suggested edits due to infer package 1.0.0 update (#263)

Author: vsass

04-foundations/02-lesson/04-02-lesson.Rmd

@@ -1144,59 +1144,65 @@ This function has three arguments (inputs):
 2. the observed statistic (`obs_stat`) 
 3. the direction of the alternative hypothesis ("greater", "less", or "two-sided")
 
-We can also use the `visualize()` function to visualize where the observed statistic falls in the distribution of permuted statistics, and shade the direction that the p-value was calculated from. 
-The `visualize()` function has many inputs (find out more by typing `?visualize` in your console), but the most important ones are the __same__ as the `get_p_value()` function!  
+We can also use the `visualize()` and `shade_p_value()` functions to visualize where the observed statistic falls in the distribution of permuted statistics, and shade the direction that the p-value was calculated from. 
+The `shade_p_value()` function has many inputs (find out more by typing `?shade_p_value` in your console), but the most important ones are the __same__ as the `get_p_value()` function!  
 
-Now, use the `visualize()` and `get_p_value()` functions for the original, small, and big datasets. 
-First `visualize()` where the p-value lies on the distibution, and then calculate the p-value. 
+Now, use the `visualize()`, `shade_p_value()`, and `get_p_value()` functions for the original, small, and big datasets. 
+First use `shade_p_value()` to see where the p-value lies on the distribution, and then calculate the p-value. 
 
 - You can test out the different methods for calculating the p-value by trying out: `direction = "greater"`, `direction = "two_sided"`, and `direction = "less"`. 
 
 ```{r pvalue, exercise=TRUE}
 # Visualize and calculate the p-value for the original dataset
 gender_discrimination_perm |>
+  visualize() +
   ___(obs_stat = ___, direction = "___")
 
 gender_discrimination_perm |>
   ___(___, ___)
 
 # Visualize and calculate the p-value for the small dataset
 ___ |>
+  visualize() +
   ___(___, ___)
 
 ___ |>
   ___(___, ___)
 
 # Visualize and calculate the p-value for the big dataset
 ___ |>
+  visualize() +
   ___(___, ___)
 
 ___ |>
   ___(___, ___)
 ```
 
 ```{r pvalue-hint}
- Argument of the both functions should be `obs_stat = diff_orig, direction = "greater"`, but remember to use the correct dataset! 
+ Arguments of both `shade_p_value()` and `get_p_value()` functions should be `obs_stat = diff_orig, direction = "greater"`, but remember to use the correct dataset! 
 ```
 
 ```{r pvalue-solution}
 # Visualize and calculate the p-value for the original dataset
 gender_discrimination_perm |>
-  visualize(obs_stat = diff_orig, direction = "greater")
+  visualize() +
+  shade_p_value(obs_stat = diff_orig, direction = "greater")
 
 gender_discrimination_perm |>
   get_p_value(obs_stat = diff_orig, direction = "greater")
 
 # Visualize and calculate the p-value for the small dataset
 gender_discrimination_small_perm |>
-  visualize(obs_stat = diff_orig_small, direction = "greater")
+  visualize() +
+  shade_p_value(obs_stat = diff_orig_small, direction = "greater")
 
 gender_discrimination_small_perm |>
   get_p_value(obs_stat = diff_orig_small, direction = "greater")
 
 # Visualize and calculate the p-value for the big dataset
 gender_discrimination_big_perm |>
-  visualize(obs_stat = diff_orig_big, direction = "greater")
+  visualize() +
+  shade_p_value(obs_stat = diff_orig_big, direction = "greater")
 
 gender_discrimination_big_perm |>
   get_p_value(obs_stat = diff_orig_big, direction = "greater")

04-foundations/03-lesson/04-03-lesson.Rmd

@@ -428,16 +428,17 @@ Now that you've created the randomization distribution, you'll use it to assess
 
 The permuted dataset and the original observed statistic are available in your workspace as `opp_perm` and `diff_obs` respectively.
 
-`visualize()` and `get_p_value()` using the built in infer functions. Remember that the null statistics are above the original difference, so the p-value (which represents how often a null value is more *extreme*) is calculated by counting the number of null values which are `less` than the original difference.
+`visualize()`, `shade_p_value()`, and `get_p_value()` using the built-in infer functions. Remember that the null statistics are above the original difference, so the p-value (which represents how often a null value is more *extreme*) is calculated by counting the number of null values which are `less` than the original difference.
 
-- First `visualize()` the sampling distribution of the permuted statistics indicating the place where `obs_stat = diff_obs`, and coloring in values below with the command `direction = "less"`.
+- First `visualize()` the sampling distribution of the permuted statistics indicating the place where `obs_stat = diff_obs`, and coloring in values below with the command `direction = "less"` using `shade_p_value()`.
 - Then `get_p_value()` is calculated as the proportion of permuted statistics which are `direction = "less"` than `obs_stat = diff_obs`.
 - As an alternative way to calculate the p-value, use `summarize()` and `mean()` to find the proportion of times the permuted differences in `opp_perm` (called `stat`) are less than or equal to the observed difference (called `diff_obs`).
 - You can test your knowledge by trying out: `direction = "greater"`, `direction = "two_sided"`, and `direction = "less"` before submitting your answer to both `visualize()` and `get_p_value()`.
 
