SCB Statistics Sweden

Statistics Sweden Data

baseURL <- "http://api.scb.se/OV0104/v1/doris/en/ssd"
## flow <- "NR0103ENS2010T08A"
## path <- as.character(scb_tables$Path[scb_tables$ID==flow])
flow <- "LE0101F73"
path <- "LE/LE0101/LE0101F"
req.uri <- file.path(baseURL, path, flow)
bottom_node <- pxweb::get_pxweb_metadata(req.uri)
dims <- pxweb::get_pxweb_dims(bottom_node)
## dims_list <- as.list(rep("*", length(names(dims))))
## names(dims_list) <- names(dims)
dims_list <- lapply(dims, function(x) sample(x$values, min(length(x$values), 3)))
pxweb_data <-                           # retrieve data
  pxweb::get_pxweb_data(
           url = req.uri,
           dims = dims_list,
           clean = TRUE)
knitr::kable(pxweb_data[1:10,])

type of activity	how often during the last 12 months	level of education	sex	period	observations	values
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2008-2009	Proportion of persons in percent	3.6
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2010-2011	Proportion of persons in percent	2.7
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2012-2013	Proportion of persons in percent	2.8
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2008-2009	Estimated numbers in thousands	277.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2010-2011	Estimated numbers in thousands	207.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2012-2013	Estimated numbers in thousands	223.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2008-2009	Margin of error, Estimated numbers in thousands	24.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2010-2011	Margin of error, Estimated numbers in thousands	22.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men and women	2012-2013	Margin of error, Estimated numbers in thousands	24.0
used the Internet in spare time to search for something, download things, look at pictures or listen to music	a few times a month	total	men	2008-2009	Proportion of persons in percent	2.9

library(ggplot2)

unique(pxweb_data$observations)

## [1] Proportion of persons in percent               
## [2] Estimated numbers in thousands                 
## [3] Margin of error, Estimated numbers in thousands
## 3 Levels: Estimated numbers in thousands ...

## unique(pxweb_data$`type of activity`)
## dput(unique(pxweb_data$`how often during the last 12 months`))

data_plot <- pxweb_data

data_plot$`how often during the last 12 months` <-
  factor(data_plot$`how often during the last 12 months`,
         levels =
           ## c(
           ##   "not at all",
           ##   "a few times during the last three months or less often", 
           ##   "every day (by and large)"
           ## )
           ## c(
           ##   "a few times during the last three months or less often",
           ##   "every week but not every day",
           ##   "several times a week or every day"
           ## )
           c(
             "not at all",
             "several times a week or every day",
             "every day (by and large)"
           )
         )

p <-
  data_plot %>%
  filter((sex %in% c("men and women"))) %>%
  filter(!(`level of education` %in% c("total"))) %>%
  ## filter(observations %in% c("Estimated numbers in thousands")) %>%
  filter(observations %in% c("Proportion of persons in percent")) %>%
  ggplot(aes(x = period, y = values, fill = `how often during the last 12 months`)) +
  ## geom_point() +
  geom_bar(position = "stack", stat = "identity", color = "black") +
  facet_grid(`level of education` ~ `type of activity`) +
  scale_fill_manual(values = c("black", "transparent", "#F92672")) +
scale_x_discrete(name = NULL, expand = c(0,0)) +
  scale_y_continuous(name = "Percent", expand = c(0,0)) +
  theme_bw() +
  theme(legend.position = "top")
p