final polishing/updating for new episodes

add time estimates for exercises

Author: jenna-jordan

_episodes/03-data-wrangling.md

@@ -1,7 +1,7 @@
 ---
 title: "Data Wrangling"
 teaching: 15
-exercises: 0
+exercises: 5
 questions:
 - "What format should my data be in for Plotly Express?"
 - "Why can't I use the data in its current format?"
@@ -26,17 +26,15 @@ You can click on the `Data` folder and double click on `gapminder_all.csv` to vi
 We are going take this very wide dataset and make it very long, so the unit of observation will be each country + year + metric combination, rather than just the country. This process is made much simpler by a couple of functions in the `pandas` library.
 
 > ## Tidy Data
+>
 > The term "tidy data" may be most popular in the R ecosystem (the "tidyverse" is a collection of R packages designed around the tidy data philosophy), but it is applicable to all tabular datasets, not matter what programming language you are using to wrangle your data.
-> You can ready more about the tidy data philosophy in Hadley Wickham's 2014 paper, "Tidy Data", available [here](https://vita.had.co.nz/papers/tidy-data.pdf).
 >
-> Tidy data follows 3 rules:
-> 1. Each variable forms a column
-> 2. Each observation forms a row
-> 3. Each type of observational unit forms a table
+> You can ready more about the tidy data philosophy in Hadley Wickham's 2014 paper, "Tidy Data", available [here](https://vita.had.co.nz/papers/tidy-data.pdf).
 >
 > Wickham later refined and revised the tidy data philosophy, and published it in the 12th chapter of his open access textbook "R for Data Science" - available [here](https://r4ds.had.co.nz/tidy-data.html). 
 >
 > The revised rules are:
+>
 > 1. Each variable must have its own column
 > 2. Each observation must have its own row
 > 3. Each value must have its own cell
@@ -75,6 +73,7 @@ df
 The first function we are going to use to wrangle this dataset is `pd.melt()`. This function's entire purpose to to make wide dataframes into long dataframes.
 
 > ## Check out the documentation
+>
 > To learn more about `pd.melt()`, you can look at the function's [documentation](https://pandas.pydata.org/docs/reference/api/pandas.melt.html)
 > To see this documentation within Jupyter Lab, you can type `pd.melt()` in a cell and then hold down the shift + tab keys.
 > You can also open a "Show Contextual Help" window from the Launcher.
@@ -91,7 +90,6 @@ df.columns
 
