The specs for the two plots above have just one property that is different between them. Complete the spec below for both plots.
scales: [
{
name: "xScale",
domain: [2014, 2018],
range: "width",
zero: false
},
{
name: "yScale",
domain: { data: "aggregated", field: "m" },
range: "height",
zero: true
}
]
scales: [
{
name: "xScale",
domain: [2014, 2018],
range: "width",
zero: false
},
{
name: "yScale",
domain: { data: "aggregated", field: "m" },
range: "height",
zero: false
}
]
What are the pros and cons of each scale? Which scale reflects the data more accurately in your opinion?
The first plot shows the scale starting at zero for dollars (y) which makes the differences in price point less drastic but more realistic. If you want to make the point that avocados were more expensive in 2017, you'd use the non-zero scale (plot 2).
The specs for the two plots above have just one property that is different between them. Complete the spec below for plot 2 to sort the bars.
...
data: [
{
name: "lego_themes",
url: "https://raw.githubusercontent.com/picoral/csc-444-data/main/lego_themes.json",
transform: [
{
type: "collect",
sort: { field: "n", order: "descending" }
}
]
}
],
scales: [
{
name: "xScale",
type: "band",
domain: { data: "lego_themes", field: "theme"},
range: "width",
padding: 0.6
},
{
name: "yScale",
type: "linear",
domain: { data: "lego_themes", field: "n" },
range: "height"
}
],
...
Why is sorting bars by the numeric variable recommended in this case?
Theme is an unordered categorical variable, so there's no reason to order them by alphabetical order. By ordering them by the numeric variable, it's much easier to identify the categories with the highest and lowest counts.
The specs for the two plots above have just one property that is different between them. Complete the spec below for plot 2 to add values to the bars.
...
marks: [
{
type: "rect",
from: { data: "lego_themes" },
encode: {
enter: {
x: { field: "theme", scale: "xScale"},
y2: { value: 0, scale: "yScale"},
y: { field: "n", scale: "yScale"},
width: { value: 30}
}
}
},
{
type: "text",
from: { data: "lego_themes" },
encode: {
enter: {
x: { field: "theme", scale: "xScale" },
y: { field: "n", scale: "yScale" },
text: { field: "n"},
dy: { value: 10 },
dx: { value: 5 },
fill: { value: "white" }
}
}
}
],
...
List one advantage and one disadvantage of adding values to bars in a bar plot.
The specs for the two plots above have just two properties that are different between them. Complete the spec below for plot 2 to sort the bars, and add a legend. You can look at the data and the variables you have available.
// create spec
var spec = {
$schema: "https://vega.github.io/schema/vega/v5.json",
description: "A bar plot of Artists in the USA",
width: 800,
height: 500,
padding: 50,
autosize: "fit",
data: [
{
name: "artists",
url: "https://raw.githubusercontent.com/picoral/csc-444-data/main/artists.json",
transform: [
{
type: "stack",
groupby: ["type"],
field: "percent",
as: ["x0", "x1"],
sort: { field: "race" }
},
{
type: "collect",
sort: { field: "max" }
}
]
}
],
scales: [
{
name: "xScale",
type: "linear",
domain: [0, 1],
range: "width"
},
{
name: "yScale",
type: "band",
domain: { data: "artists", field: "type"},
range: "height",
padding: .5
},
{
name: "colorScale",
type: "ordinal",
domain: { data: "artists", field: "race" },
range: {"scheme": "category20"}
}
],
axes: [
{
scale: "xScale",
orient: "bottom"
},
{
scale: "yScale",
orient: "left"
}
],
marks: [
{
type: "rect",
from: { data: "artists" },
encode: {
enter: {
x: { field: "x0", scale: "xScale"},
x2: { field: "x1", scale: "xScale"},
y: { field: "type", scale: "yScale" },
height: { value: 20 },
fill: { field: "race", scale: "colorScale"}
}
}
}
],
legends: [
{
fill: "colorScale"
}
]
};
Explain why the sorted version is better.
Why is plot 1 preferable over plot 2?
Identify the differences between the two plots above. What are they? What would the differences in the spec be? (you can use prose to answer this) You can look at the data and the variables you have available.
Identify the differences between the two plots above. What are they? What would the differences in the spec be? (you can use prose to answer this) You can look at the data and the variables you have available.
Complete the spec below for plot 2 to aggregate the data for sum for every week in every year each show was on Broadway. You can look at the data and the variables you have available.
transform: [
{
type: "aggregate",
groupby: ["week_number", "show"],
fields: [weekly_gross],
ops: [sum],
as: ["total_gross"]
}
]
Identify the differences between the two plots above. What are they? What would the differences in the spec be? (you can use prose to answer this) You can look at the data and the variables you have available.