thinking about graphics scales in stata. level of measurement categorical versus continuous...
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![Page 1: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/1.jpg)
Thinking about Graphics
Scales in Stata
![Page 2: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/2.jpg)
Level of measurement
• Categorical versus continuous
• Categorical data may be represented as• Position along a categorical axis• Aesthetics such as color, marker type• Paneling
• Continuous data may be represented as• Position along a continuous axis• Aesthetics such as color, marker size
![Page 3: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/3.jpg)
Categories as axes
010
2030
4050
freq
uenc
y
Domestic Foreign
graph bar
0 10 20 30 40 50frequency
Foreign
Domestic
graph hbar
010
2030
4050
Fre
quen
cy
Domestic ForeignCar type
histogram foreign, discrete
Dom
estic
For
eign
Car
type
0 10 20 30 40 50Frequency
dotplot foreign
Categorical axis
![Page 4: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/4.jpg)
Categories as aesthetics
0 20 40 60 80 100percent
graph hbar, asyvars stack
Domestic ForeignDomestic Foreign
graph pie
Categories as color
![Page 5: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/5.jpg)
Bivariate category axes & aesthetics
010
2030
40pe
rcen
t
Domestic Foreign
1 2 3 4 5 1 2 3 4 5
graph bar, over() over()
010
2030
40pe
rcen
t
Domestic Foreign
graph bar, over() asyvars
020
4060
80pe
rcen
t
Domestic Foreign
graph bar, asyvars stack
0
25
50
75
100
perc
ent b
y R
epai
r R
ecor
d 19
78
0 25 50 75 100percent by Car type
Domestic ForeignCar type
spineplot (user-written)
Two categorical variables
![Page 6: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/6.jpg)
Bivariate category paneling
Domestic Foreign
Graphs by Car type
010
2030
1 2 3 4 5 1 2 3 4 5
Domestic Foreign
Fre
quen
cy
Repair Record 1978Graphs by Car type
12
34
5R
epai
r R
ecor
d 19
78
Domestic ForeignCar type
dotplot, over()
Categories by paneling
![Page 7: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/7.jpg)
Univariate continuous axis
1020
3040
Mile
age
(mpg
)0 2 4 6 8 10
Frequency
dotplot
02
46
810
Fre
quen
cy
10 20 30 40Mileage (mpg)
histogram, discrete
1020
3040
Mile
age
(mpg
)
0 20 40 60 80index
scatter vs. index
Univariate continuous axes
![Page 8: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/8.jpg)
Bivariate continuous axes
1020
3040
Mile
age
(mpg
)
0 20 40 60 80index
scatter vs. sorted index
1020
3040
Mile
age
(mpg
)
0 5,000 10,000 15,000Price
scatter
Bivariate continuous axes
![Page 9: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/9.jpg)
Summary (continuous) by category
02,
000
4,00
06,
000
mea
n of
pric
e
1 2 3 4 5
graph bar
0 2,000 4,000 6,000mean of price
5
4
3
2
1
graph dot
0 2,000 4,000 6,000 8,000mean of price
5
4
3
2
1
Domestic Foreign
Mean price by category
![Page 10: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/10.jpg)
Scatter by category
1020
3040
5,000 10,000 15,000 5,000 10,000 15,000
Domestic Foreign
Mile
age
(mpg
)
PriceGraphs by Car type
1020
3040
0 5,000 10,000 15,000Price
Color and symbol
Scatter by category
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Coordinates
• Systems• Cartesian, mostly• Polar, for pie
• Dimension• 2 to 4, as an overlay
• Order• Category order• Continuous order, forward or reversed
• Measurement units• Linear, the usual• Log
![Page 12: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/12.jpg)
Cartesian and polar
![Page 13: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/13.jpg)
Layering
![Page 14: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/14.jpg)
Layering, continuous
05,
000
10,0
0015
,000
0 50 100 150 200 250
Price Weight (lbs.)
2,00
03,
000
4,00
05,
000
Wei
ght (
lbs.
)
05,
0001
0,00
015,
000
Pric
e
0 50 100 150 200 250Mileage (mpg)...
Price Weight (lbs.)
2,00
03,00
04,00
05,00
0W
eigh
t (lb
s.)
05,
00010
,00015
,000
Pric
e
140 160 180 200 220 240Length (in.)
10 20 30 40Mileage (mpg)
Price Weight (lbs.)
Superimposed coordinates
![Page 15: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/15.jpg)
Layered, categorical
0 10 20 30 40 50
Linc.Peugeot
Cad.VolvoMerc.BuickOlds
Pont.Audi
DodgeAMCFiat
Chev.Toyota
FordBMW
DatsunRenault
Plym.Honda
VWMazda
Subaru
mean of mpg mean of weight100
SubaruMazda
VWHondaPlym.
RenaultDatsun
BMWFord
ToyotaChev.
FiatAMC
DodgeAudi
Pont.Olds
BuickMerc.VolvoCad.
PeugeotLinc.
2,000 2,500 3,000 3,500 4,000 4,500lbs.
10 15 20 25 30 35mpg
mean mpg mean weight
Layered dot plots
![Page 16: Thinking about Graphics Scales in Stata. Level of measurement Categorical versus continuous Categorical data may be represented as Position along a categorical](https://reader036.vdocuments.mx/reader036/viewer/2022082818/56649f135503460f94c26a79/html5/thumbnails/16.jpg)
Order, categorical
0 10 20 30 40mean of mpg
VolvoVW
ToyotaSubaruRenault
Pont.Plym.
PeugeotOlds
Merc.Mazda
Linc.Honda
FordFiat
DodgeDatsunChev.Cad.
BuickBMWAudiAMC
0 10 20 30 40mean of mpg
Linc.Peugeot
Cad.VolvoMerc.BuickOlds
Pont.Audi
DodgeAMCFiat
Chev.Toyota
FordBMW
DatsunRenault
Plym.Honda
VWMazda
Subaru
Reorder categorical coordinates