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2d:{"title":"Week 1 Tutorial 3: Data visualization with Chart cell","cells":[{"id":0,"value":"# Week 1 Tutorial 3: Data visualization with Chart cell","pinned":false,"hidden":false,"mode":"md"},{"id":15,"value":"- In Observable, the easiest way to get going with data visualization is the **Chart cell**.","pinned":false,"hidden":false,"mode":"md"},{"id":31,"value":"## Adding a Chart cell","pinned":false,"hidden":false,"mode":"md"},{"id":38,"value":"There are two options. First, you should see a **Chart button** with icon ${getIcon('chartCell')} as the top option when you begin searching for \"chart\" in the **Add cell menu**:\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":34,"value":"

The **Add cell menu** with **Chart cell** option highlighted as the top choice after being typed in the search bar.

","pinned":false,"hidden":false,"mode":"html"},{"id":94,"value":"Selecting this button will create a Chart cell for you:","pinned":false,"hidden":false,"mode":"md"},{"id":106,"value":"\n \n

The Chart cell with no data source selected.

\n","pinned":false,"hidden":false,"mode":"html"},{"id":110,"value":"Once you select a data source, the Chart cell options are revealed. ","pinned":false,"hidden":false,"mode":"md"},{"id":120,"value":" \n

The Chart cell with a data source selected.

","pinned":false,"hidden":false,"mode":"html"},{"id":587,"value":"In this example, we will the [cars](https://observablehq.com/@observablehq/sample-datasets#-cars) dataset. It needs to be added to a notebook with something like: `carData = cars` (cannot use `cars` directly)","pinned":false,"hidden":false,"mode":"md"},{"id":46,"value":"## X, Y and Mark fields","pinned":false,"hidden":false,"mode":"md"},{"id":56,"value":"Let's first modify the X variable. Use the **X dropdown** to look at and select from existing fields in the data source:","pinned":false,"hidden":false,"mode":"md"},{"id":161,"value":" \n

Viewing the dropdown options for the X field.

","pinned":false,"hidden":false,"mode":"html"},{"id":57,"value":"- Suppose we pick _economy (mpg)_; the result is a histogram of observation counts, with _economy (mpg)_ as the X variable and a reducer on the Y axis counting the observations of vehicles in each of the X-axis bins.\n- Both the reducer on the Y axis (_Auto: count_) and the mark designating the type of chart (_Auto: bar_) are automatically selected to create the visualization:","pinned":false,"hidden":false,"mode":"md"},{"id":170,"value":" \n

A histogram counting cars by economy.

","pinned":false,"hidden":false,"mode":"html"},{"id":64,"value":"- If we add a field for the Y axis, it will automatically take away the count reducer on the Y axis and replace it with the field chosen.\n- At this point it may likely update the chart to a type it determines to be more useful.\n- In this case it chooses a scatter plot (**Mark field**: _Auto: dot_) for visualizing the relationship between fuel economy (X axis) and 0-60 mph times (Y axis):","pinned":false,"hidden":false,"mode":"md"},{"id":68,"value":" \n

A scatter plot with fuel economy along the X axis with 0-60 speed along the Y axis.

","pinned":false,"hidden":false,"mode":"html"},{"id":195,"value":"- We expect the **Chart cell** to pick a reasonable type of chart based on the type of data selected for each field.\n- We can also change the **Mark dropdown** to something other than what is automatically selected.\n- For example, we can override _Auto: dot_ here to a line chart by changing the value of the **Mark dropdown** to _line_, which quickly reveals that the _dot_ mark is the better choice for this data.","pinned":false,"hidden":false,"mode":"md"},{"id":197,"value":" \n

A line chart with fuel economy along the X axis with 0-60 speed along the Y axis.

","pinned":false,"hidden":false,"mode":"html"},{"id":70,"value":"---\n","pinned":false,"hidden":false,"mode":"md"},{"id":72,"value":"## Color, Size, and Facet channels","pinned":false,"hidden":false,"mode":"md"},{"id":74,"value":"### Color channel","pinned":false,"hidden":false,"mode":"md"},{"id":76,"value":"Below, we choose the _power (hp)_ field, which will distribute colors in a continuous gradient across the dots according to how much power the car has:\n","pinned":false,"hidden":false,"mode":"md"},{"id":211,"value":" \n

The scatter plot updated with dot color distributed by power (hp).

