Week 1A: Data Visualization: Grammar of Graphics

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Grammar

English

• Nouns
• Article
• Adjective
• Verb
• Adverb
• Proposition

Graphics

Grammar - Example

English

The little monkey hangs confidently by a branch.

Graphics

<ggplot: (383084815)>

Grammar - Example

English

Article: The

Adjective: little

Noun: monkey

Verb: hangs

Adverb: Confidently

Proposition: by

Noun: a branch

Graphics

Graphics - Grammar components

• Plot
• Code

<ggplot: (383478448)>

import pandas as pd
import numpy as np

from plotnine import *
from plotnine.data import *

%matplotlib inline
(
    ggplot(economics, aes(x='date', y='uempmed')) 
    + geom_line() 
)

geom_line

• Plot
• Code

<ggplot: (-9223372036471287173)>

import pandas as pd
import numpy as np

from plotnine import *
from plotnine.data import *

%matplotlib inline
(
    ggplot(economics, aes(x='date', y='uempmed')) 
    + geom_line() 
)

geom_point

• Plot
• Code

<ggplot: (-9223372036470940265)>

import pandas as pd
import numpy as np

from plotnine import *
from plotnine.data import *

%matplotlib inline
(
    ggplot(economics, aes(x='date', y='uempmed')) 
    + geom_point() # line plot
    + labs(x='date', y='median duration of unemployment, in week')
)

Making your first plot with plotnine

Data

Date: data to be plotted

Packages

import pandas as pd
import plotnine

from plotnine import *
from plotnine.data import *

Dataset: economics

economics.head(3)

	date	pce	pop	psavert	uempmed	unemploy
0	1967-07-01	507.4	198712	12.5	4.5	2944
1	1967-08-01	510.5	198911	12.5	4.7	2945
2	1967-09-01	516.3	199113	11.7	4.6	2958

Dataset: economics

economics.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 574 entries, 0 to 573
Data columns (total 6 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   date      574 non-null    datetime64[ns]
 1   pce       574 non-null    float64       
 2   pop       574 non-null    int64         
 3   psavert   574 non-null    float64       
 4   uempmed   574 non-null    float64       
 5   unemploy  574 non-null    int64         
dtypes: datetime64[ns](1), float64(3), int64(2)
memory usage: 27.0 KB

Dataset: economics

economics['year'] = economics['date'].dt.year
economics['month'] = economics['date'].dt.month

Dataset: economics

economics.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 574 entries, 0 to 573
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   date      574 non-null    datetime64[ns]
 1   pce       574 non-null    float64       
 2   pop       574 non-null    int64         
 3   psavert   574 non-null    float64       
 4   uempmed   574 non-null    float64       
 5   unemploy  574 non-null    int64         
 6   year      574 non-null    int64         
 7   month     574 non-null    int64         
dtypes: datetime64[ns](1), float64(3), int64(4)
memory usage: 36.0 KB

Dataset: economics

economics.head(3)

	date	pce	pop	psavert	uempmed	unemploy	year	month
0	1967-07-01	507.4	198712	12.5	4.5	2944	1967	7
1	1967-08-01	510.5	198911	12.5	4.7	2945	1967	8
2	1967-09-01	516.3	199113	11.7	4.6	2958	1967	9

Tidy data

• Every column is a variable.

• Every row is an observation.

• Every cell is a single value.

Tidy data - Example

Tidy data - Example

Tidy data - Example

Tidy data - Example

Grammar of Graphics - Plot 1 with economics

Data

ggplot(data=economics)

<ggplot: (383844195)>

Aesthetics: mapping variables

Aesthetics: mapping variables

Aesthetic means “something you can see”.

• position (i.e., on the x and y axes)

• color (“outside” color)

• fill (“inside” color)

• shape (of points)

Aesthetic: position

from plotnine.data import mtcars

ggplot(mtcars, aes('wt', 'mpg')) + geom_point()

<ggplot: (-9223372036470736247)>

Aesthetic: color

ggplot(mtcars, aes('wt', 'mpg', color='factor(cyl)')) + geom_point()

<ggplot: (383119922)>

Aesthetic: shape

ggplot(mtcars, aes('wt', 'mpg', shape='factor(cyl)')) + geom_point()

<ggplot: (384289123)>

Aesthetic: size

ggplot(mtcars, aes('wt', 'mpg', size='factor(cyl)')) + geom_point()

