data2<-read_csv("data-Uvm9r.csv", skip=1, col_names = c("year","Other","Sales &amp commerce, transportation professions","Hotel industry, restaurants, personal services sector","Health, teaching, culture, science professions","Management, administration, banking &amp insurance, legal professions","Management, administration, banking & insurance, legal professions"
) )

data22 <- data2[-c(7)] %>% gather("type", "val", -year)

data22 %>%
  ggplot(aes(year, val, col=type)) +
  geom_point(data=filter(data22, year %in% c(min(year), max(year))), shape=18, size=6) +
  geom_smooth(se=FALSE) +
  geom_text(data = filter(data22, year==max(year)), aes(label=type, hjust=-.1)) +
  theme(panel.background = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1, color="gray" ),
        axis.line.x = element_line(color = "black"),
        axis.ticks = element_blank(),
        legend.position = "none") +

  scale_y_continuous(breaks = seq(0, 600000, 50000), limits=c(0,650000)) +
  scale_x_continuous(breaks = seq(1970, 2016, 5), limits = c(1970, 2060)) +
  #scale_x_discrete(breaks=NULL) +
  scale_colour_brewer(palette = "Reds") +
  ylab("") +
  xlab("") +
  labs (
    title = "The rise in the employment rate of women has mostly happened in higher-qualified sectors",
    subtitle = "Female labour participation in Switzerland by job cluster, 1970-2016",
    caption = "Source: Federal Statistical Office  Get the data"
  )