","pinned":false,"hidden":false,"mode":"html"},{"id":226,"value":"From this it is easy to see, albeit unsurprising, that, in general, the more power, the faster and the less economical the car. ","pinned":false,"hidden":false,"mode":"md"},{"id":78,"value":"### Size channel\nIn this case, we choose _weight (lb)_ from our **Size dropdown**, which lets us additionally explore how weight relates to a car's velocity, power, and fuel economy:\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":222,"value":" \n

The scatter plot updated with dot size distributed by weight (lb).

","pinned":false,"hidden":false,"mode":"html"},{"id":429,"value":"Interestingly, maybe somewhat surprisingly, we can see that higher weight tends to correlate with faster speeds.","pinned":false,"hidden":false,"mode":"md"},{"id":236,"value":"### Facet channel","pinned":false,"hidden":false,"mode":"md"},{"id":242,"value":"Sometimes it is helpful to facet a chart according to a variable on either the X or Y axis, for example to explore relationships or distributions by a certain category. Here, we have a histogram showing how power is distributed amongst the cars by count. \n\nWe use the **Facet Y** dropdown to choose _cylinders_ while choosing _power (hp)_ on the X axis and the reducer _Auto: count_ to to utilize the Y axis for counting the cars in each bin:","pinned":false,"hidden":false,"mode":"md"},{"id":245,"value":" \n

A histogram of power (hp) faceted by number of cylinders .

","pinned":false,"hidden":false,"mode":"html"},{"id":462,"value":"Plot.plot({\n marks: [\n Plot.auto(null, {})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":249,"value":"In this view, you can see there is a general trend cars having higher power the greater the number of cylinders they have.","pinned":false,"hidden":false,"mode":"md"},{"id":80,"value":"## Using reducers","pinned":false,"hidden":false,"mode":"md"},{"id":85,"value":"- **Reducers** are for summary statistics (e.g. mean values or sums).\n- For X, Y, Color, and Size, you can pick a reducer from the menu above the field dropdown.\n- The following reducing functions can be applied: count, distinct, sum, min, max, mean, median and mode. \n- Below we apply the mean reducer to the **Y** axis field(_weight (lb)_) to visualize the average car weight for each of 3, 4, 5, 6, and 8 cylinders.\n","pinned":false,"hidden":false,"mode":"md"},{"id":257,"value":" \n

A bar chart showing average weight per engine type - 3, 4, 5, 6, or 8 cylinders.

\n","pinned":false,"hidden":false,"mode":"html"},{"id":268,"value":"From this, we can see that the 3 cylinder vehicles do not conform to the pattern the others are beholden. While the 4-8 cylinder vehicles follow a pattern of getting heavier the more cylinders are present, the 3 cylinders are heavier than the 4 cylinders on average.","pinned":false,"hidden":false,"mode":"md"},{"id":86,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":89,"value":"## Customization\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":278,"value":"- Open the customization pane by clicking the **Customize button** () in the top right of the cell header.\n- From there, you can customize chart height and width, margins, insets, add or remove a legend, update _X_ and _Y_ labels, adjust the color scheme, add a caption, and more:","pinned":false,"hidden":false,"mode":"md"},{"id":282,"value":" \n

Customization pane.

","pinned":false,"hidden":false,"mode":"html"},{"id":289,"value":"These changes produce real-time results:","pinned":false,"hidden":false,"mode":"md"},{"id":291,"value":" \n

Updated with changes from the Customization pane.

\n","pinned":false,"hidden":false,"mode":"html"},{"id":301,"value":"## Convert to JavaScript","pinned":false,"hidden":false,"mode":"md"},{"id":303,"value":"- The Chart cell uses the JavaScript library [Plot](https://observablehq.com/plot/) under the hood.\n- It can easily convert your Chart cell to JavaScript Plot code.\n- To do this, select the gold **Convert to JavaScript button** in the following **Add cell menu** of the chart after the Chart cell:","pinned":false,"hidden":false,"mode":"md"},{"id":309,"value":" \n

Select the Convert to JavaScript button in the Add Cell menu.

","pinned":false,"hidden":false,"mode":"html"},{"id":318,"value":"A duplicate cell will appear immediately below the one you just converted, exposing the entire Plot code used to create the chart:","pinned":false,"hidden":false,"mode":"md"},{"id":312,"value":" \n

The converted Chart cell with JavaScript Plot code.

","pinned":false,"hidden":false,"mode":"html"},{"id":325,"value":"- With the Plot code exposed, you can then make even further customizations.","pinned":false,"hidden":false,"mode":"md"},{"id":443,"value":"import {getIcon} from \"@observablehq/product-education-icon-helpers\"\n","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}