## ----echo = FALSE, results = 'hide',   eval=TRUE, message = FALSE-------------

if(!require(Rilostat)){install.packages('stringr', repos='http://cran.us.r-project.org')}
require(Rilostat)


## ----eval=FALSE---------------------------------------------------------------
# install.packages("Rilostat")

## ----eval=FALSE---------------------------------------------------------------
# if(!require(devtools)){install.packages('devtools')}
# install_github("ilostat/Rilostat")

## ----echo=FALSE, eval=TRUE----------------------------------------------------
options(ilostat_quiet = TRUE)

## ----eval=TRUE----------------------------------------------------------------
toc <- get_ilostat_toc( quiet = TRUE)


## ----echo=FALSE, eval=TRUE, results = 'asis'----------------------------------
knitr::kable(head(toc[1:3,1:5]), caption = "Table 1a. Extract, 'Table of contents by indicator in English'")

## ----eval=TRUE----------------------------------------------------------------
toc <- get_ilostat_toc(segment = 'ref_area', lang = 'es', quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(toc[1:3,1:9]),caption = "Table 1b. Extract, 'Table of contents by ref_area in Spanish'")

## ----eval=TRUE----------------------------------------------------------------
toc <- get_ilostat_toc(search = 'bargaining', quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(toc[,1:8],caption = "Table 1c. 'Table of contents by indicator with search word 'bargaining''")

## ----eval=TRUE----------------------------------------------------------------
toc <- get_ilostat_toc(segment = 'ref_area', search = c('France|Albania', 'Annual'), 
											fixed = FALSE, quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(toc[1:2,1:9],caption = "Table 1d. 'Table of contents by ref_area with search words 'France' or 'Albania' and 'Annual''")

## ----eval=TRUE----------------------------------------------------------------
toc <-  dplyr::filter(get_ilostat_toc(), freq == 'M', quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(toc[1:5,1:5]),caption = "Table 1e. Extract, 'Table of contents by indicator filtered for Monthly Short term indicators'")

## ----eval=TRUE----------------------------------------------------------------
dat <- get_ilostat(id = 'UNE_2UNE_SEX_AGE_NB_A', segment = 'indicator', quiet = TRUE) 

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(dat[1:3,1:7]),caption = "Table 2a. Extract, 'Annual unemployment by sex and age, ILO modelled estimates, Nov. 2018'")

## ----eval=TRUE----------------------------------------------------------------
dat <- get_ilostat(id = 'ARM_A', segment = 'ref_area', quiet = TRUE) 


## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(dat[1:3, c(1:5,7:8)]),caption = "Table 2b. Extract, 'Armenia, annual data'")

## ----eval=TRUE----------------------------------------------------------------
dat <- get_ilostat(id = c('AFG_A', 'TTO_A'), segment = 'ref_area',quiet = TRUE)

dplyr::count(dat, ref_area)


## ----eval=TRUE----------------------------------------------------------------
toc <- get_ilostat_toc(search = 'CPI_', quiet = TRUE)

dat <- get_ilostat(id = toc, segment = 'indicator', quiet = TRUE) 

dplyr::count(dat, indicator)


## ----eval=TRUE----------------------------------------------------------------
dat <- get_ilostat(id = 'UNE_TUNE_SEX_AGE_NB_Q', time_format = 'num', quiet = TRUE) 

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(dat[1:3, 1:7],caption = "Table 3a. Extract, 'Quarterly unemployment by sex and age'")

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(dat[1:3, 1:7]),caption = "Table 3b. Extract, Monthly time-related underemployment by sex and age")

## ----eval=FALSE---------------------------------------------------------------
# dat <- get_ilostat(id = 'TRU_TTRU_SEX_AGE_NB_M', cache_dir = 'c:/temp', cache_format = 'dta', quiet = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# get_ilostat(id = get_ilostat_toc(search = 'SDG'), 	cache_dir = 'c:/temp', cache_format = 'dta',
# 													back = FALSE, quiet = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# options(ilostat_cache_dir = 'C:/temp')
# dat <- get_ilostat(id = 'UNE_DEAP_SEX_AGE_RT_A', filters = list(
# 													ref_area = c('BRA', 'ZAF'),
# 													sex = 'T',
# 													classif1 = '_Y15-24'), quiet = TRUE)
# dplyr::count(dat, ref_area, sex, classif1)

## ----echo =FALSE, eval=TRUE---------------------------------------------------
# options(ilostat_cache_dir = 'C:/temp')
dat <- get_ilostat(id = 'UNE_DEAP_SEX_AGE_RT_A', filters = list(
													ref_area = c('BRA', 'ZAF'), 
													sex = 'T', 
													classif1 = '_Y15-24'), quiet = TRUE)
dplyr::count(dat, ref_area, sex, classif1)

