World map of NEET
library(Rilostat)
library(tidyverse)
library(plotly)
X <- get_ilostat(id = 'EIP_2EET_SEX_RT_A', segment = 'indicator', filters = list(time = '2018', sex='T')) %>%
filter(str_sub(ref_area,1,1) != 'X') %>%
select(ref_area, obs_value) %>%
left_join(Rilostat:::ilostat_ref_area_mapping %>%
select(ref_area, ref_area_plotly) %>%
label_ilostat(code = 'ref_area'),
by = "ref_area") %>%
filter(!obs_value %in% NA) %>%
mutate(tot_obs_value = cut(obs_value, quantile(obs_value, na.rm = TRUE), include.lowest = TRUE))
X %>% plot_geo( width = 900, height = 600) %>%
add_trace(
z = ~obs_value,
color = ~obs_value,
colors=c("green", "blue"),
text = ~ref_area.label,
locations = ~ref_area_plotly,
marker = list(line = list(color = toRGB("grey"), width = 0.5)),
showscale = TRUE) %>%
colorbar(title = '(%)', len = 0.5, ticksuffix="%") %>%
layout(
title = list( text = "Share of youth not in employment, education or training (NEET) in 2018",
font = list(size = 18)),
font = (size = 1),
geo = list( showframe = TRUE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showCountries = TRUE,
resolution = 110),
annotations =
list( x = 1, y = 1,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
Stacked area graph of Employment distribution by sector
#--------------
# Stacked area graph
# Data: Global employment distribution by occupation -- ILO modelled estimates, November 2018
#--------------
library(Rilostat)
library(tidyverse)
library(viridis)
library(hrbrthemes)
library(scales)
library(stringr)
library(officer)
# -- Relative distribution
dat_emp1 <- get_ilostat(id = 'EMP_2EMP_SEX_OCU_DT_A',
segment = 'indicator',
type = "both",
time_format = "num",
filters = list(ref_area = 'X01',
sex = 'SEX_T')) %>%
filter(classif1 != 'OCU_DETAILS_TOTAL') %>%
mutate(distribution = obs_value/100) %>%
select(time, classif1, distribution)
# Plot (With the relative distribution)
dat_emp1 %>%
ggplot( aes(x=time,
y=distribution,
fill=classif1,
color=classif1,
text=classif1)) +
geom_area() +
scale_fill_viridis(discrete = TRUE) +
scale_color_viridis(discrete = TRUE) +
labs(x="", y="") +
scale_y_continuous(breaks = pretty_breaks(n = 10), labels=percent, expand = c(0.01,0.01)) +
scale_x_continuous(breaks = seq(1991, 2023, 2), lim = c(1991, 2023), expand = c(0.01,0.01)) +
theme_ipsum() +
theme(axis.text.x=element_text(size=8),
axis.text.y=element_text(size=8),
legend.position="none") +
annotate( "text",
x=1992,
y= c(0.989, 0.95, 0.89, 0.83, 0.75, 0.40, 0.12, 0.04),
label =c( "Managers",
"Professionals",
"Technicians and associate professionals",
"Clerical support workers",
"Service and sales workers",
"Craft and related trades workers",
"Plant and machine operators, and assemblers",
"Elementary occupations and skills agricultural, forestry and fishery workers"),
hjust = 0, size=I(3)) +
annotate("rect", xmin = 2018, xmax = 2023, ymin = 0, ymax = 1, alpha = 0.3, fill = "gray") +
annotate("text", label = "Projections", x=2019, y=0.9, vjust=1, hjust=0, size=I(4)) +
geom_vline(xintercept = 2018, colour = "red") +
labs(caption = "Source: ilostat",
title = "Global employment distribution by occupation -- modelled estimates, Nov. 2018") +
theme(plot.title = element_text(size=12, face="bold.italic"))
