I have a large time series data set, which I've used xts to summarize in 30 second periods. Not sure how to make this set easily reproducible but it looks like this
> str(taonedf)
'data.frame': 480 obs. of 2 variables:
$ time : POSIXct, format: "2013-01-06 13:00:29" "2013-01-06 13:00:59" "2013-01-06 13:01:29" ...
$ count: int 20763 12030 22188 12183 21112 11628 21543 12609 20095 12992 ...
> head(taonedf)
time count
1 2013-01-06 13:00:29 20763
2 2013-01-06 13:00:59 12030
3 2013-01-06 13:01:29 22188
4 2013-01-06 13:01:59 12183
5 2013-01-06 13:02:29 21112
6 2013-01-06 13:02:59 11628
I've plotted a normal line plot of this and it works fine.
ggplot(data=taonedf, aes(x=time, y=count/30)) + #
geom_line(color="#009E73") +
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
I want to add some vline annotations. I've created a second dataframe called EV, it looks like this:
> str(ev)
'data.frame': 10 obs. of 2 variables:
$ dt : POSIXct, format: "2013-01-06 13:45:00" "2013-01-06 14:18:00" "2013-01-06 14:49:00" ...
$ event: Factor w/ 9 levels "Event 1",..: 7 8 3 2 5 6 1 4 2 9
> head(ev)
dt event
1 2013-01-06 13:45:00 Event 1
Now, when I add the vline option I get odd results. I'm using the same date time format between the two so the scale should align.
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xtintercept=dt))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
What am I missing? This doesn't appear to be that hard. All of the documentation and examples show simple numeric X axis so I'm assuming there is some issue with dates in the X axis but I can't pinpoint it. Any help would be appreciated.
> dput(taonedf)
structure(list(time = structure(c(1357506029.996, 1357506059.999,
1357506089.997, 1357506119.998, 1357506149.998, 1357506179.996,
1357506209.996, 1357506239.993, 1357506269.999, 1357506299.996,
1357506329.998, 1357506359.998, 1357506389.999, 1357506419.998,
1357506449.986, 1357506479.996, 1357506509.99, 1357506539.988,
1357506569.996, 1357506599.999, 1357506629.991, 1357506659.998,
1357506689.999, 1357506719.995, 1357506749.996, 1357506779.998,
1357506809.998, 1357506839.997, 1357506869.996, 1357506899.996,
1357506929.997, 1357506959.994, 1357506989.998, 1357507019.999,
1357507049.999, 1357507079.998, 1357507109.998, 1357507139.999,
1357507169.998, 1357507199.99, 1357507229.999, 1357507259.999,
1357507289.999, 1357507319.998, 1357507349.997, 1357507379.997,
1357507409.999, 1357507439.998, 1357507469.994, 1357507499.996,
1357507529.996, 1357507559.996, 1357507589.995, 1357507619.988,
1357507649.999, 1357507679.994, 1357507709.996, 1357507739.996,
1357507769.994, 1357507799.991, 1357507829.999, 1357507859.999,
1357507889.999, 1357507919.999, 1357507949.999, 1357507979.999,
1357508009.999, 1357508039.999, 1357508069.998, 1357508099.999,
1357508129.999, 1357508159.999, 1357508189.999, 1357508219.998,
1357508249.999, 1357508279.999, 1357508309.999, 1357508339.999,
1357508369.999, 1357508399.999, 1357508429.998, 1357508459.999,
1357508489.999, 1357508519.999, 1357508549.999, 1357508579.999,
1357508609.999, 1357508639.999, 1357508669.999, 1357508699.999,
1357508729.999, 1357508759.998, 1357508789.999, 1357508819.998,
1357508849.999, 1357508879.998, 1357508909.999, 1357508939.996,
1357508969.999, 1357508999.999, 1357509029.999, 1357509059.999,
1357509089.999, 1357509119.999, 1357509149.999, 1357509179.999,
1357509209.999, 1357509239.999, 1357509269.999, 1357509299.999,
1357509329.999, 1357509359.999, 1357509389.999, 1357509419.999,
1357509449.999, 1357509479.999, 1357509509.999, 1357509539.999,
1357509569.976, 1357509599.999, 1357509629.999, 1357509659.999,
1357509689.999, 1357509719.999, 1357509749.996, 1357509779.999,
1357509809.999, 1357509839.999, 1357509869.999, 1357509899.999,
1357509929.999, 1357509959.996, 1357509989.999, 1357510019.997,
1357510049.998, 1357510079.997, 1357510109.999, 1357510139.999,
1357510169.999, 1357510199.999, 1357510229.999, 1357510259.999,
1357510289.999, 1357510319.999, 1357510349.999, 1357510379.999,
1357510409.999, 1357510439.999, 1357510469.999, 1357510499.999,
1357510529.999, 1357510559.999, 1357510589.999, 1357510619.999,
1357510649.999, 1357510679.999, 1357510709.999, 1357510739.983,
1357510769.999, 1357510799.999, 1357510829.999, 1357510859.999,
1357510889.999, 1357510919.999, 1357510949.999, 1357510979.999,
1357511009.997, 1357511039.999, 1357511069.999, 1357511099.999,
1357511129.999, 1357511159.999, 1357511189.999, 1357511219.999,
1357511249.999, 1357511279.999, 1357511309.999, 1357511339.999,
1357511369.999, 1357511399.999, 1357511429.999, 1357511459.999,
1357511489.999, 1357511519.999, 1357511549.999, 1357511579.999,
1357511609.999, 1357511639.999, 1357511669.999, 1357511699.999,
1357511729.999, 1357511759.999, 1357511789.996, 1357511819.999,
1357511849.999, 1357511879.999, 1357511909.999, 1357511939.993,
1357511969.999, 1357511999.998, 1357512029.999, 1357512059.999,
1357512089.999, 1357512119.999, 1357512149.999, 1357512179.998,
1357512209.999, 1357512239.999, 1357512269.999, 1357512299.999,
1357512329.997, 1357512359.993, 1357512389.997, 1357512419.999,
1357512449.999, 1357512479.998, 1357512509.999, 1357512539.999,
1357512569.999, 1357512599.999, 1357512629.999, 1357512659.995,
1357512689.999, 1357512719.999, 1357512749.999, 1357512779.995,
1357512809.999, 1357512839.999, 1357512869.999, 1357512899.999,
1357512929.999, 1357512959.999, 1357512989.997, 1357513019.996,
1357513049.999, 1357513079.999, 1357513109.999, 1357513139.999,
1357513169.999, 1357513199.993, 1357513229.999, 1357513259.999,
1357513289.999, 1357513319.999, 1357513349.998, 1357513379.999,
1357513409.999, 1357513439.999, 1357513469.999, 1357513499.999,
1357513529.999, 1357513559.999, 1357513589.999, 1357513619.999,
1357513649.999, 1357513679.999, 1357513709.999, 1357513739.999,
1357513769.999, 1357513799.998, 1357513829.997, 1357513859.999,
1357513889.999, 1357513919.999, 1357513949.999, 1357513979.998,
1357514009.999, 1357514039.996, 1357514069.999, 1357514099.999,
1357514129.999, 1357514159.999, 1357514189.999, 1357514219.999,
1357514249.999, 1357514279.999, 1357514309.999, 1357514339.993,
1357514369.999, 1357514399.999, 1357514429.999, 1357514459.999,
1357514489.999, 1357514519.999, 1357514549.988, 1357514579.997,
1357514609.999, 1357514639.998, 1357514669.984, 1357514699.999,
1357514729.999, 1357514759.999, 1357514789.999, 1357514819.999,
1357514849.999, 1357514879.999, 1357514909.999, 1357514939.996,
1357514969.999, 1357514999.999, 1357515029.999, 1357515059.998,
1357515089.999, 1357515119.97, 1357515149.998, 1357515179.999,
1357515209.999, 1357515239.999, 1357515269.999, 1357515299.999,
1357515329.999, 1357515359.999, 1357515389.999, 1357515419.999,
1357515449.999, 1357515479.999, 1357515509.999, 1357515539.999,
1357515569.999, 1357515599.999, 1357515629.995, 1357515659.999,
1357515689.999, 1357515719.999, 1357515749.999, 1357515779.999,
1357515809.995, 1357515839.999, 1357515869.999, 1357515899.999,
1357515929.999, 1357515959.999, 1357515989.999, 1357516019.999,
1357516049.999, 1357516079.999, 1357516109.999, 1357516139.999,
1357516169.999, 1357516199.999, 1357516229.999, 1357516259.998,
1357516289.998, 1357516319.999, 1357516349.999, 1357516379.999,
1357516409.999, 1357516439.999, 1357516469.999, 1357516499.999,
1357516529.999, 1357516559.999, 1357516589.999, 1357516619.999,
1357516649.999, 1357516679.999, 1357516709.999, 1357516739.999,
1357516769.999, 1357516799.999, 1357516829.999, 1357516859.999,
1357516889.999, 1357516919.999, 1357516949.999, 1357516979.999,
1357517009.999, 1357517039.999, 1357517069.999, 1357517099.999,
1357517129.999, 1357517159.998, 1357517189.999, 1357517219.999,
1357517249.999, 1357517279.999, 1357517309.999, 1357517339.999,
1357517369.999, 1357517399.998, 1357517429.999, 1357517459.999,
1357517489.999, 1357517519.999, 1357517549.999, 1357517579.999,
1357517609.999, 1357517639.999, 1357517669.999, 1357517699.999,
1357517729.999, 1357517759.999, 1357517789.999, 1357517819.999,
1357517849.999, 1357517879.999, 1357517909.999, 1357517939.999,
1357517969.999, 1357517999.999, 1357518029.999, 1357518059.976,
1357518089.999, 1357518119.998, 1357518149.998, 1357518179.999,
1357518209.987, 1357518239.999, 1357518269.998, 1357518299.991,
1357518329.998, 1357518359.999, 1357518389.994, 1357518419.994,
1357518449.995, 1357518479.999, 1357518509.999, 1357518539.998,
1357518569.983, 1357518599.999, 1357518629.998, 1357518659.994,
1357518689.999, 1357518719.988, 1357518749.999, 1357518779.999,
1357518809.999, 1357518839.999, 1357518869.999, 1357518899.999,
1357518929.999, 1357518959.999, 1357518989.999, 1357519019.999,
1357519049.999, 1357519079.998, 1357519109.999, 1357519139.999,
1357519169.999, 1357519199.999, 1357519229.999, 1357519259.999,
1357519289.999, 1357519319.999, 1357519349.999, 1357519379.999,
1357519409.999, 1357519439.999, 1357519469.999, 1357519499.999,
1357519529.999, 1357519559.999, 1357519589.999, 1357519619.999,
1357519649.999, 1357519679.999, 1357519709.999, 1357519739.999,
1357519769.999, 1357519799.999, 1357519829.997, 1357519859.999,
1357519889.999, 1357519919.999, 1357519949.999, 1357519979.999,
1357520009.999, 1357520039.999, 1357520069.999, 1357520099.999,
1357520129.999, 1357520159.999, 1357520189.999, 1357520219.999,
1357520249.999, 1357520279.999, 1357520309.999, 1357520339.999,
1357520369.999, 1357520399.999), tzone = "", tclass = c("POSIXct",
"POSIXt"), class = c("POSIXct", "POSIXt")), count = c(20763L,
