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I have time series data of one month plotted day-wise as enter image description here

Notice every day follows a different pattern. Now, I want to show this "diversity in pattern" of each day in much compact form in a research paper. What are the different ways/options of representing this in compact form using R.

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  • $\begingroup$ Are you thinking visual representation, or mathematical? If it's the former, look at sparklines. If it's the latter, look at PCA; the PCA coefficients will give you a succinct summary. $\endgroup$
    – Emre
    Feb 8 '16 at 6:05
  • $\begingroup$ Thanks @Emre for pointing to sparklines, but sparklines still take enough space on paper,i.e, thirty rows (for each day). I want something more compact. $\endgroup$ Feb 8 '16 at 6:17
  • $\begingroup$ How about 2D PCA? Each time series will be represented as a point on one chart. $\endgroup$
    – Emre
    Feb 8 '16 at 7:40
  • $\begingroup$ Sparklines is a good idea, maybe just comma-seperated next to each other (depending on what you wanna show) - just like flowing text, Or plot several lines in one chart - maybe 4 to 5 lines per 7 charts if you aggregate by weekday. But I guess you already tried that... $\endgroup$
    – lukeA
    Feb 8 '16 at 9:08
  • $\begingroup$ Difficult to say but if you might have expected periodicity then Panel of 4 (and 1 of 3), with 7 colours for 7 days would be compact and show/not show the relation? You would also want to do something like ARIMA plot? $\endgroup$ Feb 8 '16 at 14:28
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Simulate some data:

library(ggplot2)
library(purrr)
library(ggthemes)

days <- seq(as.Date("2015-08-01"), as.Date("2015-08-31"), by="1 day")
hours <- sprintf("%02d", 0:23)

map_df(days, function(x) {
  map_df(hours, function(y) {
    data.frame(day=x, hour=y, val=sample(2500, 1), stringsAsFactors=FALSE)
  })
}) -> df

Check it:

ggplot(df, aes(x=hour, y=val, group=day)) +
  geom_line() +
  facet_wrap(~day) +
  theme_tufte(base_family="Helvetica") +
  labs(x=NULL, y=NULL)

enter image description here

Since you're only trying to convey the scope of the variation, perhaps use a boxplot of the values of hours across days?

ggplot(df, aes(x=hour, y=val)) +
  geom_boxplot(fill="#2b2b2b", alpha=0.25, width=0.75, size=0.25) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_continuous(expand=c(0,0)) +
  coord_flip() +
  theme_tufte(base_family="Helvetica") +
  theme(axis.ticks=element_blank()) +
  labs(x=NULL, y=NULL)

enter image description here

That can be tweaked to fit into most publication graphics slots and the boxplot shows just how varied each day's readings are.

You could also use boxplot.stats to get the summary data and plot it on a line chart:

library(dplyr)
library(tidyr)

bps <- function(x) {
  cnf <- boxplot.stats(x)$conf
  data.frame(as.list(set_names(cnf, c("lwr", "upr"))), mean=mean(x))
}

group_by(df, hour) %>% 
  do(bps(.$val)) %>% 
  ggplot(aes(x=hour, y=mean, ymin=lwr, ymax=upr, group=1)) +
  geom_ribbon(fill="#2b2b2b", alpha=0.25) +
  geom_line(size=0.25) +
  theme_tufte(base_family="Helvetica") +
  theme(axis.ticks=element_blank()) +
  labs(x=NULL, y=NULL)

enter image description here

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Two alternative way are a density plot for each hour and level plot of hour and factorized value.

I'm showing ganarated data with two different unified distribution (for day and night hours)

Densityplot

library(lattice)
xy <- densityplot(~ value,  groups = hour ,   
plot.points=FALSE, 
data =  df,   
scales=list(x=list(rot=90, cex= .9 ),y=list(cex=.9)),par.strip.text=list(cex=.8),   
ylab="density", xlab="value", main=paste(  "DensityPlot"  ) )
print (xy)

enter image description here

Levelplot

library(RColorBrewer)
xy <- levelplot(cnt ~    hour + value,   
 col.regions=colorRampPalette(brewer.pal(9,"YlOrRd"))(16) ,
data =  df,   
scales=list(x=list(rot=90, cex= .9 ),y=list(cex=.9)),par.strip.text=list(cex=.8),   
ylab="value", xlab="hour", main=paste( "LevelPlot"  ) )
print (xy)

enter image description here

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