Box plot of Labour force participation
#--------------
# BoxPlot
# Data: Labour force participation rate (5 points in time) by sex
#--------------
library(Rilostat)
library(tidyverse)
library(scales)
dat_emp4 <- get_ilostat(id = 'EAP_2WAP_SEX_AGE_RT_A', segment = 'indicator', filters = list(time = c('1990', '2000', '2010', '2018', '2030'), sex=c('F', 'M'), classif1='AGGREGATE_TOTAL')) %>%
filter(str_sub(ref_area,1,1) != 'X') %>%
mutate(sex_lab = ifelse(sex=='SEX_F', 'Women', ifelse(sex=='SEX_M', 'Men', NA))) %>%
mutate(sex_year = ifelse((sex=='SEX_F' & time=='2030'), 'Women - Projection', ifelse((sex=='SEX_M' & time=='2030'), 'Men - Projection', ifelse((sex=='SEX_F' & time!='2030'), 'Women - Estimate', ifelse((sex=='SEX_M' & time!='2030'), 'Men - Estimate', NA))))) %>%
mutate(lfpr = obs_value/100) %>%
select(ref_area, sex_lab, sex_year, time, lfpr)%>%
group_by(sex_year, time) %>%
mutate(MD = median(lfpr)) %>%
ungroup() %>%
mutate(MD = as.character(MD))
ggplot(dat_emp4, aes(x=time, y=lfpr, fill=sex_year, alpha=sex_year)) +
geom_boxplot() +
facet_wrap(~sex_lab) +
theme_bw() +
theme (legend.position="none") +
scale_alpha_manual(values=c(0.7,0.2, 0.7, 0.2)) +
scale_fill_manual(values=c("yellow","yellow","turquoise4","turquoise4")) +
labs( x="",
y="LFPR (%)",
title = "Labour force participation rate by sex ILO modelled estimates, July 2018",
caption = "Source: ilostat") +
scale_y_continuous(labels=percent)
Principal component analysis of SDG indicators
#-------------------------------------------------------
# PCA of SDG indicators
# author : Mabelin Villarreal-Fuentes
#-------------------------------------------------------
library(Rilostat)
library(tidyverse)
library(missMDA)
library(FactoMineR)
library(factoextra)
# SDG's:
# 1 [1.1.1] Working poverty rate, Total, 15+ (150 obs)
# 2 [1.3.1] 2016 -- Proportion of population covered by SP floors/systems, T = (Population covered by at least one social protection benefit)
# 3 [5.5.2] Female share of employment in managerial positions (%), Total managment
# 4 [8.2.1] Annual growth rate of output per worker (measured as GDP in constant 2011 international $ in PPP)
# 5 [8.3.1] Proportion of informal employment in non-agricultural employment -- Harmonized series (%)
# Not included -- 6 [8.5.1] 2014 -- Average hourly earnings of female and male employees
# 7 [8.5.2] Unemployment rate (%)
# 8 [8.6.1] 2014 -- Proportion of youth (aged 15-24 years) not in education, employment or training
# Not included -- 9 [8.7.1] 2014 -- Proportion of children engaged in economic activity and household chores (%)
# 10 [8.8.1] 2015 -- Non-fatal occupational injuries per 100'000 workers (%)
# 11 [8.8.1] 2015 -- Fatal occupational injuries per 100'000 workers (%)
# 12 [9.2.2] Manufacturing employment as a proportion of total employment (%)
# 13 [10.4.1] 2015 -- Labour income share as a percent of GDP (%)
#Retrieving the information
dat_sdg_1 <- get_ilostat(id = 'SDG_0111_SEX_AGE_RT_A', segment = 'indicator', filter = list(time = '2017', sex = 'T', classif1='AGE_YTHADULT_YGE15')) %>% mutate(SDG_0111 = obs_value) %>% select(ref_area, SDG_0111)
dat_sdg_2 <- get_ilostat(id = 'SDG_0131_SEX_SOC_RT_A', segment = 'indicator', filter = list(time = '2016', sex = 'T', classif1 = 'SOC_CONTIG_TOTAL')) %>% mutate(SDG_0131 = obs_value) %>% select(ref_area, SDG_0131)