 ```{r summarizing_opportunity_cost, exercise=TRUE}
 # Visualize the statistic 
 opp_perm |>
+  ___() +
   ___(___, ___)
 
 # Calculate the p-value using `get_p_value()`
@@ -451,7 +452,8 @@ opp_perm |>
 
 ```{r summarizing_opportunity_cost-hint-1}
 opp_perm |>
-  visualize(obs_stat = diff_obs, direction = "less")
+  visualize() +
+  shade_p_value(obs_stat = diff_obs, direction = "less")
 ```
 
 ```{r summarizing_opportunity_cost-hint-2}
@@ -462,7 +464,8 @@ opp_perm |>
 ```{r summarizing_opportunity_cost-solution}
 # Visualize the statistic 
 opp_perm |>
-  visualize(obs_stat = diff_obs, direction = "less")
+  visualize() +
+  shade_p_value(obs_stat = diff_obs, direction = "less")
 
 # Calculate the p-value using `get_p_value()`
 opp_perm |>

04-foundations/04-lesson/04-04-lesson.Rmd

@@ -943,7 +943,7 @@ percentile_ci
  (3)
 
 
-- Finally, use the `visualize()` function to plot the distribution of bootstrapped proportions with the middle 95 percent highlighted.  
+- Finally, use the `visualize()` function together with `shade_confidence_interval()` to plot the distribution of bootstrapped proportions with the middle 95 percent highlighted.  
     - Set the `endpoints` argument to be `percentile_ci`.
     - Set the `direction` of the shading to `"between"`, to highlight in-between those endpoints.
 
@@ -955,11 +955,12 @@ percentile_ci <- one_poll_boot |>
   
 one_poll_boot |> 
   # Visualize in-between the endpoints given by percentile_ci
-  ___
+  ___() +
+  ___(endpoints = ___, direction = ___)
 ```
 
 ```{r bootstrap_percentile_3-hint}
- After the pipe, visualize the interval by calling `visualize()`, setting `endpoints` to `percentile_ci` and `direction` to `"between"`.
+ After the pipe, visualize the distribution by calling `visualize()` and the interval using `shade_confidence_interval()`, setting `endpoints` to `percentile_ci` and `direction` to `"between"`.
 ```
 
 ```{r bootstrap_percentile_3-solution}
@@ -969,7 +970,8 @@ percentile_ci <- one_poll_boot |>
   
 one_poll_boot |> 
   # Visualize in-between the endpoints given by percentile_ci
-  visualize(endpoints = percentile_ci, direction = "between")
+  visualize() +
+  shade_confidence_interval(endpoints = percentile_ci, direction = "between")
 ```
 
 Excellent! Again, the same caveat applies: because the two intervals were created using different methods, the intervals are expected to be a bit different as well. In the long run, however, the intervals should provide the same information.