How can I generate a GG Diagram in R Studio?

General
,
13

The simple answer is that @ron's example code will draw the same type of diagram. The challenge comes in tweaking the details. It's a little hard to make out what's going on in the original plot due to the resolution of the screenshot being low (can you possibly get a higher-res screenshot?), but what I see:

  1. The g values may have been rescaled or normalized
  2. The four plots have different x and y axis limits, and different main panel sizes. This probably means they were created independently of each other, and then composed into this grid as a separate step
  3. I have no idea what the colors represent. Is there a caption or legend with the original plot that might explain it? Without knowing this, it's hard to guess how to recreate the effect.
  4. The lines are very smooth and loopy — maybe tree branches accelerate this way (?), but I wonder if the data has been smoothed somehow.

I found some accelerometer data to play with, and so far this is the best I've got. Here, the colors just indicate the order in which the readings were taken (earliest = gray, mid = blue, latest = black).

library(tidyverse)

# Data downloaded from:
#   https://archive.ics.uci.edu/ml/machine-learning-databases/00287/Activity%20Recognition%20from%20Single%20Chest-Mounted%20Accelerometer.zip
# Metadata:
#   https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer

file_list <- list.files(
  "Activity Recognition from Single Chest-Mounted Accelerometer/", 
  pattern = "\\.csv$",
  full.names = TRUE
)

accel <- map_dfr(
  file_list,
  read_csv,
  col_names = c("id", "x", "y", "z", "activity"),
  col_types = "nnnnn",
  .id = "participant"
) %>% 
  mutate(
    activity = factor(
      activity, 
      labels = c(
        "Working at Computer",
        "Standing Up, Walking and Going Up/Down Stairs",
        "Standing",
        "Walking",
        "Going Up/Down Stairs",
        "Walking and Talking with Someone",
        "Talking while Standing"
      ),
      exclude = "0"
    ),
    participant = factor(participant, levels = as.character(1:15))
  ) %>% 
  filter(!is.na(activity))

hz <- 52 # frequency of readings
three_mins <- (3 * 60) * hz

# Filter data to manageable subset and prep for plotting
accel_3min <- accel %>%
  filter(participant %in% c(1:4), activity == "Standing") %>%
  group_by(participant) %>%
  slice(1:three_mins) %>%
  mutate(
    x = scales::rescale(x, to = c(-1, 1)),
    y = scales::rescale(y, to = c(-1, 1)),
    z = scales::rescale(z, to = c(-1, 1)),
    id = row_number()
  )

# Color first third of readings lightest color, second third mid color,
# last third darkest color
accel_3min %>% 
  ggplot() +
  geom_path(aes(x = y, y = z, colour = cut(id, 3)), alpha = 0.8, size = 0.5) +
  facet_wrap( ~ participant, labeller = label_both) + 
  coord_fixed() +
  scale_color_manual(
    values = c("gray80", "dodgerblue", "black"),
    guide = "none"
  ) + 
  theme_bw() + 
  labs(x = expression(a[y]), y = expression(a[z]))

TA8QBWJ.png860×503 100 KB

If anybody else wants to play with this, I put my attempt in a shared RStudio Cloud project:

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