gistfile1.txt

try <- dataset %>%
  dplyr::select(VO2cdi, Meting, ID) %>%
  drop_na() %>% # must drop NAs for clustering to work
  glimpse()
spread_try <- try %>%
  spread(ID,VO2cdi)  %>%
  glimpse()
VO2_cluster <- t(spread_try[-1])
VO2_cluster_dist <- dist(VO2_cluster , method="euclidean")  
fit <- hclust(VO2_cluster_dist, method="ward.D")   
fit
plot(fit, family="Arial")
rect.hclust(fit, k=4, border="cadetblue")
ggdendrogram(fit, rotate = TRUE, theme_dendro = FALSE) +
  theme_minimal() + xlab("") + ylab("")
clustered_data <- cutree(fit, k=4)
clustered_data_tidy <- as.data.frame(as.table(clustered_data)) %>% glimpse()
colnames(clustered_data_tidy) <- c("ID","cluster")
clustered_data_tidy$ID <- as.character(clustered_data_tidy$ID)
joined_clusters <- try %>%
  inner_join(clustered_data_tidy, by = "ID") %>%
  glimpse()
table(clustered_data_tidy$cluster)
cluster2<-joined_clusters %>%dplyr::filter(cluster==2) 
ggplot(cluster2, aes(Meting, VO2cdi)) +
  geom_line(color="grey") +
  theme_minimal() +
  ylab("VO2 cdi") + xlab("") +
  geom_smooth(method="auto",color="red", se=F, size=0.5) +
  facet_wrap(~ID)