## ----eval=FALSE---------------------------------------------------------------
# options(ilostat_cache_dir = 'C:/temp')
# dat <- get_ilostat(id = 'UNE_DEAP_SEX_AGE_RT_A', filters = list(
# 													ref_area = c('BRA', 'ZAF'),
# 													sex = 'M',
# 													classif1 = '_YGE15'),
# 													quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(dat[1:2,]),caption = "Table 4a. Extract, Youth male unemployment rate in Brazil and South Africa (ILO modelled estimates, Nov. 2018), 'Series Key'")

## ----echo =FALSE, eval=TRUE---------------------------------------------------
# options(ilostat_cache_dir = 'C:/temp')
dat <- get_ilostat(id = 'UNE_DEAP_SEX_AGE_RT_A', filters = list(ref_area = c('BRA', 'ZAF'), 
													sex = 'F', 
													classif1 = '_YGE15'), 
													quiet = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# # options(ilostat_cache_dir = 'C:/temp')
# dat <- get_ilostat(id = 'UNE_DEAP_SEX_AGE_RT_A', filters = list(
# 													ref_area = c('BRA', 'ZAF'),
# 													sex = 'F',
# 													classif1 = '_YGE15'),
# 												 quiet = TRUE)

## ----echo=FALSE, eval=TRUE----------------------------------------------------
knitr::kable(head(dat[1:3,1:7]),caption = "Table 4b. Extract, Youth female unemployment rate in Brazil and South Africa (ILO modelled estimates, Nov. 2018)")

## ----eval=TRUE----------------------------------------------------------------
clean_ilostat_cache()


## ----eval=FALSE---------------------------------------------------------------
# require(Rilostat)
# require(ggplot2, quiet = TRUE)
# require(dplyr, quiet = TRUE)
# 
#   get_ilostat(id = 'EAP_DWAP_SEX_AGE_RT_A',
#               time_format = 'num',
#               filters = list( ref_area = c('FRA', 'USA', 'DEU'),
#                               sex = 'SEX_M',
# 							  classif1 = '_YGE15',
# 							  timefrom = 2005, timeto = 2019), quiet = TRUE)  %>%
#   distinct(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, 2005-2019') +
#   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))
# 
# 
# 

## ----eval=FALSE---------------------------------------------------------------
# require(Rilostat)
# if(!require(ggplot2)){install.packages('ggplot2')}
# if(!require(dplyr)){install.packages('dplyr')}
# 
#     get_ilostat(id = 'EMP_2EMP_SEX_AGE_CLA_NB_A',
#                 filters = list( ref_area = 'ZAF',
#                                 time = '2019',
#                                 sex = c('M', 'F'),
#                                 classif1 = c('Y15-24', 'YGE25')), quiet = TRUE) %>%
#     group_by(ref_area, source , indicator , sex  , classif1, time) %>%
# 	mutate(obs_value = obs_value / max(obs_value) * 100) %>%
# 	ungroup %>%
# 	filter(!classif2 %in% c('CLA_ECOCLA_TOTAL', 'CLA_ECOCLA_USDGE3')) %>%
# 	distinct(obs_value, sex, classif1, classif2) %>%
# 	label_ilostat() %>%
# 	
#     ggplot(aes(y=obs_value, x=as.factor(classif1.label), fill=classif2.label)) +
#     geom_bar(stat="identity") +
#     facet_wrap(~as.factor(sex.label)) + coord_flip() +
#     theme(legend.position="top") +
#     ggtitle("Employment by economic class, sex and age, South Africa, 2019") +
#     labs(x="Age group", y="Distribution of economic class (%)", fill="Economic class : ") +
# 	theme(plot.title = element_text(hjust = 0.5)) +
#     scale_fill_brewer(type = "div")
# 	

## ----eval=FALSE---------------------------------------------------------------
# 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"
#   ),
#   quiet = TRUE
# ) %>%
#   distinct(ref_area, indicator, obs_value) %>%   # replaces cmd
#   label_ilostat() %>%
#   group_by(ref_area.label) %>%
#   mutate(obs_value = obs_value / sum(obs_value)) %>%
#   ungroup() %>%
#   mutate(
#     indicator.label = str_replace(
#       indicator.label,
#       fixed(" by sex and age (thousands)"),
#       ""
#     )
#   ) %>%
#   pivot_wider(
#     names_from = indicator.label,
#     values_from = 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)
# 

## ----eval=FALSE---------------------------------------------------------------
# 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_2EAP_SEX_AGE_RT_A', filters = list(
# 								time = '2019',
# 								sex = 'SEX_T',
# 								classif1 = 'AGE_YTHADULT_Y15-24'
# 								), quiet = TRUE) %>%
# 			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 2019 (ILO modelled estimates, Nov. 2020)',
# 		  geo = list(	
# 					showframe = FALSE,
# 					showcoastlines = TRUE,
# 					projection = list(type = 'natural earth'),
# 					showcountries = TRUE,
# 					resolution = 110) # or 50
# 			)