diff --git a/R/plots.R b/R/plots.R
index 4aa0a18..ae824ba 100644
--- a/R/plots.R
+++ b/R/plots.R
@@ -128,7 +128,7 @@ vis.number.count <- function (.data, .ncol = 3, .name = 'Histogram of clonotypes
   }
   
   ggplot() + 
-    xlim(min(.data$Read.count), 300) +
+    xlim(min(.data$Read.count), 300) + 
     ylab('Number of clonotypes') +
     geom_histogram(aes(x = Read.count, fill = ..count..), data = .data, binwidth = 1, colour = 'black') +
     coord_trans(xtrans = 'log10') + scale_y_log10() +
diff --git a/vignettes/tcrvignette.Rnw b/vignettes/tcrvignette.Rnw
index 118b926..039ca69 100644
--- a/vignettes/tcrvignette.Rnw
+++ b/vignettes/tcrvignette.Rnw
@@ -432,7 +432,12 @@ The package implements rich data visualisation procedures. All of them are descr
 
 \subsection{CDR3 length and read count distributions}
 Plots of the distribution of CDR3 nucleotide sequences length (function \code{vis.count.len}) and the histogram of "Read.count" number (function \code{vis.number.count}). Input data is either a data frame or a list with data frames.
-<<eval=TRUE,fig=TRUE,height=5,width=18,height=6>>=
+%<<eval=TRUE,fig=TRUE,height=5,width=18,height=6>>=
+%p1 <- vis.count.len(twb[[1]])
+%p2 <- vis.number.count(twb[[1]])
+%grid.arrange(p1, p2, ncol = 2)
+%@
+<<eval=FALSE>>=
 p1 <- vis.count.len(twb[[1]])
 p2 <- vis.number.count(twb[[1]])
 grid.arrange(p1, p2, ncol = 2)