data science: data visualization boot camp comparison bar ... · information graphics: a...

Data Science: Data Visualization Boot CampComparison

Bar Charts Across Time

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

26 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 2020

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Type Sample data Hands on Q & A Conclusion References Files

Table of contents (1 of 1)

1 TypeUsesGeneral considerations

2 Sample data

3 Hands on

4 Q & A

5 Conclusion6 References7 Files

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A definition

“Grouped column graph (multiple data se-ries all referenced from the same zero axis) Whentwo or more data series are plotted side-by-sideon the same column graph, the result is referredto as a clustered, grouped, multiple, or side-by-side column graph. Grouped column graphs en-able the user to:

Compare multiple items at various pointsin timeShow how relationships between multipleitems change with timeLook for correlations or meaningfulrelationships between multiple data seriesCondense onto one graph what otherwisewould required multiple graphs

With this type of graph, each data element of

each data series is represented by a separate col-

umn.”

R. L. Harris [2]

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Federal Reserve Economic Data (1 of 2)

“The Federal Reserve Bank of St. Louis is

the center of the Eighth District of the Federal

Reserve System. . . . The Division monitors the

economic and financial literature and produces

research in the areas of money and banking,

macroeconomics, and international and regional

economics. . . . The Research Division also fur-

nishes its working papers to provide insight into

current Bank interests and developing theories

and to stimulate discussion. . . . The widely used

database FRED is updated regularly and allows

24/7 access to regional and national financial and

economic data.”

Fed. Res. Bank Staff [1]

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Federal Reserve Economic Data (2 of 2)

We select the data of interest. https://fred.stlouisfed.org

https://fred.stlouisfed.org

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Selecting data of interest

For manual, singular download: https:

//fred.stlouisfed.org/series/PCE

For programtic, singular download:

https:

//github.com/tidyverse/ggplot2/

blob/master/data-raw/economics.R

We elected to download a series programtically.

https://fred.stlouisfed.org/series/PCEhttps://fred.stlouisfed.org/series/PCEhttps://github.com/tidyverse/ggplot2/blob/master/data-raw/economics.Rhttps://github.com/tidyverse/ggplot2/blob/master/data-raw/economics.Rhttps://github.com/tidyverse/ggplot2/blob/master/data-raw/economics.R

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The first codes. (1 of 6)

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rm(list=ls())

library(ggplot2)

saveFileName

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prettyNum(sum(df[,c("y1", "y2")]),

big.mark=",")),

title="Total imports for 2017",

y="Millions of dollars",

caption=paste0("Using data from:https://www.census.gov/",

"foreign-trade/data/index.html"),

fill="Countries"

) +

theme(plot.title=element_text(hjust = 0.5)) +

theme(plot.title=element_text(colour = "blue")) +

theme(plot.subtitle=element_text(hjust = 0.5)) +

theme(plot.subtitle=element_text(colour = "black")) +

theme(plot.caption=element_text(hjust = 0.0)) +

theme(plot.caption=element_text(colour = "red")) +

theme(legend.title.align=0.5) +

theme(axis.text.x=element_text(angle=0, hjust = 0)) +

theme(axis.text.x=element_blank()) +

theme(axis.ticks.x = element_blank())

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g + geom_rect(alpha=0.5, color="black")

df

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fill="Countries"

) +











g + geom_rect(alpha=0.5, color="black") +

geom_hline(yintercept=0)

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The second codes. (1 of 8)

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rm(list=ls())

library(ggplot2)

saveFileName

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df$y1

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caption=paste0("Using data from:https://www.census.gov/",


fill="Countries"

) +











g + geom_rect(data=df, aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2),

fill="red", alpha=0.5,

color="black", inherit.aes=FALSE) +

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geom_rect(data=df, aes(xmin=x2, xmax=x3, ymin=y1, ymax=y3),

fill="green", alpha=0.5,



fill="blue", alpha=0.5,



fill="yellow",alpha=0.5, color="black",

inherit.aes=FALSE) +

geom_text(aes(x=1:length(countriesOfInterest),

y=c(rep(max(df[,7:11]), length(countriesOfInterest)))

),

label=df$country,

color="blue",

angle=0,

nudge_x=0.0,

nudge_y=0.0,

inherit.aes=FALSE

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)

newOrder

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) +











g + geom_rect( aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), fill="red",

alpha=0.5, color="black", inherit.aes=FALSE) +

geom_rect( aes(xmin=x2, xmax=x3, ymin=y1, ymax=y3), fill="green",


geom_rect( aes(xmin=x3, xmax=x4, ymin=y1, ymax=y4), fill="blue",


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geom_rect( aes(xmin=x4, xmax=x5, ymin=y1, ymax=y5), fill="yellow",


geom_text(aes(x=1:length(countriesOfInterest),

y=c(rep(max(df[,7:11]), length(countriesOfInterest)))

),

label=df$country,

color="blue",

angle=0,

nudge_x=0.0,

nudge_y=0.0,

inherit.aes=FALSE

)

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Hands-on exercises

1 In the first set of slides, your supervisor wants number ofchanges:

1 The last slide shows the magnitude of our country’s import andexport with various countries. Change the data to show thebalance of trade, vice the magnitude of the import and export.

2 In the second set of slides:1 Your supervisor wants the country names to not collide with

the plotted data.2 Change the countries to be: China, Hong Kong, Japan, South

Korea, Singapore, and Taiwan.

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Q & A time.

Q: How many Oregonians does ittake to screw in a light bulb?A: Three. One to screw in thelight bulb and two to fend off allthose Californians trying to sharethe experience.

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What have we covered?

Bar charts with few items are:

Useful to show the relative sizesof different components acrossdifferent categories.Can be easily overwhelmed ifthere are too many categories.

Good for showing componentrelationships across categories.

Next: Comparing a many categories across a few periods.

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References (1 of 1)

[1] Fed. Res. Bank Staff,About Economic Research at the St. Louis Fed,https://research.stlouisfed.org/about.html, 2018.

[2] Robert L. Harris,Information Graphics: A Comprehensive Illustrated Reference,Oxford University Press, 2000.

https://research.stlouisfed.org/about.html

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Files of interest

1 Code snippet to createimages in this presentation

2 Extract Federal Export and

Import data

## First codesrm(list=ls())

library(ggplot2)

saveFileName

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