No more changes

learn/models/sub-sampling/index.html.md

@@ -28,7 +28,6 @@ Consider a two-class problem where the first class has a very low rate of occurr
 
 ```{.r .cell-code}
 imbal_data <- 
-
   readr::read_csv("https://tidymodels.org/learn/models/sub-sampling/imbal_data.csv") %>% 
   mutate(Class = factor(Class))
 dim(imbal_data)
@@ -62,6 +61,8 @@ Here is a simple recipe implementing oversampling:
 ```{.r .cell-code}
 library(tidymodels)
 library(themis)
+set.seed(1234)
+
 imbal_rec <- 
   recipe(Class ~ ., data = imbal_data) %>%
   step_rose(Class)
@@ -152,8 +153,8 @@ collect_metrics(qda_rose_res)
 #> # A tibble: 2 × 6
 #>   .metric .estimator  mean     n std_err .config             
 #>   <chr>   <chr>      <dbl> <int>   <dbl> <chr>               
-#> 1 j_index binary     0.804    50 0.0178  Preprocessor1_Model1
-#> 2 roc_auc binary     0.953    50 0.00459 Preprocessor1_Model1
+#> 1 j_index binary     0.777    50 0.0199  Preprocessor1_Model1
+#> 2 roc_auc binary     0.949    50 0.00508 Preprocessor1_Model1
 ```
 :::
 

learn/models/sub-sampling/index.qmd

@@ -54,7 +54,6 @@ Consider a two-class problem where the first class has a very low rate of occurr
 #| label: "load-data"
 #| message: false
 imbal_data <- 
-
   readr::read_csv("https://tidymodels.org/learn/models/sub-sampling/imbal_data.csv") %>% 
   mutate(Class = factor(Class))
 dim(imbal_data)
@@ -82,6 +81,8 @@ Here is a simple recipe implementing oversampling:
 #| label: "rec"
 library(tidymodels)
 library(themis)
+set.seed(1234)
+
 imbal_rec <- 
   recipe(Class ~ ., data = imbal_data) %>%
   step_rose(Class)

learn/statistics/infer/index.html.md

@@ -44,6 +44,9 @@ Throughout this vignette, we make use of `gss`, a data set available in infer co
 ```{.r .cell-code}
 library(tidymodels) # Includes the infer package
 
+# Set seed
+set.seed(1234)
+
 # load in the data set
 data(gss)
 
@@ -263,15 +266,15 @@ gss %>%
 #> # Groups:   replicate [5,000]
 #>    replicate hours
 #>        <int> <dbl>
-#>  1         1  53.6
-#>  2         1  38.6
-#>  3         1  48.6
-#>  4         1  39.6
-#>  5         1  53.6
+#>  1         1  58.6
+#>  2         1  35.6
+#>  3         1  28.6
+#>  4         1  38.6
+#>  5         1  28.6
 #>  6         1  38.6
 #>  7         1  38.6
-#>  8         1  46.6
-#>  9         1  28.6
+#>  8         1  57.6
+#>  9         1  58.6
 #> 10         1  38.6
 #> # ℹ 2,499,990 more rows
 ```
@@ -295,16 +298,16 @@ gss %>%
 #> # Groups:   replicate [5,000]
 #>    partyid   age replicate
 #>    <fct>   <dbl>     <int>
-#>  1 dem        36         1
-#>  2 dem        34         1
-#>  3 dem        24         1
+#>  1 ind        36         1
+#>  2 ind        34         1
+#>  3 ind        24         1
 #>  4 rep        42         1
-#>  5 ind        31         1
+#>  5 dem        31         1
 #>  6 dem        32         1
-#>  7 ind        48         1
-#>  8 ind        36         1
-#>  9 rep        30         1
-#> 10 ind        33         1
+#>  7 dem        48         1
+#>  8 rep        36         1
+#>  9 ind        30         1
+#> 10 dem        33         1
 #> # ℹ 2,499,990 more rows
 ```
 :::
@@ -326,16 +329,16 @@ gss %>%
 #> # A tibble: 5,000 × 2
 #>    replicate  stat
 #>        <int> <dbl>
-#>  1         1  39.7
-#>  2         2  39.4
-#>  3         3  39.3
-#>  4         4  39.6
-#>  5         5  40.1
-#>  6         6  40.9
-#>  7         7  39.0
-#>  8         8  40.9
-#>  9         9  38.3
-#> 10        10  39.6
+#>  1         1  39.8
+#>  2         2  39.6
+#>  3         3  39.8
+#>  4         4  39.2
+#>  5         5  39.0
+#>  6         6  39.8
+#>  7         7  40.6
+#>  8         8  40.6
+#>  9         9  40.4
+#> 10        10  39.0
 #> # ℹ 4,990 more rows
 ```
 :::
@@ -356,16 +359,16 @@ gss %>%
 #> # A tibble: 5,000 × 2
 #>    replicate    stat
 #>        <int>   <dbl>
-#>  1         1 -2.89  
-#>  2         2 -2.62  
-#>  3         3 -0.620 
-#>  4         4 -0.320 
-#>  5         5  0.0680
-#>  6         6  0.112 
-#>  7         7  1.47  
-#>  8         8 -1.47  
-#>  9         9  0.139 
-#> 10        10 -0.390 
+#>  1         1 -0.0378
+#>  2         2  1.55  
+#>  3         3  0.465 
+#>  4         4  1.39  
+#>  5         5 -0.161 
+#>  6         6 -0.179 
+#>  7         7  0.0151
+#>  8         8  0.914 
+#>  9         9 -1.32  
+#> 10        10 -0.426 
 #> # ℹ 4,990 more rows
 ```
 :::
@@ -439,11 +442,11 @@ p_value
 #> # A tibble: 1 × 1
 #>   p_value
 #>     <dbl>
-#> 1  0.0292
+#> 1   0.046
 ```
 :::
 
-It looks like the p-value is 0.0292, which is pretty small---if the true mean number of hours worked per week was actually 40, the probability of our sample mean being this far (1.382 hours) from 40 would be 0.0292. This may or may not be statistically significantly different, depending on the significance level $\alpha$ you decided on *before* you ran this analysis. If you had set $\alpha = .05$, then this difference would be statistically significant, but if you had set $\alpha = .01$, then it would not be.
+It looks like the p-value is 0.046, which is pretty small---if the true mean number of hours worked per week was actually 40, the probability of our sample mean being this far (1.382 hours) from 40 would be 0.046. This may or may not be statistically significantly different, depending on the significance level $\alpha$ you decided on *before* you ran this analysis. If you had set $\alpha = .05$, then this difference would be statistically significant, but if you had set $\alpha = .01$, then it would not be.
 
 To get a confidence interval around our estimate, we can write:
 

learn/statistics/infer/index.qmd

@@ -62,6 +62,9 @@ Throughout this vignette, we make use of `gss`, a data set available in infer co
 #| message: false
 library(tidymodels) # Includes the infer package
 
+# Set seed
+set.seed(1234)
+
 # load in the data set
 data(gss)
 

learn/statistics/survival-case-study/index.qmd

@@ -26,6 +26,8 @@ source(here::here("common.R"))
 library(tidymodels)
 library(sessioninfo)
 library(leaflet)
+library(htmlwidgets)
+setWidgetIdSeed(1234)
 pkgs <- c("tidymodels", "censored", "modeldatatoo", "glmnet", "aorsf")
 theme_set(theme_bw() + theme(legend.position = "top"))
 ```