COMMUNICATING WITH DATA IN THE TIDYVERSE€¦ · DataCamp Communicating with Data in the Tidyverse...

DataCamp CommunicatingwithDataintheTidyverse

Visualizingaspectsofdatawithfacets

COMMUNICATINGWITHDATAINTHETIDYVERSE

TimoGrossenbacherDataJournalist


Thefacet_grid()functionilo_data<-ilo_data%>%filter(year=="1996"|year=="2006")

ggplot(ilo_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+facet_grid(.~year)

ilo_data<-ilo_data%>%filter(year=="1996"|year=="2006")

ggplot(ilo_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+facet_grid(year~.)


Thefacet_grid()functionilo_data<-ilo_data%>%filter(year=="1996"|year=="2006")

ggplot(ilo_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+facet_grid(.~year)

ilo_data<-ilo_data%>%filter(year=="1996"|year=="2006")

ggplot(ilo_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+facet_wrap(facets=~year)


Afacetedscatterplot


Stylingfacetedplotsstrip.backgroundstrip.text...


Definingyourownthemefunctiontheme_green<-function(){theme(plot.background=element_rect(fill="green"),panel.background=element_rect(fill="lightgreen"))}

ggplot(ilo_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+

theme_green()


Let'spractice!



Acustomplottoemphasizechange




Thedotplot

[1]NewYorkTimes(https://www.nytimes.com/2017/11/17/upshot/income-inequality-united-states.html){{0}}


Dotplotswithggplot2ggplot((ilo_data%>%filter(year==2006)))+geom_dotplot(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Shareofcountries")


Dotplotswithggplot2:thegeom_path()function

geom_path()connectstheobservationsintheorderinwhichtheyappearinthedata.

?geom_path

ilo_data%>%arrange(country)

#Atibble:34x4countryyearhourly_compensationworking_hours<fctr><fctr><dbl><dbl>1Austria199624.7531.998082Austria200630.4631.817313Belgium199625.2531.653854Belgium200631.8530.211545CzechRep.19962.9439.726926CzechRep.20066.7738.40000#...with28morerows


Dotplotswithggplot2:thegeom_path()functionggplot()+geom_path(aes(x=numeric_variable,y=numeric_variable))

ggplot()+geom_path(aes(x=numeric_variable,y=factor_variable))

ggplot()+geom_path(aes(x=numeric_variable,y=factor_variable),arrow=arrow(___))


Let'stryoutgeom_path!



Polishingthedotplot




FactorlevelsTheorderoffactorlevelsdeterminetheorderofappearanceinggplot2.

ilo_data$country

[1]AustriaBelgiumCzechRep.Finland[5]FranceGermanyHungary......17Levels:AustriaBelgiumCzechRep.FinlandFrance...UnitedKingdom


ReorderingfactorswiththeforcatspackageNeedstobeloadedwithlibrary(forcats)

fct_dropfordroppinglevels

fct_revforreversingfactorlevels

fct_reorderforreorderingthem.

[1]Learnmoreattidyverse.org(http://forcats.tidyverse.org/)


Thefct_reorderfunctionilo_data

#Atibble:34x4countryyearhourly_compensationworking_hours<fctr><fctr><dbl><dbl>1Austria199624.7531.998082Austria200630.4631.817313Belgium199625.2531.653854Belgium200631.8530.211545CzechRep.19962.9439.726926CzechRep.20066.7738.40000

ilo_data<-ilo_data%>%mutate(country=fct_reorder(country,working_hours,mean))

ilo_data$country

[1]AustriaBelgiumCzechRep.Finland[5]FranceGermanyHungary......17Levels:NetherlandsNorwayGermanySweden...CzechRep.


Thefct_reorderfunction


Nudginglabelswithhjustandvjustggplot(ilo_data)+geom_path(aes(...))+geom_text(aes(...,hjust=ifelse(year=="2006",1.4,-0.4)))


Let'spractice!



Finalizingtheplotfordifferentaudiences

anddevices




coord_cartesianvs.xlim/ylimggplot_object+coord_cartesian(xlim=c(0,100),ylim=c(10,20))

ggplot_object+xlim(0,100)+ylim(10,20)


coord_cartesianvs.xlim/ylim

[1]TakenfromRStudioDataVisualizationCheatSheet(https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)


Desktopvs.Mobileaudiences


Let'sproducetheseplots!


COMMUNICATING WITH DATA IN THE TIDYVERSE€¦ · DataCamp Communicating with Data in the Tidyverse...

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