 Our "id" columns are `country` and `continent`. Our "value" columns are all of the rest. That's a lot of columns! But no worries - we can programmatically make a list of all of these columns.
 
-
 ~~~
 cols = list(df.columns)
 cols.remove('continent')
@@ -109,6 +107,7 @@ df_melted
 {: .language-python}
 
 > ## New dataframe variable names
+>
 > When wrangling a dataframe in a Jupyter notebook, it's a good idea to assign transformed dataframes to a new variable name.
 > You don't have to do this with every transformation, but do try to do this with every substantial transformation.
 > This way, we don't have to re-run the entire notebook when we are experimenting with transformations on a dataframe.
@@ -127,6 +126,7 @@ df_melted
 {: .language-python}
 
 > ## Wide vs long data
+>
 > Take a moment to compare this dataframe to the one we started with. 
 >
 > What are some advantages to having the data in this format?
@@ -152,6 +152,8 @@ df_final.to_csv("Data/gapminder_tidy.csv", index=False)
 
 We set the index to False so that the index column does not get saved to the CSV file.
 
+## Exercises
+
 > ## Imagining other tidy ways to wrangle
 > We wrangled our data into a tidy form. However, there is no single "true tidy" form for any given dataset.
 >

_episodes/04-create-visualizations.md

@@ -1,7 +1,7 @@
 ---
 title: "Create Visualizations"
-teaching: 10
-exercises: 0
+teaching: 15
+exercises: 5
 questions:
 - "How can I create an interactive visualization using Plotly Express?"
 objectives:
@@ -80,7 +80,6 @@ fig.show()
 
 There it is! Our first line plot.
 
-
 ## When you want to compare - adding more lines and labels
 
 By itself, this plot of New Zealand's GDP isn't especially interesting. Let's add another line, to compare it to Australia.
@@ -116,6 +115,7 @@ fig.show()
 ![Plot of Oceania's GDP over time with correct labels](../fig/L3_thirdplot.png)
 
 > ## Interactivity is baked in to Plotly charts
+>
 > When you have many more lines, the interactive features of Plotly become very useful. 
 > Notice how hovering over a line will tell you more information about that point. 
 > You will also see several options in the upper right corner to further interact with the plot - including saving it as a PNG file!
@@ -124,6 +124,7 @@ fig.show()
 ## Exercises
 
 > ## Visualize Population in Europe
+>
 > Create a plot that visualizes the population of countries in Europe over time.
 > > ## Solution
 > > ~~~
@@ -136,6 +137,7 @@ fig.show()
 {: .challenge}
 
 > ## Visualize Average Life Expectancy in Asia
+>
 > Create a plot that visualizes the average life expectancy of countries in Asia over time.
 > > ## Solution
 > > ~~~

_episodes/05-create-streamlit-app.md

@@ -1,7 +1,7 @@
 ---
 title: "Create the Streamlit App"
-teaching: 10
-exercises: 0
+teaching: 15
+exercises: 5
 questions:
 - "How do I create a Streamlit app?"
 - "How can I see a live preview of my app?"
@@ -109,8 +109,9 @@ You can make this title whatever you want. Save the file, and go back to the bro
 We can add other text to our app with `st.write()` and other functions.
 
 > ## Check out the documentation
+>
 > Whenever you are working with a new library - or even one that you are familiar with! - it's a good idea to look through the documentation.
-> You can find Streamlit's documentation [here](https://docs.streamlit.io/en/stable/api.html#)
+> You can find Streamlit's documentation [here](https://docs.streamlit.io/library/api-reference)
 {: .callout}
 
 ## Add a plot to the app
@@ -158,16 +159,10 @@ We now have a web application that can allow you to share your interactive visua
 
 ![Streamlit app after this lesson](../fig/streamlit_app_lesson4fin.png)
 
-> ## Share your app online
-> Right now, our app only lives on our computer. Like Jupyter Lab, the app is displaying in a web browser but has the URL `localhost:####` (where #### represents the port number).
-> To easily make this app public and shared online, you can sign up for a [Streamlit Sharing](https://streamlit.io/sharing-sign-up) account. This will let you share up to 3 apps. 
-> All you need (in addition to an account) is to have your code in a public GitHub repo.
-> Detailed instructions can be found in [Streamlit's Documentation](https://docs.streamlit.io/en/stable/deploy_streamlit_app.html)
-{: .callout}
-
 ## Exercises
 
 > ## Add a description
+>
 > After the plot is displayed, add some text describing the plot.
 > > ## Solution
 > > ~~~
@@ -179,6 +174,7 @@ We now have a web application that can allow you to share your interactive visua
 {: .challenge}
 
 > ## Show me the data!
+>
 > After the plot is displayed, also display the dataframe used to generate the plot.
 > > ## Solution
 > > ~~~

_episodes/06-refactoring-for-flexibility.md

@@ -1,7 +1,7 @@
 ---
 title: "Refactoring Code for Flexibility (Prepping for Widgets)"
-teaching: 15
-exercises: 0
+teaching: 20
+exercises: 5
 questions:
 - "How does the current code need to change in order to incorporate widgets?"
 - "What aspects of the code need to change?"
@@ -243,6 +243,7 @@ st.plotly_chart(fig, use_container_width=True)
 ## Exercises
 
 > ## Add a (flexible) description
+>
 > After the plot is displayed, add some text describing the plot. 
 >
 > This time, use F-strings so the description can change with the plot
@@ -255,6 +256,7 @@ st.plotly_chart(fig, use_container_width=True)
 {: .challenge}
 
 > ## Show me the data! (Maybe)
+>
 > After the plot is displayed, also display the dataframe used to generate the plot.
 >
 > This time, make it optional - only display the dataframe if a variable is set to True.

_episodes/07-add-widgets.md

@@ -1,7 +1,7 @@
 ---
 title: "Add Widgets to the Streamlit App"
-teaching: 10
-exercises: 0
+teaching: 15
+exercises: 15
 questions:
 - "Why do I want to have widgets in my app?"
 - "What kinds of widgets are there?"
@@ -23,6 +23,7 @@ We are going to add two widgets to our app. One widget will let us select the co
 You may have said "Dropdown box". In Streamlit, this is called a "Select box", and is created with `st.selectbox()`.
 
 > ## Look through the documentation for widget options
+>
 > Streamlit has many types of widgets available. You can see the documentation for them [here](https://docs.streamlit.io/en/stable/api.html#display-interactive-widgets)
 {: .callout}
 
@@ -174,6 +175,7 @@ Save, Rerun, and... Share!
 ## Exercises
 
 > ## Show me the data! (If the user wants it)
+>
 > After the plot is displayed, also display the dataframe used to generate the plot.
 >
 > Use a widget so that the user can decide whether to display the data. 
@@ -192,6 +194,7 @@ Save, Rerun, and... Share!
 {: .challenge}
 
 > ## Limit the countries displayed in the plot
+>
 > Add a widget that allows users to limit the countries that will be displayed on the plot.
 > 
 > (Hint: look at the multiselect!)
@@ -209,6 +212,7 @@ Save, Rerun, and... Share!
 {: .challenge}
 
 > ## Limit the dates displayed in the plot
+>
 > Add a widget that allows users to limit the range of years that will be displayed on the plot.
 > 
 > (Hint: look at the slider!)
@@ -227,6 +231,7 @@ Save, Rerun, and... Share!
 {: .challenge}
 
 > ## Improve the description
+>
 > After the plot is displayed, add some text describing the plot. 
 >
 > This time, add more to the description based on the information specified by the newly added widgets.

_episodes/08-publish-your-app.md

@@ -1,7 +1,7 @@
 ---
 title: "Publish Your Streamlit App"
 teaching: 20
-exercises: 0
+exercises: 30
 questions:
 - "How do I deploy my app so other people can see it?"
 - "How do I create a `requirements.txt` file?"
@@ -115,5 +115,25 @@ When you have signed in to your Streamlit account, click "New app". Copy and pas
 
 You can read the documentation about this process for deploying an app [here](https://docs.streamlit.io/streamlit-cloud/get-started/deploy-an-app).
 
+## Exercises
+
+> ## Add your own twist
+>
+> Now that you have successfully deployed the app we created together, it's time for you to add your own twist.
+>
+> You can add more visualizations, more widgets, or make use of Streamlit's other capabilities to add to and enhance the app. Make it your own!
+{: .challenge}
+
+> ## Share & Present Your Apps
+>
+> After everyone has had time to work on adding their own twist to their Streamlit apps, take some time to share your apps with each other and present what was added to the base app.
+>
+> What did you find challenging?
+>
+> What feature that you added did you most like?
+>
+> What is something else you would like to add in the future?
+{: .discussion}
+
 {% include links.md %}