Format the code

vignettes/ml100k.Rmd

@@ -57,15 +57,21 @@ After fitting the MFAI model, we can use the `getImportance()` function to obtai
 
 ```{r}
 # Get importance score
-importance <- as.data.frame(getImportance(mfairObject, which_factors = 1:3))
+importance <- as.data.frame(
+ getImportance(mfairObject, which_factors = 1:3)
+)
 importance$Genre <- rownames(importance)
+# Convert the wide table to the long table
 importance_long <- melt(
  data = importance,
  id.vars = "Genre",
  variable.name = "Factor",
  value.name = "Importance"
 )
-importance_long$Genre <- factor(importance_long$Genre, levels = rev(colnames(X)))
+importance_long$Genre <- factor(
+ importance_long$Genre,
+ levels = rev(colnames(X))
+)
 # head(importance_long)
 ```
 
@@ -126,15 +132,21 @@ mfairObject_pmt <- fitGreedy(mfairObject_pmt,
 
 ```{r}
 # Get importance score
-importance_pmt <- as.data.frame(getImportance(mfairObject_pmt, which_factors = 1:3))
+importance_pmt <- as.data.frame(
+ getImportance(mfairObject_pmt, which_factors = 1:3)
+)
 importance_pmt$Genre <- rownames(importance_pmt)
+# Convert the wide table to the long table
 importance_pmt_long <- melt(
  data = importance_pmt,
  id.vars = "Genre",
  variable.name = "Factor",
  value.name = "Importance"
 )
-importance_pmt_long$Genre <- factor(importance_pmt_long$Genre, levels = rev(colnames(X_pmt)))
+importance_pmt_long$Genre <- factor(
+ importance_pmt_long$Genre,
+ levels = rev(colnames(X_pmt))
+)
 # head(importance_pmt_long)
 
 # Visualize the importance score
@@ -179,15 +191,21 @@ mfairObject_both <- fitGreedy(mfairObject_both,
 
 ```{r}
 # Get importance score
-importance_both <- as.data.frame(getImportance(mfairObject_both, which_factors = 1:3))
+importance_both <- as.data.frame(
+ getImportance(mfairObject_both, which_factors = 1:3)
+)
 importance_both$Genre <- rownames(importance_both)
+# Convert the wide table to the long table
 importance_both_long <- melt(
  data = importance_both,
  id.vars = "Genre",
  variable.name = "Factor",
  value.name = "Importance"
 )
-importance_both_long$Genre <- factor(importance_both_long$Genre, levels = rev(colnames(X_both)))
+importance_both_long$Genre <- factor(
+ importance_both_long$Genre,
+ levels = rev(colnames(X_both))
+)
 # head(importance_both_long)
 
 # Visualize the importance score

vignettes/neocortex.Rmd

@@ -101,7 +101,9 @@ p <- ggplot(
  data = FX_long,
  aes(
  x = `Time period`, y = F,
- linetype = `Neocortex area`, colour = `Neocortex area`, group = `Neocortex area`
+ linetype = `Neocortex area`,
+ colour = `Neocortex area`,
+ group = `Neocortex area`
  )
 ) +
  geom_line(linewidth = 0.5) +