Microsoft PowerPoint - How to make nice figures and tables in scientific publications.pptx

24/03/2021

Howtomakenicefigures

andtablesinscientific

publications

ThorkildTylleskär

CentreforInternationalHealth,

UniversityofBergen(UiB)

Kjønn Bolig-

standard

Trang-

boddhet

Deler

latrine

Gutt

Jente

semi-permanent

permanent

≥ 7

2-4

nei

Sex Sharing

latrine

Numberof

household

members

Housing

standard

Boys

Girls

Yes

24/03/2021

Overview

Fourparts:

1. Basicsabouttablesandfiguresinscientificpapers

2. Choosingtopresentdataasatableorasafigure

3. Tables

4. Figures

Scientificpapers

Inscientificpapersdat aarepresented,either:

1. inthetextor

2. inatableor

3. inafigure

Donotduplicatethedata:intable+intextorintable+infigure

24/03/2021

Whatisafigureinscientifictext?

• Anythingthatisnotatableortext

• Itincludesphotographs,pictures,illustrations,drawings,diagrams,

anything

• Itisnumbered:

• Figure1.OverviewofXYZ

• Figure2.Nextillustration

• Thereisalwaysananchorinthetext,figure2.

Classicalwaytoorga niseamanuscript

• Manuscriptsarecommonlyinparts:

• Onefilewithtitlepage(title,authorsandaffiliations),abstract,maintextand

references

• Onefilewithtables

• Onefileforeachfigure

• Agood way toavoid headaches

24/03/2021

Hinttomakeitneat(forinstanceinyourthesis)

2typesoffonts:

1. With‘serifs’(=feet)

2. Without‘serifs’,sans‐serifs

Classicrule1:

1. Seriffontformaintext

2. Sans‐seriffontforfiguresandtables

Classicrule2:Maximum:

• 2differentfontsand

• 2differentsizesand

• 2differenttypes(regular+italic)or(regular+bold)

Withserifs Sans‐serifs

Times New Roman

Arial

Bodoni MT

Calibri

Palatino Linotype

Ta h o m a

DejaVu Serif DejaVu Sans

Figureortable?

• Usefiguresfor:

• simplemessages,trends,relationships

• Usetablesarefor

• complexdata,exactnumbers,datawithawiderange

24/03/2021

TableheadingsalwaysABOVEthetable

FigurelegendsalwaysBELOWthefigure

Number

N=345

Percent

Educationstatus

Noteducated 181 52.46

Primary 109 31.59

Secondary 36 10.43

Tertiary 18 5.22

Maritalstatus

Single 5 1.45

Married 329 95.36

Separated 10 2.90

Widowed 1 0.29

Employmentstatus

Unemployed 221 64.06

Self‐employed 94 27.25

Employed 30 8.70

Table1.Baselinecharacteristics.

0<1 1<3 3<6 6<9 9<12

Agegroups(years)

Prevalence(%)

Figure2.Prevalence(± 95%CI)ofH.Pyloribyage

groupinKampala,Uganda.

Tables

24/03/2021

Number

N=345

Percent

Educationstatus

Noteducated 181 52.46

Primary 109 31.59

Secondary 36 10.43

Terti a ry 18 5.22

Maritalstatus

Single 5 1.45

Married 329 95.36

Separated 10 2.90

Widowed 1 0.29

Employmentstatus

Unemployed 221 64.06

Self‐employed 94 27.25

Employed 30 8.70

Number

N=345

n(%)

Education

Noformaleducation 181(52.5)

Primary 109(31.6)

Secondaryorhigher 54(15.6)

Maritalstatus

Married 329(95.4)

Single,separated,widowed 16(4.6)

Employmentstatus

Unemployed 221(64.0)

Self‐employed 94(27.2)

Employed 30(8.7)

Table1.Baselinecharacteristics. Table1.Baselinecharacteristics.

Aclassical table (forprinting)hasonly 3horisontallines: Keep itsimple,give OVERVIEW

Number

N=345

n(%)

Age(year s),mean(SD) 29.09(5.63)

Parity,mean(SD) 3.28(1.75)

Gestationage(weeks),median(IQR) 28(22‐ 32)

Distancefromhospital(Km),median(IQR) 4(3‐ 5)

Timetohospital(mins),mean(SD) 32.87(15.28)

Durationontreatment(months),median(IQR) 36(12‐ 60)

Education

Noformaleducation 181(52.5)

Primary 109(31.6)

Secondary

orhigher 54(15.6)

Maritalstatus

Married 329(95.4)

Single,separated,widowed 16(4.6)

Employmentstatus

Unemployed 221(64.0)

Self‐employed 94(27.2)

Employed 30(8.7)

Table1.Baselinecharacteristics.

Try toavoid mixing

differenttypesofdatain

the sametable.

Atleast donotput an

incorrect headingon the

column.

24/03/2021

Unintendedpregnancy

N=345

Bivariablelogistic

regression

Multivariablelogistic

regression

n UnadjustedOR(95%CI) AdjustedOR(95%CI)

Age(years)

<19 20 1 1

20‐24 54 0.87(0.27‐2.80) 0.64(0.17‐2.38)

25‐29 104 0.86(0.29‐2.59) 0.76(0.22‐2.64)

30‐34 99 0.47(0.16‐1.4) 0.40(0.11‐1.44)

>35 68 0.31(0.10‐0.96) 0.33(0.09‐1.26)

EducationStatus

Noteducated 182 1 1

Primary 109 1.41(0.86‐2.31) 1.14(0.67‐1.98)

Secondary 36 1.88(0.86‐4.13) 1.69(0.70‐4.10)

Tertiary 18 5.79(1.29‐25.93) 4.3(0.89‐20.87)