<ggplot: (-9223372036470265845)>

Data + Aesthetics

ggplot(economics, aes(x='date', y='uempmed'))

<ggplot: (-9223372036470514358)>

Geometrics

Actual marks we put on a plot

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_point()

<ggplot: (-9223372036469989302)>

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_point(alpha=0.5)

<ggplot: (-9223372036469942633)>

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_point(size=0.3)

<ggplot: (-9223372036469989246)>

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_line()

<ggplot: (-9223372036469721351)>

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_line() + geom_point(size=0.3)

<ggplot: (-9223372036469621511)>

Data + Aesthetics + Geometrics

ggplot(economics, aes(x='date', y='uempmed')) + geom_line() + geom_point(size=0.3, colour="blue")

<ggplot: (-9223372036469501724)>

Geoms

source: https://nbisweden.github.io/RaukR-2019/ggplot/presentation/ggplot_presentation.html#17

Grammar of Graphics - Plot 2 with mpg

Dataset

mpg

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
...	...	...	...	...	...	...	...	...	...	...	...
229	volkswagen	passat	2.0	2008	4	auto(s6)	f	19	28	p	midsize
230	volkswagen	passat	2.0	2008	4	manual(m6)	f	21	29	p	midsize
231	volkswagen	passat	2.8	1999	6	auto(l5)	f	16	26	p	midsize
232	volkswagen	passat	2.8	1999	6	manual(m5)	f	18	26	p	midsize
233	volkswagen	passat	3.6	2008	6	auto(s6)	f	17	26	p	midsize

234 rows × 11 columns

Dataset: variable types

mpg.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 234 entries, 0 to 233
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   manufacturer  234 non-null    category
 1   model         234 non-null    category
 2   displ         234 non-null    float64 
 3   year          234 non-null    int64   
 4   cyl           234 non-null    int64   
 5   trans         234 non-null    category
 6   drv           234 non-null    category
 7   cty           234 non-null    int64   
 8   hwy           234 non-null    int64   
 9   fl            234 non-null    category
 10  class         234 non-null    category
dtypes: category(6), float64(1), int64(4)
memory usage: 14.0 KB

Data

ggplot(mpg)

<ggplot: (385284458)>

Data + Aesthetics

ggplot(mpg, aes(x='displ', y='hwy'))

<ggplot: (385284528)>

Data + Aesthetics

ggplot(mpg, aes(x='displ', y='hwy'))

<ggplot: (385505775)>

displ - a car’s engine size, in litres.

hwy - a car’s fuel efficiency on the highway, in miles per gallon (mpg)

Data + Aesthetics + Geom

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point()

<ggplot: (385599443)>

Facets: small multiples

Subplots that each display one subset of the data.

Data + Aesthetics + Geom + Facets

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point() + facet_wrap("class", nrow=2)

<ggplot: (-9223372036469377946)>

Data + Aesthetics + Geom + Facets

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point() + facet_wrap("class", nrow=2) 

<ggplot: (383391510)>

Statistics

Data + Aesthetics + Geom + Facets + Statistics

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point() + facet_wrap("class", nrow=2)+ stat_smooth(method = "lm")

<ggplot: (385913671)>

Coordinate

Data + Aesthetics + Geometrics + Facets + Statistics + Coordinate

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point() + facet_wrap("class", nrow=2)+ stat_smooth(method = "lm") + coord_flip()

<ggplot: (385251876)>

Theme

Data + Aesthetics + Geometrics + Facets + Statistics + Coordinate+ Theme

ggplot(mpg, aes(x='displ', y='hwy')) + geom_point() + facet_wrap("class", nrow=2)+ stat_smooth(method = "lm") + coord_flip() + theme_dark()

<ggplot: (383847323)>

Scale

Data + Aesthetics + Geometrics + Scale

ggplot(mpg, aes(x='displ', y='hwy', color='class')) + geom_point() 

<ggplot: (383875883)>

Data + Aesthetics + Geometrics + Scale

ggplot(mpg, aes(x='displ', y='hwy', color='class')) + geom_point() + scale_color_brewer()

<ggplot: (383894269)>

Data + Aesthetics + Geometrics + Scale

ggplot(mpg, aes(x='displ', y='hwy', color='class')) + geom_point() + scale_color_manual(values=['blue', 'red', 'green'])

<ggplot: (-9223372036468854114)>

Your Turn

Visualize AirPassengers dataset.

Dataset: available at https://thiyanga-spatiotemporal.netlify.app/posts/data/