Use R to recreate contour plot made in Igor

Question

This contour plot, made with the Igor program, is popular in atmospheric chemistry and pollution studies:

I'm trying to recreate it with R for a friend who wants to stop using Igor, and we can't quite get it. Here's the dataset (the same data used to make the plot with Igor), and here's what I've got so far to make the plot with R:

# read in the data
dat <- read.csv("contour_plot_data.csv")

# focus on the untransformed values
dat <- dat[, 1:108]

# get Diameter value from col names
Diameter <- as.numeric(gsub("X", "", names(dat)[-1]))

# interpolate between the Diameter values for a smoother contour,
# a seperate interpolation for each row (date value)
# this takes a moment or two...

interp <-  seq(min(Diameter), max(Diameter), 0.2)
dat_interp <- data.frame(matrix(0, ncol =  length(interp), nrow = nrow(dat)))
for(i in 1:nrow(dat)){
  # get the values from row i
  vec <- unlist(dat[i, 2:108], use.names = FALSE)
  # compute loess interpolations
  lo <- loess(vec ~ Diameter)
  # predict interpolated values
  pr <- predict(lo, newdata = data.frame(Diameter = interp))
  # store in a data frame
  df <- data.frame(ct = unname(pr), Diameter = interp)
  # add as new row to new data frame
  dat_interp[i, ] <- df$ct
  print(i) # so we can see that it's working
}

# add date col and col names to the interpolated data
names(dat_interp) <- interp
dat_interp$date <- as.character(dat$Time)

# melt data into long format
# see http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/
library(tidyr)
gather_cols <- interp
dat_long <- gather_(dat_interp, "Diameter", "dN_dlogDp", gather_cols)

# we want diameter as a numeric
dat_long$Diameter <- as.numeric(as.character(dat_long$Diameter))
# we want date as a date format
x <-  as.character(dat_long$date)
date_ <- as.Date(x, format = "%d/%m/%Y") 
time_ <- gsub(" ", "", substr(x, nchar(x) - 4, nchar(x)))
dat_long$date_time <- as.POSIXct(paste0(date_, " ", time_))

# The Igor plot seems to use log dN_dlogDp values, so let's get those
dat_long$dN_dlogDp_log <- log10(dat_long$dN_dlogDp)
dat_long$dN_dlogDp_log <- ifelse(dat_long$dN_dlogDp_log == "NaN", 0, dat_long$dN_dlogDp_log)

# get on with plottong...
library(ggplot2)   
library(scales)

labels_breaks <- seq(0, max(Diameter), 100)
mytheme <- theme_bw(base_size = 14) +  theme(aspect.ratio = 1/4) 
ggplot(dat_long, aes(y = Diameter, x = date_time,  fill=dN_dlogDp_log)) +
  geom_raster(interpolate = TRUE)  +
  scale_fill_gradientn(name=expression(log(dN/dlogD[p])), colours = rainbow(7)) +
  scale_y_continuous(expand = c(0,0), breaks = labels_breaks ) +
  scale_x_datetime(expand = c(0,0), breaks = date_breaks("12 hours")) +
    ylab("Diameter (nm)") +
  xlab("Date and time") +
  mytheme

My plot could do with a little more finessing with labels and tick marks, etc. However, my main question is why my contour fill looks so different from the Igor plot. The scale seems reversed, and the interpolation looks very different.

How can I make my plot look more like the Igor plot?

Note that these other questions of mine are closely related to the task of recreating this plot:

And after I asked this question I have been keeping an updated gist of R code that combines details from the answers to these questions, and successfully replicates these plots (example output included in the gist). That gist is here: https://gist.github.com/benmarwick/9a54cbd325149a8ff405.

UPDATE I've now made a package that will produce these plots: https://github.com/benmarwick/smps

I'm not going to download anything from that site. Can you generate some example data perhaps? Reversing the color scale should be as easy as `scale_fill_gradientn(colours = rev(rainbow(7))`. For a faithful recreation you'll need to know the details of that colour scale. Transitions seems to very sharp though, so I'm not sure it is worth recreating in detail. I would use `scale_fill_viridis` from the `viridis` package. — Axeman, Mar 15 '16 at 16:12
What site would you download data from? I found that one suggested on meta. — Ben, Mar 15 '16 at 21:32
@Ben Hi, I'm learning your method.How to download your data? When I open your link it's just an upload page. — YQ.Wang, Feb 23 '17 at 11:41
@YQ.Wang Hi, yes, the data file are included in the package that I made, you can download if by following the instructions here: https://github.com/benmarwick/smps — Ben, Feb 24 '17 at 07:30

Ben · Accepted Answer · 2016-03-18T22:45:42.070

I can get a lot closer to the Igor plot using akima::interp instead of loess for the interpolation:

# read in the data
dat <- read.csv("contour_plot_data.csv")

# focus on the untransformed values
dat <- dat[, 1:108]

# get Diameter value from col names
Diameter <- as.numeric(gsub("X", "", names(dat)[-1]))

# melt data into long format
# see http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/
library(tidyr)
dat_long <- gather(dat, "Diameter", "dN_dlogDp", 2:108)

# we want diameter as a numeric
dat_long$Diameter <- as.numeric(gsub("X", "", dat_long$Diameter ))
# we want time as a date-formatted variable
x <-  as.character(dat_long$Time)
date_ <- as.Date(x, format = "%d/%m/%Y") 
time_ <- gsub(" ", "", substr(x, nchar(x) - 4, nchar(x)))
dat_long$Time <- as.POSIXct(paste0(date_, " ", time_))

# The Igor plot seems to use log dN_dlogDp values, so let's get those
dat_long$dN_dlogDp_log <- log10(dat_long$dN_dlogDp)
dat_long$dN_dlogDp_log <- ifelse(dat_long$dN_dlogDp_log == "NaN" |
                                   dat_long$dN_dlogDp_log == "-Inf"  , 0, dat_long$dN_dlogDp_log)


# interpolate between the  values for a smoother contour
# this takes a moment or two...

library(akima)
xo <- with(dat_long, seq(min(Time), max(Time), 120))
yo <- with(dat_long, seq(min(Diameter), max(Diameter), 0.5))
dat_interp <- with(dat_long, interp(Time, Diameter, dN_dlogDp_log, xo = xo, yo = yo) )

# get on with plotting...

# make into a data frame for ggplot
dat_interp_df <-  data.frame(matrix(data = dat_interp$z, ncol = length(dat_interp$y), nrow = length(dat_interp$x)))
names(dat_interp_df) <- dat_interp$y
dat_interp_df$Time <- as.POSIXct(dat_interp$x, origin = "1970-01-01")

# wide to long
dat_interp_df_long <- gather(dat_interp_df, "Diameter", "dN_dlogDp_log", 1:(ncol(dat_interp_df)-1))
dat_interp_df_long$Diameter <- as.numeric(as.character(dat_interp_df_long$Diameter))

# plot
library(ggplot2) 
library(scales)
y_labels_breaks <- seq(0, max(Diameter), 100)
ggplot(dat_interp_df_long, aes(y = Diameter, x = Time,  fill = dN_dlogDp_log)) +
  geom_raster(interpolate = TRUE)  +
  scale_fill_gradientn(name=expression(log(dN/dlogD[p])), colours = rev(rainbow(50))) +
  scale_y_continuous(expand = c(0,0), breaks = y_labels_breaks ) +
  scale_x_datetime(expand = c(0,0), breaks = date_breaks("1 day"))

But there is still quite a big difference in the colour mapping, with the Igor plot having wide bands with sharp boundaries, and my plot has fewer colour bands and fuzzy boundaries between them. So I guess I don't quite have the interpolation method that the Igor plot uses.

UPDATE after experimenting with a bunch of colour ramps, I've found a pretty good match in colorRamps::blue2green2red. I've also put a bit of effort here into fancy tickmarks:

# plot
library(ggplot2) 
library(scales) # for date_breaks
library(colorRamps) # for blue2green2red

# function for minor tick marks
every_nth <- function(x, nth, empty = TRUE, inverse = FALSE) 
{
  if (!inverse) {
    if(empty) {
      x[1:nth == 1] <- ""
      x
    } else {
      x[1:nth != 1]
    }
  } else {
    if(empty) {
      x[1:nth != 1] <- ""
      x
    } else {
      x[1:nth == 1]
    }
  }
}

