is ", 
    z$delta, " ", ifelse(z$delta == 1, "unit", "units"), ". This is based on the assumption that the standard deviation of the response variable is ", 
    z$sd, "."
)

if(onesample) {
    cat(txt1, "\n")
} else {
    cat(txt2, "\n")
}