introduction to the data...datacamp communicating with data in the tidyverse the data you are going...

DataCamp CommunicatingwithDataintheTidyverse

Introductiontothedata

COMMUNICATINGWITHDATAINTHETIDYVERSE

TimoGrossenbacherDataJournalist


ThisismeFindexamplesofdatajournalismonsrfdata.github.io

https://projector-prod.herokuapp.com/srfdata.github.io


ThelaststepintheTidyverseprocess

[1]RforDataScience(http://r4ds.had.co.nz/communicate-intro.html)


Whatyouaregoingtocreate


Thedatayouaregoingtoworkwithilo_working_hours

#Atibble:737x3countryyearworking_hours<chr><chr><dbl>1Australia1980.034.578852Canada1980.034.850003Denmark1980.031.898084Finland1980.035.563465France1980.035.423086Iceland1980.035.846157Italy1980.035.746358Japan1980.040.788469Korea,Rep.1980.055.3076910Norway1980.030.37885#...with727morerows


Thedatayouaregoingtoworkwithilo_hourly_compensation

#Atibble:831x3countryyearhourly_compensation<chr><chr><dbl>1Australia1980.08.442Austria1980.08.873Belgium1980.011.744Canada1980.08.875Denmark1980.010.836Finland1980.08.617France1980.08.908Greece1980.03.729HongKong,China1980.01.5010Ireland1980.06.44#...with821morerows


Theinner_join()verb/function

x%>%inner_join(y,by="key")

#>#Atibble:2×3#>keyval_xval_y#><dbl><chr><chr>#>11x1y1#>22x2y2

[1]RforDataScience(http://r4ds.had.co.nz/relational-data.html#inner-join)


Let'sdothis!



Filteringandplottingthedata




FilterthedataforEuropeancountries

ilo_data%>%filter(country=="Switzerland")

#Atibble:27x4countryyearhourly_compensationworking_hours<fctr><fctr><dbl><dbl>1Switzerland198010.9634.703852Switzerland198110.0134.334623Switzerland198210.3134.123084Switzerland198310.3333.842315Switzerland19849.5233.478856Switzerland19859.5533.359617Switzerland198613.6233.196158Switzerland198716.9033.173089Switzerland198817.8133.1626910Switzerland198916.5432.87308#...with17morerows


The%in%operator

...equivalentto:

ilo_data%>%filter(country%in%c("Sweden","Switzerland"))

#Atibble:54x4countryyearhourly_compensationworking_hours<fctr><fctr><dbl><dbl>1Sweden198012.4029.169232Switzerland198010.9634.703853Sweden198111.7029.007694Switzerland198110.0134.334625Sweden19829.9929.27885#...with49morerows

ilo_data%>%filter(country=="Sweden"|country=="Switzerland")


Therelationshipbetweenbothindicatorsplot_data<-ilo_data%>%filter(year==2006)

ggplot(plot_data)+geom_histogram(aes(x=working_hours))

plot_data<-ilo_data%>%filter(year==2006)

ggplot(plot_data)+geom_histogram(aes(x=hourly_compensation))


Therelationshipbetweenbothindicators


Addinglabelstotheplot


Somedplyrfunctionrepetition

ilo_data%>%group_by(country)%>%summarize(median_working_hours=median(working_hours))

#Atibble:17x2countrymedian_working_hours<fctr><dbl>1Austria31.699042Belgium32.038463CzechRep.39.100004Finland34.048085France32.34615#...with12morerows


Let'spractice!



Customggplot2themes




Theadvantagesofacustomlook


Thetheme()functionggplot(plot_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+

theme(text=element_text(family="Bookman",color="gray25"))


Defaultggplot2themesggplot(plot_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+

theme_classic()


Chainingtheme()callsggplot(plot_data)+geom_histogram(aes(x=working_hours))+labs(x="Workinghoursperweek",y="Numberofcountries")+

theme_classic()+

theme(text=element_text(family="Bookman",color="gray25"))


Themeconfigurationoptions

axis.titlelabelofaxes(element_text;inheritsfromtext)

axis.title.xxaxislabel(element_text;inheritsfromaxis.title)

axis.title.x.topxaxislabelontopaxis(element_text;inheritsfromaxis.title.x)

axis.title.x.bottomxaxislabelonbottomaxis(element_text;inheritsfromaxis.title.x)

?theme


Theelement_*functionfamilyelement_text()element_rect()element_line()element_blank()

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

theme(text=element_blank())


Let'stryoutthemes!


introduction to the data...datacamp communicating with data in the tidyverse the data you are going...

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