dat_sdg_3 <- get_ilostat(id = 'SDG_0552_OCU_RT_A', segment = 'indicator', filter = list(time = '2017', classif1 = 'OCU_MGMT_TOTAL')) %>% mutate(SDG_0552 = obs_value) %>% select(ref_area, SDG_0552)
dat_sdg_4 <- get_ilostat(id = 'SDG_0821_NOC_RT_A', segment = 'indicator', filter = list(time = '2017')) %>% mutate(SDG_0821 = obs_value) %>% select(ref_area, SDG_0821)
dat_sdg_5 <- get_ilostat(id = 'SDG_B831_SEX_RT_A', segment = 'indicator', filter = list(time = '2017', sex = 'T')) %>% mutate(SDG_B831 = obs_value) %>% select(ref_area, SDG_B831)
dat_sdg_7 <- get_ilostat(id = 'SDG_0852_SEX_AGE_RT_A', segment = 'indicator', filter = list(time = '2017', sex = 'T', classif1 = 'AGE_YTHADULT_YGE15')) %>% mutate(SDG_0852 = obs_value) %>% select(ref_area, SDG_0852)
dat_sdg_8 <- get_ilostat(id = 'SDG_0861_SEX_RT_A', segment = 'indicator', filter = list(time = '2014', sex = 'T')) %>% mutate(SDG_0861 = obs_value) %>% select(ref_area, SDG_0861)
dat_sdg_10 <- get_ilostat(id = 'SDG_N881_SEX_MIG_RT_A', segment = 'indicator', filter = list(time = '2015', sex = 'T', classif1 = 'MIG_STATUS_TOTAL')) %>% mutate(SDG_N881 = obs_value) %>% select(ref_area, SDG_N881)
dat_sdg_11 <- get_ilostat(id = 'SDG_F881_SEX_MIG_RT_A', segment = 'indicator', filter = list(time = '2015', sex = 'T', classif1 = 'MIG_STATUS_TOTAL')) %>% mutate(SDG_F881 = obs_value) %>% select(ref_area, SDG_F881)
dat_sdg_12 <- get_ilostat(id = 'SDG_0922_NOC_RT_A', segment = 'indicator', filter = list(time = '2017')) %>% mutate(SDG_0922 = obs_value) %>% select(ref_area, SDG_0922)
dat_sdg_13 <- get_ilostat(id = 'SDG_1041_NOC_RT_A', segment = 'indicator', filter = list(time = '2015')) %>% mutate(SDG_1041 = obs_value) %>% select(ref_area, SDG_1041)
# Merging of all indicators; PSE left out of the analysis to avoid distorting the relationships between the rest
# of reference areas
dat_sdg <- full_join(dat_sdg_1, dat_sdg_2, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_3, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_4, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_5, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_7, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_8, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_10, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_11, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_12, by='ref_area')
dat_sdg <- full_join(dat_sdg, dat_sdg_13, by='ref_area') %>%
filter(str_sub(ref_area,1,1) != 'X') %>% filter(ref_area != 'PSE')
# Missing values treatment
# 1. Counting values equal to NA
dat_sdg$na_count <- apply(is.na(dat_sdg), 1, sum)
# keeping only those countries with 70% or more information -> na_count no more that 3
# 54 of 183 countries;
dat_sdg <- dat_sdg %>% subset(na_count<=3) %>% select(ref_area:SDG_1041) %>%
column_to_rownames(var = "ref_area")
# 2. Imputation using missMDA
# Estimate the number of dimensions necessary retain avoid overfitting
nb <- estim_ncpPCA(dat_sdg, ncp.min = 1, ncp.max = 5)
imp_dat_sdg <- imputePCA(dat_sdg, ncp = 1)$completeObs %>%
as.data.frame(.) %>%
select(SDG_0131, SDG_0552, SDG_0821, SDG_0852, SDG_0861, SDG_N881, SDG_F881, SDG_0922, SDG_1041, SDG_0111, SDG_B831)
