Heat (and hexagon) plots in Stata Ben Jann University of Bern, - - PowerPoint PPT Presentation

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Heat (and hexagon) plots in Stata Ben Jann University of Bern, - - PowerPoint PPT Presentation

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Heat (and hexagon) plots in Stata Ben Jann University of Bern, ben.jann@soz.unibe.ch 2019 German Stata Users Group meeting Munich, May 24, 2019 Ben Jann (University of Bern) heatplot Munich, 24.05.2019 1 Outline Introduction 1 Syntax of

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Heat (and hexagon) plots in Stata

Ben Jann

University of Bern, ben.jann@soz.unibe.ch

2019 German Stata Users Group meeting Munich, May 24, 2019

Ben Jann (University of Bern) heatplot Munich, 24.05.2019 1

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Outline

1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

Ben Jann (University of Bern) heatplot Munich, 24.05.2019 2

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What is a heat plot?

Generally speaking, a heat plot is a graph in which some aspect of the data is displayed as a color gradient. A simple example is a bivariate histogram; the color gradient is used to illustrate (relative) frequencies within bins of X and Y .

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. quietly drawnorm y x, n(10000) corr(1 .5 1) cstorage(lower) clear . heatplot y x, backfill colors(plasma)

  • 4
  • 2

2 4 y

  • 4
  • 2

2 4 x

.84893 .78679 .72464 .6625 .60036 .53821 .47607 .41393 .35179 .28964 .2275 .16536 .10321 .04107

percent

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What about hexagons?

Hexagons are great because they look a bit like circles, but you can join them together without leaving gaps. Bees found out how awesome hexagons are long time ago.

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What about hexagons?

Latter on, gully cover designers found out that hexagons look great

  • n gully covers.

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What about hexagons?

Finally, also statisticians discovered the virtues of hexagons. “The here are many reasons for using hexagons, at least over

  • squares. Hexagons have symmetry of nearest neighbors which is

lacking in square bins. Hexagons are the maximum number of sides a polygon can have for a regular tesselation of the plane, so in terms of packing a hexagon is 13% more efficient for covering the plane than squares. This property translates into better sampling efficiency at least for elliptical shapes. Lastly hexagons are visually less biased for displaying densities than

  • ther regular tesselations. For instance with squares our eyes

are drawn to the horizontal and vertical lines of the grid.”1

1Lewin-Koh, N. (2018). Hexagon Binning: an Overview. Available from

https://cran.r-project.org/web/packages/hexbin/vignettes/hexagon_binning.pdf

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Example from above using hexagons

. hexplot y x, backfill colors(plasma)

  • 4
  • 2

2 4 y

  • 4
  • 2

2 4 x

.8875 .8225 .7575 .6925 .6275 .5625 .4975 .4325 .3675 .3025 .2375 .1725 .1075 .0425

percent

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Main commands

Bivariate histogram

heatplot Y X ⇥ if ⇤ ⇥ in ⇤ ⇥ weight ⇤ ⇥ , options ⇤

Trivariate heat plot (color gradient for Z)

heatplot Z Y X ⇥ if ⇤ ⇥ in ⇤ ⇥ weight ⇤ ⇥ , options ⇤

Heat plot from Stata matrix

heatplot matname ⇥ , options ⇤

Heat plot from Mata matrix

heatplot mata(name) ⇥ , options ⇤

Heat plot using hexagons

hexplot ...

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Main options

Color gradient options

levels(#) number of color bins cuts(numlist) custom cutpoints for color bins colors(palette) color map to be used for the color bins statistic(stat) how Z is aggregated size ⇥ (exp) ⇤ | sizeprop size of color fields values(options) display values as marker labels scatter ⇥ (...) ⇤ render color fields as scatter plot keylabels(spec) how legend keys are labeled . . .

Binning of Y and X

⇥ x|y ⇤ bins(spec) how continuous Y and X are binned ⇥ x|y ⇤ bwidth(spec) alternative to bins() ⇥ x|y ⇤ discrete ⇥ (#) ⇤ treat variables as discrete and omit binning (note: categorical X and Y can be specified as i.varname) . . .

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Main options

Matrix options

drop(numlist) drop elements equal to values in numlist lower display lower triangle only lower display upper triangle only nodiagonal

  • mit diagonal

Graph options

addplot(plots) add other plots to the graph by(varlist ⇥ , options ⇤ ) repeat plot by subgroups twoway_options general twoway options . . .

Some more options related to storing results . . .

