Is there a way to produce multiple x-y scatterplots at once based on grouping value, ordered by a th-CodePudding

I have multi-level data. The group level is individual persons, which are designated by id. The variable index indicates different time points. Is there a way to make a separate scatterplot (x vs. y) for each individual, all displayed in the same output, and ordered based on a third variable (z)? If so, can color then be added to indicate degree of third variable (z)? Data below, Thanks.

> dput(dat1.1)
  structure(list(id = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
  3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
  4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
  5L, 5L, 5L, 5L, 5L, 5L), index = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 
  8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
  1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
  15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
  9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 
  2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
  16L, 17L, 18L, 19L, 20L), x = c(7.443917, 7.520429, 7.446833, 
  8.07893, 8.534033, 8.263931, 7.598647, 6.902987, 7.672617, 7.739256, 
  7.591341, 8.101125, 7.811751, 6.596834, 6.637652, 8.467165, 7.835399, 
  6.500149, 7.083198, 7.531798, 6.110208, 6.368534, 5.26318, 6.735778, 
  5.580152, 5.460161, 5.844303, 6.258181, 7.191627, 5.105033, 6.760193, 
  5.857215, 5.866264, 6.769086, 6.547294, 5.623804, 4.675815, 6.153901, 
  6.040519, 6.236045, 8.216397, 6.097841, 5.491311, 5.831432, 6.297337, 
  6.655688, 5.553445, 6.37449, 6.271961, 6.959645, 7.080341, 6.46092, 
  6.476955, 7.221111, 6.219023, NA, NA, NA, NA, NA, 8.21752, 7.589581, 
  8.363739, 8.849697, 7.78645, 7.494006, 7.827766, 9.11352, 7.80884, 
  6.701855, 6.259061, 5.523358, 6.186617, 6.548538, 6.6937, 7.213297, 
  5.243428, 7.510827, 7.054297, 7.603241), y = c(106L, 114L, 50L, 
  50L, 56L, 46L, 50L, 52L, 114L, 50L, 56L, 26L, 48L, 52L, 48L, 
  54L, 54L, 56L, 52L, 50L, 84L, 86L, 88L, 86L, 82L, 84L, 88L, 84L, 
  86L, 84L, 86L, 86L, 84L, 84L, 88L, 88L, 88L, 84L, 86L, 120L, 
  106L, 168L, 116L, 56L, 108L, 68L, 68L, 70L, 74L, 76L, 76L, 76L, 
  72L, 70L, 118L, NA, NA, NA, NA, NA, 60L, 62L, 52L, 90L, 50L, 
  50L, 54L, 56L, 52L, 30L, 78L, 30L, 52L, 54L, 52L, 80L, 86L, 46L, 
  54L, 84L), z = c(33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 
  33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 54L, 54L, 
  54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 
  54L, 54L, 54L, 54L, 54L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 
  56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 50L, 
  50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 
  50L, 50L, 50L, 50L, 50L, 50L)), class = "data.frame", row.names = c(NA, 
  -80L))

CodePudding user response：

Does this come close to giving you what you want?

library(tidyverse)

d %>% 
  group_by(id) %>% 
  mutate(z=as.factor(z)) %>% 
  group_map(
    function(.x, .y) {
      .x %>% 
        ggplot()  
          geom_point(aes(x=x, y=y, colour=z))  
          facet_wrap(vars(z))  
          scale_colour_manual(drop=FALSE, values=d %>% distinct(z) %>% pull(z))   
          labs(title=.x$id[1])
    },
    .keep=TRUE
  )

Points to note:

group_map applies a function to each group of a grouped data frame. .x refers to the data in the current group, .y is a one row tibble defining the group. .keep requests that the grouping variables are kept in .x.
drop=FALSE in the call to scale_colour_manual() ensures that unused factor levels are retained in the legend (and hence different levels of z are distinguishable between plots).