# 3. -- PCA ---
results_0 <- PCA(imp_dat_sdg, quanti.sup = 10:11)
barplot(results_0$eig[,1],main="Eigenvalues",names.arg=1:nrow(results$eig))
# Retaining 3 principal components
results <- PCA(imp_dat_sdg, quanti.sup = 10:11, ncp = 3)
summary(results, nbelements=Inf)
#------------------------------
# Description of the dimensions
#dimdesc(results)
#------------------------------
# Plots
# plot(results, cex=0.8, auto = "y", shadow = TRUE)
# plot(results, cex=0.8, choix="var", shadow = TRUE)
# Plots for the 2nd and 3rd axis
# plot(results, cex=0.5, choix="ind", axes=2:3)
# plot(results, cex=0.5, choix="var", axes=2:3)
#-------------------------------
# Visualization (correlation circle and individuals cloud in one graph)
fviz_pca_biplot(results, col.ind="cos2", geom = "text") +
scale_color_gradient2(low="white", mid="blue", high="red", midpoint=0.50)
Pyramid chart of Labour force distribution
require(dplyr)
require(tidyverse)
require(plotrix)
require(Rilostat)
test_ref_area = 'FRA'
test_time <- '2018'
test_indicator <- c('EIP_2EIP_SEX_AGE_NB', 'EAP_2EAP_SEX_AGE_NB')
test_age <- c("AGE_5YRBANDS_Y15-19", "AGE_5YRBANDS_Y20-24", "AGE_5YRBANDS_Y25-29", "AGE_5YRBANDS_Y30-34", "AGE_5YRBANDS_Y35-39", "AGE_5YRBANDS_Y40-44", "AGE_5YRBANDS_Y45-49", "AGE_5YRBANDS_Y50-54", "AGE_5YRBANDS_Y55-59", "AGE_5YRBANDS_Y60-64")
test_sex <- c('SEX_M', 'SEX_F')
test_source = 'ILO modelled estimates, May 2017'
ref_area_name <- label_ilostat(test_ref_area, dic = 'ref_area')
ref_age <- label_ilostat(test_age, dic = 'classif1') %>% str_split( pattern = ': ',simplify = TRUE) %>% .[,2]
ref_sex <- label_ilostat(test_sex, dic = 'sex') %>% str_split( pattern = ': ',simplify = TRUE) %>% .[,2]
color_code <- c("#3585E7","#F0D285")
X <- get_ilostat(id = paste0(test_ref_area, '_A'),
segment = 'ref_area',
filters = list( indicator = test_indicator,
time = test_time,
ref_area = test_ref_area,
classif1 = test_age,
sex = test_sex),
cache = FALSE) %>%
select(indicator, classif1, obs_value, sex) %>%
mutate( classif1 = label_ilostat(classif1, dic = 'classif1') %>% str_split( pattern = ': ',simplify = TRUE) %>% .[,2],
sex = label_ilostat(sex, dic = 'sex') %>% str_split( pattern = ': ',simplify = TRUE) %>% .[,2]) %>%
spread(indicator, obs_value) %>%
select_(.dots = list("sex", "classif1", val1 = test_indicator[1], val2 = test_indicator[2] ))
sideLeft <- X %>%
filter(sex %in% ref_sex[1]) %>%
select(-sex,-classif1) %>%
select(val2, val1) %>%
mutate( #val1 = val1 - val2,
TOT = val2 + val1,
STOT = TOT / sum(TOT) * 100,
SHval2 = val2 / TOT * STOT,
SHval1 = val1 / TOT * STOT) %>%
select(SHval2, SHval1) %>% as.matrix
sideright <- X %>%
filter(sex %in% ref_sex[2])%>%
select(val2, val1) %>%
mutate( # val1 = val1 - val2,
TOT = val2 + val1,
STOT = TOT / sum(TOT) * 100,
SHval2 = val2 / TOT * STOT,
SHval1 = val1 / TOT * STOT) %>%
select(SHval2, SHval1) %>% as.matrix
Label <- X %>% distinct(classif1) %>% .$classif1
FirstPar <- pyramid.plot(
lx = sideLeft,
rx = sideright,
labels = Label,
unit = "%",
lxcol = color_code,
rxcol = color_code,
laxlab = c(0, 5, 10), # left side axes
raxlab = c(0, 5, 10), # right side axes
top.labels = c("Male", "Age", "Female"),
gap = 1.5 # place for age labels
) +
mtext( # give it a title
