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Plotting a datable with multiple columns (all 1:7 rows) via ggplot with a single geom_point() using aesthetics to color them differently
algorithm$algorithm <- 1:nrow(algorithm)
ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") +
scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
library(ggplot2)
library(microbenchmark)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)
# make a vector with your different conditions
conds <- c('f1', 'f2')
# initalizing a table from the getgo is much cleaner
# than doing everything in separate variables
dat <- data.frame(
datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
cond = rep(NA, l*length(conds))
)
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)
for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative
# I don't have f1 & f2 so I did what I could...
s <- summary(microbenchmark(
"f1" = rpois(datasetsizes[i],10),
"f2" = {length(rpois(datasetsizes[i],10))}))
dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}
dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) +
geom_point(aes(color = cond)) +
geom_line(aes(color = cond)) + # added to see a clear difference btw conds
labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') +
scale_y_continuous(trans = 'log10')
-----------------------
algorithm$algorithm <- 1:nrow(algorithm)
ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") +
scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
library(ggplot2)
library(microbenchmark)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)
# make a vector with your different conditions
conds <- c('f1', 'f2')
# initalizing a table from the getgo is much cleaner
# than doing everything in separate variables
dat <- data.frame(
datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
cond = rep(NA, l*length(conds))
)
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)
for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative
# I don't have f1 & f2 so I did what I could...
s <- summary(microbenchmark(
"f1" = rpois(datasetsizes[i],10),
"f2" = {length(rpois(datasetsizes[i],10))}))
dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}
dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) +
geom_point(aes(color = cond)) +
geom_line(aes(color = cond)) + # added to see a clear difference btw conds
labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') +
scale_y_continuous(trans = 'log10')
how to add max(or min) fitness halting condition to multiprocessing deap python genetic algorithm
import multiprocessing
pool = multiprocessing.Pool()
toolbox.register("map", pool.map)
QUESTION
Plotting a datable with multiple columns (all 1:7 rows) via ggplot with a single geom_point() using aesthetics to color them differently
Asked 2019-Dec-05 at 10:21I intend to compare timings between two algorithm-based functions f1,f2 via microbenchmark which work on a rpois simulated dataset with sizes of: [1:7] vector given by 10^seq(1,4,by=0.5) i.e. :
[1] 10.00000 31.62278 100.00000 316.22777 1000.00000 3162.27766 10000.00000
Am working on to plot them as well, with all of the information required from microbenchmark (i.e. min,lq,mean,median,uq and max - yes all of them are required, except for expr and neval). I require this via ggplot on a log-log scale with a single geom_point() and aesthetics with each of the information being of different colours and here is my code for that:
library(ggplot2)
library(microbenchmark)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
f1_min<-integer(length(datasetsizes))
f1_lq<-integer(length(datasetsizes))
f1_mean<-integer(length(datasetsizes))
f1_median<-integer(length(datasetsizes))
f1_uq<-integer(length(datasetsizes))
f1_max<-integer(length(datasetsizes))
f2_min<-integer(length(datasetsizes))
f2_lq<-integer(length(datasetsizes))
f2_mean<-integer(length(datasetsizes))
f2_median<-integer(length(datasetsizes))
f2_uq<-integer(length(datasetsizes))
f2_max<-integer(length(datasetsizes))
for(loopvar in 1:(length(datasetsizes)))
{
s<-summary(microbenchmark(f1(rpois(datasetsizes[loopvar],10), max.segments=3L),f2(rpois(datasetsizes[loopvar],10), maxSegments=3)))
f1_min[loopvar] <- s$min[1]
f2_min[loopvar] <- s$min[2]
f1_lq[loopvar] <- s$lq[1]
f2_lq[loopvar] <- s$lq[2]
f1_mean[loopvar] <- s$mean[1]
f2_mean[loopvar] <- s$mean[2]
f1_median[loopvar] <- s$median[1]
f2_median[loopvar] <- s$median[2]
f1_uq[loopvar] <- s$uq[1]
f2_uq[loopvar] <- s$uq[2]
f1_max[loopvar] <- s$max[1]
f2_max[loopvar] <- s$max[2]
}
algorithm<-data.table(f1_min ,f2_min,
f1_lq, f2_lq,
f1_mean, f2_mean,
f1_median, f2_median,
f1_uq, f2_uq,
f1_max, cdpa_max, datasetsizes)
ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
I debug my code at each step and uptil the assignment of computed values to a datatable by the name of 'algorithm' it works fine. Here are the computed runs which are passed as [1:7]vecs into the data table along with datasetsizes (1:7 as well) at the end:
> algorithm
f1_min f2_min f1_lq f2_lq f1_mean f2_mean f1_median f2_median f1_uq f2_uq f1_max f2_max datasetsizes
1: 86.745000 21.863000 105.080000 23.978000 113.645630 24.898840 113.543500 24.683000 120.243000 25.565500 185.477000 39.141000 10.00000
2: 387.879000 52.893000 451.880000 58.359000 495.963480 66.070390 484.672000 62.061000 518.876500 66.116500 734.149000 110.370000 31.62278
3: 1608.287000 341.335000 1845.951500 382.062000 1963.411800 412.584590 1943.802500 412.739500 2065.103500 443.593500 2611.131000 545.853000 100.00000
4: 5.964166 3.014524 6.863869 3.508541 7.502123 3.847917 7.343956 3.851285 7.849432 4.163704 9.890556 5.096024 316.22777
5: 23.128505 29.687534 25.348581 33.654475 26.860166 37.576444 26.455269 37.080149 28.034113 41.343289 35.305429 51.347386 1000.00000
6: 79.785949 301.548202 88.112824 335.135149 94.248141 370.902821 91.577462 373.456685 98.486816 406.472393 135.355570 463.908240 3162.27766
7: 274.367776 2980.122627 311.613125 3437.044111 337.287131 3829.503738 333.544669 3820.517762 354.347487 4205.737045 546.996092 4746.143252 10000.00000
The microbenchmark computed values fine as expected but the ggplot throws up this error:
Don't know how to automatically pick scale for object of type data.table/data.frame. Defaulting to continuous.
Error: Aesthetics must be either length 1 or the same as the data (7): colour, x
Am not being able to resolve this, can anyone let me know what is possibly wrong and correct the plotting procedure for the same?
Also on a sidenote I had to extract all the values (min,lq,mean,median,uq,max) seperately from the computed benchmark seperately since I cant take that as a datatable from the summary itself since it contained expr (expression) and neval columns. I was able to eliminate one of the columns using
algorithm[,!"expr"] or algorithm[,!"neval"]
but I can't eliminate two of them together, i.e.
algorithm[,!"expr",!"neval"] or algorithm[,!("expr","neval")] or algorithm[,!"expr","neval"]
- all possible combinations like that don't work (throws 'invalid argument type' error).
Any possible workaround or solution to this and the plotting (main thing) would be highly appreciated!
ANSWER
Answered 2019-Dec-05 at 10:21Your problem lies mainly with the fact that you're referring to an algorithm
column in the ggplot formula that does not exist in your object.
From what you gave, I could do the following :
algorithm$algorithm <- 1:nrow(algorithm)
ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") +
scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
and plot this fine :
EDIT : let's clean this up a bit...
As per OP's request, I've cleaned up his code a bit.
There are a lot of things you can work on to improve on your code's readability, but I'm focusing more on the practical aspect here. Basically, join your variables together in a table if you know they'll end up as such. There are a bunch of tricks you can use to assign the values to the correct spots, a few of which you'll see in the code below.
library(ggplot2)
library(microbenchmark)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)
# make a vector with your different conditions
conds <- c('f1', 'f2')
# initalizing a table from the getgo is much cleaner
# than doing everything in separate variables
dat <- data.frame(
datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition
cond = rep(NA, l*length(conds))
)
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
head(dat)
for(i in 1:l){ # for the love of god, don't use something as long as 'loopvar' as an iterative
# I don't have f1 & f2 so I did what I could...
s <- summary(microbenchmark(
"f1" = rpois(datasetsizes[i],10),
"f2" = {length(rpois(datasetsizes[i],10))}))
dat[which(dat$datasetsizes == datasetsizes[i]), # select rows of current ds size
c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")]
}
dat <- data.table(dat)
ggplot(dat, aes(x=datasetsizes,y=mean)) +
geom_point(aes(color = cond)) +
geom_line(aes(color = cond)) + # added to see a clear difference btw conds
labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') +
scale_y_continuous(trans = 'log10')
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