diff --git a/3-Data-Visualization/R/11-visualization-proportions/README.md b/3-Data-Visualization/R/11-visualization-proportions/README.md
index 00a06e3..39fad50 100644
--- a/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -59,6 +59,7 @@ The output is:
 Take this data and convert the 'class' column to a category:
 
 ```r
+library(dplyr)
 grouped=mushrooms %>%
   group_by(class) %>%
   summarise(count=n())
@@ -96,6 +97,7 @@ A somewhat more visually interesting pie chart is a donut chart, which is a pie
 Take a look at the various habitats where mushrooms grow:
 
 ```r
+library(dplyr)
 habitat=mushrooms %>%
   group_by(habitat) %>%
   summarise(count=n())
@@ -116,20 +118,21 @@ The output is:
 Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:
 
 ```r
+library(ggplot2)
 library(webr)
 PieDonut(habitat, aes(habitat, count=count))
 ```
 
 ![donut chart](images/donut-wb.png)
 
-This code draws a chart and a center circle, then adds that center circle in the chart. Edit the width of the center circle by changing `0.40` to another value.
+This code uses the two libraries- ggplot2 and webr. Using the PieDonut function of the webr library, we can create a donut chart easily!
 
-Donut charts can be tweaked in several ways to change the labels. The labels in particular can be highlighted for readability. Learn more in the [docs](https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_and_donut_labels.html?highlight=donut).
+Donut charts in R can be made using only the ggplot2 library as well. You can learn more about it [here](https://www.r-graph-gallery.com/128-ring-or-donut-plot.html) and try it out yourself.
 
 Now that you know how to group your data and then display it as a pie or donut, you can explore other types of charts. Try a waffle chart, which is just a different way of exploring quantity.
 ## Waffles!
 
-A 'waffle' type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called [waffle](https://r-charts.com/part-whole/waffle-chart-ggplot2/) and use it to generate your visualization:
+A 'waffle' type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called [waffle](https://cran.r-project.org/web/packages/waffle/waffle.pdf) and use it to generate your visualization:
 
 ```r
 install.packages("waffle", repos = "https://cinc.rud.is")
@@ -137,9 +140,12 @@ install.packages("waffle", repos = "https://cinc.rud.is")
 
 Select a segment of your data to group:
 
-```python
-capcolor=mushrooms.groupby(['cap-color']).count()
-capcolor
+```r
+library(dplyr)
+cap_color=mushrooms %>%
+  group_by(cap.color) %>%
+  summarise(count=n())
+View(cap_color)
 ```
 
 Create a waffle chart by creating labels and then grouping your data:
@@ -169,8 +175,6 @@ Using a waffle chart, you can plainly see the proportions of cap colors of this
 
 ![waffle chart](images/waffle.png)
 
-✅ Pywaffle supports icons within the charts that use any icon available in [Font Awesome](https://fontawesome.com/). Do some experiments to create an even more interesting waffle chart using icons instead of squares.
-
 In this lesson, you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset.
 
 ## 🚀 Challenge
diff --git a/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index 612827f..ccb4bf8 100644
--- a/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -1,6 +1,6 @@
 # Making Meaningful Visualizations
 
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|
+|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)|
 |:---:|
 | Meaningful Visualizations - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |