r - Creating a curve to fit x-y data where X is categorical -


i've got dataset of diving behavior tagged animals, , i'm struggling fit curve data, think because x variable in case categorical, , not continuous data. let me give bit of background:

my dataset has 184 observations of 14 variables: 

      tagid    ddmmyy hour.gmt.hour.local.  x0   x3   x10   x20   x50    x100   x150  x200  x300  x400 1     122097   250912     0            9   0.0  0.0   0.3  12.0   15.3   59.6   12.8  0.0    0    0 2     122097   260912     0            9   0.0  2.4   6.9  5.5    13.7   66.5   5.0   0.0    0    0 3     122097   260912     6            15  0.0  1.9   3.6  4.1    12.7   39.3   34.6  3.8    0    0 4     122097   260912     12           21  0.0  0.2   5.5  8.0    18.1   61.4   6.7   0.0    0    0 5     122097   280912     6            15  2.4  9.3   6.0  3.4    7.6    21.1   50.3  0.0    0    0 6     122097   290912     18           3   0.0  0.2   1.6  6.4    41.4   50.4   0.0   0.0    0    0

the variables i'm interested in x0:x400. these depth bins, , values represent percent of total time period of day animal spent in depth bin. on first line, spent 0% of time between 0-3meters, 59.6% of time between 100-150 meters, etc. bit of answers last question here on stackoverflow, calculated mean % time spent in each depth bin animal:

diving.means <- colmeans(diving[, -(1:4)]) lowerintervalbound <- gsub("x", "", names(diving)[-(1:4)]) lowints <- as.numeric(lowerintervalbound) plot(x=factor(lowints), y=diving.means, xlab="depth bin (meters—lower bound)", ylab="% time spent")

which provided me plot:

enter image description here

unfortunately because data means (a single value), , not frequencies, couldn't figure out how plot them histogram... that's neither here nor there, can input these values , make desired plot if necessary.. trick analytically now.

now i've got multiple animals , different time bins i'd compare. i'll work out system weight time spent in bins average depth compare statistically, want compare them visually, qualitatively, produce plots can use in presentations , publications. i'd create density curve representing 'histogram,' , plot curves multiple scenarios on single plot compare. however, can't seem make work density() function, don't have frequency data. sort of have densities calculated already, % time spent in each bin.. they're not represented in raw format in dataset frequencies of categories, can make histograms , density curves out of.

this how data look:

> diving.means           x0           x3          x10          x20          x50         x100         x300         x400           x150         x200   3.330978261  3.299456522  8.857608696 17.646195652 30.261413043 29.356521739  6.445108696  0.664130435    0.135869565  0.001630435

or: 

> df<-data.frame(lowints, diving.means) > df  lowints diving.means x0         0  3.330978261 x3         3  3.299456522 x10       10  8.857608696 x20       20 17.646195652 x50       50 30.261413043 x100     100 29.356521739 x150     150  6.445108696 x200     200  0.664130435 x300     300  0.135869565 x400     400  0.001630435

and produce looks more or less (pulled randomly publication—axes unrelated data):

enter image description here

and able isolate curves , plot them together.

thanks can provide!

you have frequencies, hist cannot used. can use plot spline interpolation density:

df <- read.table(text=" lowints diving.means x0         0  3.330978261 x3         3  3.299456522 x10       10  8.857608696 x20       20 17.646195652 x50       50 30.261413043 x100     100 29.356521739 x150     150  6.445108696 x200     200  0.664130435 x300     300  0.135869565 x400     400  0.001630435")  require(splines) dens <-predict(interpspline(df[,1], df[,2])) plot(df[,1], df[,2], type="s", ylim=c(0,40)) lines(dens, col="red",lwd=2)

enter image description here


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