12030L, 22188L, 12183L, 21112L, 11628L, 21543L, 12609L, 20095L,
12992L, 21552L, 12447L, 21113L, 12236L, 21705L, 12018L, 21140L,
11820L, 21571L, 12803L, 21146L, 12081L, 21171L, 12440L, 21353L,
11708L, 21476L, 12210L, 21364L, 12041L, 21907L, 11934L, 22207L,
12403L, 21629L, 12676L, 21046L, 12196L, 21673L, 12190L, 21830L,
11652L, 20943L, 12350L, 20848L, 11800L, 21085L, 12367L, 21519L,
12325L, 22217L, 12195L, 22405L, 11869L, 21380L, 12145L, 21842L,
12224L, 21793L, 12856L, 34934L, 24073L, 41005L, 33964L, 46240L,
41287L, 52697L, 62618L, 78594L, 68193L, 76617L, 63747L, 90556L,
75830L, 104609L, 51063L, 67046L, 66977L, 82513L, 87228L, 107474L,
141878L, 127290L, 70953L, 98879L, 87814L, 117309L, 113463L, 150979L,
198271L, 170456L, 108325L, 119583L, 111803L, 117067L, 186768L,
226191L, 235546L, 228039L, 165570L, 159472L, 161707L, 137614L,
180049L, 254616L, 302166L, 336723L, 234902L, 202560L, 210679L,
173053L, 162839L, 262536L, 306859L, 249385L, 300646L, 219594L,
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202234L, 236882L, 217502L, 181157L, 196976L, 201901L, 228233L,
221241L, 220140L, 122623L, 76699L, 105589L, 381687L, 264571L,
187083L, 175972L, 202483L, 198547L, 196964L, 206402L, 181260L,
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184240L, 160864L, 156540L, 150392L, 157610L, 138447L, 148423L,
147318L, 148463L, 114389L, 163761L, 126624L, 167519L, 138240L,
133005L, 120187L, 155814L, 132751L, 140000L, 120323L, 124415L,
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118641L, 114330L, 135960L, 148066L, 130787L, 130230L, 130436L,
107109L, 129405L, 116093L, 135293L, 119048L, 147364L, 127028L,
145576L, 139960L, 139896L, 139433L, 127806L, 124845L, 141319L,
132821L, 129279L, 111905L, 130898L, 133135L, 138201L, 121460L,
143846L, 92964L, 100614L, 85637L, 139594L, 124302L, 106071L,
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105855L, 122387L, 152357L, 103217L, 134069L, 106021L, 91796L,
103335L, 99422L, 115839L, 147787L, 128868L, 123416L, 109312L,
129782L, 109397L, 130418L, 113709L, 103774L, 133272L, 137311L,
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187156L, 139426L, 159207L, 187435L, 198519L, 132559L, 163582L,
179069L, 150413L, 161463L, 173357L, 162457L, 136248L, 144086L,
151073L, 130237L, 144066L, 179840L, 135843L, 147757L, 206373L,
140734L, 177374L, 176168L, 154999L, 136136L, 187568L, 142357L,
152180L, 168528L, 131228L, 140622L, 145363L, 93070L, 58613L,
82024L, 86640L, 77493L, 71205L, 87641L, 89232L, 99214L, 89311L,
87948L, 90790L, 91326L, 106916L, 97318L, 89452L, 91658L, 82069L,
92559L, 89194L, 81721L, 83490L, 96388L, 90145L, 79861L, 90301L,
77676L, 262966L, 227355L, 256477L, 238905L, 241260L, 206168L,
229477L, 215515L, 245217L, 232026L, 225308L, 223537L, 198524L,
237840L, 233483L, 193081L, 216570L, 212949L, 203150L, 240861L,
209596L, 200673L, 180099L, 187726L, 187642L, 188402L, 176871L,
216090L, 203310L, 184723L, 195702L, 204137L, 276952L, 313717L,
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354317L, 366915L, 339465L, 346781L, 394895L, 355176L, 349618L,
417590L, 335474L, 405686L, 362581L, 356525L, 354142L, 383487L,
334305L, 327489L, 336201L, 374153L, 341485L, 321473L, 308773L,
15709L, 8870L, 15563L, 8944L, 15941L, 9342L, 16303L, 8951L, 14969L,
9385L, 14537L, 9963L, 15676L, 9011L, 16552L, 9587L, 16802L, 9693L,
15267L, 8946L, 14189L, 9067L, 14359L, 9776L, 167922L, 337364L,
350941L, 362928L, 364922L, 319641L, 348687L, 321356L, 400161L,
334171L, 332829L, 323842L, 397809L, 375694L, 384432L, 356825L,
350846L, 395942L, 359471L, 296926L, 418481L, 322144L, 335658L,
347212L, 334421L, 375769L, 364300L, 317370L, 373192L, 346713L,
356341L, 327225L, 305538L, 347815L, 276914L, 322149L, 303627L,
292363L, 284724L, 305082L, 373363L, 304386L, 438592L, 403579L,
430549L, 450536L, 432445L, 389779L, 434888L, 375010L, 456096L,
577393L, 451122L, 432354L, 425547L, 417729L)), .Names = c("time",
"count"), row.names = c(NA, -480L), class = "data.frame")
> dput(ev)
structure(list(dt = structure(c(1357508700, 1357510680, 1357512540,
1357515360, 1357517220, 1357517700, 1357518000, 1357518000, 1357519140,
1357519140), class = c("POSIXct", "POSIXt"), tzone = ""), event = structure(c(7L,
8L, 3L, 2L, 5L, 6L, 1L, 4L, 2L, 9L), .Label = c("Event 1",
"Event 2", "Event 3",
"Event 4", "Event 5",
"Event 6", "Event 7",
"Event 8", "Event 9"
), class = "factor")), .Names = c("dt", "event"), row.names = c(NA,
-10L), class = "data.frame")
Library Versions:
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: x86_64-redhat-linux-gnu (64-bit)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.2.2 xts_0.9-1 zoo_1.7-9 gdata_2.12.0 data.table_1.8.6 caTools_1.14
[7] scales_0.2.3 ggplot2_0.9.3
Simplied code - this still doesnt work
library(scales)
library(ggplot2)
taonedf<-dget("taonedf") #in this thread
ev<-dget("ev") #in this thread
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line() +
geom_vline(data=ev,aes(xtintercept=as.numeric(dt)))
To get geom_vline() display lines as intended, first, library scales should be loaded. Then use as.numeric() in geom_vline().
library(scales)
+ geom_vline(data=ev,aes(xintercept=as.numeric(dt)))
Two things
You need to wrap the datetimes for the vline in as.numeric
You misspelled xintercept
Fixing those:
library("ggplot2")
library("scales")
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xintercept=as.numeric(dt)))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests")) +
theme(legend.position = "none")
Related
I tried to adapt a code from an earlier version of R to process some data. I got most of it working again but ran into an issue.... I am trying to use vegan to run a permanova and permdisp, however when I get to the betadisper part I get the error "missing observations due to 'group' removed
Error in eigen(-x/2, symmetric = TRUE) : 0 x 0 matrix" I am not the best at R.... but I have tried to futz with it and don't know where I went wrong....
Thank you for reading and any Help would be appreciated.
require (vegan)
require (ggplot2)
require (gridExtra)
require (pals)
require (ape)
require (RColorBrewer)
legpro<- read.table('legpro.txt', sep='\t', header=T,row.names = 1)
legpro.std<-legpro[,3:ncol(legpro)]/legpro$Overall.body.Size.Estimator
legpro.std<-cbind(legpro$Code,legpro.std)
names(legpro.std)[1]<- 'Code'
legpro.std<-cbind(legpro$Species.Name,legpro.std)
names(legpro.std)[1]<- 'Species.Name'
#legpro.std<-legpro.std[-54]
#excluding body size from data
legpro.std<-legpro.std[-54]
#-6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-21,-22,-23,-29,-35,-41,-45,-46,-48,-49,-51,-52, can eliminate
head(legpro.std)
tail(legpro.std)
plot(legpro.std$claw1,legpro.std$claw2)
plot(legpro.std$claw1,legpro.std$claw3)
plot(legpro.std$Forewing.width,legpro.std$Forewing.length)
plot(legpro$Overall.body.Size.Estimator,legpro$Forewing.length)
legpro.std$Code <- factor(legpro.std$Code,levels = c('A.adnixa','A.modesta','A.bicolor','A.plomleyi','C.rastricornis','S.itasca','S.mohri','C.fitchi','C.simplicior','S.flinti','S.vicaria','C.areolaris','O.fulvicephalus','D.macleodi','N.americanus','D.binocula','H.costalis','H.stigma','S.angustus','M.tasmaniae','L.banksi','L.squamosa','S.pavida','D.sayi','C.tenuistriga','C.cincta','A.eureka','C.collaris','P.prasinus','C.coloradensis','N.myrmeleonoides','A.occidens','P.capicola','P.immensus','P.libelluloides','U.macleayanus','U.floridanus','U.quadripunctatus','U.bicolor','L.longicornis','L.coccajus','D.speciosus','C.pusillus','B.mexicanus','B.californicus','B.abdominalis','C.abdominalis','C.schwarzi','M.trigrammus','M.californicus','M.exitialis','P.hageni','V.fallax','S.carrizonus','S.dissimilis','S.eiseni','B.furcatus','B.lethalis','E.sinuatum','E.ornatum','E.arizonense','G.luniger','M.bilineatus','C.plumbeus','D.tetragrammicus'))#for setting species order
###Permanova
permanova<-adonis2(log10(legpro.std[,3:ncol(legpro.std)])~legpro.std$Species.Name,method='euclidean')
permanova
###Permdisp
Name.Code<- as.factor(legpro.std$Code)
legpro.dis<- vegdist(log10(legpro.std[,3:ncol(legpro.std)]),'euclidean')
perm.legpro <- betadisper (legpro.dis,Name.Code,type=c('median'))
perm.legpro2<- permutest(perm.legpro,pairwise=T, permutations=9999)
anova(perm.legpro)
TukeyHSD(perm.legpro)
pcoa<- pcoa(vegdist(log10(legpro.std[,3:ncol(legpro.std)]),'euclidean'))
print(pcoa)#Cumum_eig or
pcoa.cm<-cmdscale(vegdist(log10(legpro.std[,3:ncol(legpro.std)]),'euclidean'),add = F)
print(pcoa.cm) #% explanation of each axis is given by the variance of the axis/total pcoa variance or check above
The data from head is:
head(legpro.std)
Species.Name Code claw1 claw2 claw3 Totleglength1 Totleglength2 Totleglength3 Coxa1.anterior
1 Sialis.itasca.Ross 0.04570004 0.04528458 0.05359368 2.080806 2.397590 3.108226 0.3477358
2 Sialis.itasca.Ross 0.04510309 0.05111684 0.05025773 2.026632 2.348797 2.907216 0.3311856
3 Sialis.itasca.Ross 0.04593070 0.05116841 0.04472200 1.904915 2.350322 2.831184 0.2912973
4 Sialis.itasca.Ross 0.04196933 0.04237288 0.04640840 1.945924 2.228612 2.730024 0.2824859
5 Sialis.itasca.Ross 0.04725473 0.05220522 0.05445545 2.048830 2.428218 2.951395 0.3280828
6 Sialis.itasca.Ross 0.04471005 0.05046481 0.05002213 2.318504 2.488490 3.011067 0.3333333
coxa1.posterior Coxa1average Coxa2.anterior coxa2.posterior Coxa2.average Coxa3.anterior coxa3.posterior
1 0.3169921 0.3323639 0.3402576 0.3485667 0.3444121 0.2937266 0.3223930
2 0.3144330 0.3228093 0.3784364 0.3625429 0.3704897 0.2916667 0.3487972