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Default

. webuse nhanes2, clear . heatplot weight height 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

.86884 .80958 .75033 .69108 .63182 .57257 .51332 .45406 .39481 .33556 .2763 .21705 .15779 .09854 .03929

percent

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Change resolution

. heatplot weight height, xbins(20) ybwidth(10 30) 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

4.2682 3.9745 3.6808 3.3871 3.0934 2.7997 2.506 2.2123 1.9187 1.625 1.3313 1.0376 .74389 .4502 .15651

percent

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Use counts, change color ramp, change binning, and labeling

. heatplot weight height, statistic(count) color(plasma, reverse) /// > cut(1(5)@max) keylabels(, range(1)) 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

91-93 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-45 36-40 31-35 26-30 21-25 16-20 11-15 6-10 1-5

count

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Use hexagons instead of squares

. hexplot weight height, statistic(count) color(plasma, reverse) /// > cut(1(5)@max) keylabels(, range(1)) 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

96-98 91-95 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-45 36-40 31-35 26-30 21-25 16-20 11-15 6-10 1-5

count

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Scale size of hexagons by relative frequency

. hexplot weight height, statistic(count) color(plasma) /// > cut(1(5)@max) keylabels(, range(1)) size 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

96-98 91-95 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-45 36-40 31-35 26-30 21-25 16-20 11-15 6-10 1-5

count

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Scaling also available with squares

. heatplot weight height, statistic(count) color(plasma) /// > cut(1(5)@max) keylabels(, range(1)) size 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

91-93 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-45 36-40 31-35 26-30 21-25 16-20 11-15 6-10 1-5

count

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Adding other plots

. hexplot weight height, statistic(count) color(plasma) /// > cut(1(5)@max) keylabels(, range(1)) size /// > addplot(lpolyci weight height, degree(1) psty(p2) lw(*1.5) ac(%50) alc(%0)) 50 100 150 200 weight (kg) 140 160 180 200 height (cm)

96-98 91-95 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-45 36-40 31-35 26-30 21-25 16-20 11-15 6-10 1-5

count

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Gender distribution (proportion female) by weight and height

. webuse nhanes2, clear . hexplot female weight height, color(PiYG) ylabel(25(25)175) cuts(0(.05)1) 25 50 75 100 125 150 175 weight (kg) 140 160 180 200 height (cm)

.975 .925 .875 .825 .775 .725 .675 .625 .575 .525 .475 .425 .375 .325 .275 .225 .175 .125 .075 .025

female

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Same graph taking account relative frequencies

. hexplot female weight height, color(PiYG) ylabel(25(25)175) cuts(0(.05)1) /// > sizeprop recenter p(lcolor(black) lwidth(vthin) lalign(center)) 25 50 75 100 125 150 175 weight (kg) 140 160 180 200 height (cm)

.975 .925 .875 .825 .775 .725 .675 .625 .575 .525 .475 .425 .375 .325 .275 .225 .175 .125 .075 .025

female

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Distribution of the body mass index by gender and its relation to high blood pressure

. heatplot highbp bmi i.female, xdiscrete(0.9) yline(18.5 25) cuts(0(.05).75) /// > sizeprop recenter colors(inferno) plotregion(color(gs11)) ylabel(, nogrid) 10 20 30 40 50 60 Body Mass Index (BMI) 1 1=female, 0=male

.725 .675 .625 .575 .525 .475 .425 .375 .325 .275 .225 .175 .125 .075 .025

highbp

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Sea surface temperature by longitude, latitude, and date

. sysuse surface, clear (NOAA Sea Surface Temperature) . heatplot temperature longitude latitude, discrete(.5) statistic(asis) /// > by(date, legend(off)) ylabel(30(1)38) aspectratio(1)

30 31 32 33 34 35 36 37 38 142 144 146 148 150 142 144 146 148 150

01mar2011 11mar2011 30N to 38.5N 142E to 150E

Graphs by date Ben Jann (University of Bern) heatplot Munich, 24.05.2019 32

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Same plot using hexagons

. hexplot temperature longitude latitude, discrete(.5) statistic(asis) /// > by(date, legend(off)) ylabel(30(1)38) aspectratio(1)

30 31 32 33 34 35 36 37 38 142 144 146 148 150 142 144 146 148 150

01mar2011 11mar2011 30N to 38.5N 142E to 150E

Graphs by date Ben Jann (University of Bern) heatplot Munich, 24.05.2019 33

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Same plot using hexagons

. hexplot temperature longitude latitude, discrete(.5) statistic(asis) clip /// > by(date, legend(off)) ylabel(30(1)38) aspectratio(1)

