Merge pull request #354 from jasleen101010/main
Add R for Data Visualizations- Lessons 11,12,13pull/356/head
Jen Looper
4 years ago
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# Visualizing Proportions
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| ](../../../sketchnotes/11-Visualizing-Proportions.png)|
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|:---:|
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|Visualizing Proportions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
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In this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as:
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- Pie charts 🥧
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- Donut charts 🍩
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- Waffle charts 🧇
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> 💡 A very interesting project called [Charticulator](https://charticulator.com) by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: [Charticulator tutorial](https://charticulator.com/tutorials/tutorial4.html).
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## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/20)
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## Get to know your mushrooms 🍄
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Mushrooms are very interesting. Let's import a dataset to study them:
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```r
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mushrooms = read.csv('../../data/mushrooms.csv')
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head(mushrooms)
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```
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A table is printed out with some great data for analysis:
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| class | cap-shape | cap-surface | cap-color | bruises | odor | gill-attachment | gill-spacing | gill-size | gill-color | stalk-shape | stalk-root | stalk-surface-above-ring | stalk-surface-below-ring | stalk-color-above-ring | stalk-color-below-ring | veil-type | veil-color | ring-number | ring-type | spore-print-color | population | habitat |
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| --------- | --------- | ----------- | --------- | ------- | ------- | --------------- | ------------ | --------- | ---------- | ----------- | ---------- | ------------------------ | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
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| Poisonous | Convex | Smooth | Brown | Bruises | Pungent | Free | Close | Narrow | Black | Enlarging | Equal | Smooth | Smooth | White | White | Partial | White | One | Pendant | Black | Scattered | Urban |
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| Edible | Convex | Smooth | Yellow | Bruises | Almond | Free | Close | Broad | Black | Enlarging | Club | Smooth | Smooth | White | White | Partial | White | One | Pendant | Brown | Numerous | Grasses |
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| Edible | Bell | Smooth | White | Bruises | Anise | Free | Close | Broad | Brown | Enlarging | Club | Smooth | Smooth | White | White | Partial | White | One | Pendant | Brown | Numerous | Meadows |
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| Poisonous | Convex | Scaly | White | Bruises | Pungent | Free | Close | Narrow | Brown | Enlarging | Equal | Smooth | Smooth | White | White | Partial | White | One | Pendant | Black | Scattered | Urban
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| Edible | Convex |Smooth | Green | No Bruises| None |Free | Crowded | Broad | Black | Tapering | Equal | Smooth | Smooth | White | White | Partial | White | One | Evanescent | Brown | Abundant | Grasses
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|Edible | Convex | Scaly | Yellow | Bruises | Almond | Free | Close | Broad | Brown | Enlarging | Club | Smooth | Smooth | White | White | Partial | White | One | Pendant | Black | Numerous | Grasses
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Right away, you notice that all the data is textual. You will have to convert this data to be able to use it in a chart. Most of the data, in fact, is represented as an object:
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```r
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names(mushrooms)
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```
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The output is:
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```output
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[1] "class" "cap.shape"
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[3] "cap.surface" "cap.color"
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[5] "bruises" "odor"
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[7] "gill.attachment" "gill.spacing"
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[9] "gill.size" "gill.color"
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[11] "stalk.shape" "stalk.root"
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[13] "stalk.surface.above.ring" "stalk.surface.below.ring"
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[15] "stalk.color.above.ring" "stalk.color.below.ring"
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[17] "veil.type" "veil.color"
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[19] "ring.number" "ring.type"
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[21] "spore.print.color" "population"
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[23] "habitat"
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```
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Take this data and convert the 'class' column to a category:
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```r
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library(dplyr)
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grouped=mushrooms %>%
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group_by(class) %>%
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summarise(count=n())
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```
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Now, if you print out the mushrooms data, you can see that it has been grouped into categories according to the poisonous/edible class:
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```r
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View(grouped)
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```
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| class | count |
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| --------- | --------- |
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| Edible | 4208 |
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| Poisonous| 3916 |
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If you follow the order presented in this table to create your class category labels, you can build a pie chart.
