update slides and links

05-data-visualization.Rmd

@@ -32,7 +32,7 @@ Categorical (between 1 categorical and 1 continuous variable)
 
 -   Violin plots
 
-[![Image source: Seaborn\'s overview of plotting functions](https://seaborn.pydata.org/_images/function_overview_8_0.png)](https://seaborn.pydata.org/tutorial/function_overview.html)
+[![Image source: Seaborn's overview of plotting functions](https://seaborn.pydata.org/_images/function_overview_8_0.png)](https://seaborn.pydata.org/tutorial/function_overview.html)
 
 Why do we focus on these common plots? Our eyes are better at distinguishing certain visual features more than others. All of these plots are focused on their position to depict data, which gives us the most effective visual scale.
 
@@ -182,4 +182,4 @@ plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", palette=
 
 ## Exercises
 
-Exercise for week 5 can be found [here](https://colab.research.google.com/drive/1kT3zzq2rrhL1vHl01IdW5L1V7v0iK0wY?usp=sharing).
+Exercise for week 5 can be found [here](https://colab.research.google.com/drive/17iwr8NwLLrmzRj4a6zRZucETXpPkmDNR?usp=sharing).

slides/community2.qmd

@@ -0,0 +1,39 @@
+---
+title: "Python Community Session 1"
+format: 
+  revealjs:
+    smaller: true
+    scrollable: true
+    echo: true
+output-location: fragment
+---
+
+## How to find new functions and methods
+
+Suppose you want to learn more about Pandas Dataframes:
+
+[Newcomer tutorials](https://pandas.pydata.org/docs/user_guide/10min.html)
+
+[User guides](https://pandas.pydata.org/docs/user_guide/index.html)
+
+[Package documentation page](https://pandas.pydata.org/docs/index.html)
+
+-   How to install the module?
+
+-   How to load in the module? What are some module functions?
+
+-   What kind of Objects are available for use?
+
+## What kind of Objects are available for use?
+
+-   How to construct or load an Object?
+
+-   *What does it contain?*
+
+    -   **Value** that holds the essential data for the object.
+
+    -   **Attributes** that hold subset of the data or additional data for the object.
+
+-   *What can it do?*
+
+    -   Functions called **Methods** specific to the data type and automatically takes the object as input.

slides/lesson5_slides.qmd

@@ -92,7 +92,7 @@ Once you load in the Dataframe, `my_df` is a Dataframe Object.
 
 . . .
 
-Ex. When we make a plot from `displot`, we get a FacetGrid object.
+When we make a plot from `displot`, it returns a FacetGrid object.
 
 ## [What's your favorite plot??](https://docs.google.com/document/d/1-v4pYC7jh7EYHq7MtRLd0rfhurojGrskWy7Oy_GQfZY/edit?usp=sharing)
 
@@ -114,7 +114,7 @@ Ex. When we make a plot from `displot`, we get a FacetGrid object.
 
 More to come!
 
-## Why we like these common plots
+## Why we like these plots
 
 ![](https://www.oreilly.com/api/v2/epubs/9781466508910/files/image/fig5-1.png)
 
@@ -134,31 +134,31 @@ Adjust the binwidth:
 plot = sns.displot(data=metadata, x="Age", binwidth = 10)
 ```
 
-## Distributions
+## Conditional Distributions
 
-What is the distribution of age when compared with sex? Age for Female, Age for Male, and Age for Unknown:
+What is the distribution of age conditional on sex? Age for Female, Age for Male, and Age for Unknown:
 
 ```{python}
 plot = sns.displot(data=metadata, x="Age", hue="Sex", binwidth=10)
 ```
 
-## Distributions
+## Conditional Distributions
 
 We add a new option that we want to separate each bar category via `multiple="dodge"` input argument:
 
 ```{python}
 plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", binwidth=10)
 ```
 
-## Distributions
+## Conditional Distributions
 
 Or, separate the conditions via a subplot:
 
 ```{python}
 plot = sns.displot(data=metadata, x="Age", col="Sex", binwidth=10)
 ```
 
-You can find a lot more details about distributions and histograms in [the Seaborn tutorial](https://seaborn.pydata.org/tutorial/distributions.html).
+More info [here](https://seaborn.pydata.org/tutorial/distributions.html).
 
 ## Relational
 
@@ -168,15 +168,7 @@ Specify the input argument `data` as our dataframe, and the input arguments `x`
 plot = sns.relplot(data=expression, x="KRAS_Exp", y="EGFR_Exp")
 ```
 
-## Relational
-
-You can change the plot style via the `kind` argument to "line", but the visual interpretation changes.
-
-```{python}
-plot = sns.relplot(data=expression, x="KRAS_Exp", y="EGFR_Exp", kind="line")
-```
-
-## Relational
+## Condition on Relational
 
 To conditional on other variables, plotting features are used to distinguish conditional variable values:
 
@@ -194,22 +186,24 @@ Let's merge `expression` and `metadata` together, so that we can examine KRAS an
 expression_metadata = expression.merge(metadata)
 ```
 
-## Relational
+## Condition on Relational
 
 Examine KRAS and EGFR relationships conditional on primary vs. metastatic cancer status:
 
 ```{python}
 plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis")
 ```
 
-## Relational
+## Condition on Relational
 
 More conditioning:
 
 ```{python}
 plot = sns.relplot(data=expression_metadata, x="KRAS_Exp", y="EGFR_Exp", hue="PrimaryOrMetastasis", col="AgeCategory")
 ```
 
+More on relational plots [here](https://seaborn.pydata.org/tutorial/relational.html).
+
 ## Categorical
 
 Between one categorical and one continuous variable:
@@ -228,19 +222,22 @@ You can add additional conditional variables via the input arguments:
 
 -   `col`
 
-## Categorical
+## Condtion on Categorical
 
 ```{python}
 plot = sns.catplot(data=expression_metadata, x="PrimaryOrMetastasis", y="EGFR_Exp", hue="AgeCategory")
 ```
 
+More on categorical plots [here](https://seaborn.pydata.org/tutorial/categorical.html).
+
 ## "Kind" of plots
 
 ![Image source: Seaborn’s overview of plotting functions](https://seaborn.pydata.org/_images/function_overview_8_0.png)
 
 For example, `sns.catplot(data=expression_metadata, x="PrimaryOrMetastasis", y="EGFR_Exp", hue="AgeCategory", kind="box")`
 
-Try it out!
+[Try it out, share with what you tried](https://docs.google.com/document/d/1-v4pYC7jh7EYHq7MtRLd0rfhurojGrskWy7Oy_GQfZY/edit?usp=sharing)
+
 
 ## Basic plot customization: color
 
@@ -255,6 +252,11 @@ plot = sns.displot(data=metadata, x="Age", hue="Sex", multiple="dodge", palette=
 
 ## [Cheatsheet, updated with Modules](https://docs.google.com/document/d/1si-4suESej1Vjopkv5KiI-hGylXk11osmO3oEfpYOuc/edit?usp=sharing)
 
+
+## What to do next week?
+
+<https://docs.google.com/document/d/17FsFWDOEuqTcHoWe-wl2Bz-xlsK0vGzqeiNBxMDkwXI/edit?usp=sharing>
+
 ## How was class for you today?
 
 <https://forms.gle/8EGdEqcsp82GP82U9>