R/plot1d.R

In interca: Multiple Correspondence Analysis Based on Interpretive Coordinates

plot1d <- function(res, dim=1) {
    e1 <- as.vector(res$ecoords[,dim])
  n <- length(e1)
    avge <- mean(abs(e1))
   lbl <- res$lbl[abs(e1)> avge]
  e1 <- round(e1[abs(e1)> avge],2)

  #first interpretive axis
  ye1 <- rep(0,length(e1))

  auto_mca_tablee1 <- data.frame(cbind(e1,ye1,lbl))

  auto_mca_tablee1$e1 <- as.numeric(auto_mca_tablee1$e1)
  auto_mca_tablee1$ye1 <- as.numeric(auto_mca_tablee1$ye1)
  auto_mca_tablee1$lbl <- as.factor(auto_mca_tablee1$lbl)
  auto_mca_tablee1 <- auto_mca_tablee1 %>% filter(abs(e1)>avge)


  interpretive1 <- auto_mca_tablee1%>%
    ggplot() +
    aes(x = e1, y = ye1) +
    geom_point(shape = "circle", size = 1, colour = "#B22222") +
    labs(x = paste("Interpretive axis",dim), y="")+theme(axis.text.y=element_blank(),
     axis.ticks.y=element_blank())+geom_hline(yintercept = 0)+geom_vline(xintercept = 0)+
    geom_label_repel(
      aes(label = paste(lbl)),
      size = 6.5,max.overlaps = Inf,
      point.padding = 0.9,
      min.segment.length = 0.6,
      box.padding = 0.1
    )

  interpretive1<- interpretive1+geom_vline(color="red",xintercept = avge)+
    geom_vline(xintercept = -avge,color='red')

  interpretive1
}