3 0.2538275 0.2725624 0.3352135 0.3231265 0.3291700 0.2828364 0.3227236
4 0.2861178 0.2843019 0.2744149 0.2836965 0.2790557 0.2655367 0.2869250
5 0.3352835 0.3316832 0.2812781 0.3096310 0.2954546 0.2736274 0.3105311
6 0.3196104 0.3264719 0.3430721 0.3413015 0.3421868 0.3005755 0.3594511
Coxa3.average femur1 femur2 femur3 Forewing.length Forewing.width Forewingsurice.area tarsi1_1 tarsi1_2
1 0.3080598 0.5334441 0.6248442 0.7714998 3.905692 1.140839 10.72503 0.1479020 0.08516826
2 0.3202319 0.5945017 0.6224227 0.7096220 3.717354 1.240550 10.73572 0.1271478 0.08161512
3 0.3027800 0.5302175 0.6877518 0.7304593 3.708702 1.221595 11.24479 0.1373892 0.07775987
4 0.2762308 0.5145279 0.5952381 0.7094431 3.672720 1.142454 10.39747 0.1323648 0.07627119
5 0.2920792 0.5234024 0.6710172 0.7178218 3.894690 1.299730 11.24786 0.1314131 0.08190819
6 0.3300133 0.7242143 0.6573705 0.7312970 3.911022 1.100044 9.71889 0.1496237 0.08632138
tarsi1_3 tarsi1_4 tarsi1_5 tarsi1tot tarsi2_1 tarsi2_2 tarsi2_3 tarsi2_4 tarsi2_5 tarsi2tot tarsi3_1
1 0.05899460 0.03406730 0.07810552 0.4042376 0.1798920 0.09056917 0.08890735 0.03199003 0.1574574 0.5488159 0.3103448
2 0.06185567 0.03006873 0.09235395 0.3930412 0.1859966 0.12113402 0.08376288 0.03565292 0.1245704 0.5511168 0.2843643
3 0.06124093 0.02739726 0.08622079 0.3900080 0.1833199 0.11724416 0.07534246 0.03424657 0.1446414 0.5547945 0.2123288
4 0.06133979 0.02986279 0.09604520 0.3958838 0.1949153 0.10895885 0.07506054 0.02945924 0.1142050 0.5225989 0.2566586
5 0.05940594 0.02880288 0.09180918 0.3933394 0.1908191 0.10531054 0.08235824 0.03915392 0.1345635 0.5522052 0.2808281
6 0.05843293 0.03320053 0.09871625 0.4262948 0.2173528 0.12926073 0.09606020 0.03231518 0.1372289 0.6122178 0.2855246
tarsi3_2 tarsi3_3 tarsi3_4 tarsi3_5 tarsi3tot tibia1 tibia2 tibia3 Trochanter1.anterior
1 0.1458247 0.12172829 0.04154549 0.1728292 0.7922725 0.6726215 0.7349398 1.0793519 0.14291649
2 0.1658076 0.10438144 0.03651203 0.1507732 0.7418385 0.6065292 0.6808419 0.9746564 0.09879725
3 0.1305399 0.07896857 0.03505237 0.1543110 0.6112006 0.6176470 0.6458501 1.0209508 0.08944399
4 0.1513317 0.09564165 0.04317999 0.1452785 0.6920904 0.6432607 0.7171106 0.9196933 0.10290557
5 0.1588659 0.09675968 0.04095410 0.1332133 0.7106211 0.7011701 0.7803781 1.0756077 0.09315932
6 0.1651173 0.10358565 0.03497123 0.1412129 0.7304117 0.7348384 0.7290836 1.0624170 0.09517486
Trochanter1.posterior Trochanter1.average Trochanter2.anterior Trochanter2.posterior Trochanter2.average
1 0.13336103 0.13813876 0.15828833 0.1308683 0.1445783
2 0.12070446 0.10975085 0.12070446 0.1271478 0.1239261
3 0.09951651 0.09448025 0.10999194 0.1555197 0.1327558
4 0.11299435 0.10794996 0.09483455 0.1343826 0.1146086
5 0.10531054 0.09923493 0.10711072 0.1512151 0.1291629
6 0.11819388 0.10668437 0.14121292 0.1540505 0.1476317
Trochanter3.anterior Trochanter3.posterior Trochanter3.average
1 0.1819692 0.1321147 0.1570420
2 0.1503436 0.1713917 0.1608677
3 0.1651894 0.1663981 0.1657937
4 0.1315577 0.1335755 0.1325666
5 0.1480648 0.1624663 0.1552655
6 0.1460823 0.1677733 0.1569278
dput
structure(list(Species.Name = c("Sialis.itasca.Ross", "Sialis.itasca.Ross",
"Sialis.itasca.Ross", "Sialis.itasca.Ross", "Sialis.itasca.Ross",
"Sialis.itasca.Ross"), Code = structure(c(6L, 6L, 6L, 6L, 6L,
6L), levels = c("A.adnixa", "A.modesta", "A.bicolor", "A.plomleyi",
"C.rastricornis", "S.itasca", "S.mohri", "C.fitchi", "C.simplicior",
"S.flinti", "S.vicaria", "C.areolaris", "O.fulvicephalus", "D.macleodi",
"N.americanus", "D.binocula", "H.costalis", "H.stigma", "S.angustus",
"M.tasmaniae", "L.banksi", "L.squamosa", "S.pavida", "D.sayi",
"C.tenuistriga", "C.cincta", "A.eureka", "C.collaris", "P.prasinus",
"C.coloradensis", "N.myrmeleonoides", "A.occidens", "P.capicola",
"P.immensus", "P.libelluloides", "U.macleayanus", "U.floridanus",
"U.quadripunctatus", "U.bicolor", "L.longicornis", "L.coccajus",
"D.speciosus", "C.pusillus", "B.mexicanus", "B.californicus",
"B.abdominalis", "C.abdominalis", "C.schwarzi", "M.trigrammus",
"M.californicus", "M.exitialis", "P.hageni", "V.fallax", "S.carrizonus",
"S.dissimilis", "S.eiseni", "B.furcatus", "B.lethalis", "E.sinuatum",
"E.ornatum", "E.arizonense", "G.luniger", "M.bilineatus", "C.plumbeus",
"D.tetragrammicus"), class = "factor"), claw1 = c(0.0457000398959275,
0.045103090158027, 0.0459306989564186, 0.0419693321653473, 0.0472547265893557,
0.0447100492179136), claw2 = c(0.045284584153096, 0.0511168382615714,
0.051168411852526, 0.042372881410651, 0.0522052218973023, 0.050464806029534
), claw3 = c(0.0535936807297095, 0.050257730039612, 0.0447219967552032,
0.0464083960590078, 0.0544554479373681, 0.0500221314070853),
Totleglength1 = c(2.080805936746, 2.02663219508693, 1.90491528797122,
1.94592417110508, 2.04882996069499, 2.31850370337949), Totleglength2 = c(2.3975902975308,
2.3487971821018, 2.35032225197969, 2.22861185065265, 2.42821795709869,
2.48849039363661), Totleglength3 = c(3.10822595137736, 2.90721641469161,
2.83118434974866, 2.7300243220427, 2.95139534984782, 3.0110667765838
), Coxa1.anterior = c(0.347735766263887, 0.331185569221734,
0.291297323563015, 0.282485879299417, 0.328082812807204,
0.333333333333333), coxa1.posterior = c(0.316992102366229,
0.314432977364315, 0.253827544849914, 0.286117841877611,
0.335283548106612, 0.31961043951507), Coxa1average = c(0.332363934522785,
0.322809273078248, 0.272562434407915, 0.28430186079029, 0.33168318068193,
0.326471886424202), Coxa2.anterior = c(0.340257575356567,
0.378436407168337, 0.335213529057563, 0.27441486170569, 0.281278142496969,
0.343072135629241), coxa2.posterior = c(0.348566665701357,
0.362542936849028, 0.323126490526473, 0.283696541563174,
0.309630989611699, 0.341301463699871), Coxa2.average = c(0.344412120321235,
0.370489672008682, 0.329170009993469, 0.279055701634432,
0.295454566279356, 0.342186799664556), Coxa3.anterior = c(0.293726629791346,
0.291666666612973, 0.282836418227029, 0.2655367278651, 0.273627368020151,
0.300575476345173), coxa3.posterior = c(0.32239302120667,
0.348797230641319, 0.322723594896146, 0.286924958124474,
0.310531068416563, 0.359451064344658), Coxa3.average = c(0.308059825499008,
0.320231948412369, 0.302780006360137, 0.276230843196563,
0.292079218218357, 0.330013270123579), femur1 = c(0.53344412311015,
0.594501676537536, 0.530217536295495, 0.514527850716328,
0.523402362157819, 0.724214250276451), femur2 = c(0.624844166341973,
0.62242266239383, 0.687751788591478, 0.595238122698798, 0.67101716194329,
0.657370505792677), femur3 = c(0.771499768530334, 0.709621950616875,
0.73045925924984, 0.709443126916381, 0.71782183270357, 0.731296991557455
), Forewing.length = c(3.90569163653097, 3.71735396284475,
3.70870245967632, 3.67271997731286, 3.89468979918273, 3.91102248411048
), Forewing.width = c(1.14083920932507, 1.24054979613491,
1.22159545808208, 1.1424536620426, 1.29973004580049, 1.10004419021568
), Forewingsurice.area = c(10.7250294361749, 10.7357182857541,
11.2447860996532, 10.3974705849124, 11.2478641830568, 9.71889046379367
), tarsi1_1 = c(0.14790195113684, 0.127147764272682, 0.137389192375109,
0.132364817915656, 0.131413146374404, 0.149623723140197),
tarsi1_2 = c(0.0851682561130301, 0.0816151169968028, 0.0777598691430841,
0.0762711871041436, 0.0819081932949372, 0.086321375636013
), tarsi1_3 = c(0.05899459957015, 0.0618556656924223, 0.0612409299273872,
0.0613397899673706, 0.0594059432453148, 0.0584329328546814
), tarsi1_4 = c(0.0340673036084858, 0.0300687276311245, 0.0273972594295365,
0.0298627938699947, 0.0288028831418247, 0.0332005316106093
), tarsi1_5 = c(0.0781055209485422, 0.0923539508709525, 0.0862207865660963,
0.0960452026260472, 0.0918091839108305, 0.0987162455869307
), tarsi1tot = c(0.404237631377048, 0.393041225034431, 0.390008037038313,
0.395883791483213, 0.393339350417356, 0.426294808828432),
tarsi2_1 = c(0.179891974784803, 0.185996557201992, 0.183319900195347,
0.194915264093515, 0.190819089619718, 0.217352803588656),
tarsi2_2 = c(0.0905691687216469, 0.121134019176011, 0.117244156225387,
0.108958845465276, 0.10531053520073, 0.129260729462848),
tarsi2_3 = c(0.0889073519821446, 0.0837628811084026, 0.0753424647406533,
0.0750605365433736, 0.0823582392230224, 0.0960601978522388
), tarsi2_4 = c(0.0319900282179677, 0.0356529186168164, 0.0342465741861955,
0.0294592417998321, 0.039153916085443, 0.032315182365712),
tarsi2_5 = c(0.157457414941948, 0.124570439606804, 0.144641410417212,
0.114205004217583, 0.134563461120325, 0.137228859386712),
tarsi2tot = c(0.548815938233055, 0.551116815710026, 0.554794505764795,
0.522598892523131, 0.552205241699284, 0.612217772213493),
tarsi3_1 = c(0.310344810480925, 0.284364252740063, 0.212328761485435,
0.256658594428061, 0.280828103319559, 0.285524552019457),
tarsi3_2 = c(0.145824678654506, 0.165807558229931, 0.130539881647459,
0.151331723647517, 0.158865888131613, 0.165117302168043),
tarsi3_3 = c(0.12172829459396, 0.104381440634743, 0.0789685681210926,
0.0956416505558846, 0.0967596828191374, 0.103585653153654
), tarsi3_4 = c(0.0415454916076207, 0.0365120268387758, 0.0350523743106695,
0.0431799855509762, 0.0409540966474686, 0.0349712270016878
), tarsi3_5 = c(0.172829247098504, 0.150773196132581, 0.154311026818233,
0.145278460351334, 0.133213330086744, 0.141212924791317),
tarsi3tot = c(0.792272522435516, 0.741838474576093, 0.61120061278579,
0.692090414533774, 0.710621101004522, 0.730411659134159),
tibia1 = c(0.672621484952058, 0.606529166301326, 0.617647027480212,
0.64326070605379, 0.701170139665048, 0.734838388979717),
