translation of titles

code/030_Exploratory.R

@@ -132,7 +132,7 @@ corrplot = ggplotly( ggcorrplot(corr_matrix, hc.order = TRUE,
                                 outline.col = "white",
                                 #ggtheme = ggplot2::theme_gray,
                                 colors = c("#6D9EC1", "white", "#E46726")) +
-                       ggtitle("Matrice di correlazione"))
+                       ggtitle("Correlation matrix"))
 # ********** Saving a file ******************* #
 file_name = paste0( folder, "/corrplot.Rdata")
 save( corrplot, file = file_name)
@@ -168,7 +168,7 @@ data_corr = data.frame( Variables = colnames( wine[ , 1:11]),
 corr_Y = ggplot(data_corr, aes( Variables, Spearman, fill = Variables, text = paste('Kendall:', Kendall, "\n", 
                                                                                     'Pearson:', Pearson, "\n" ))) +
          geom_bar(  stat = "identity", position='stack') + 
-         ggtitle( "Correlazione della variabile Qualità" ) + guides( fill = FALSE ) +
+         ggtitle( "Quality variable correlation" ) + guides( fill = FALSE ) +
          theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
          ylab("Spearman's correlation") 
 #  theme(axis.text.x = element_text(angle = 45, vjust = 1,  size = 12, hjust = 1))
@@ -189,7 +189,7 @@ df$Relative_Freq = paste( round( df$Frequencies/sum(df$Frequencies)*100, 1), '%'
 quality_distr = ggplot(data=df, aes(x=Quality, y=Frequencies, fill = Quality)) +
                 geom_bar(stat="identity") + 
                 geom_text(aes(label=Relative_Freq),  vjust=-5, color="black") + #
-                ggtitle( 'Qualità - Distribuzione di frequenza')
+                ggtitle( 'Quality - Frequency Distribution')
 
 quality_distr = ggplotly( quality_distr )
 
@@ -201,4 +201,3 @@ save( quality_distr, file = file_name)
 
 rm(list= ls())
 
-cat('\n\n SCRIPT ESEGUITO CORRETTAMENTE!! \n\n')

code/076_Modeling_Classification.R

@@ -327,11 +327,11 @@ elnet_validation = ggplot(data = elnet_data, aes( x = lambda, group = alpha,
                                           #              "Precisione nominale:", Precision) +
                           geom_line( aes( y = Accuracy, color = alpha )) +
                           geom_point( aes( y = Accuracy, color = alpha ), show.legend = F) +    
-                          ggtitle( "Validazione parametri Elastic Net" ) +
+                          ggtitle( "Elastic Net validation" ) +
                           theme(plot.title = element_text(size = 15, face = "bold"))
 
 ply_val_elnet = ggplotly( elnet_validation ) %>%
-                layout(title = "Validazione parametri Elastic Net",
+                layout(title = "Elastic Net validation",
                        legend = list(orientation = "v")) # , y = 0, x = 0))
 
 save_plot( ply_val_elnet, type = "CLASSIFICATION")
@@ -392,12 +392,12 @@ gam_validation = ggplot(data = gam_data, aes( x = df)) +
                  geom_line( aes( y = Accuracy, color = 'red' )) +
                  geom_point( aes( y = Accuracy, color = 'red' ), show.legend = F) + 
                  geom_ribbon(aes(ymin=Accuracy-AccuracySD, ymax=Accuracy+AccuracySD), linetype=2, alpha=0.1) +
-                 ggtitle( "Validazione parametri GAM" ) +
+                 ggtitle( "GAM validation" ) +
                  theme(plot.title = element_text(size = 15, face = "bold")) + 
                  scale_x_continuous(breaks=1:10)
 
 ply_val_gam = ggplotly( gam_validation ) %>%
-                layout(title = "Validazione parametri GAM", showlegend = F)
+                layout(title = "GAM validation", showlegend = F)
                    # legend = list(orientation = "v")) # , y = 0, x = 0))
 save_plot( ply_val_gam, type = "CLASSIFICATION")
 
@@ -467,7 +467,7 @@ knn_kappa = ggplot(data = cv_df, aes(x = k)) +
 knn_kappa = ggplotly( knn_kappa )
 
 knn_cv_plot = subplot( knn_acc, knn_kappa ) %>% 
-              layout(title = "Validazione parametri knn - Accuratezza vs Kappa", legend = list(orientation = "v"))
+              layout(title = "knn validation - Accuracy vs Kappa", legend = list(orientation = "v"))
 
 
 save_plot( knn_cv_plot, type = 'CLASSIFICATION')

markdown/100_main.Rmd

@@ -60,7 +60,7 @@ library('DT')
 ```
 
 
-```{r child = '250_REGURALISED_METHODS.Rmd'}
+```{r child = '250_REGULARISED_METHODS.Rmd'}
 ```
 
 

markdown/150_Introduction.Rmd

@@ -82,7 +82,7 @@ For this purpose, the correlation matrix computed on the whole dataset is report
 
 
 ```{r, fig.width=9.5, fig.height=6, echo=FALSE}
-file = "results/EXPLORATORY_ANALYSES/corrplot.Rdata"
+file = "../results/EXPLORATORY_ANALYSES/corrplot.Rdata"
 load( file )
 corrplot
 

markdown/220_LDA.Rmd

@@ -38,7 +38,7 @@ load( file )
 file = "../results/MODELING/CLASSIFICATION/canonical_variable2.Rdata"
 load( file )
 
-subplot( canonical_variable, canonical_variable2) %>% layout(  title = 'Variabile Canonica')
+subplot( canonical_variable, canonical_variable2) %>% layout(  title = 'Canonical variable')
 
 ```