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## Pie!
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```r
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pie(grouped$count,grouped$class, main="Edible?")
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```
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Voila, a pie chart showing the proportions of this data according to these two classes of mushrooms. It's quite important to get the order of the labels correct, especially here, so be sure to verify the order with which the label array is built!
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## Donuts!
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A somewhat more visually interesting pie chart is a donut chart, which is a pie chart with a hole in the middle. Let's look at our data using this method.
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Take a look at the various habitats where mushrooms grow:
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```r
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library(dplyr)
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habitat=mushrooms %>%
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group_by(habitat) %>%
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summarise(count=n())
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View(habitat)
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```
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The output is:
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| habitat| count |
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| --------- | --------- |
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| Grasses | 2148 |
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| Leaves| 832 |
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| Meadows | 292 |
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| Paths| 1144 |
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| Urban | 368 |
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| Waste| 192 |
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| Wood| 3148 |
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Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:
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```r
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library(ggplot2)
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library(webr)
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PieDonut(habitat, aes(habitat, count=count))
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```
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This code uses the two libraries- ggplot2 and webr. Using the PieDonut function of the webr library, we can create a donut chart easily!
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Donut charts in R can be made using only the ggplot2 library as well. You can learn more about it [here](https://www.r-graph-gallery.com/128-ring-or-donut-plot.html) and try it out yourself.
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Now that you know how to group your data and then display it as a pie or donut, you can explore other types of charts. Try a waffle chart, which is just a different way of exploring quantity.
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## Waffles!
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A 'waffle' type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called [waffle](https://cran.r-project.org/web/packages/waffle/waffle.pdf) and use it to generate your visualization:
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```r
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install.packages("waffle", repos = "https://cinc.rud.is")
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```
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Select a segment of your data to group:
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```r
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library(dplyr)
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cap_color=mushrooms %>%
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group_by(cap.color) %>%
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summarise(count=n())
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View(cap_color)
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```
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Create a waffle chart by creating labels and then grouping your data:
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```r
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library(waffle)
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names(cap_color$count) = paste0(cap_color$cap.color)
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waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual(values=c("brown", "#F0DC82", "#D2691E", "green",
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"pink", "purple", "red", "grey",
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"yellow","white"))
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```
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Using a waffle chart, you can plainly see the proportions of cap colors of this mushrooms dataset. Interestingly, there are many green-capped mushrooms!
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In this lesson, you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset.
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## 🚀 Challenge
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Try recreating these tasty charts in [Charticulator](https://charticulator.com).
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## [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/21)
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## Review & Self Study
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Sometimes it's not obvious when to use a pie, donut, or waffle chart. Here are some articles to read on this topic:
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https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart
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https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce
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https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm
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https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402
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Do some research to find more information on this sticky decision.
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## Assignment
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[Try it in Excel](assignment.md)
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# Visualizing Relationships: All About Honey 🍯
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| ](../../../sketchnotes/12-Visualizing-Relationships.png)|
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|:---:|
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|Visualizing Relationships - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
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Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php).
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This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state.
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It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternately, you could visualize the relationship between states' honey yield per colony. This year span covers the devastating 'CCD' or 'Colony Collapse Disorder' first seen in 2006 (http://npic.orst.edu/envir/ccd.html), so it is a poignant dataset to study. 🐝
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## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/22)
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In this lesson, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of ggplot2's `ggplot`and `geom_point` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://ggplot2.tidyverse.org/)', which allow the data scientist to better understand how variables relate to each other.
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## Scatterplots
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Use a scatterplot to show how the price of honey has evolved, year over year, per state. ggplot2, using `ggplot` and `geom_point`, conveniently groups the state data and displays data points for both categorical and numeric data.