30 31 32 33 34 35 36 37 38 142 144 146 148 150 142 144 146 148 150

01mar2011 11mar2011 30N to 38.5N 142E to 150E

Graphs by date Ben Jann (University of Bern) heatplot Munich, 24.05.2019 34

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Same plot using hexagons

. quietly sysuse auto, clear . hexplot price weight mpg, values(format(%9.0f)) legend(off) aspectratio(1) /// > colors(plasma, intensity(.6)) p(lc(black) lalign(center))

5147 4294 4425 4296 5837 3876 3866 4194 5397 7103 4647 5651 11995 4976 4569 4647 12990 4888 9298 8814 11385 15906 12546

1000 2000 3000 4000 5000 Weight (lbs.) 10 20 30 40 Mileage (mpg)

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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First store correlations in a matrix and then plot from there

. quietly sysuse auto, clear . quietly correlate price mpg trunk weight length turn foreign . matrix C = r(C) . heatplot C, values(format(%9.3f)) color(hcl, diverging intensity(.6)) /// > legend(off) aspectratio(1)

1.000

  • 0.469

0.314 0.539 0.432 0.310 0.049

  • 0.469

1.000

  • 0.582
  • 0.807
  • 0.796
  • 0.719

0.393 0.314

  • 0.582

1.000 0.672 0.727 0.601

  • 0.359

0.539

  • 0.807

0.672 1.000 0.946 0.857

  • 0.593

0.432

  • 0.796

0.727 0.946 1.000 0.864

  • 0.570

0.310

  • 0.719

0.601 0.857 0.864 1.000

  • 0.631

0.049 0.393

  • 0.359
  • 0.593
  • 0.570
  • 0.631

1.000

price mpg trunk weight length turn foreign price mpg trunk weight length turn foreign Ben Jann (University of Bern) heatplot Munich, 24.05.2019 38

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Plot lower triangle only

. heatplot C, values(format(%9.3f)) color(hcl, diverging intensity(.6)) /// > legend(off) aspectratio(1) lower nodiagonal

  • 0.469

0.314

  • 0.582

0.539

  • 0.807

0.672 0.432

  • 0.796

0.727 0.946 0.310

  • 0.719

0.601 0.857 0.864 0.049 0.393

  • 0.359
  • 0.593
  • 0.570
  • 0.631

mpg trunk weight length turn foreign price mpg trunk weight length turn

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Copy some data

. copy http://www.stata-press.com/data/r15/homicide1990.dta . . copy http://www.stata-press.com/data/r15/homicide1990_shp.dta .

Compute spacial weights matrix (this might take a while)

. use homicide1990 (S.Messner et al.(2000), U.S southern county homicide rates in 1990) . spmatrix create contiguity W . spmatrix matafromsp W id = W . mata mata describe W # bytes type name and extent 15,949,952 real matrix W[1412,1412]

(matrix W has about 2 million cells)

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Heat plot of W with default settings, ignoring cells (i.e. weights) that are equal to zero

. heatplot mata(W), drop(0) aspectratio(1) 500 1000 1500 Rows 500 1000 1500 Columns

22.732 21.175 19.617 18.06 16.503 14.945 13.388 11.831 10.273 8.7161 7.1587 5.6014 4.0441 2.4867 .92938

sum

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Hexagon plot with fine-grained resolution

. heatplot mata(W), drop(0) aspectratio(1) hexagon bins(100) 500 1000 1500 Rows 500 1000 1500 Columns

5.8325 5.4406 5.0488 4.657 4.2651 3.8733 3.4814 3.0896 2.6977 2.3059 1.914 1.5222 1.1303 .73848 .34663

sum

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Plot each cell individually using the discrete option

. heatplot mata(W), drop(0) aspectratio(1) discrete color(black) p(lalign(center)) 500 1000 1500 Rows 500 1000 1500 Columns

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Could also use the scatter option

. heatplot mata(W), drop(0) aspectratio(1) discrete color(black) scatter p(ms(p)) 500 1000 1500 Rows 500 1000 1500 Columns

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1

Introduction

2

Syntax of heatplot and hexplot

3

Examples Bivariate histogram Trivariate distributions Display values as marker labels Correlation matrix Spacial weights matrix

4

Installation

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Installation

To install heatplot (and hexplot) type

. ssc install heatplot, replace

heatplot depends on the palettes package, which itself depends

  • n the ColrSpace Mata library, so you may also want to type

. ssc install palettes, replace . ssc install colrspace, replace

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