text = paste0(ref_area_name , ", labour force distribution, ", test_time),
side = 3,
line = 2,
cex=1.5)
# legend( ### stick in a legend
# par('usr')[1],
# par('usr')[4],
# c("Labour force","Outside Labour force"),
# fill = color_code,
# border = '#FFFFFF',
# bty = 'n')
# par( ### don't forget to restore the margins and background
# mar = FirstPar,
# bg = "transparent")
Column chart of unemployment rate (vertical bars)
require(tidyverse)
require(plotly)
require(Rilostat)
ref_region <- c('World', 'Asia and the Pacific', 'Americas', 'Europe and Central Asia', 'Africa', 'Arab States')
code_region <- get_ilostat_toc(segment = 'ref_area') %>% filter(ref_area.label %in% ref_region)
X <- get_ilostat(id = 'UNE_2EAP_SEX_AGE_RT_A',
filters = list( ref_area = code_region$ref_area,
sex = 'SEX_T',
classif1 = 'AGE_YTHADULT_YGE15',
time = '2017'),
cache = FALSE) %>%
mutate(ref_area.label = label_ilostat(ref_area, dic = 'ref_area') %>% factor(.,levels = ref_region)) %>%
arrange(obs_value) %>%
spread(sex, obs_value)
plot_ly(X,
x = ~ref_area.label,
y = ~SEX_T,
type = 'bar') %>%
layout(
title = list( text = "Global and regional unemployment rates, 2017",
font = list(size = 18)),
xaxis = list( title = "",
tickfont = list(size = 18)
),
yaxis = list( title = "Percentage",
titlefont = list(size = 18),
tickfont = list(size = 18)
),
annotations =
list( # x = 1.18, y = - 0.058,
x = 1 , y = 1,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
Time series of Labour force participation rate
require(Rilostat)
require(ggplot2, quiet = TRUE)
require(dplyr, quiet = TRUE)
get_ilostat(id = 'EAP_DWAM_NOC_RT_A',
time_format = 'num',
filters = list( ref_area = c('FRA', 'USA', 'DEU'),
timefrom = 2005, timeto = 2017)) %>%
select(ref_area, time, obs_value) %>%
ggplot(aes(x = time, y = obs_value, colour = ref_area)) +
geom_line() +
ggtitle('Male labour force participation rate in selected countries, 2005-2017') +
scale_x_continuous(breaks = seq(2005, 2017, 3)) +
labs(x="Year", y="Male LFPR (%)", colour="Country:") +
theme(legend.position = "top", plot.title = element_text(hjust = 0.5))
Column chart of unemployment rate (vertical bars)
require(tidyverse)
require(plotly)
require(Rilostat)
ref_region <- c('World', 'Asia and the Pacific', 'Americas', 'Europe and Central Asia', 'Africa', 'Arab States')
code_region <- get_ilostat_toc(segment = 'ref_area') %>% filter(ref_area.label %in% ref_region)
X <- get_ilostat(id = 'UNE_2EAP_SEX_AGE_RT_A',
filters = list( ref_area = code_region$ref_area,
sex = c('SEX_M', 'SEX_F'),
classif1 = 'AGE_YTHADULT_YGE15',
time = '2017'),
cache = FALSE) %>%
mutate(ref_area.label = label_ilostat(ref_area, dic = 'ref_area') %>% factor(.,levels = ref_region)) %>%
arrange(obs_value) %>%
spread(sex, obs_value)
plot_ly( data = X ,
x = ~ref_area.label,
y = ~SEX_M,
type = 'bar',
name = 'Male') %>%
add_trace( y = ~SEX_F,
name = 'Female') %>%
layout( title = list( text = "Global and regional unemployment rates by sex, 2017"),
xaxis = list( title = "",
tickfont = list(size = 12)
),
yaxis = list( title = "Percentage",
titlefont = list(size = 12),
tickfont = list(size = 12)
),
barmode = 'group',
annotations =
list( x = 0.1, y = -0.07,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