Maritalstatus

single 16 1 1

marriedorcohabiting 329 4.15(1.41‐12.23) 4.44(1.30‐15.14)

Parity

0to4children 262 1 1

5to9children 83 0.32(0.20‐0.54) 0.37(0.20‐0.68)

Donotuse small groups as

reference groupinlogistic

regression

Besttool fortables

• Excel

24/03/2021

Figures

Typesofgraphics

• Photographs

• Consistsofpixels

• Whenyousearchoninternetyoucansee,forinstance2500x2282

• Diagramsareoftwotypes

• Pixel‐based,alsocalledbitmap(notscalable),likephotographs

• Vector‐based(scalable)

24/03/2021

Pixel‐based filetypes(Raster)

• .tif

• commonforphotographs/graphics(canbehigh‐resolution)

• .jpg

• commonforphotographs/graphics

• .gif

• GraphicsInterchangeFormat,commonforgraphics(low‐resolution)

• .png

• PortableNetworkGraphics,commonforgraphics

Vector‐based filetypes

• .eps

• Encapsulatedpos t‐script,commonforgraphics

• .svg

• ScalableVectorGraphics(SVG)/graphics

• .ai

• AdobeIllustrator

24/03/2021

Resolution

• Resolutionisdefinedin

• Dotsperinch(dpi)

• Screenresolutionis72dpi

• Minimumresolutionforprintingis300dpi

• Highresolutionprintingis1200dpi

Ifyou use photographs

• Donottorturethem!

24/03/2021

EdwardR.Tufte

• JeffreyNicols PowerPoint:

• http://www.cs.cmu.edu/~jeffreyn/talk

s/tufte‐lecture.ppt

• Tuftehimself:

• https://www.youtube.com/watch?v=T

h_1azZA2OY

• Anexplanation:

• https://www.youtube.com/watch?v=q

QGcK20pJk0

Displaying

QuantitativeInformation

AnexplorationofEdwardR.Tufte’s

TheVisualDisplayofQuantitativeInformation

Jeffrey Nichols

Programming Usable Interfaces

May 2, 2003

24/03/2021

Howcanwemakebettergraphics?

Tuftepresentssomeprinciplesofdatagraphics

• Aboveallelse,showthedata

• Maximizethedata‐inkratio

• Withinreason

• Everybitofinkonagraphicrequiresareason

• Erasenon‐data‐ink

• Eraseredundantdata‐ink

• Reviseandedit

Histogram of Midterm Results

D/F C B- B B+ A- A A+

Scoring Buckets

# of Students

Histogram of Midterm Results

D/F C B- B B+ A- A A+

Scoring Buckets

# of Students

24/03/2021

Example1

Adjusted

Allvisits (n=202,610)

NoEDvisits/admissions

(n=125,218)

Asylum‐seekingvisits

(n=572)

Visitbefore1.1.15

(n=126)

Asylum‐seekingvisits

(n=446)

Multipleregistration

admissions(n=2067)

Emergencydepartment

visits(n= 63,108)

Admissions (n=11,645)

Ambulatorycaresensitive:

11%(n=1279/11645)

Admissions (n=149)

Ambulatorycaresensitive:

12%(n=18/149)

Emergencydepartment

visits(n= 297)

Non‐asylum‐seekingvisits

(n=74,753)

24/03/2021

• NEVERuse MicrosoftWord

forANYgraphics EVER!

• Forflowchartsandsimilar:

• UseMicrosoftPowerPoint

tomakethegraphics!

• Learnsomeofthebasics

• Copyasmuchaspossible

• Ifyouhavemadeonebox,

justcopyandrecycleitforall

otherboxes

Moreprinciples:

1. Reducedata“Simplify,simplify,simplify”

2. Usealogicalorder,forinstancegofrombigtosmall

3. Keepcomparisonsclose verticallyclose

4. Self‐explaininglegend:

• What,Where,When,Units,Source

• Avoidtheuseofabbreviations,reducethenumberoffootnotes

24/03/2021

Usecolouraccordingtotheaudience

• Ifyourreaderwillreadaphotocopiedversionofthearticle:useB/W

• Ifyouthinkyourreaderswillreadfromscreen:youmayusecolour

• ConsiderTufte

0 10 20 30 40 50 60 70

percent

Skin infections (0.33)

Severe ENT & upper airway infection (0.01)

Neonatal jaundice (0.39)

Nutritional deficiency (0.00)

Kidney & urinary infections (0.19)

Iron-deficiency anemia (0.79)

Disease of female pelvic organs (0.9)

Immunisation preventable (0.9)

Gastroenteritis & dehydration (0.09)

Gastritis (0.76)

Failure to thrive (0.61)

Diabetes mellitus (0.97)

Dental conditions (0.57)

Convulsions (0.4)

Asthma (0.3)

Allergies & allergic reactions (0.06)

Department of Paediatrics and Child health,

Mulago National Referral Hospital, Kampala

246 HIV infected HAART naïve

children aged 0-<12 years

236 HIV infected HAART naïve

children aged 0-<12 years

10 children excluded:

• failed to provide stools (9)

• incomplete data (1)

219 HIV infected HAART naïve children

aged 0-<12 years with CD4 percent available

17 children:

• CD4 percent not available

193 HIV infected HAART naïve children

aged 0-<12 years with CD4 percent and FC

26 samples lost in transport

134 children

≤ 4 years

59 children

> 4 years

24/03/2021

• NEVERaccepttheoutputfromanystatisticalsoftwareorMSExcelas

afinalgraph!

• Theyalwaysbreachthedata‐inkrule

• Needsalotoftidyingup

• Payattentiontothelook!

• Usethinlines,0.5pt

• Avoidlargeemptyspaces