Style code (GHA)

vignettes/wpgsd_corr_example.Rmd

@@ -41,8 +41,8 @@ library(gt)
 ```{r}
 event_tb <- tribble(
   ~Population, ~"Number of Event in IA", ~"Number of Event in FA",
-  "Population 1", 100,200,
-  "Population 2",  110,220,
+  "Population 1", 100, 200,
+  "Population 2", 110, 220,
   "Overlap of Population 1 and 2", 80, 160,
   "Overall Population", 225, 450
 )
@@ -60,7 +60,7 @@ The number of events are listed as
 event_tbl <- tribble(
   ~Population, ~"Number of Event in IA",
   "Population 1", 100,
-  "Population 2",  110,
+  "Population 2", 110,
   "Overlap in population 1 and 2", 80
 )
 event_tbl %>%
@@ -70,8 +70,8 @@ event_tbl %>%
 The the corrleation could be simply calculated as 
 $$Corr(Z_{11},Z_{21})=\frac{80}{\sqrt{100*110}}=0.76$$
 ```{r}
-Corr1=80/sqrt(100*110)
-round(Corr1,2)
+Corr1 <- 80 / sqrt(100 * 110)
+round(Corr1, 2)
 ```
 
 ### Example 2 - Correlation of different analyses within the same population
@@ -82,7 +82,7 @@ The number of events are listed as
 ```{r}
 event_tb2 <- tribble(
   ~Population, ~"Number of Event in IA", ~"Number of Event in FA",
-  "Population 1", 100,200
+  "Population 1", 100, 200
 )
 event_tb2 %>%
   gt() %>%
@@ -92,8 +92,8 @@ The the corrleation could be simply calculated as
 $$Corr(Z_{11},Z_{12})=\frac{100}{\sqrt{100*200}}=0.71$$
 The 100 in the numerator is the overlap number of events of interim analysis and final analysis in population 1.
 ```{r}
-Corr1=100/sqrt(100*200)
-round(Corr1,2)
+Corr1 <- 100 / sqrt(100 * 200)
+round(Corr1, 2)
 ```
 ### Example 3 - Correlation of different analyses and different population
 Let's consider the situation that we want to compare population 1 in interim analyses and population 2 in final analyses. Then for different population, $i=1$ and $i=2$, and to compare IA and FA, the $k$ will be $k=1$ and $k=2$. 
@@ -103,10 +103,9 @@ The number of events are listed as
 ```{r}
 event_tb3 <- tribble(
   ~Population, ~"Number of Event in IA", ~"Number of Event in FA",
-  "Population 1", 100,200,
+  "Population 1", 100, 200,
   "Population 2", 110, 220,
-  "Overlap in population 1 and 2", 80,160
-
+  "Overlap in population 1 and 2", 80, 160
 )
 event_tb3 %>%
   gt() %>%
@@ -116,31 +115,31 @@ The correlation could be simply calculated as
 $$Corr(Z_{11},Z_{22})=\frac{80}{\sqrt{100*220}}=0.54$$
 The 80 in the numerator is the overlap number of events of population 1 in interim analysis and population 2 in final analysis.
 ```{r}
-Corr1=80/sqrt(100*220)
-round(Corr1,2)
+Corr1 <- 80 / sqrt(100 * 220)
+round(Corr1, 2)
 ```
 Now we know how to calculate the correlation values under different situations, and the generate_corr() function was built based on this logic. We can directly calculate the results for each cross situation via the function. 
 
 First, we need a event table including the information of the study.
 
 ```{r}
 library(wpgsd)
-#The event table
+# The event table
 event <- tibble::tribble(
-   ~ H1, ~H2, ~Analysis, ~Event,
-   1, 1, 1, 100,
-   2, 2, 1, 110,
-   3, 3, 1, 225,
-   1, 2, 1, 80,
-   1, 3, 1, 100,
-   2, 3, 1, 110,
-   1, 1, 2, 200,
-   2, 2, 2, 220,
-   3, 3, 2, 450,
-   1, 2, 2, 160,
-   1, 3, 2, 200,
-   2, 3, 2, 220
- )
+  ~H1, ~H2, ~Analysis, ~Event,
+  1, 1, 1, 100,
+  2, 2, 1, 110,
+  3, 3, 1, 225,
+  1, 2, 1, 80,
+  1, 3, 1, 100,
+  2, 3, 1, 110,
+  1, 1, 2, 200,
+  2, 2, 2, 220,
+  3, 3, 2, 450,
+  1, 2, 2, 160,
+  1, 3, 2, 200,
+  2, 3, 2, 220
+)
 event %>%
   gt() %>%
   tab_header(title = "Number of events at each population & analyses")
@@ -155,4 +154,4 @@ For example: H1=1, H2=1, Analysis=1, Event=100 indicates that in the first popul
 
 Another example: H1=1, H2=2, Analysis=2, Event=160 indicates that the number of overlapping cases where the experimental treatment is superior to the control in population 1 and 2 in the final analysis is 160.
 
-*To be noticed, the column names in this function are fixed to be 'H1, H2, Analysis, Event'.
+*To be noticed, the column names in this function are fixed to be 'H1, H2, Analysis, Event'.