# add tick marks every two hours
start_date <- min(dat_interp_df_long$Time)
end_date <-  max(dat_interp_df_long$Time)
date_breaks_2h <-  seq(from = start_date, to = end_date, by = "2 hours")
date_breaks_1_day <- seq(from = start_date, to = end_date, by = "1 day")
multiple <- length(date_breaks_2h) / length(date_breaks_1_day)

insert_minor <- function(major_labs, n_minor) {labs <- 
  c( sapply( major_labs, function(x) c(x, rep("", multiple) ) ) )
labs[1:(length(labs)-n_minor)]}


y_labels_breaks <- seq(0, max(Diameter), 100)
mytheme <- theme_bw(base_size = 14) +  theme(aspect.ratio = 1/5)
ggplot(dat_interp_df_long, aes(y = Diameter, x = Time,  fill = dN_dlogDp_log)) +
  geom_raster(interpolate = TRUE)  +
  scale_fill_gradientn(name=expression(log(dN/dlogD[p])), colours = blue2green2red(100)) +
  scale_y_continuous(expand = c(0,0), 
                     labels = every_nth(y_labels_breaks, 2, inverse = TRUE),
                     breaks = y_labels_breaks) +
  scale_x_datetime(expand = c(0,0), 
                   breaks=date_breaks_2h, 
                   labels=insert_minor(format(date_breaks_1_day, "%d %b"),  
                                       length(date_breaks_1_day))) +
  xlab("Day and time") +
  ylab("Diameter (nm)") +
  mytheme

The green-blue gradient is still a bit different from the Igor plot. I have very little green at all! Perhaps further experimentation with colour ramps might improve the match there.

To get the y-axis on a log scale, some additional effort is required. We have to use geom_rect and adjust the sizes of each rectangle to fit in the log scale:

##################  y-axis with log scale ###########################
# get visually diminishing axis ticks
base_breaks <- function(n = 10){
  function(x) {
    axisTicks(log10(range(x, na.rm = TRUE)), log = TRUE, n = n)
  }
}

# Now with log axis, we need to replace the ymin and ymax
distance <- diff((unique(dat_interp_df_long$Diameter)))/2
upper <- (unique(dat_interp_df_long$Diameter)) + c(distance, distance[length(distance)])
lower <- (unique(dat_interp_df_long$Diameter)) - c(distance[1], distance) 

# Create xmin, xmax, ymin, ymax
dat_interp_df_long$xmin <- dat_interp_df_long$Time - 1000 # default of geom_raster is 0.5
dat_interp_df_long$xmax <- dat_interp_df_long$Time + 1000
idx <- rle(dat_interp_df_long$Diameter)$lengths[1]
dat_interp_df_long$ymin <- unlist(lapply(lower, function(i) rep(i, idx)))
dat_interp_df_long$ymax <- unlist(lapply(upper, function(i) rep(i, idx)))


ggplot(dat_interp_df_long, aes(y = Diameter, x = Time, 
                               xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, 
                               fill = dN_dlogDp_log)) +
  geom_rect()  +
  scale_fill_gradientn(name=expression(log(dN/dlogD[p])), colours = blue2green2red(1000)) +
  scale_y_continuous(expand = c(0,0), 
                     trans = log_trans(), breaks = base_breaks()) +
  scale_x_datetime(expand = c(0,0), 
                   breaks=date_breaks_2h, 
                   labels=insert_minor(format(date_breaks_1_day, "%d %b"),  
                                       length(date_breaks_1_day))) +
  xlab("Day and time") +
  ylab("Diameter (nm)") +
  mytheme

UPDATE After some experimentation with colour ramps, I've found a pretty close match:

# adjust the colour ramp to match the Igor plot (their colour ramp is pretty uneven! lots of red and blue, it seems.)
colfunc <- colorRampPalette(c( rep("red", 3), 
                               rep("yellow", 1), 
                               rep("green", 2), 
                               "cyan", 
                               rep("blue", 3), 
                               "purple"))
y_labels_breaks <- seq(0, max(Diameter), 100)
mytheme <- theme_bw(base_size = 14) +  theme(aspect.ratio = 1/5)
ggplot(dat_interp_df_long, aes(y = Diameter, x = Time,  fill = dN_dlogDp_log)) +
  geom_raster(interpolate = TRUE)  +
  scale_fill_gradientn(name=expression(log(dN/dlogD[p])), colours = rev(colfunc(100))) +
  scale_y_continuous(expand = c(0,0), 
                     labels = every_nth(y_labels_breaks, 2, inverse = TRUE),
                     breaks = y_labels_breaks) +
  scale_x_datetime(expand = c(0,0), 
                   breaks=date_breaks_2h, 
                   labels=insert_minor(format(date_breaks_1_day, "%d %b"),  
                                       length(date_breaks_1_day))) +
  xlab("Day and time") +
  ylab("Diameter (nm)") +
  mytheme

The code from this post is also at https://gist.github.com/benmarwick/9a54cbd325149a8ff405

UPDATE I've now made a package that will produce these plots: https://github.com/benmarwick/smps

Use R to recreate contour plot made in Igor

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