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Behind the scenes of the SDG Atlas 2018 Creating a reproducible, data visualization-driven World Bank publication

Behind the scenes of the SDG Atlas 2018pubdocs.worldbank.org/.../071718-morning-seminar-11-SDGAtlas.pdf · The Bank’s SDG Indicators Group • Members from across the Bank’s thematic

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Behind the scenes of the SDG Atlas 2018

Creating a reproducible, data visualization-driven World Bank publication

What is the World Bank’s role in SDG monitoring?

How are the SDGs Monitored?

• Inter-agency and Expert Group on SDG Indicators (IEAG-SDGs)

• Develops global indicator framework

• Includes member states; regional and international agencies are observers

The Bank’s SDG Indicators Group

• Members from across the Bank’s thematic global practices providing technical input to all 17 goals

• Bank responsible for reporting on 21 indicators covering 9 goals and involved in another 20+.

• All work closely aligned with the Bank’s own Twin Goals

Why an SDG Atlas?

1966

1983

1985

Under new stricter definitions, fewer people have access to water

80% of Pakistanis can “access an improved water source” within a 30-minute round-trip.

In Nigeria, 2/3 of the population have similar access, but new data show that only 10% have access at home.

2017 2018

95% 5%

funkytraditional

Atlas 2018 Requirements● Be graphics-led

○ tight control over design and layout ○ ability to iterate and experiment

● Work in print & online● Computationally reproducible

Might have to do things a little differently...

2017 2018

years = 1990:2016indicators <- c("SL.AGR.EMPL.ZS","SL.IND.EMPL.ZS","SL.SRV.EMPL.ZS"

)

df <- wbgdata(wbgref$incomes$iso3c,indicators,years = years,indicator.wide = FALSE

)

ggplot(df, aes(date, value,fill = indicatorID

)) +geom_col() +facet_wrap(~iso3c, nrow=1)

years = 1990:2016indicators <- c("SL.AGR.EMPL.ZS","SL.IND.EMPL.ZS","SL.SRV.EMPL.ZS"

)

df <- wbgdata(wbgref$incomes$iso3c,indicators,years = years,indicator.wide = FALSE

)

ggplot(df, aes(date, value,color = indicatorID

)) +geom_line() +facet_wrap(~iso3c, nrow=1)

fig_sdg8_emp_sector_panel <- function(years = 1990:2016) {indicators <- c("SL.AGR.EMPL.ZS", "SL.IND.EMPL.ZS", "SL.SRV.EMPL.ZS")

df <- wbgdata(wbgref$incomes$iso3c,indicators,years = years,indicator.wide = FALSE,# Comment the next two lines to use live API dataoffline = "only",offline.file = "inputs/cached_api_data/fig_sdg8_emp_sector_panel.csv"

)

figure(data = df,plot = function(df, style = style_atlas()) {

df <- df %>% mutate(iso3c = factor(iso3c, rev(wbgref$incomes$iso3c)))iso3c_labeller <- as_labeller(function(l) wbgref$incomes$labels[l])ggplot(df, aes(date, value, group = indicatorID, color = indicatorID)) +

geom_line(size=style$linesize) +scale_y_continuous(expand=c(0,0), limits = c(0, 80)) +scale_x_continuous(expand=c(0,0), limits=c(1990,2020), breaks = c(1990,2016 scale_color_manual(

values = c(SL.AGR.EMPL.ZS = style$colors$spot.secondary,SL.IND.EMPL.ZS = style$colors$spot.secondary.light,SL.SRV.EMPL.ZS = style$colors$spot.primary

),labels = c(

SL.AGR.EMPL.ZS = "Agriculture",SL.IND.EMPL.ZS = "Industry",SL.SRV.EMPL.ZS = "Services"

)) +facet_wrap(~iso3c, nrow=1, labeller = iso3c_labeller) +style$theme() +style$theme_legend("top") +theme(panel.spacing.x = unit(0.03, "npc"))

},aspect_ratio = 1.2,title = "In the early 2000s the service sector overtook agriculture to become th

employer. Globally, services account for 50 percent of employment, agriculture 30 p 20 percent.",

subtitle = paste0("Employment by sector (% of total employment)"),source = paste0("Source: ILO. World Development Indicators (SL.AGR.EMPL.ZS; SL.

SL.SRV.EMPL.ZS)."))

}

?

Source code for publication

Source code for toolsgithub.com/worldbank

Thanks!Tariq Khokhar: [email protected] Emi Suzuki: [email protected]