tibia2 = c(0.734939761208523, 0.680841920542057, 0.645850109596671,
0.717110574280138, 0.780378064142149, 0.729083625085317),
tibia3 = c(1.07935188194521, 0.974656351266629, 1.02095076737815,
0.9196933462701, 1.07560766416314, 1.06241701109683), Trochanter1.anterior = c(0.142916494686509,
0.0987972496490506, 0.0894439939133073, 0.102905570062555,
0.0931593216950861, 0.0951748552474476), Trochanter1.posterior = c(0.133361030881401,
0.120704458621732, 0.0995165119881685, 0.112994354060364,
0.10531053520073, 0.118193883379276), Trochanter1.average = c(0.138138762783955,
0.109750854135391, 0.0944802527492875, 0.10794996206146,
0.0992349286729306, 0.106684369313362), Trochanter2.anterior = c(0.158288326427611,
0.120704458621732, 0.109991937780383, 0.0948345464155595,
0.107110718913071, 0.141212924791317), Trochanter2.posterior = c(0.130868296424412,
0.127147764272682, 0.155519737883267, 0.1343825722132, 0.151215127156145,
0.154050456084472), Trochanter2.average = c(0.144578311426012,
0.123926111447207, 0.132755838033276, 0.114608559516156,
0.129162923034608, 0.147631690437894), Trochanter3.anterior = c(0.181969248928957,
0.150343635578303, 0.165189354284288, 0.131557713775331,
0.148064812410224, 0.146082338998146), Trochanter3.posterior = c(0.132114657421083,
0.171391742772324, 0.166398053665197, 0.133575468072875,
0.162466255556294, 0.167773349902735), Trochanter3.average = c(0.157041952967292,
0.16086768939009, 0.165793703974743, 0.132566591125879, 0.155265534208282,
0.156927844671778), Overall.body.Size.Estimator = c(1, 1,
1, 1, 1, 1)), row.names = c(NA, 6L), class = "data.frame")
I am trying to create a stratigraphic plot of geochemical element data which should be possible using package tidypaleo.
I want multiple plots of the different element data with Depth (cm) downcore set as the y axis. The data look as follows.
Image of data
I am using this code:
ggplot(wapITRAX, aes(x =BrTi , y = wapITRAX$Depth))+
labs(y = "Depth (cm)")+
geom_lineh()+
theme_classic()+
scale_y_reverse()
However, this only plots one element and I am trying to achieve a plot like this Image of plot
> dput(head(wapITRAX))
structure(list(Depth = 0:5, IncCoh = c(6.049230907, 5.975282432,
5.736199822, 5.658584418, 5.659008377, 5.597103404), BrTi =
c(50.50197628,
22.09236453, 23.48370927, 18.62638581, 14.36924414, 17.48777896
), AlIncCOh = c(16.69633736, 8.200449193, 23.70907643, 20.32310407,
28.62692352, 26.44224866), BrCl = c(8.04090623, 4.306048968,
3.417836951, 3.156895904, 2.787628518, 2.059316731), FeTi =
c(332.715415,
235.9371921, 372.726817, 390.7871397, 396.986099, 495.2624867
), CaTi = c(4.071146245, 3.27955665, 4.395989975, 3.677383592,
3.028670721, 4.523910733), ZrRb = structure(c(363L, 447L, 407L,
395L, 450L, 410L), .Label = c("#DIV/0!", "0.447638604",
"0.478169284",
"0.54554134", "0.548501778", "0.561420163", "0.579454254",
"0.579498861",
"0.580801291", "0.589758019", "0.590194076", "0.590277778",
"0.591357754",
"0.592870544", "0.593851133", "0.598519653", "0.599931082",
"0.600979737",
"0.601426307", "0.611710677", "0.617065868", "0.618499405",
"0.621310093",
"0.627720871", "0.63775246", "0.64005168", "0.643958869",
"0.644371941",
"0.645605974", "0.645661658", "0.646672915", "0.647348952",
"0.651578947",
"0.652401176", "0.656186383", "0.657906264", "0.658835905",
"0.662074554",
"0.662361624", "0.669589393", "0.67103429", "0.671371769",
"0.674335863",
"0.674781688", "0.676097561", "0.676639083", "0.677849462",
"0.680497925",
"0.680610514", "0.680725971", "0.683906537", "0.68855859",
"0.689067202",
"0.692353115", "0.692732291", "0.695411392", "0.696067091",
"0.696794872",
"0.699376436", "0.701762744", "0.702015197", "0.702432938",
"0.70361991",
"0.705235754", "0.705426357", "0.708084164", "0.708258528",
"0.708925221",
"0.715226656", "0.715263314", "0.717828827", "0.718975706",
"0.719799305",
"0.720363636", "0.72476489", "0.725426857", "0.725461098",
"0.726030739",
"0.7267645", "0.726998188", "0.727170554", "0.727533265",
"0.730362368",
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0.009460859,
0.004488071, 0.0033725, 0.003435313), MnIncCoh = c(169.4430276,
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), CuRb = c(0.392971246, 1.484304933, 0.735426009, 0.491651206,
1.142857143, 0.4345898)), row.names = c(NA, 6L), class =
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Using your posted data. This should approximate the desired design.
First step, Transform the data from a wide format to a long format using the pivot_longer function from tidyr.
Then plot the data using "depth" as the independent variable and the parameters' values as the dependent variables.
Then use facet_wrap() to separate the plots. coord_flip() will make the independent variable (Depth) appear on the y-axis.
#fixed 1 column of data.
originaldata$ZrRb <- as.numeric(as.character(originaldata$ZrRb))
library(tidyr)
#Make wide
wapITRAX<-pivot_longer(originaldata, -1, names_to="parameter", values_to = "value")
library(ggplot2)
ggplot(wapITRAX, aes(x = Depth , y = value))+
labs(x = "Depth (cm)")+
geom_line() +
theme_classic() +
coord_flip() +
scale_x_reverse() +
facet_wrap(vars(parameter), nrow=1, scales = "free_x")
I am trying to plot, using ggplot, a series of scatter plots with regression lines for several datasets. I started with the following dataset, "onepectCO2MEDIAN". The data for this dataset is as follows:
onepctCO2MEDIAN
x y
layer.1 0.000000000 0.0000000
layer.2 0.006794447 4.9002490
layer.3 0.014288058 0.1608000
layer.4 0.022087920 6.6349133
layer.5 0.030797357 -1.2429506
layer.6 0.038451072 1.5643374
layer.7 0.048087904 -2.2659035
layer.8 0.058677729 2.2070045
layer.9 0.069261406 -2.3677001
layer.10 0.080524530 -1.0913506
layer.11 0.092760246 0.4099940
layer.12 0.103789609 -0.1259727
layer.13 0.116953168 -2.4138253
layer.14 0.129253298 7.0890257
layer.15 0.141710050 -0.7593539
layer.16 0.156002052 0.0454416
layer.17 0.170648172 -1.5349683
layer.18 0.185318425 6.5524201
layer.19 0.199463055 -0.8312563
layer.20 0.213513337 -2.5099183
layer.21 0.228839271 0.1365968
layer.22 0.246981293 -1.3719845
layer.23 0.263012767 -0.8712988
layer.24 0.278505564 0.6632584
layer.25 0.293658361 0.7938036
layer.26 0.310747266 3.4880637
layer.27 0.325990349 -4.4612208
layer.28 0.342517540 0.0871734
layer.29 0.362751633 -1.4171578
layer.30 0.380199537 -0.9956508
layer.31 0.394992948 0.3215526
layer.32 0.414373398 3.1403866
layer.33 0.430690214 -0.7376099
layer.34 0.449738145 -2.4860541
layer.35 0.470167458 -3.4235858
layer.36 0.489019871 0.4824748
layer.37 0.507242471 -0.9785386
layer.38 0.524314284 8.5359684
layer.39 0.543750525 5.4844742
layer.40 0.564234197 3.2149367
layer.41 0.583679616 3.9168916
layer.42 0.601459444 4.4907020
layer.43 0.619924664 6.5410410
layer.44 0.639932007 4.8068650
layer.45 0.661347181 8.1510170
layer.46 0.684117317 0.2697413
layer.47 0.704829752 -0.1807501
layer.48 0.725045770 9.7181249
layer.49 0.745165825 1.5406466
layer.50 0.765016139 -1.6476041
layer.51 0.783461511 4.8024603
layer.52 0.806382924 4.0421516
layer.53 0.829241335 9.3756512
layer.54 0.849924415 5.3305050
layer.55 0.871352434 7.5445803
layer.56 0.893632233 6.4679547
layer.57 0.916052133 2.8096065
layer.58 0.938579470 5.3921661
layer.59 0.959907651 7.2043689
layer.60 0.981643587 3.3350806
layer.61 1.004116774 8.8690707
layer.62 1.028363466 1.7861299
layer.63 1.054009140 6.2555038
layer.64 1.072440803 7.6079236
layer.65 1.094457805 7.6871483
layer.66 1.123176277 4.7787764
layer.67 1.149430871 12.7110502
layer.68 1.170912921 -0.7156284
layer.69 1.196743071 1.6490899
layer.70 1.218625903 3.0363024
layer.71 1.241868377 4.2974769
layer.72 1.267941594 1.9543778
layer.73 1.290708780 3.9986964
layer.74 1.313222289 4.5179472
layer.75 1.339045882 0.9337905
layer.76 1.362803459 3.3050770
layer.77 1.384450197 3.5422970
layer.78 1.409720302 5.9973660
layer.79 1.435851157 0.5081869
layer.80 1.455592215 7.9661630
layer.81 1.479495347 9.9460496
layer.82 1.506051958 3.7908372
layer.83 1.525728464 2.5735847
layer.84 1.549362063 10.1404974
layer.85 1.573440671 13.7408304
layer.86 1.600278735 0.9335771
layer.87 1.623879492 9.7588742
layer.88 1.650029302 1.2769395
layer.89 1.672362328 13.4970906
layer.90 1.700221121 10.2087502
layer.91 1.724793375 1.6811275
layer.92 1.751070559 6.1178992
layer.93 1.778022110 -0.1567626
layer.94 1.803022087 3.8237479
layer.95 1.830668867 4.4331468
layer.96 1.855736911 5.9790707
layer.97 1.882615030 11.3104333
layer.98 1.909218490 8.2142607
layer.99 1.938130021 15.3209674
layer.100 1.963727593 5.8178217
layer.101 1.993271947 9.6004907
layer.102 2.022548139 3.4063646
layer.103 2.050679922 4.7375010
layer.104 2.078064442 3.0133019
layer.105 2.104113460 5.5659522
layer.106 2.133597612 12.0346333
layer.107 2.164026260 -0.4028320
layer.108 2.194852829 10.5996780
layer.109 2.224257946 5.4479584
layer.110 2.252194643 4.7052374
layer.111 2.277335048 14.0962019
layer.112 2.304058313 5.7149016
layer.113 2.330930233 3.7780072
layer.114 2.357022762 4.4120620
layer.115 2.386489272 4.1866085
layer.116 2.417503953 6.9078802
layer.117 2.448524356 2.7825739
layer.118 2.478698969 7.6171786
layer.119 2.510175705 10.2410603
layer.120 2.539697886 8.1820711
layer.121 2.567915559 4.8275494
layer.122 2.597463250 19.1624883
layer.123 2.627518773 16.0677109
layer.124 2.658759236 12.5897081
layer.125 2.692401528 9.2907988
layer.126 2.721903205 7.4262502
layer.127 2.753021359 9.3902518
layer.128 2.786313415 12.6193550