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Let's start by importing the data and Seaborn:
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```r
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honey=read.csv('../../data/honey.csv')
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head(honey)
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```
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You notice that the honey data has several interesting columns, including year and price per pound. Let's explore this data, grouped by U.S. state:
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| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year |
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| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- |
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| AL | 16000 | 71 | 1136000 | 159000 | 0.72 | 818000 | 1998 |
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| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 |
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| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 |
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| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 |
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| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 |
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| FL | 230000 | 98 |22540000 | 4508000 | 0.64 | 14426000 | 1998 |
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Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Make the `y` axis tall enough to display all the states:
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```r
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library(ggplot2)
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ggplot(honey, aes(x = priceperlb, y = state)) +
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geom_point(colour = "blue")
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```
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Now, show the same data with a honey color scheme to show how the price evolves over the years. You can do this by adding a 'scale_color_gradientn' parameter to show the change, year over year:
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> ✅ Learn more about the [scale_color_gradientn](https://www.rdocumentation.org/packages/ggplot2/versions/0.9.1/topics/scale_colour_gradientn) - try a beautiful rainbow color scheme!
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```r
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ggplot(honey, aes(x = priceperlb, y = state, color=year)) +
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geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7))
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```
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With this color scheme change, you can see that there's obviously a strong progression over the years in terms of honey price per pound. Indeed, if you look at a sample set in the data to verify (pick a given state, Arizona for example) you can see a pattern of price increases year over year, with few exceptions:
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| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year |
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| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |
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| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 |
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| AZ | 52000 | 62 | 3224000 | 1548000 | 0.62 | 1999000 | 1999 |
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| AZ | 40000 | 59 | 2360000 | 1322000 | 0.73 | 1723000 | 2000 |
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| AZ | 43000 | 59 | 2537000 | 1142000 | 0.72 | 1827000 | 2001 |
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| AZ | 38000 | 63 | 2394000 | 1197000 | 1.08 | 2586000 | 2002 |
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| AZ | 35000 | 72 | 2520000 | 983000 | 1.34 | 3377000 | 2003 |
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| AZ | 32000 | 55 | 1760000 | 774000 | 1.11 | 1954000 | 2004 |
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| AZ | 36000 | 50 | 1800000 | 720000 | 1.04 | 1872000 | 2005 |
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| AZ | 30000 | 65 | 1950000 | 839000 | 0.91 | 1775000 | 2006 |
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| AZ | 30000 | 64 | 1920000 | 902000 | 1.26 | 2419000 | 2007 |
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| AZ | 25000 | 64 | 1600000 | 336000 | 1.26 | 2016000 | 2008 |
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| AZ | 20000 | 52 | 1040000 | 562000 | 1.45 | 1508000 | 2009 |
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| AZ | 24000 | 77 | 1848000 | 665000 | 1.52 | 2809000 | 2010 |
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| AZ | 23000 | 53 | 1219000 | 427000 | 1.55 | 1889000 | 2011 |
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| AZ | 22000 | 46 | 1012000 | 253000 | 1.79 | 1811000 | 2012 |
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Another way to visualize this progression is to use size, rather than color. For colorblind users, this might be a better option. Edit your visualization to show an increase of price by an increase in dot circumference:
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```r
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ggplot(honey, aes(x = priceperlb, y = state)) +
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geom_point(aes(size = year),colour = "blue") +
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scale_size_continuous(range = c(0.25, 3))
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```
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You can see the size of the dots gradually increasing.
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Is this a simple case of supply and demand? Due to factors such as climate change and colony collapse, is there less honey available for purchase year over year, and thus the price increases?
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To discover a correlation between some of the variables in this dataset, let's explore some line charts.
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## Line charts
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Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart:
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```r
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qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab = "priceperlb")
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```
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Answer: Yes, with some exceptions around the year 2003:
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Question: Well, in 2003 can we also see a spike in the honey supply? What if you look at total production year over year?
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```python
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qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod")
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```
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Answer: Not really. If you look at total production, it actually seems to have increased in that particular year, even though generally speaking the amount of honey being produced is in decline during these years.
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Question: In that case, what could have caused that spike in the price of honey around 2003?
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To discover this, you can explore a facet grid.
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## Facet grids
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Facet grids take one facet of your dataset (in our case, you can choose 'year' to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison?
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Create a facet grid by using `facet_wrap` as recommended by [ggplot2's documentation](https://ggplot2.tidyverse.org/reference/facet_wrap.html).
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```r
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ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) +
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geom_line() + facet_wrap(vars(year))
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```
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In this visualization, you can compare the yield per colony and number of colonies year over year, side by side with a wrap set at 3 for the columns:
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For this dataset, nothing particularly stands out with regards to the number of colonies and their yield, year over year and state over state. Is there a different way to look at finding a correlation between these two variables?
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## Dual-line Plots
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Try a multiline plot by superimposing two lineplots on top of each other, using R's `par` and `plot` function. We will be plotting the year in the x axis and display two y axes. So, display the yield per colony and number of colonies, superimposed:
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```r
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par(mar = c(5, 4, 4, 4) + 0.3)
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plot(honey$year, honey$numcol, pch = 16, col = 2,type="l")
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par(new = TRUE)
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plot(honey$year, honey$yieldpercol, pch = 17, col = 3,
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axes = FALSE, xlab = "", ylab = "",type="l")
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axis(side = 4, at = pretty(range(y2)))
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mtext("colony yield", side = 4, line = 3)
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```
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While nothing jumps out to the eye around the year 2003, it does allow us to end this lesson on a little happier note: while there are overall a declining number of colonies, the number of colonies is stabilizing even if their yield per colony is decreasing.
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Go, bees, go!
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🐝❤️
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## 🚀 Challenge
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In this lesson, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these lessons. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques.
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## [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/23)
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## Review & Self Study
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Line plots can be simple or quite complex. Do a bit of reading in the [ggplot2 documentation](https://ggplot2.tidyverse.org/reference/geom_path.html#:~:text=geom_line()%20connects%20them%20in,which%20cases%20are%20connected%20together) on the various ways you can build them. Try to enhance the line charts you built in this lesson with other methods listed in the docs.
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## Assignment
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[Dive into the beehive](assignment.md)
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# Making Meaningful Visualizations
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| ](../../../sketchnotes/13-MeaningfulViz.png)|
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||||
|:---:|
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||||
| Meaningful Visualizations - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
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> "If you torture the data long enough, it will confess to anything" -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase)
|
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|
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One of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been cleaned and prepared, as you did in prior lessons. After that, you can start deciding how best to present the data.
|
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|
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In this lesson, you will review:
|
||||
|
||||
1. How to choose the right chart type
|
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2. How to avoid deceptive charting
|
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3. How to work with color
|
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4. How to style your charts for readability
|
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5. How to build animated or 3D charting solutions
|
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6. How to build a creative visualization
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||||
|
||||
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/24)
|
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|
||||
## Choose the right chart type
|
||||
|
||||
In previous lessons, you experimented with building all kinds of interesting data visualizations using Matplotlib and Seaborn for charting. In general, you can select the [right kind of chart](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/) for the question you are asking using this table:
|
||||
|
||||
| You need to: | You should use: |
|
||||
| -------------------------- | ------------------------------- |
|
||||
| Show data trends over time | Line |
|
||||
| Compare categories | Bar, Pie |
|
||||
| Compare totals | Pie, Stacked Bar |
|
||||
| Show relationships | Scatter, Line, Facet, Dual Line |
|
||||
| Show distributions | Scatter, Histogram, Box |
|
||||
| Show proportions | Pie, Donut, Waffle |
|
||||
|
||||
> ✅ Depending on the makeup of your data, you might need to convert it from text to numeric to get a given chart to support it.
|
||||
|
||||
## Avoid deception
|
||||
|
||||
Even if a data scientist is careful to choose the right chart for the right data, there are plenty of ways that data can be displayed in a way to prove a point, often at the cost of undermining the data itself. There are many examples of deceptive charts and infographics!
|
||||
|
||||
[](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie")
|
||||
|
||||
> 🎥 Click the image above for a conference talk about deceptive charts
|
||||
|
||||
This chart reverses the X axis to show the opposite of the truth, based on date:
|
||||
|
||||
|
||||
|
||||
[This chart](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) is even more deceptive, as the eye is drawn to the right to conclude that, over time, COVID cases have declined in the various counties. In fact, if you look closely at the dates, you find that they have been rearranged to give that deceptive downward trend.
|
||||
|
||||
|
||||
|
||||
This notorious example uses color AND a flipped Y axis to deceive: instead of concluding that gun deaths spiked after the passage of gun-friendly legislation, in fact the eye is fooled to think that the opposite is true:
|
||||
|
||||
|
||||
|
||||
This strange chart shows how proportion can be manipulated, to hilarious effect:
|
||||
|
||||
|
||||
|
||||
Comparing the incomparable is yet another shady trick. There is a [wonderful web site](https://tylervigen.com/spurious-correlations) all about 'spurious correlations' displaying 'facts' correlating things like the divorce rate in Maine and the consumption of margarine. A Reddit group also collects the [ugly uses](https://www.reddit.com/r/dataisugly/top/?t=all) of data.
|
||||
|
||||
It's important to understand how easily the eye can be fooled by deceptive charts. Even if the data scientist's intention is good, the choice of a bad type of chart, such as a pie chart showing too many categories, can be deceptive.
|
||||
|
||||
## Color
|
||||
|
||||
You saw in the 'Florida gun violence' chart above how color can provide an additional layer of meaning to charts, especially ones not designed using libraries such as ggplot2 and RColorBrewer which come with various vetted color libraries and palettes. If you are making a chart by hand, do a little study of [color theory](https://colormatters.com/color-and-design/basic-color-theory)
|
||||
|
||||
> ✅ Be aware, when designing charts, that accessibility is an important aspect of visualization. Some of your users might be color blind - does your chart display well for users with visual impairments?
|
||||
|
||||
Be careful when choosing colors for your chart, as color can convey meaning you might not intend. The 'pink ladies' in the 'height' chart above convey a distinctly 'feminine' ascribed meaning that adds to the bizarreness of the chart itself.
|
||||
|
||||
While [color meaning](https://colormatters.com/color-symbolism/the-meanings-of-colors) might be different in different parts of the world, and tend to change in meaning according to their shade. Generally speaking, color meanings include:
|
||||
|
||||
| Color | Meaning |
|
||||
| ------ | ------------------- |
|
||||
| red | power |
|
||||
| blue | trust, loyalty |
|
||||
| yellow | happiness, caution |
|
||||
| green | ecology, luck, envy |
|
||||
| purple | happiness |
|
||||
| orange | vibrance |
|
||||
|
||||
If you are tasked with building a chart with custom colors, ensure that your charts are both accessible and the color you choose coincides with the meaning you are trying to convey.
|
||||
|
||||
## Styling your charts for readability
|
||||
|
||||
Charts are not meaningful if they are not readable! Take a moment to consider styling the width and height of your chart to scale well with your data. If one variable (such as all 50 states) need to be displayed, show them vertically on the Y axis if possible so as to avoid a horizontally-scrolling chart.
|
||||
|
||||
Label your axes, provide a legend if necessary, and offer tooltips for better comprehension of data.
|
||||
|
||||
If your data is textual and verbose on the X axis, you can angle the text for better readability. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) offers 3d plotting, if you data supports it. Sophisticated data visualizations can be produced using it.
