add visualization - Githubissues

Example code to load cell profiler output, track cells and plot windrose plot to visualize migration pattern.

# define column name for track label
strata <- 'TrackObjects_Label'

# read cellprofiler output
profiles <- read_csv("../profiles/201903_testsequence/IdentifyPrimaryObjects.csv") %>% 
  mutate(Metadata_timepoint = as.numeric(Metadata_timepoint)) %>%
  group_by(TrackObjects_Label) %>% 

# summarize tracks 
tracks <- track(profiles, strata = strata,  t_var = "Metadata_timepoint"  ) 

# windrose plot 
plot_windrose(tracks)

plot_windrose <- function(population, title_name = "windrose plot", legend = "speed in pixel/frame", dir_res = 30, spd_res = 0.5){
  # rotate track angles 
  population <- tracks %>%
    filter(Track_Length > 19) %>%
    mutate(Track_Angle = Track_Angle + pi/2) %>% # rotate all angles by 90 degree or pi/2
    mutate(Track_Angle = ifelse(Track_Angle > pi, Track_Angle - 2*pi, Track_Angle) ) %>%
    mutate(Track_Angle = ifelse(Track_Angle < 0, Track_Angle + 2*pi, Track_Angle) ) 

  h1 <- plot.windrose(
    spd = population %>% extract2("Track_Speed"), 
    dir = (180 * (population$Track_Angle) / pi), 
    spdmin = 0, 
    spdmax = max(population$Track_Speed), 
    spdres = spd_res, 
    dirres = dir_res, 
    title_name =  title_name,
    scale_name = legend
  )
} 

plot_windrose(tracks)

Code for the complete windrose plot comes from following https://stackoverflow.com/questions/17266780/wind-rose-with-ggplot-r/17266781#17266781

# WindRose.R
#
# following https://stackoverflow.com/questions/17266780/wind-rose-with-ggplot-r/17266781#17266781
require(RColorBrewer)

plot.windrose <- function(data,
  spd,
  dir,
  spdres = 2,
  dirres = 30,
  spdmin = 2,
  spdmax = 20,
  spdseq = NULL,
  palette = "YlGnBu",
  countmax = NA,
  debug = 0,
  scale_name = "speed in µm/s",
  title_name = "Distribution of angle and speed"){

  # Look to see what data was passed in to the function
  if (is.numeric(spd) & is.numeric(dir)){
    # assume that we've been given vectors of the speed and direction vectors
    data <- data.frame(spd = spd,
      dir = dir)
    spd = "spd"
    dir = "dir"
  } else if (exists("data")){
    # Assume that we've been given a data frame, and the name of the speed 
    # and direction columns. This is the format we want for later use.    
  }  

  # Tidy up input data ----
  n.in <- NROW(data)
  dnu <- (is.na(data[[spd]]) | is.na(data[[dir]]))
  data[[spd]][dnu] <- NA
  data[[dir]][dnu] <- NA

  # figure out the wind speed bins ----
  if (missing(spdseq)){
    spdseq <- seq(spdmin,spdmax,spdres)
  } else {
    if (debug >0){
      cat("Using custom speed bins \n")
    }
  }
  # get some information about the number of bins, etc.
  n.spd.seq <- length(spdseq)
  n.colors.in.range <- n.spd.seq - 1

  # create the color map
  spd.colors <- colorRampPalette(brewer.pal(min(max(3,
    n.colors.in.range),
    min(9,
      n.colors.in.range)),                                               
    palette))(n.colors.in.range)

  if (max(data[[spd]],na.rm = TRUE) > spdmax){    
    spd.breaks <- c(spdseq,
      max(data[[spd]],na.rm = TRUE))
    spd.labels <- c(paste(c(spdseq[1:n.spd.seq-1]),
      '-',
      c(spdseq[2:n.spd.seq])),
      paste(spdmax,
        "-",
        max(data[[spd]],na.rm = TRUE)))
    spd.colors <- c(spd.colors, "grey50")
  } else{
    spd.breaks <- spdseq
    spd.labels <- paste(c(spdseq[1:n.spd.seq-1]),
      '-',
      c(spdseq[2:n.spd.seq]))    
  }
  data$spd.binned <- cut(x = data[[spd]],
    breaks = spd.breaks,
    labels = spd.labels,
    ordered_result = TRUE)
  # clean up the data
  data. <- na.omit(data)

  # figure out the wind direction bins
  dir.breaks <- c(-dirres/2,
    seq(dirres/2, 360-dirres/2, by = dirres),
    360+dirres/2)  
  # 
  dir.labels <- c(paste(360-dirres/2,"-",dirres/2),
    paste(seq(dirres/2, 360-3*dirres/2, by = dirres),
      "-",
      seq(3*dirres/2, 360-dirres/2, by = dirres)),
    paste(360-dirres/2,"-",dirres/2))

  #dir.labels <- c(paste(0),
  #  paste(seq(dirres/2, 360-3*dirres/2, by = dirres)),
  #"-",
  #seq(3*dirres/2, 360-dirres/2, by = dirres)),
  #  paste(360-dirres/2,"-",dirres/2))

  # assign each wind direction to a bin
  dir.binned <- cut(data[[dir]],
    breaks = dir.breaks,
    ordered_result = TRUE)
  levels(dir.binned) <- dir.labels
  data$dir.binned <- dir.binned

  # Run debug if required ----
  if (debug>0){    
    cat(dir.breaks,"\n")
    cat(dir.labels,"\n")
    cat(levels(dir.binned),"\n")       
  }  

  # deal with change in ordering introduced somewhere around version 2.2
  if(packageVersion("ggplot2") > "2.2"){    
    #cat("Hadley broke my code\n")
    data$spd.binned = with(data, factor(spd.binned, levels = rev(levels(spd.binned))))
    spd.colors = rev(spd.colors)
  }

  # create the plot ----
  p.windrose <- ggplot(data = data,
    aes(x = dir.binned,
      fill = spd.binned)) +
    geom_bar() + 
    scale_x_discrete(drop = FALSE,
      labels = waiver()) +
    coord_polar(start = -((dirres/2)/360) * 2*pi) +
    scale_fill_manual(name = scale_name, 
      values = spd.colors,
      drop = FALSE) +
    theme(axis.title.x = element_blank()) +
    labs(title = title_name)

  # adjust axes if required
  if (!is.na(countmax)){
    p.windrose <- p.windrose +
      ylim(c(0,countmax))
  }

  # print the plot
  print(p.windrose)  

  # return the handle to the wind rose
  return(p.windrose)
}

cells2numbers / migrationminer

add visualization #8