layer.129 2.819564104 11.1121040
layer.130 2.850823164 15.7907100
layer.131 2.880394101 10.7425287
layer.132 2.911391258 7.7971430
layer.133 2.942965150 8.8060858
layer.134 2.974468350 17.5606266
layer.135 3.008983612 17.3088605
layer.136 3.040015221 13.4500543
layer.137 3.072668672 14.6377884
layer.138 3.105982423 8.0798552
dput(onepctCO2MEDIAN)
dput(onepctCO2MEDIAN)
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4.51794724689848, 0.933790484492299, 3.30507700050003, 3.5422970157433,
5.99736597322524, 0.508186860060022, 7.96616300581067, 9.94604963036295,
3.79083717222623, 2.57358468532258, 10.1404974171776, 13.7408303595752,
0.933577123801399, 9.75887417074129, 1.27693947132921, 13.4970905965787,
10.2087501765735, 1.68112753028756, 6.1178991508927, -0.156762622680077,
3.82374791691426, 4.43314678736265, 5.97907067167507, 11.3104332518482,
8.21426074201525, 15.320967360602, 5.81782169471483, 9.6004907412354,
3.40636455909704, 4.73750103921864, 3.0133019468806, 5.56595224859066,
12.0346332527215, -0.40283199827104, 10.5996779538754, 5.44795836991128,
4.70523736412729, 14.096201892183, 5.71490161813391, 3.77800720810782,
4.41206200639436, 4.18660847858423, 6.90788020044911, 2.78257393345915,
7.61717857379431, 10.2410602647684, 8.18207106836167, 4.82754943871433,
19.1624882857155, 16.0677109398509, 12.589708067017, 9.29079879799404,
7.42625019725314, 9.39025179806185, 12.6193550331438, 11.1121039747257,
15.7907099734986, 10.7425286789233, 7.79714300307344, 8.80608578166101,
17.5606266346039, 17.3088604929222, 13.4500543478523, 14.6377884248645,
8.07985518296064)), class = "data.frame", row.names = c("layer.1",
"layer.2", "layer.3", "layer.4", "layer.5", "layer.6", "layer.7",
"layer.8", "layer.9", "layer.10", "layer.11", "layer.12", "layer.13",
"layer.14", "layer.15", "layer.16", "layer.17", "layer.18", "layer.19",
"layer.20", "layer.21", "layer.22", "layer.23", "layer.24", "layer.25",
"layer.26", "layer.27", "layer.28", "layer.29", "layer.30", "layer.31",
"layer.32", "layer.33", "layer.34", "layer.35", "layer.36", "layer.37",
"layer.38", "layer.39", "layer.40", "layer.41", "layer.42", "layer.43",
"layer.44", "layer.45", "layer.46", "layer.47", "layer.48", "layer.49",
"layer.50", "layer.51", "layer.52", "layer.53", "layer.54", "layer.55",
"layer.56", "layer.57", "layer.58", "layer.59", "layer.60", "layer.61",
"layer.62", "layer.63", "layer.64", "layer.65", "layer.66", "layer.67",
"layer.68", "layer.69", "layer.70", "layer.71", "layer.72", "layer.73",
"layer.74", "layer.75", "layer.76", "layer.77", "layer.78", "layer.79",
"layer.80", "layer.81", "layer.82", "layer.83", "layer.84", "layer.85",
"layer.86", "layer.87", "layer.88", "layer.89", "layer.90", "layer.91",
"layer.92", "layer.93", "layer.94", "layer.95", "layer.96", "layer.97",
"layer.98", "layer.99", "layer.100", "layer.101", "layer.102",
"layer.103", "layer.104", "layer.105", "layer.106", "layer.107",
"layer.108", "layer.109", "layer.110", "layer.111", "layer.112",
"layer.113", "layer.114", "layer.115", "layer.116", "layer.117",
"layer.118", "layer.119", "layer.120", "layer.121", "layer.122",
"layer.123", "layer.124", "layer.125", "layer.126", "layer.127",
"layer.128", "layer.129", "layer.130", "layer.131", "layer.132",
"layer.133", "layer.134", "layer.135", "layer.136", "layer.137",
"layer.138"))
I started with the following to generate the first regression line and scatter plot:
lm<-ggplot(onepctCO2MEDIAN) +
geom_jitter(aes(RCP1pctCO2cumulativeMedian[1:138], departurea),
colour="blue") + geom_smooth(aes(RCP1pctCO2cumulativeMedian[1:138],
departurea), method=lm)
But I receive this error:
Warning message:
Computation failed in `stat_smooth()`:
'what' must be a function or character string
A blue scatter plot is successfully generated, but the problem is that the regression line does not appear, presumably related to the above warning.
Is there a reason for this? I would appreciate any assistance!
You should be able to get a scatter plot with a regression line by doing the following:
library(tidyverse)
#> Registered S3 methods overwritten by 'ggplot2':
#> method from
#> [.quosures rlang
#> c.quosures rlang
#> print.quosures rlang
onepctCO2MEDIAN <- structure(list(x = c(0, 0.00679444684647024, 0.014288058038801,
0.0220879195258021, 0.0307973567396402,0.0384510718286037,0.0480879042297602,
0.0586777292191982, 0.0692614056169987, 0.080524530261755,0.0927602462470531,
0.103789608925581, 0.116953168064356, 0.129253298044205, 0.141710050404072,
0.156002052128315, 0.170648172497749, 0.185318425297737, 0.199463054537773,
0.21351333707571, 0.22883927077055, 0.246981292963028, 0.263012766838074,
0.278505563735962, 0.29365836083889, 0.310747265815735, 0.325990349054337,
0.342517539858818, 0.362751632928848, 0.380199536681175, 0.39499294757843,
0.414373397827148, 0.430690214037895, 0.449738144874573, 0.470167458057404,
0.489019870758057, 0.507242470979691, 0.524314284324646, 0.543750524520874,
0.56423419713974, 0.583679616451263, 0.601459443569183, 0.619924664497375,
0.639932006597519, 0.661347180604935, 0.684117317199707, 0.704829752445221,
0.725045770406723, 0.745165824890137, 0.765016138553619, 0.783461511135101,
0.806382924318314, 0.829241335391998, 0.84992441534996, 0.871352434158325,
0.893632233142853, 0.916052132844925, 0.938579469919205, 0.959907650947571,
0.981643587350845, 1.00411677360535, 1.02836346626282, 1.05400913953781,
1.07244080305099, 1.09445780515671, 1.12317627668381, 1.14943087100983,
1.17091292142868, 1.19674307107925, 1.21862590312958, 1.24186837673187,
1.26794159412384, 1.2907087802887, 1.31322228908539, 1.33904588222504,
1.36280345916748, 1.38445019721985, 1.40972030162811, 1.43585115671158,
1.45559221506119, 1.47949534654617, 1.50605195760727, 1.52572846412659,
1.5493620634079, 1.5734406709671, 1.60027873516083, 1.62387949228287,
1.65002930164337, 1.67236232757568, 1.70022112131119, 1.72479337453842,
1.75107055902481, 1.77802211046219, 1.80302208662033, 1.83066886663437,
1.85573691129684, 1.88261502981186, 1.90921849012375, 1.93813002109528,
1.96372759342194, 1.99327194690704, 2.02254813909531, 2.05067992210388,
2.07806444168091, 2.1041134595871, 2.13359761238098, 2.16402626037598,
2.19485282897949, 2.2242579460144, 2.25219464302063, 2.27733504772186,
2.30405831336975, 2.33093023300171, 2.35702276229858, 2.38648927211761,
2.41750395298004, 2.44852435588837, 2.47869896888733, 2.51017570495605,
2.53969788551331, 2.567915558815, 2.59746325016022, 2.62751877307892,
2.65875923633575, 2.69240152835846, 2.72190320491791, 2.75302135944366,
2.78631341457367, 2.8195641040802, 2.85082316398621, 2.88039410114288,
2.91139125823975, 2.94296514987946, 2.97446835041046, 3.00898361206055,
3.04001522064209, 3.07266867160797, 3.10598242282867),
y = c(0,
4.90024901723162, 0.160799993152722, 6.63491326258641, -1.24295055804536,
1.56433744259162, -2.26590352245208, 2.20700446463354, -2.36770012911069,
-1.09135061899174, 0.409993989292701, -0.125972681525582, -2.41382533818026,
7.08902570153028, -0.759353880417294, 0.0454415959640926, -1.53496826259972,
6.55242014096194, -0.831256280861552, -2.50991825629084, 0.136596820654013,
-1.37198445498419, -0.871298832596736, 0.663258363762466, 0.793803634291308,
3.48806373666998, -4.46122081238949, 0.0871733966938564, -1.41715777257774,
-0.995650815648318, 0.32155262317503, 3.14038657369241, -0.737609879885404,
-2.48605406511292, -3.423585843908, 0.482474753780281, -0.978538630093809,
8.53596837794201, 5.48447420320695, 3.21493665820644, 3.91689160157513,
4.49070195980797, 6.54104103157039, 4.80686500146557, 8.15101701282067,
0.26974132191657, -0.180750068063062, 9.71812491230244, 1.54064657400204,
-1.64760408795688, 4.80246028991894, 4.04215159914344, 9.37565121768513,
5.33050496938428, 7.54458026088508, 6.46795470819342, 2.80960651433971,
5.39216613235986, 7.20436888038562, 3.3350806460997, 8.86907069895943,
1.78612988613659, 6.25550382050395, 7.60792364896564, 7.68714830528144,
4.77877638957615, 12.7110501777314, -0.715628443181046, 1.64908991824022,
3.03630240714679, 4.29747688442346, 1.95437780501881, 3.99869636910933,
4.51794724689848, 0.933790484492299, 3.30507700050003, 3.5422970157433,
5.99736597322524, 0.508186860060022, 7.96616300581067, 9.94604963036295,
3.79083717222623, 2.57358468532258, 10.1404974171776, 13.7408303595752,
0.933577123801399, 9.75887417074129, 1.27693947132921, 13.4970905965787,
10.2087501765735, 1.68112753028756, 6.1178991508927, -0.156762622680077,
3.82374791691426, 4.43314678736265, 5.97907067167507, 11.3104332518482,
8.21426074201525, 15.320967360602, 5.81782169471483, 9.6004907412354,
3.40636455909704, 4.73750103921864, 3.0133019468806, 5.56595224859066,
12.0346332527215, -0.40283199827104, 10.5996779538754, 5.44795836991128,
4.70523736412729, 14.096201892183, 5.71490161813391, 3.77800720810782,
4.41206200639436, 4.18660847858423, 6.90788020044911, 2.78257393345915,
7.61717857379431, 10.2410602647684, 8.18207106836167, 4.82754943871433,
19.1624882857155, 16.0677109398509, 12.589708067017, 9.29079879799404,
7.42625019725314, 9.39025179806185, 12.6193550331438, 11.1121039747257,
15.7907099734986, 10.7425286789233, 7.79714300307344, 8.80608578166101,
17.5606266346039, 17.3088604929222, 13.4500543478523, 14.6377884248645,
8.07985518296064)),
class = "data.frame", row.names = c("layer.1",
"layer.2", "layer.3", "layer.4", "layer.5", "layer.6", "layer.7",
"layer.8", "layer.9", "layer.10", "layer.11", "layer.12", "layer.13",
