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96 changes: 28 additions & 68 deletions docs/05-data-visualization.md
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Expand Up @@ -61,52 +61,54 @@ plot = sns.displot(data=metadata, x="Age")

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-3-1.png)<!-- -->

(For the webpage's purpose, assign the plot to a variable `plot`. In practice, you don't need to do that. You can just write `sns.displot(data=metadata, x="Age")`).

A common parameter to consider when making histogram is how big the bins are. You can specify the bin width via `binwidth` argument, or the number of bins via `bins` argument.


``` python
sns.displot(data=metadata, x="Age", binwidth = 10)
plot = sns.displot(data=metadata, x="Age", binwidth = 10)
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-4-3.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-4-4.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-4-3.png)<!-- -->

Our histogram also works for categorical variables, such as "Sex".


``` python
sns.displot(data=metadata, x="Sex")
plot = sns.displot(data=metadata, x="Sex")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-5-7.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-5-8.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-5-5.png)<!-- -->

**Conditioning on other variables**

Sometimes, you want to examine a distribution, such as Age, conditional on other variables, such as Age for Female, Age for Male, and Age for Unknown: what is the distribution of age when compared with sex? There are several ways of doing it. First, you could color variables by color, using the `hue` input argument:


``` python
sns.displot(data=metadata, x="Age", hue="Sex")
plot = sns.displot(data=metadata, x="Age", hue="Sex")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-6-11.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-6-12.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-6-7.png)<!-- -->

It is rather hard to tell the groups apart from the coloring. So, we add a new option that we want to separate each bar category via `multiple="dodge"` input argument:


``` python
sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge")
plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-7-15.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-7-16.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-7-9.png)<!-- -->

Lastly, an alternative to using colors to display the conditional variable, we could make a subplot for each conditional variable's value via `col="Sex"` or `row="Sex"`:


``` python
sns.displot(data=metadata, x="Age", col="Sex")
plot = sns.displot(data=metadata, x="Age", col="Sex")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-8-19.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-8-20.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-8-11.png)<!-- -->

You can find a lot more details about distributions and histograms in [the Seaborn tutorial](https://seaborn.pydata.org/tutorial/distributions.html).

Expand All @@ -116,10 +118,10 @@ To visualize two continuous variables, it is common to use a scatterplot or a li


``` python
sns.relplot(data=expression, x="KRAS_Exp", y="EGFR_Exp")
plot = sns.relplot(data=expression, x="KRAS_Exp", y="EGFR_Exp")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-9-23.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-9-24.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-9-13.png)<!-- -->

To conditional on other variables, plotting features are used to distinguish conditional variable values:

Expand All @@ -135,37 +137,37 @@ Let's merge `expression` and `metadata` together, so that we can examine KRAS an
``` python
expression_metadata = expression.merge(metadata)

sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis")
plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-10-27.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-10-28.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-10-15.png)<!-- -->

Here is the scatterplot with different shapes:


``` python
sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", style="PrimaryOrMetastasis")
plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", style="PrimaryOrMetastasis")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-11-31.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-11-32.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-11-17.png)<!-- -->

You can also try plotting with `size=PrimaryOrMetastasis"` if you like. None of these seem pretty effective at distinguishing the two groups, so we will try subplot faceting as we did for the histogram:


``` python
sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", col="PrimaryOrMetastasis")
plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", col="PrimaryOrMetastasis")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-12-35.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-12-36.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-12-19.png)<!-- -->

You can also conditional on multiple variables by assigning a different variable to the conditioning options:


``` python
sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis", col="AgeCategory")
plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis", col="AgeCategory")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-13-39.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-13-40.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-13-21.png)<!-- -->

You can find a lot more details about relational plots such as scatterplots and lineplots [in the Seaborn tutorial](https://seaborn.pydata.org/tutorial/relational.html).

Expand Down Expand Up @@ -203,64 +205,22 @@ exp_plot = sns.relplot(data=expression, x="KRAS_Exp", y="EGFR_Exp")
exp_plot.set(xlabel="KRAS Espression", ylabel="EGFR Expression", title="Gene expression relationship")
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-14-43.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-14-44.png)<!-- -->
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-14-23.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-14-24.png)<!-- -->

You can change the color palette by setting adding the `palette` input argument to any of the plots. You can explore available color palettes [here](https://www.practicalpythonfordatascience.com/ap_seaborn_palette):


``` python
sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", palette=sns.color_palette(palette='rainbow')
plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", palette=sns.color_palette(palette='rainbow')
)
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-15-47.png)<!-- -->![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-15-48.png)<!-- -->

## Exercises

Exercise for week 5 can be found [here](https://colab.research.google.com/drive/1kT3zzq2rrhL1vHl01IdW5L1V7v0iK0wY?usp=sharing).


``` r
hist(iris$Sepal.Length)
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-16-51.png)<!-- -->


``` r
hist(iris$Sepal.Length)
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-17-1.png){width=200%}

matplotlib


``` python
import matplotlib.pyplot as plt

fig, ax = plt.subplots()

fruits = ['apple', 'blueberry', 'cherry', 'orange']
counts = [40, 100, 30, 55]
bar_labels = ['red', 'blue', '_red', 'orange']
bar_colors = ['tab:red', 'tab:blue', 'tab:red', 'tab:orange']

ax.bar(fruits, counts, label=bar_labels, color=bar_colors)

ax.set_ylabel('fruit supply')
ax.set_title('Fruit supply by kind and color')
ax.legend(title='Fruit color')

## <string>:1: UserWarning: The palette list has more values (6) than needed (3), which may not be intended.
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-18-1.png)<!-- -->

now show
![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-15-27.png)<!-- -->

## Exercises

``` python
plt.show()
```

![](resources/images/05-data-visualization_files/figure-docx/unnamed-chunk-19-3.png)<!-- -->
Exercise for week 5 can be found [here](https://colab.research.google.com/drive/1kT3zzq2rrhL1vHl01IdW5L1V7v0iK0wY?usp=sharing).
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