Facet distribution of Employment by economic class
require(Rilostat)
if(!require(ggplot2)){install.packages('ggplot2')}
if(!require(dplyr)){install.packages('dplyr')}
get_ilostat(id = 'EMP_2EMP_SEX_AGE_CLA_DT_A',
filters = list( ref_area = 'ZAF',
time = '1999',
sex = c('M', 'F'),
classif1 = c('Y15-24', 'YGE25'))) %>%
filter(!classif2 %in% 'CLA_ECOCLA_TOTAL') %>%
mutate( sex = factor(sex) %>% recode_factor(
'SEX_M' = 'Male',
'SEX_F' = 'Female'),
classif1 = factor(classif1) %>% recode_factor(
'AGE_YTHADULT_YGE25' = 'Adult',
'AGE_YTHADULT_Y15-24' = 'Youth'),
classif2 = factor(classif2) %>% recode_factor(
'CLA_ECOCLA_USDLT2' = '<US$1.9',
'CLA_ECOCLA_USDGE2LT3' = '>=US$1.9 & <US$3.2',
'CLA_ECOCLA_USDGE3LT5' = '>=US$3.2 & <US$5.5',
'CLA_ECOCLA_USDGE5' = '>=US$5.5')) %>%
ggplot(aes(y=obs_value, x=as.factor(classif1), fill=classif2)) +
geom_bar(stat="identity") +
facet_wrap(~as.factor(sex)) + coord_flip() +
theme(legend.position="top") +
labs(x="Age group", y="Distribution of economic class (%)", fill="Economic class : ",
title = "Employment by economic class, sex and age, South Africa, 1999") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_brewer(type = "div")
Bar chart of unemployment rate (Horizontal)
require(tidyverse)
require(plotly)
require(Rilostat)
ref_region <- c('World', 'South-Eastern Asia and the Pacific', 'Southern Asia', 'Eastern Asia', 'Latin America and the Caribbean', 'Sub-Saharan Africa', 'Central Asia', 'Arab States','Northern Africa', 'Northern America', 'Eastern Europe', 'Northern, Southern and Western Europe' )
code_region <- get_ilostat_toc(segment = 'ref_area') %>% filter(ref_area.label %in% ref_region)
test <- get_ilostat(id = 'UNE_2EAP_SEX_AGE_RT_A',
filters = list( ref_area = code_region$ref_area,
sex = c('SEX_M', 'SEX_F'),
classif1 = 'AGE_YTHADULT_YGE15',
time = '2017'),
cache = FALSE) %>%
mutate(ref_area.label = label_ilostat(ref_area, dic = 'ref_area') %>% factor(.,levels = ref_region)) %>%
arrange(obs_value) %>%
spread(sex, obs_value)
plot_ly( data = test ,
y = ~ref_area.label,
x = ~SEX_M,
type = 'bar',
name = 'Male',
orientation = 'h') %>%
add_trace( x = ~SEX_F,
name = 'Female') %>%
layout( title = list(text = "Global and regional unemployment rates by sex, 2017",
font = list(size = 18)),
yaxis = list( title = "",
tickfont = list(size = 12)
),
xaxis = list( title = "Percentage",
spikethickness = 30,
titlefont = list(size = 12),
tickfont = list(size = 12)
),
barmode = 'group',
margin = list(
t = 50, # top margin
r = 200, # right side margin
b = 50, # bottom margin
l = 400, # left side margin
pad = 5),
annotations =
list( x = 1.4, y = - 0.11,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
Histogram of Labour force
require(tidyverse)
require(plotly)
require(Rilostat)
ref_region <- c('World')
code_region <- get_ilostat_toc(segment = 'ref_area') %>% filter(ref_area.label %in% ref_region)
test_age <- c("AGE_5YRBANDS_Y15-19", "AGE_5YRBANDS_Y20-24", "AGE_5YRBANDS_Y25-29", "AGE_5YRBANDS_Y30-34", "AGE_5YRBANDS_Y35-39", "AGE_5YRBANDS_Y40-44", "AGE_5YRBANDS_Y45-49", "AGE_5YRBANDS_Y50-54", "AGE_5YRBANDS_Y55-59", "AGE_5YRBANDS_Y60-64")
X <- get_ilostat(id = 'EAP_2EAP_SEX_AGE_NB_A',
filters = list( ref_area = code_region$ref_area,
sex = c('SEX_T'),
classif1 = test_age,