"layer.14", "layer.15", "layer.16", "layer.17", "layer.18", "layer.19",
"layer.20", "layer.21", "layer.22", "layer.23", "layer.24", "layer.25",
"layer.26", "layer.27", "layer.28", "layer.29", "layer.30", "layer.31",
"layer.32", "layer.33", "layer.34", "layer.35", "layer.36", "layer.37",
"layer.38", "layer.39", "layer.40", "layer.41", "layer.42", "layer.43",
"layer.44", "layer.45", "layer.46", "layer.47", "layer.48", "layer.49",
"layer.50", "layer.51", "layer.52", "layer.53", "layer.54", "layer.55",
"layer.56", "layer.57", "layer.58", "layer.59", "layer.60", "layer.61",
"layer.62", "layer.63", "layer.64", "layer.65", "layer.66", "layer.67",
"layer.68", "layer.69", "layer.70", "layer.71", "layer.72", "layer.73",
"layer.74", "layer.75", "layer.76", "layer.77", "layer.78", "layer.79",
"layer.80", "layer.81", "layer.82", "layer.83", "layer.84", "layer.85",
"layer.86", "layer.87", "layer.88", "layer.89", "layer.90", "layer.91",
"layer.92", "layer.93", "layer.94", "layer.95", "layer.96", "layer.97",
"layer.98", "layer.99", "layer.100", "layer.101", "layer.102",
"layer.103", "layer.104", "layer.105", "layer.106", "layer.107",
"layer.108", "layer.109", "layer.110", "layer.111", "layer.112",
"layer.113", "layer.114", "layer.115", "layer.116", "layer.117",
"layer.118", "layer.119", "layer.120", "layer.121", "layer.122",
"layer.123", "layer.124", "layer.125", "layer.126", "layer.127",
"layer.128", "layer.129", "layer.130", "layer.131", "layer.132",
"layer.133", "layer.134", "layer.135", "layer.136", "layer.137",
"layer.138"))
# Create scatterplot from dataframe "onepctCO2MEDIAN" with "x" and "y" variables and add "lm"
onepctCO2MEDIAN %>%
ggplot(aes(x = x, y = y)) +
geom_point() +
xlab("x") +
ylab("y") +
geom_smooth(method = "lm")
Created on 2019-06-07 by the reprex package (v0.3.0)
We used the R library forecast to make predictions for the next 24 hours. We have the following:
fore_cast=forecast.tbats(model,h=24,level=90)
fore_cast
Point Forecast Lo 90 Hi 90
5.380952 6270.778 5389.089 7296.643
5.386905 5458.096 4557.375 6536.743
5.392857 5219.995 4248.967 6412.814
5.398810 5187.102 4126.390 6520.328
Now we have 2 problems:
We need 'time' (in hour e.g. 01,23,19 etc) instead of 'point'.
We wish to plot the trendline against time showing the actual observed
values against these predicted values. We have loaded actual observed
values from a CSV file.
We tried:
actual_data = read.csv('actualdata.csv')
plot(actual_data,fore_cast)
Doesn't work, and using plot(actual_data) just shows some points in a straight line instead of curved trendline.
EDIT:
Sample output of fore_cast from dput:
structure(list(model = structure(list(lambda = 0.000438881055939422,
alpha = 0.65694875480321, beta = -0.0983972877836753, damping.parameter = 0.800419363290521,
gamma.one.values = c(-0.00150031474145603, -0.00124696854910294
), gamma.two.values = c(0.0023600487982342, -0.002465549595849
), ar.coefficients = NULL, ma.coefficients = NULL, likelihood = 13202.294346586,
optim.return.code = 0L, variance = 0.00855092137349485, AIC = 13258.294346586,
parameters = structure(list(vect = c(0.000438881055939422,
0.65694875480321, 0.800419363290521, -0.0983972877836753,
-0.00150031474145603, -0.00124696854910294, 0.0023600487982342,
-0.002465549595849), control = structure(list(use.beta = TRUE,
use.box.cox = TRUE, use.damping = TRUE, length.gamma = 4L,
p = 0, q = 0), .Names = c("use.beta", "use.box.cox",
"use.damping", "length.gamma", "p", "q"))), .Names = c("vect",
"control")), seed.states = structure(c(7.44188559667267,
0.00357069100887873, -0.0664300680553579, 0.0229067500159256,
0.00460111570469819, -0.00772324725408007, -0.000610110386029883,
0.00568378752162509, -0.0084050648066819, -0.0324093004247092,
-0.000720936399990958, -0.00705790547321605, -0.00738992950838566,
0.00180424326179638, -0.00107745502434416, 0.00242014705705761,
-0.01824679745657, 0.0123019701003545, -0.0245935735677402,
0.0181321397860132), .Dim = c(20L, 1L)), fitted.values = structure(c(1598.57443298879,
1435.74973092922, 1397.92464316794, 1296.90202189518, 1440.3201303663,
1544.11695101118, 1777.97079874181, 1766.50571671645, 1925.27360388028,
1863.26963233038, 1773.08363764691, 1887.26580055295, 1887.48006609474,
1841.66200850472, 1991.90290660363, 2233.04775631848, 2081.30246965768,
1872.12639817609, 1899.38583561568, 2213.43437455052, 2214.00832820531,
1745.36311914995, 1678.67975050944, 1502.35472259274, 1512.27350460399,
1456.14165844166, 1464.3803467642, 1517.99443293857, 1484.54280422369,
1382.37041287489, 1452.43700910726, 1545.16934543365, 1440.50974319508,
1475.59742668699, 1544.88546424501, 1790.95280713647, 1916.4267023671,
1928.72804180587, 1819.15839770808, 1916.43079357329, 1836.80043977753,
1720.25638746452, 1730.03629161412, 1614.6048115754, 1599.23641723244,
1635.86950932066, 1543.46360784778, 1641.35066985679, 1608.60556151299,
1651.47649465456, 1475.15006990464, 1403.67294742438, 1507.58932406857,
1666.3170708439, 1696.06132797576, 1543.32187293056, 1704.58043626911,
1914.72424191575, 2109.33624862625, 2092.98934458578, 2222.13355258602,
2084.68677709368, 1962.9230489947, 2045.61547393981, 2140.30565941261,
2097.46130996426, 2126.07936955385, 2226.18935508502, 2269.54492801286,
2300.37314952852, 2398.48786829541, 2303.31270702723, 2332.74139979969,
2146.51487558643, 2101.27480789243, 2111.61910899422, 2053.57840714969,
2046.56606362537, 2073.82870990658, 2094.88831798868, 2334.85185938782,
2541.72156227893, 2502.36031483721, 2398.12240784327, 2266.35832277135,
2151.05248890962, 2266.88803633019, 2366.19453856405, 2399.97570044332,
2341.74959623409, 2144.33465155869, 2102.91952061083, 2214.48622101851,
2179.48115699957, 2288.28092735955, 2224.55218736155, 2195.1506809087,
2163.94619334319, 2161.41843642149, 2134.75060670667, 2138.77895768654,
2142.84680080931, 2258.55072549978, 2297.90237035988, 2314.94197015208,
2300.99928929609, 2277.39754662665, 2291.06980363364, 2487.04257346235,
2381.05768214413, 2509.40078456481, 2657.61336243367, 2528.65026804303,
2434.2722174014, 2366.04811963942, 2270.6647135766, 2231.33965004538,
2376.51043520344, 2249.42861599343, 2193.98771109322, 2252.12327312365,
2210.76969838623, 2180.50451255189, 2221.92898123682, 2537.84678083006,
2329.57350097532, 2252.82349908982, 2143.92033677754, 2092.3142840022,
2084.70304624685, 2111.18929138546, 2160.05383108999, 2280.94409931504,
2118.22029344747, 2214.65738250204, 2269.05911898631, 2084.26658709038,
2016.04764576402, 2095.57091797435, 2161.07354463394, 2427.77607700887,
2333.91103594967, 2234.23838054763, 2250.71557301013, 2186.97925802073,
2129.51096829218, 2115.40228652934, 2094.89231085691, 2086.41044567131,
2180.94542608489, 2105.38187642016, 2459.45788915933, 2292.36325639374,
2410.75372754831, 2375.56640249604, 2491.11938114866, 2470.51372278037,
2464.95765202085, 2600.85929020727, 2709.48518695182, 2779.77558137814,
2518.29927341458, 2344.06621605191, 2391.56719713269, 2368.68842788795,
2199.93530349068, 2113.92970206565, 2458.96718445444, 3121.97852988865,
2559.40932439262, 2331.12829078836, 2238.54586985577, 2241.91440620202,
2225.29804576634, 2154.14147781021, 2060.57980596908, 2037.30100544426,
2215.93410789353, 2364.42668160056, 2518.72871618042, 2537.34279365294,
2473.76096855791, 2623.63387707374, 2589.08335304697, 2577.0563838788,
2349.53279218826, 2305.52193868551, 2232.63712180453, 2167.50003597208,
2320.23187534213, 2281.86365949586, 2281.21119271599, 2323.2014703372,
2185.94404743238, 2140.21863271207, 2011.67723856012, 1966.52063119589,
2002.67344212857, 1952.41101080662, 1988.37461163105, 2126.75137749373,
2239.14722292367, 2320.98046489603, 2444.91847853015, 2431.69548763034,
2514.73820659393, 2505.85249387343, 2888.19773974179, 2853.20690693738,
2502.20865871069, 2524.56894781003, 2659.52271740553, 2615.9025930681,
2923.69327019152, 2754.76074569658, 2784.59488335761, 2874.24378479002,
2683.41908597168, 2733.83011888159, 2774.1325162997, 2906.41593326865,
2726.06821502751, 2460.21579967528, 2450.8035097605, 2547.39389733175,
2625.60323572861, 2827.94083526683, 2971.92012845614, 3042.90981987278,
2835.00811374845, 2846.98066660519, 2871.21876763166, 2901.99696373824,
2627.47532996657, 2583.75084300313, 2602.68041642846, 2632.8054092953,
2667.85374690972, 2639.10586730146, 2466.95799545022, 2381.06823502402,
2531.32611053776, 2407.14812148706, 2342.75701798463, 2401.73791085847,
2365.50645844524, 2404.50408575777, 2452.57343738519, 2613.15332739214,
2665.50965844576, 2723.8237337447, 2915.09266385617, 2890.17498445896,
2853.6278331055, 2868.1228183545, 2917.07803535669, 2876.59409770233,
2577.82035337979, 2581.91435020803, 2520.20342021937, 2603.37973251208,
2536.03988578365, 2510.83398648802, 2472.80606784857, 2425.51212342113,
2442.02863541673, 2465.73405821711, 2384.42988766816, 2555.51500549788,
2737.77091706275, 2425.00224845814, 2460.17325671183, 2639.16650619329,
2816.37024420397, 2755.69999167982, 2802.64991688288, 2685.12803367301,
2521.77568128564, 2500.99980614696, 2620.41659854805, 2529.25134423133,
2590.14804885984, 2318.80485234464, 2341.88940012276, 2460.21008281205,
2513.70688167177, 2437.71670675479, 2383.29782281743, 2499.36244454453,
2472.98602901478, 2491.10649022417, 2350.1405559119, 2362.78308814045,
2431.3911847573, 2321.15216823049, 2355.74203614213, 2429.60523843166,
2355.61947983433, 2346.3751018515, 2453.82214513707, 2542.98125962684,
2342.43364707529, 2302.17741211575, 2388.93541944219, 2435.41878657221, ....
