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How to set fixed y-axis scale for certain rows of facet #494

Open aramcb opened 7 years ago

aramcb commented 7 years ago

I would like to produce a facet that looks like the one below:

image

Note that each row of the facet has a fixed-y but the scale differs for each row.

I have accomplished this by making "fake" data points which are invisible and are the bottom left and top right of each facet. It just seems a bit hacky and has magic numbers.

fake data points (made invisible with alpha = 0 when plotted with ggplot later

df_scale_setter_max <- data.frame(strain = rep(unique(df_group.tidy$strain),5), 
                              variable = c("spd","mag","prob","dist", "dura"),
                              value = c(0.4,1.0000005,1.0000005,1.15, 3.5),
                              train_or_test = "train",
                              tap_num = 30
                              )

df_scale_setter_min <- data.frame(strain = rep(unique(df_group.tidy$strain),5), 
                              variable = c("spd","mag","prob","dist", "dura"),
                              value = c(0,0,0,0, 0),
                              train_or_test = "train",
                              tap_num = 1
)

data-frame Sorry for how big it is (plz bear with me when highlighting)

df_group.tidy <- structure(list(strain = c("DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", 
"DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "DA1371", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", "N2", 
"N2", "N2", "N2", "N2", "N2", "N2"), variable = c("dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dist", "mag", "spd", 
"dura", "prob", "dist", "mag", "spd", "dura", "prob", "dist", 
"mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob", "dist", "mag", "spd", "dura", 
"prob", "dist", "mag", "spd", "dura", "prob", "dist", "mag", 
"spd", "dura", "prob", "dist", "mag", "spd", "dura", "prob", 
"dist", "mag", "spd", "dura", "prob"), avg = c(2.87333333333333, 
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0.0166468807604684, 0.0134964428195558, 0.00705010237389504, 
0.0510574569022532, 0.0298419397286911, 0.0182034085500556, 0.0137269505316061, 
0.00697942424759759, 0.0631805902334611, 0.0291429616790544, 
0.0150466729499882, 0.0109322132412112, 0.00756199894251764, 
0.0704999895085812, 0.0285185452628927, 0.0180246396118229, 0.011506069726699, 
0.00748234461044907, 0.0677414157534667, 0.0257604332979408, 
0.0160242346049525, 0.0102438265912977, 0.00842853191549877, 
0.0622360937153574, 0.0300966041039866, 0.0196683086935279, 0.00916280590461734, 
0.00861731504075017, 0.0670064908981601, 0.029597401877196, 0.0172528104219464, 
0.00965819885429942, 0.00766730136187936, 0.0677005239768908, 
0.0249130496745065, 0.0159143212642505, 0.00952843128416083, 
0.00671177248850447, 0.0702521241440112, 0.0310376997927253, 
0.0143282338068468, 0.0086844378101734, 0.00789255770980658, 
0.0858698960827043, 0.0328477466756521, 0.0146998711905156, 0.0066063276191937, 
0.00888107006107158, 0.0801569466401669, 0.0305521925499406, 
0.0171749663014406, 0.00756472237294103, 0.00648096255569042, 
0.0753326351401553, 0.0329934710445946)), .Names = c("strain", 
"variable", "avg", "SEM"), row.names = c(NA, -300L), class = "data.frame")

plot 

(scatter_raw_curves <- df_group.tidy %>% 
    ggplot(mapping = aes (x = tap_num, y = avg, color = train_or_test)) +
    geom_point(size = 0.5) +
    geom_line(aes(group = train_or_test), size = 0.95) +
    geom_pointrange(mapping = aes(x = tap_num, 
                                  ymin = avg - SEM, 
                                  ymax = avg + SEM)) +
    geom_point(data = df_scale_setter_max, aes(x = tap_num, y = value), alpha = 0) +
    geom_point(data = df_scale_setter_min, aes(x = tap_num, y = value), alpha = 0) +
    theme(panel.background = element_blank()) +
    theme(text = element_text(size = 20),
          axis.line = element_line(colour = "black")) +
    ggtitle("") +
    theme(plot.title = element_text(size = 30, hjust = 0.5, face = "bold"),
          axis.text = element_text(size = 40),
          strip.text = element_text(size = 40),
          axis.title.y = (element_text(size =60))
    ) +
    labs(colour = "",
         y = "average",
         x = "tap #")+ 
    facet_wrap(~ variable + strain, 
               scales = "free_y",
               ncol = length(unique(df_group.tidy$strain)) #sets col num to #of strains
               )

)

# I am wondering if someone has a more elegant solution?

ghost commented 7 years ago

I don't have an answer, but your question is similar to this one: https://stackoverflow.com/questions/33444013/ggplot2-facet-wrap-fixed-y-scale-for-each-row-free-scale-between-rows

Looks like the solution offered was pretty similar, although the idea of setting the alpha to 0 is a good one.

aramcb commented 7 years ago

@philstraforelli Aw yes it seems like a very similar strategy. I find, however, that even when I set my fake data points, the freey scale only sets the scale to within the vicinity_ making it even more frustrating. (that is, if I set an invisible point to 1.00, the scale that plots is 1.2)