time = '2017'),
cache = FALSE) %>%
mutate( ref_area.label = label_ilostat(ref_area, dic = 'ref_area') ,
classif1 = label_ilostat(classif1, dic = 'classif1') %>% str_split( pattern = ': ',simplify = TRUE) %>% .[,2] %>% as.factor,
obs_value = obs_value / 1000) %>%
select(classif1, obs_value)
plot_ly(X,
x = ~classif1,
y = ~obs_value,
type = 'bar') %>%
layout(title = list(text = "Global labour force by age band (ages 15 to 64), 2017",
font = list(size = 18)),
xaxis = list( title = "",
tickfont = list(size = 12)
),
yaxis = list( title = "Percentage",
titlefont = list(size = 12),
tickfont = list(size = 12)
),
annotations =
list( x = 1, y = - 0.07,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
World map of Youth unemployment rate
require(Rilostat)
if(!require(plotly)){install.packages('plotly')}
if(!require(dplyr)){install.packages('dplyr')}
if(!require(stringr)){install.packages('stringr')}
dat <- get_ilostat(id = 'UNE_2YAP_NOC_RT_A', filters = list(time = '2017')) %>%
filter(!str_sub(ref_area,1,1) %in% 'X') %>%
select(ref_area, obs_value) %>%
left_join( Rilostat:::ilostat_ref_area_mapping %>%
select(ref_area, ref_area_plotly)%>%
label_ilostat(code = 'ref_area'),
by = "ref_area") %>%
filter(!obs_value %in% NA)
dat %>%
plot_geo(
z = ~obs_value,
text = ~ref_area.label,
locations = ~ref_area_plotly
) %>%
add_trace(
colors = 'Blues',
marker = list(
line = list(
color = toRGB("grey"),
width = 0.5)
),
showscale = TRUE
) %>%
colorbar(
title = '%',
len = 0.5
) %>%
layout(
title = 'Youth unemployment rate in 2017 (ILO modelled estimates, Nov. 2018)',
geo = list(
showframe = FALSE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showcountries = TRUE,
resolution = 110), # or 50
annotations =
list( x = 1, y = 1,
text = "Source: ilostat",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="blue"))
)
Triangular graphs of Labour underutilization indicators
require(Rilostat)
if(!require(tidyr)){install.packages('tidyr')}
if(!require(dplyr)){install.packages('dplyr')}
if(!require(plotrix)){install.packages('plotrix')}
if(!require(stringr)){install.packages('stringr')}
triangle <- get_ilostat(id = c('EIP_WDIS_SEX_AGE_NB_A',
'UNE_TUNE_SEX_AGE_NB_A',
'TRU_TTRU_SEX_AGE_NB_A'),
filters = list(
ref_area = c('ZAF', 'MNG', 'CHL'),
source = 'BA',
sex = 'SEX_T',
classif1 = 'YGE15',
time = '2013'),
detail = 'dataonly',
cmd = "dat %>% select(ref_area, indicator, obs_value)",
quiet = TRUE) %>%
label_ilostat() %>%
group_by(ref_area.label) %>%
mutate(obs_value = obs_value / sum(obs_value)) %>% ungroup() %>%
mutate(indicator.label = indicator.label %>%
str_replace(fixed(' by sex and age (thousands)'), '')) %>%
spread(indicator.label, obs_value)
par(cex=0.75, mar=c(0,0,0,0))
positions <- plotrix::triax.plot(
as.matrix(triangle[, c(2,3,4)]),
show.grid = TRUE,
main = 'Selected labour underutilization indicators',
label.points= FALSE, point.labels = triangle$ref_area.label,
col.axis="gray50", col.grid="gray90",
pch = 19, cex.axis=1.2, cex.ticks=0.7, col="grey50")
ind <- which(triangle$ref_area.label %in% triangle$ref_area.label)
df <- data.frame(positions$xypos, geo = triangle$ref_area.label)
points(df$x[ind], df$y[ind], cex=2, col="red", pch=19)
text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5)