Sample output from dput for actual observed values:
structure(list(index12 = c(6297.416944, 5406.865556, 4718.355556,
5304.729167, 4968.014722, 5081.130833, 5544.955, 4655.009444,
4269.023056, 4346.588333, 4511.455833, 5102.57, 4818.673333,
4862.343056, 4785.176667, 5385.005278, 6469.080833, 7166.025278,
7010.708333, 511.114167)), .Names = "index12", class = "data.frame", row.names = c(NA,
-20L))
The value of Point is unusual in spite of hour unit data. I think you failed to make a model.
Here is my example:
actual_data <- structure(list(index12 = c(6297.416944, 5406.865556, 4718.355556,
5304.729167, 4968.014722, 5081.130833, 5544.955, 4655.009444,
4269.023056, 4346.588333, 4511.455833, 5102.57, 4818.673333,
4862.343056, 4785.176667, 5385.005278, 6469.080833, 7166.025278,
7010.708333, 511.114167)),
.Names = "index12", class = "data.frame", row.names = c(NA, -20L))
# I suppose that actual_data was taken per hour.
num_actual <- as.numeric(actual_data[,1])
model <- bats(num_actual)
fore_cast <- forecast(model, h=24, level=90)
fore_cast # Point is from 21 to 44 because of length(actual_data)=20 and demanding predictions for the next 24 hours
# Point Forecast Lo 90 Hi 90
# 21 5063.207 2902.187 7224.226
# 22 5108.114 2946.988 7269.241
# :
# 44 5108.114 2944.629 7271.600
# plot() has forecast method. It draws actual_data and prediction, and paints Lo90-Hi90.
plot(fore_cast, main="")
This seems relatively straightforward and possibly doable with scale_x_datetime or scale_x_discrete.
I have a column with time increments like on a stopwatch:
[1] 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06
1800 Levels: 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06 0:00:07 ... 0:30:00
Using ggplot how can I set the x-axis labels for 30 second intervals?
Reproducible data:
structure(list(`somedata$Time` = structure(1:4, .Label = c("0:00:01",
"0:00:02", "0:00:03", "0:00:04", "0:00:05", "0:00:06", "0:00:07",
"0:00:08", "0:00:09", "0:00:10", "0:00:11", "0:00:12", "0:00:13",
"0:00:14", "0:00:15", "0:00:16", "0:00:17", "0:00:18", "0:00:19",
"0:00:20", "0:00:21", "0:00:22", "0:00:23", "0:00:24", "0:00:25",
"0:00:26", "0:00:27", "0:00:28", "0:00:29", "0:00:30", "0:00:31",
"0:00:32", "0:00:33", "0:00:34", "0:00:35", "0:00:36", "0:00:37",
"0:00:38", "0:00:39", "0:00:40", "0:00:41", "0:00:42", "0:00:43",
"0:00:44", "0:00:45", "0:00:46", "0:00:47", "0:00:48", "0:00:49",
"0:00:50", "0:00:51", "0:00:52", "0:00:53", "0:00:54", "0:00:55",
"0:00:56", "0:00:57", "0:00:58", "0:00:59", "0:01:00", "0:01:01",
"0:01:02", "0:01:03", "0:01:04", "0:01:05", "0:01:06", "0:01:07",
"0:01:08", "0:01:09", "0:01:10", "0:01:11", "0:01:12", "0:01:13",
"0:01:14", "0:01:15", "0:01:16", "0:01:17", "0:01:18", "0:01:19",
"0:01:20", "0:01:21", "0:01:22", "0:01:23", "0:01:24", "0:01:25",
"0:01:26", "0:01:27", "0:01:28", "0:01:29", "0:01:30", "0:01:31",
"0:01:32", "0:01:33", "0:01:34", "0:01:35", "0:01:36", "0:01:37",
"0:01:38", "0:01:39", "0:01:40", "0:01:41", "0:01:42", "0:01:43",
"0:01:44", "0:01:45", "0:01:46", "0:01:47", "0:01:48", "0:01:49",
"0:01:50", "0:01:51", "0:01:52", "0:01:53", "0:01:54", "0:01:55",
"0:01:56", "0:01:57", "0:01:58", "0:01:59", "0:02:00", "0:02:01",
"0:02:02", "0:02:03", "0:02:04", "0:02:05", "0:02:06", "0:02:07",
"0:02:08", "0:02:09", "0:02:10", "0:02:11", "0:02:12", "0:02:13",
"0:02:14", "0:02:15", "0:02:16", "0:02:17", "0:02:18", "0:02:19",
"0:02:20", "0:02:21", "0:02:22", "0:02:23", "0:02:24", "0:02:25",
"0:02:26", "0:02:27", "0:02:28", "0:02:29", "0:02:30", "0:02:31",
"0:02:32", "0:02:33", "0:02:34", "0:02:35", "0:02:36", "0:02:37",
"0:02:38", "0:02:39", "0:02:40", "0:02:41", "0:02:42", "0:02:43",
"0:02:44", "0:02:45", "0:02:46", "0:02:47", "0:02:48", "0:02:49",
"0:02:50", "0:02:51", "0:02:52", "0:02:53", "0:02:54", "0:02:55",
"0:02:56", "0:02:57", "0:02:58", "0:02:59", "0:03:00", "0:03:01",
"0:03:02", "0:03:03", "0:03:04", "0:03:05", "0:03:06", "0:03:07",
"0:03:08", "0:03:09", "0:03:10", "0:03:11", "0:03:12", "0:03:13",
"0:03:14", "0:03:15", "0:03:16", "0:03:17", "0:03:18", "0:03:19",
"0:03:20", "0:03:21", "0:03:22", "0:03:23", "0:03:24", "0:03:25",
"0:03:26", "0:03:27", "0:03:28", "0:03:29", "0:03:30", "0:03:31",
"0:03:32", "0:03:33", "0:03:34", "0:03:35", "0:03:36", "0:03:37",
"0:03:38", "0:03:39", "0:03:40", "0:03:41", "0:03:42", "0:03:43",
"0:03:44", "0:03:45", "0:03:46", "0:03:47", "0:03:48", "0:03:49",
"0:03:50", "0:03:51", "0:03:52", "0:03:53", "0:03:54", "0:03:55",
"0:03:56", "0:03:57", "0:03:58", "0:03:59", "0:04:00", "0:04:01",
"0:04:02", "0:04:03", "0:04:04", "0:04:05", "0:04:06", "0:04:07",
"0:04:08", "0:04:09", "0:04:10", "0:04:11", "0:04:12", "0:04:13",
"0:04:14", "0:04:15", "0:04:16", "0:04:17", "0:04:18", "0:04:19",
"0:04:20", "0:04:21", "0:04:22", "0:04:23", "0:04:24", "0:04:25",
"0:04:26", "0:04:27", "0:04:28", "0:04:29", "0:04:30", "0:04:31",
"0:04:32", "0:04:33", "0:04:34", "0:04:35", "0:04:36", "0:04:37",
"0:04:38", "0:04:39", "0:04:40", "0:04:41", "0:04:42", "0:04:43",
"0:04:44", "0:04:45", "0:04:46", "0:04:47", "0:04:48", "0:04:49",
"0:04:50", "0:04:51", "0:04:52", "0:04:53", "0:04:54", "0:04:55",
"0:04:56", "0:04:57", "0:04:58", "0:04:59", "0:05:00", "0:05:01",
"0:05:02", "0:05:03", "0:05:04", "0:05:05", "0:05:06", "0:05:07",
"0:05:08", "0:05:09", "0:05:10", "0:05:11", "0:05:12", "0:05:13",
"0:05:14", "0:05:15", "0:05:16", "0:05:17", "0:05:18", "0:05:19",
"0:05:20", "0:05:21", "0:05:22", "0:05:23", "0:05:24", "0:05:25",
"0:05:26", "0:05:27", "0:05:28", "0:05:29", "0:05:30", "0:05:31",
"0:05:32", "0:05:33", "0:05:34", "0:05:35", "0:05:36", "0:05:37",
"0:05:38", "0:05:39", "0:05:40", "0:05:41", "0:05:42", "0:05:43",
"0:05:44", "0:05:45", "0:05:46", "0:05:47", "0:05:48", "0:05:49",
"0:05:50", "0:05:51", "0:05:52", "0:05:53", "0:05:54", "0:05:55",
"0:05:56", "0:05:57", "0:05:58", "0:05:59", "0:06:00", "0:06:01",
"0:06:02", "0:06:03", "0:06:04", "0:06:05", "0:06:06", "0:06:07",
"0:06:08", "0:06:09", "0:06:10", "0:06:11", "0:06:12", "0:06:13",
"0:06:14", "0:06:15", "0:06:16", "0:06:17", "0:06:18", "0:06:19",
"0:06:20", "0:06:21", "0:06:22", "0:06:23", "0:06:24", "0:06:25",
"0:06:26", "0:06:27", "0:06:28", "0:06:29", "0:06:30", "0:06:31",
"0:06:32", "0:06:33", "0:06:34", "0:06:35", "0:06:36", "0:06:37",
"0:06:38", "0:06:39", "0:06:40", "0:06:41", "0:06:42", "0:06:43",
"0:06:44", "0:06:45", "0:06:46", "0:06:47", "0:06:48", "0:06:49",
"0:06:50", "0:06:51", "0:06:52", "0:06:53", "0:06:54", "0:06:55",
"0:06:56", "0:06:57", "0:06:58", "0:06:59", "0:07:00", "0:07:01",
"0:07:02", "0:07:03", "0:07:04", "0:07:05", "0:07:06", "0:07:07",
"0:07:08", "0:07:09", "0:07:10", "0:07:11", "0:07:12", "0:07:13",
"0:07:14", "0:07:15", "0:07:16", "0:07:17", "0:07:18", "0:07:19",
"0:07:20", "0:07:21", "0:07:22", "0:07:23", "0:07:24", "0:07:25",
"0:07:26", "0:07:27", "0:07:28", "0:07:29", "0:07:30", "0:07:31",