|
||||
|
||||
|
||||
|
||||
## Animation and 3D chart display
|
||||
|
||||
Some of the best data visualizations today are animated. Shirley Wu has amazing ones done with D3, such as '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', where each flower is a visualization of a movie. Another example for the Guardian is 'bussed out', an interactive experience combining visualizations with Greensock and D3 plus a scrollytelling article format to show how NYC handles its homeless problem by bussing people out of the city.
|
||||
|
||||
|
||||
|
||||
> "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu
|
||||
|
||||
While this lesson is insufficient to go into depth to teach these powerful visualization libraries, try your hand at D3 in a Vue.js app using a library to display a visualization of the book "Dangerous Liaisons" as an animated social network.
|
||||
|
||||
> "Les Liaisons Dangereuses" is an epistolary novel, or a novel presented as a series of letters. Written in 1782 by Choderlos de Laclos, it tells the story of the vicious, morally-bankrupt social maneuvers of two dueling protagonists of the French aristocracy in the late 18th century, the Vicomte de Valmont and the Marquise de Merteuil. Both meet their demise in the end but not without inflicting a great deal of social damage. The novel unfolds as a series of letters written to various people in their circles, plotting for revenge or simply to make trouble. Create a visualization of these letters to discover the major kingpins of the narrative, visually.
|
||||
|
||||
You will complete a web app that will display an animated view of this social network. It uses a library that was built to create a [visual of a network](https://github.com/emiliorizzo/vue-d3-network) using Vue.js and D3. When the app is running, you can pull the nodes around on the screen to shuffle the data around.
|
||||
|
||||
|
||||
|
||||
## Project: Build a chart to show a network using D3.js
|
||||
|
||||
> This lesson folder includes a `solution` folder where you can find the completed project, for your reference.
|
||||
|
||||
1. Follow the instructions in the README.md file in the starter folder's root. Make sure you have NPM and Node.js running on your machine before installing your project's dependencies.
|
||||
|
||||
2. Open the `starter/src` folder. You'll discover an `assets` folder where you can find a .json file with all the letters from the novel, numbered, with a 'to' and 'from' annotation.
|
||||
|
||||
3. Complete the code in `components/Nodes.vue` to enable the visualization. Look for the method called `createLinks()` and add the following nested loop.
|
||||
|
||||
Loop through the .json object to capture the 'to' and 'from' data for the letters and build up the `links` object so that the visualization library can consume it:
|
||||
|
||||
```javascript
|
||||
//loop through letters
|
||||
let f = 0;
|
||||
let t = 0;
|
||||
for (var i = 0; i < letters.length; i++) {
|
||||
for (var j = 0; j < characters.length; j++) {
|
||||
|
||||
if (characters[j] == letters[i].from) {
|
||||
f = j;
|
||||
}
|
||||
if (characters[j] == letters[i].to) {
|
||||
t = j;
|
||||
}
|
||||
}
|
||||
this.links.push({ sid: f, tid: t });
|
||||
}
|
||||
```
|
||||
|
||||
Run your app from the terminal (npm run serve) and enjoy the visualization!
|
||||
|
||||
## 🚀 Challenge
|
||||
|
||||
Take a tour of the internet to discover deceptive visualizations. How does the author fool the user, and is it intentional? Try correcting the visualizations to show how they should look.
|
||||
|
||||
## [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/25)
|
||||
|
||||
## Review & Self Study
|
||||
|
||||
Here are some articles to read about deceptive data visualization:
|
||||
|
||||
https://gizmodo.com/how-to-lie-with-data-visualization-1563576606
|
||||
|
||||
http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/
|
||||
|
||||
Take a look at these interest visualizations for historical assets and artifacts:
|
||||
|
||||
https://handbook.pubpub.org/
|
||||
|
||||
Look through this article on how animation can enhance your visualizations:
|
||||
|
||||
https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4
|
||||
|
||||
## Assignment
|
||||
|
||||
[Build your own custom visualization](assignment.md)
|
||||
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Reference in new issue