diff --git a/3-Data-Visualization/R-11-visualization-proportions/README.md b/3-Data-Visualization/R-11-visualization-proportions/README.md
index 2f265e72..20fbedff 100644
--- a/3-Data-Visualization/R-11-visualization-proportions/README.md
+++ b/3-Data-Visualization/R-11-visualization-proportions/README.md
@@ -18,11 +18,9 @@ In this lesson, you will use a different nature-focused dataset to visualize pro
 
 Mushrooms are very interesting. Let's import a dataset to study them:
 
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-mushrooms = pd.read_csv('../../data/mushrooms.csv')
-mushrooms.head()
+```r
+mushrooms = read.csv('../../data/mushrooms.csv')
+head(mushrooms)
 ```
 A table is printed out with some great data for analysis:
 
@@ -32,24 +30,31 @@ A table is printed out with some great data for analysis:
 | Poisonous | Convex    | Smooth      | Brown     | Bruises | Pungent | Free            | Close        | Narrow    | Black      | Enlarging   | Equal      | Smooth                   | Smooth                   | White                  | White                  | Partial   | White      | One         | Pendant   | Black             | Scattered  | Urban   |
 | Edible    | Convex    | Smooth      | Yellow    | Bruises | Almond  | Free            | Close        | Broad     | Black      | Enlarging   | Club       | Smooth                   | Smooth                   | White                  | White                  | Partial   | White      | One         | Pendant   | Brown             | Numerous   | Grasses |
 | Edible    | Bell      | Smooth      | White     | Bruises | Anise   | Free            | Close        | Broad     | Brown      | Enlarging   | Club       | Smooth                   | Smooth                   | White                  | White                  | Partial   | White      | One         | Pendant   | Brown             | Numerous   | Meadows |
-| Poisonous | Convex    | Scaly       | White     | Bruises | Pungent | Free            | Close        | Narrow    | Brown      | Enlarging   | Equal      | Smooth                   | Smooth                   | White                  | White                  | Partial   | White      | One         | Pendant   | Black             | Scattered  | Urban   |
-
+| Poisonous | Convex    | Scaly       | White     | Bruises | Pungent | Free            | Close        | Narrow    | Brown      | Enlarging   | Equal      | Smooth                   | Smooth                   | White                  | White                  | Partial   | White      | One         | Pendant   | Black             | Scattered  | Urban 
+| Edible | Convex       |Smooth       | Green     | No Bruises| None   |Free            | Crowded       | Broad     | Black      | Tapering   | Equal      |  Smooth | Smooth                    | White                 | White                  | Partial    | White     | One         | Evanescent | Brown             | Abundant | Grasses
+|Edible  |  Convex      | Scaly   | Yellow         | Bruises  | Almond  | Free | Close  |   Broad   |   Brown  | Enlarging   |   Club                      | Smooth                  | Smooth    | White                 |  White                | Partial      | White    |  One  |  Pendant | Black   | Numerous | Grasses
+      
 Right away, you notice that all the data is textual. You will have to convert this data to be able to use it in a chart. Most of the data, in fact, is represented as an object:
 
-```python
-print(mushrooms.select_dtypes(["object"]).columns)
+```r
+names(mushrooms)
 ```
 
 The output is:
 
 ```output
-Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
-       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
-       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
-       'stalk-surface-below-ring', 'stalk-color-above-ring',
-       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
-       'ring-type', 'spore-print-color', 'population', 'habitat'],
-      dtype='object')
+[1] "class"                    "cap.shape"               
+ [3] "cap.surface"              "cap.color"               
+ [5] "bruises"                  "odor"                    
+ [7] "gill.attachment"          "gill.spacing"            
+ [9] "gill.size"                "gill.color"              
+[11] "stalk.shape"              "stalk.root"              
+[13] "stalk.surface.above.ring" "stalk.surface.below.ring"
+[15] "stalk.color.above.ring"   "stalk.color.below.ring"  
+[17] "veil.type"                "veil.color"              
+[19] "ring.number"              "ring.type"               
+[21] "spore.print.color"        "population"              
+[23] "habitat"            
 ```
 Take this data and convert the 'class' column to a category:
 
@@ -123,10 +128,10 @@ Donut charts can be tweaked in several ways to change the labels. The labels in
 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 [PyWaffle](https://pypi.org/project/pywaffle/) and use Matplotlib:
+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:
 
-```python
-pip install pywaffle
+```r
+install.packages("waffle", repos = "https://cinc.rud.is")
 ```
 
 Select a segment of your data to group: