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110
3-Data-Visualization/R/11-visualization-proportions/README.md
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@ -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:
@ -32,55 +30,60 @@ 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:
```python
cols = mushrooms.select_dtypes(["object"]).columns
mushrooms[cols] = mushrooms[cols].astype('category')
```r
grouped=mushrooms %>%
group_by(class) %>%
summarise(count=n())
```
```python
edibleclass=mushrooms.groupby(['class']).count()
edibleclass
```
Now, if you print out the mushrooms data, you can see that it has been grouped into categories according to the poisonous/edible class:
```r
View(grouped)
```
| class | count |
| --------- | --------- |
| Edible | 4208 |
| Poisonous| 3916 |
| | cap-shape | cap-surface | cap-color | bruises | odor | gill-attachment | gill-spacing | gill-size | gill-color | stalk-shape | ... | 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 |
| --------- | --------- | ----------- | --------- | ------- | ---- | --------------- | ------------ | --------- | ---------- | ----------- | --- | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
| class | | | | | | | | | | | | | | | | | | | | | |
| Edible | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | ... | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 |
| Poisonous | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | ... | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 |
If you follow the order presented in this table to create your class category labels, you can build a pie chart:
If you follow the order presented in this table to create your class category labels, you can build a pie chart.
## Pie!
```python
labels=['Edible','Poisonous']
plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')
plt.title('Edible?')
plt.show()
```r
pie(grouped$count,grouped$class, main="Edible?")
```
Voila, a pie chart showing the proportions of this data according to these two classes of mushrooms. It's quite important to get the order of the labels correct, especially here, so be sure to verify the order with which the label array is built!
@ -92,26 +95,29 @@ 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:
```python
habitat=mushrooms.groupby(['habitat']).count()
habitat
```r
habitat=mushrooms %>%
group_by(habitat) %>%
summarise(count=n())
View(habitat)
```
Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:
```python
labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood']
plt.pie(habitat['class'], labels=labels,
autopct='%1.1f%%', pctdistance=0.85)
center_circle = plt.Circle((0, 0), 0.40, fc='white')
fig = plt.gcf()
The output is:
| habitat| count |
| --------- | --------- |
| Grasses | 2148 |
| Leaves| 832 |
| Meadows | 292 |
| Paths| 1144 |
| Urban | 368 |
| Waste| 192 |
| Wood| 3148 |
fig.gca().add_artist(center_circle)
plt.title('Mushroom Habitats')
Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:
plt.show()
```r
library(webr)
PieDonut(habitat, aes(habitat, count=count))
```
![donut chart](images/donut-wb.png)
@ -123,10 +129,10 @@ 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:

10
3-Data-Visualization/R/12-visualization-relationships/README.md
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@ -20,12 +20,9 @@ Use a scatterplot to show how the price of honey has evolved, year over year, pe
Let's start by importing the data and Seaborn:
```python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
honey = pd.read_csv('../../data/honey.csv')
honey.head()
```r
honey=read.csv('../../data/honey.csv')
head(honey)
```
You notice that the honey data has several interesting columns, including year and price per pound. Let's explore this data, grouped by U.S. state:
@ -36,6 +33,7 @@ You notice that the honey data has several interesting columns, including year a
| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 |
| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 |
| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 |
| FL | 230000 | 98 |22540000 | 4508000 | 0.64 | 14426000 | 1998 |
Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Make the `y` axis tall enough to display all the states:

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