"0:07:32", "0:07:33", "0:07:34", "0:07:35", "0:07:36", "0:07:37",
"0:07:38", "0:07:39", "0:07:40", "0:07:41", "0:07:42", "0:07:43",
"0:07:44", "0:07:45", "0:07:46", "0:07:47", "0:07:48", "0:07:49",
"0:07:50", "0:07:51", "0:07:52", "0:07:53", "0:07:54", "0:07:55",
"0:07:56", "0:07:57", "0:07:58", "0:07:59", "0:08:00", "0:08:01",
"0:08:02", "0:08:03", "0:08:04", "0:08:05", "0:08:06", "0:08:07",
"0:08:08", "0:08:09", "0:08:10", "0:08:11", "0:08:12", "0:08:13",
"0:08:14", "0:08:15", "0:08:16", "0:08:17", "0:08:18", "0:08:19",
"0:08:20", "0:08:21", "0:08:22", "0:08:23", "0:08:24", "0:08:25",
"0:08:26", "0:08:27", "0:08:28", "0:08:29", "0:08:30", "0:08:31",
"0:08:32", "0:08:33", "0:08:34", "0:08:35", "0:08:36", "0:08:37",
"0:08:38", "0:08:39", "0:08:40", "0:08:41", "0:08:42", "0:08:43",
"0:08:44", "0:08:45", "0:08:46", "0:08:47", "0:08:48", "0:08:49",
"0:08:50", "0:08:51", "0:08:52", "0:08:53", "0:08:54", "0:08:55",
"0:08:56", "0:08:57", "0:08:58", "0:08:59", "0:09:00", "0:09:01",
"0:09:02", "0:09:03", "0:09:04", "0:09:05", "0:09:06", "0:09:07",
"0:09:08", "0:09:09", "0:09:10", "0:09:11", "0:09:12", "0:09:13",
"0:09:14", "0:09:15", "0:09:16", "0:09:17", "0:09:18", "0:09:19",
"0:09:20", "0:09:21", "0:09:22", "0:09:23", "0:09:24", "0:09:25",
"0:09:26", "0:09:27", "0:09:28", "0:09:29", "0:09:30", "0:09:31",
"0:09:32", "0:09:33", "0:09:34", "0:09:35", "0:09:36", "0:09:37",
"0:09:38", "0:09:39", "0:09:40", "0:09:41", "0:09:42", "0:09:43",
"0:09:44", "0:09:45", "0:09:46", "0:09:47", "0:09:48", "0:09:49",
"0:09:50", "0:09:51", "0:09:52", "0:09:53", "0:09:54", "0:09:55",
"0:09:56", "0:09:57", "0:09:58", "0:09:59", "0:10:00", "0:10:01",
"0:10:02", "0:10:03", "0:10:04", "0:10:05", "0:10:06", "0:10:07",
"0:10:08", "0:10:09", "0:10:10", "0:10:11", "0:10:12", "0:10:13",
"0:10:14", "0:10:15", "0:10:16", "0:10:17", "0:10:18", "0:10:19",
"0:10:20", "0:10:21", "0:10:22", "0:10:23", "0:10:24", "0:10:25",
"0:10:26", "0:10:27", "0:10:28", "0:10:29", "0:10:30", "0:10:31",
"0:10:32", "0:10:33", "0:10:34", "0:10:35", "0:10:36", "0:10:37",
"0:10:38", "0:10:39", "0:10:40", "0:10:41", "0:10:42", "0:10:43",
"0:10:44", "0:10:45", "0:10:46", "0:10:47", "0:10:48", "0:10:49",
"0:10:50", "0:10:51", "0:10:52", "0:10:53", "0:10:54", "0:10:55",
"0:10:56", "0:10:57", "0:10:58", "0:10:59", "0:11:00", "0:11:01",
"0:11:02", "0:11:03", "0:11:04", "0:11:05", "0:11:06", "0:11:07",
"0:11:08", "0:11:09", "0:11:10", "0:11:11", "0:11:12", "0:11:13",
"0:11:14", "0:11:15", "0:11:16", "0:11:17", "0:11:18", "0:11:19",
"0:11:20", "0:11:21", "0:11:22", "0:11:23", "0:11:24", "0:11:25",
"0:11:26", "0:11:27", "0:11:28", "0:11:29", "0:11:30", "0:11:31",
"0:11:32", "0:11:33", "0:11:34", "0:11:35", "0:11:36", "0:11:37",
"0:11:38", "0:11:39", "0:11:40", "0:11:41", "0:11:42", "0:11:43",
"0:11:44", "0:11:45", "0:11:46", "0:11:47", "0:11:48", "0:11:49",
"0:11:50", "0:11:51", "0:11:52", "0:11:53", "0:11:54", "0:11:55",
"0:11:56", "0:11:57", "0:11:58", "0:11:59", "0:12:00", "0:12:01",
"0:12:02", "0:12:03", "0:12:04", "0:12:05", "0:12:06", "0:12:07",
"0:12:08", "0:12:09", "0:12:10", "0:12:11", "0:12:12", "0:12:13",
"0:12:14", "0:12:15", "0:12:16", "0:12:17", "0:12:18", "0:12:19",
"0:12:20", "0:12:21", "0:12:22", "0:12:23", "0:12:24", "0:12:25",
"0:12:26", "0:12:27", "0:12:28", "0:12:29", "0:12:30", "0:12:31",
"0:12:32", "0:12:33", "0:12:34", "0:12:35", "0:12:36", "0:12:37",
"0:12:38", "0:12:39", "0:12:40", "0:12:41", "0:12:42", "0:12:43",
"0:12:44", "0:12:45", "0:12:46", "0:12:47", "0:12:48", "0:12:49",
"0:12:50", "0:12:51", "0:12:52", "0:12:53", "0:12:54", "0:12:55",
"0:12:56", "0:12:57", "0:12:58", "0:12:59", "0:13:00", "0:13:01",
"0:13:02", "0:13:03", "0:13:04", "0:13:05", "0:13:06", "0:13:07",
"0:13:08", "0:13:09", "0:13:10", "0:13:11", "0:13:12", "0:13:13",
"0:13:14", "0:13:15", "0:13:16", "0:13:17", "0:13:18", "0:13:19",
"0:13:20", "0:13:21", "0:13:22", "0:13:23", "0:13:24", "0:13:25",
"0:13:26", "0:13:27", "0:13:28", "0:13:29", "0:13:30", "0:13:31",
"0:13:32", "0:13:33", "0:13:34", "0:13:35", "0:13:36", "0:13:37",
"0:13:38", "0:13:39", "0:13:40", "0:13:41", "0:13:42", "0:13:43",
"0:13:44", "0:13:45", "0:13:46", "0:13:47", "0:13:48", "0:13:49",
"0:13:50", "0:13:51", "0:13:52", "0:13:53", "0:13:54", "0:13:55",
"0:13:56", "0:13:57", "0:13:58", "0:13:59", "0:14:00", "0:14:01",
"0:14:02", "0:14:03", "0:14:04", "0:14:05", "0:14:06", "0:14:07",
"0:14:08", "0:14:09", "0:14:10", "0:14:11", "0:14:12", "0:14:13",
"0:14:14", "0:14:15", "0:14:16", "0:14:17", "0:14:18", "0:14:19",
"0:14:20", "0:14:21", "0:14:22", "0:14:23", "0:14:24", "0:14:25",
"0:14:26", "0:14:27", "0:14:28", "0:14:29", "0:14:30", "0:14:31",
"0:14:32", "0:14:33", "0:14:34", "0:14:35", "0:14:36", "0:14:37",
"0:14:38", "0:14:39", "0:14:40", "0:14:41", "0:14:42", "0:14:43",
"0:14:44", "0:14:45", "0:14:46", "0:14:47", "0:14:48", "0:14:49",
"0:14:50", "0:14:51", "0:14:52", "0:14:53", "0:14:54", "0:14:55",
"0:14:56", "0:14:57", "0:14:58", "0:14:59", "0:15:00", "0:15:01",
"0:15:02", "0:15:03", "0:15:04", "0:15:05", "0:15:06", "0:15:07",
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"0:28:14", "0:28:15", "0:28:16", "0:28:17", "0:28:18", "0:28:19",
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"0:28:38", "0:28:39", "0:28:40", "0:28:41", "0:28:42", "0:28:43",
"0:28:44", "0:28:45", "0:28:46", "0:28:47", "0:28:48", "0:28:49",
"0:28:50", "0:28:51", "0:28:52", "0:28:53", "0:28:54", "0:28:55",
"0:28:56", "0:28:57", "0:28:58", "0:28:59", "0:29:00", "0:29:01",
"0:29:02", "0:29:03", "0:29:04", "0:29:05", "0:29:06", "0:29:07",
"0:29:08", "0:29:09", "0:29:10", "0:29:11", "0:29:12", "0:29:13",
"0:29:14", "0:29:15", "0:29:16", "0:29:17", "0:29:18", "0:29:19",
"0:29:20", "0:29:21", "0:29:22", "0:29:23", "0:29:24", "0:29:25",
"0:29:26", "0:29:27", "0:29:28", "0:29:29", "0:29:30", "0:29:31",
"0:29:32", "0:29:33", "0:29:34", "0:29:35", "0:29:36", "0:29:37",
"0:29:38", "0:29:39", "0:29:40", "0:29:41", "0:29:42", "0:29:43",
"0:29:44", "0:29:45", "0:29:46", "0:29:47", "0:29:48", "0:29:49",
"0:29:50", "0:29:51", "0:29:52", "0:29:53", "0:29:54", "0:29:55",
"0:29:56", "0:29:57", "0:29:58", "0:29:59", "0:30:00"), class = "factor"),
student = c("bob", "bob", "bob", "bob"), somemeasure = c(0L,
0L, 1L, 1L)), .Names = c("somedata$Time", "student", "somemeasure"
), row.names = c(NA, 4L), class = "data.frame")
Assuming that your data frame is named df. First, create new column which is POSIXct by pasting together some arbitrary date and original Time column and then converting with as.POSIXct().
Then use function scale_x_datetime() to set breaks and format for labels you want to see.
df$Time2<-as.POSIXct(paste("1960-01-01 ",df$Time))
library(scales)
ggplot(df,aes(Time2,somemeasure))+geom_point()+
scale_x_datetime(breaks=date_breaks("30 sec"),labels = date_format("%M:%S"))