add in one prop coerced from logical

Author: andrewpbray

inst/doc/flights_examples.R

@@ -52,6 +52,29 @@ ggplot(null, aes(x = stat)) +
 null %>%
   summarize(p_value = mean(stat < x_tilde) * 2)
 
+## ------------------------------------------------------------------------
+p_hat <- fli_small %>%
+  summarize(mean(day_hour == "morning")) %>%
+  pull()
+null <- fli_small %>%
+  specify(response = day_hour, success = "morning") %>%
+  hypothesize(null = "point", p = .5) %>%
+  generate(reps = 1000, type = "simulate") %>%
+  calculate(stat = "prop")
+ggplot(null, aes(x = stat)) +
+  geom_bar() +
+  geom_vline(xintercept = p_hat, color = "red")
+null %>%
+  summarize(p_value = mean(stat < p_hat) * 2)
+
+## ------------------------------------------------------------------------
+null <- fli_small %>%
+  mutate(day_hour_logical = (day_hour == "morning")) %>%
+  specify(response = day_hour_logical, success = "TRUE") %>%
+  hypothesize(null = "point", p = .5) %>%
+  generate(reps = 1000, type = "simulate") %>%
+  calculate(stat = "prop")
+
 ## ------------------------------------------------------------------------
 d_hat <- fli_small %>%
   group_by(season) %>%
@@ -166,29 +189,29 @@ ggplot(null, aes(x = stat)) +
 null %>% 
   summarize(p_value = mean(stat > slope_hat) * 2)   
 
-## ----eval=FALSE----------------------------------------------------------
-#  x_bar <- fli_small %>%
-#     summarize(mean(arr_delay)) %>%
-#     pull()
-#  boot <- fli_small %>%
-#     specify(response = arr_delay) %>%
-#     generate(reps = 1000, type = "bootstrap") %>%
-#     calculate(stat = "mean") %>%
-#     pull()
-#  c(lower = x_bar - 2 * sd(boot),
-#    upper = x_bar + 2 * sd(boot))
-
-## ----eval=FALSE----------------------------------------------------------
-#  p_hat <- fli_small %>%
-#   summarize(mean(day_hour == "morning")) %>%
-#   pull()
-#  boot <- fli_small %>%
-#   specify(response = day_hour, success = "morning") %>%
-#   generate(reps = 1000, type = "bootstrap") %>%
-#   calculate(stat = "prop") %>%
-#   pull()
-#  c(lower = p_hat - 2 * sd(boot),
-#   upper = p_hat + 2 * sd(boot))
+## ------------------------------------------------------------------------
+x_bar <- fli_small %>%
+   summarize(mean(arr_delay)) %>%
+   pull()
+boot <- fli_small %>%
+   specify(response = arr_delay) %>%
+   generate(reps = 1000, type = "bootstrap") %>%
+   calculate(stat = "mean") %>%
+   pull()
+c(lower = x_bar - 2 * sd(boot),
+  upper = x_bar + 2 * sd(boot))
+
+## ------------------------------------------------------------------------
+p_hat <- fli_small %>%
+ summarize(mean(day_hour == "morning")) %>%
+ pull()
+boot <- fli_small %>%
+ specify(response = day_hour, success = "morning") %>%
+ generate(reps = 1000, type = "bootstrap") %>%
+ calculate(stat = "prop") %>%
+ pull()
+c(lower = p_hat - 2 * sd(boot),
+ upper = p_hat + 2 * sd(boot))
 
 ## ------------------------------------------------------------------------
 d_hat <- fli_small %>% 

inst/doc/flights_examples.Rmd

@@ -7,8 +7,10 @@ output:
     df_print: kable
 vignette: |
   %\VignetteIndexEntry{flights example}
-  %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  chunk_output_type: console
 ---
 
 ```{r include=FALSE}
@@ -86,6 +88,36 @@ null %>%
   summarize(p_value = mean(stat < x_tilde) * 2)
 ```
 
+### One categorical (one proportion)
+
+```{r}
+p_hat <- fli_small %>%
+  summarize(mean(day_hour == "morning")) %>%
+  pull()
+null <- fli_small %>%
+  specify(response = day_hour, success = "morning") %>%
+  hypothesize(null = "point", p = .5) %>%
+  generate(reps = 1000, type = "simulate") %>%
+  calculate(stat = "prop")
+ggplot(null, aes(x = stat)) +
+  geom_bar() +
+  geom_vline(xintercept = p_hat, color = "red")
+null %>%
+  summarize(p_value = mean(stat < p_hat) * 2)
+```
+
+Logical variables will be coerced to factors:
+
+```{r}
+null <- fli_small %>%
+  mutate(day_hour_logical = (day_hour == "morning")) %>%
+  specify(response = day_hour_logical, success = "TRUE") %>%
+  hypothesize(null = "point", p = .5) %>%
+  generate(reps = 1000, type = "simulate") %>%
+  calculate(stat = "prop")
+```
+
+
 ### Two categorical (2 level) variables
 
 ```{r}
@@ -225,7 +257,7 @@ null %>%
 
 ### One numerical (one mean)
 
-```{r eval=FALSE}
+```{r}
 x_bar <- fli_small %>%
    summarize(mean(arr_delay)) %>%
    pull()
@@ -240,7 +272,7 @@ c(lower = x_bar - 2 * sd(boot),
 
 ### One categorical (one proportion)
 
-```{r eval=FALSE}
+```{r}
 p_hat <- fli_small %>%
  summarize(mean(day_hour == "morning")) %>%
  pull()