I have done species accumulation curves and would like to plot the SAC results of different substrate sizeclasses in the same ggplot, with expected species richness on y-axis and number of sites samples on x-axis. The data features a cumulative number of samples in each sizeclass (column "sites"), the expected species richness (column "richness"), and substrate size classes 10, 20 and 30 (column "sc").
sites richness sc
1 1 0.6696915 10
2 2 1.2008513 10
3 3 1.6387310 10
4 4 2.0128472 10
5 5 2.3424933 10
6 6 2.6403239 10
sites richness sc
2836 1 1.000000 20
2837 2 1.703442 20
2838 3 2.249188 20
2839 4 2.706618 20
2840 5 3.110651 20
2841 6 3.479173 20
I want each sizeclass to have unique linetype. I used the following code for ggplot:
sac_kaikki<-ggplot(sac_data, aes(x=sites, y=richness,group=sc)) +
geom_line(aes(linetype=sc))+
coord_cartesian(xlim=c(0,100))+
theme(axis.title.y = element_blank())+
theme(axis.title.x = element_blank())
However, instead of getting three neat lines in different linetypes, I got [this jumbly muddly messy thing with more stripes than a herd of zebras][1]: https://i.stack.imgur.com/iD75K.jpg. I am sure the solution is rather simple, but for my life I am not able to figure it out.
// as Brookes kindly pointed out I should add some reproducible data, here is a subset of my data with dput, featuring 10 first observations of size classes 10 and 20:
dput(head(subset(sac_data,sac_data$sc=="10"),10))
structure(list(sites = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), richness = c(0.669691470054462,
1.20085134466255, 1.63873100707468, 2.01284716414471, 2.34249332096243,
2.64032389106845, 2.91468283244696, 3.17111526890278, 3.41334794519086,
3.64392468817362), sc = c("10", "10", "10", "10", "10", "10",
"10", "10", "10", "10")), .Names = c("sites", "richness", "sc"
), row.names = c(NA, 10L), class = "data.frame")
dput(head(subset(sac_data,sac_data$sc=="20"),10))
structure(list(sites = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), richness = c(0.999999999999987,
1.70344155844158, 2.24918831168832, 2.70661814764865, 3.11065087175364,
3.47917264517669, 3.82165739030286, 4.14341144680334, 4.44765475554031,
4.73653870494466), sc = c("20", "20", "20", "20", "20", "20",
"20", "20", "20", "20")), .Names = c("sites", "richness", "sc"
), row.names = 2836:2845, class = "data.frame")
// okay so for whatever reason, the plot works just fine if I plot only two sizeclasses, but including the third one produces the absurd plot I posted a picture of.
structure(list(sites = 1:10, richness = c(0.42857142857143, 0.838095238095238,
1.22932330827066, 1.60300751879699, 1.95989974937343, 2.30075187969924,
2.62631578947368, 2.93734335839598, 3.23458646616541, 3.5187969924812
), sc = c("30", "30", "30", "30", "30", "30", "30", "30", "30",
"30")), .Names = c("sites", "richness", "sc"), row.names = c(NA,
10L), class = "data.frame")
Works fine for me with your sample data:
a <- structure(list(sites = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), richness = c(0.669691470054462,
1.20085134466255, 1.63873100707468, 2.01284716414471, 2.34249332096243,
2.64032389106845, 2.91468283244696, 3.17111526890278, 3.41334794519086,
3.64392468817362), sc = c("10", "10", "10", "10", "10", "10",
"10", "10", "10", "10")), .Names = c("sites", "richness", "sc"
), row.names = c(NA, 10L), class = "data.frame")
b <- structure(list(sites = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), richness = c(0.999999999999987,
1.70344155844158, 2.24918831168832, 2.70661814764865, 3.11065087175364,
3.47917264517669, 3.82165739030286, 4.14341144680334, 4.44765475554031,
4.73653870494466), sc = c("20", "20", "20", "20", "20", "20",
"20", "20", "20", "20")), .Names = c("sites", "richness", "sc"
), row.names = 2836:2845, class = "data.frame")
c <- structure(list(sites = 1:10, richness = c(0.42857142857143, 0.838095238095238,
1.22932330827066, 1.60300751879699, 1.95989974937343, 2.30075187969924,
2.62631578947368, 2.93734335839598, 3.23458646616541, 3.5187969924812
), sc = c("30", "30", "30", "30", "30", "30", "30", "30", "30",
"30")), .Names = c("sites", "richness", "sc"), row.names = c(NA,
10L), class = "data.frame")
sac_data <- bind_rows(a, b, c)
Plotting:
ggplot(sac_data, aes(sites, richness, group = sc)) +
geom_line(aes(linetype = sc))
Related
I would like to go over each row, and if the difference between the current row and the previous row is more than 7, then I would like to assign the current row a unique id, otherwise the current and the previous row would have the same id. Please note that the zipcode is not relevant for the unique id. Here's my data:
z<- structure(list(zipcode = c(96717L, 96730L, 96825L, 96826L, 96720L,
96756L, 96740L, 96819L, 96734L, 96740L, 96714L, 96714L, 96703L,
90017L, 96796L, 96714L, 96714L, 96761L, 96712L, 96712L), date = structure(c(8809,
8809, 8847, 8848, 8989, 9041, 9161, 9188, 9201, 9293, 9403, 9437,
9437, 9437, 9437, 9443, 9444, 9457, 9457, 9483), class = "Date")), row.names = c(NA,
-20L), class = c("data.table", "data.frame"))
Here's my desired output:
y<- structure(list(zipcode = c(96717, 96730, 96825, 96826, 96720,
96756, 96740, 96819, 96734, 96740, 96714, 96714, 96703, 90017,
96796, 96714, 96714, 96761, 96712, 96712), date = structure(c(761097600,
761097600, 764380800, 764467200, 776649600, 781142400, 791510400,
793843200, 794966400, 802915200, 812419200, 815356800, 815356800,
815356800, 815356800, 815875200, 815961600, 817084800, 817084800,
819331200), class = c("POSIXct", "POSIXt"), tzone = "UTC"), difference_date = c("NA",
"0", "38", "1", "141", "52", "120", "27", "13", "92", "110",
"34", "0", "0", "0", "6", "1", "13", "0", "26"), id = c(1, 1,
2, 2, 3, 4, 5, 6, 8, 9, 10, 11, 11, 11, 11, 11, 11, 12, 12, 13
)), class = c("data.table", "data.frame"))
Here's what I did:
library(data.table)
z[,date:=as.Date(date)][,diff_days:=c(NA,diff.Date(date,lag=1L,differences=1L))][, event_id :=1:.N,.(diff_days<=7)]
Here's what I got:
First I converted your example data to data.table format:
library(data.table)
z <- as.data.table(z)
Then calculate the difference date:
z[, date_diff := as.numeric(difftime(date, lag(date), units = "days"))]
And finally, add the id :
z[, event_id := cumsum(c(TRUE, diff(date) > 7))]
My dataframe has many arrows, ">" and "<"s in it alongside some of the element values. I want to remove these characters but keep the numbers. I only know how to replace the entire element with NA with the following code.
df <- apply(df, 1:2, gsub, pattern = "<|>", replacement = "")
Will someone please help me edit this so that it keeps the element numbers too, instead of throwing the entire thing out?
Dataframe:
structure(list(`Analyte Sample` = c(1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14), A = c("4190", "6665", "7435", "2052",
"783", "322", "199", "90", "46", "17", "8", "3", "3", "<1↓"
), B = c("11569", "6677", "3852", "983.88", "589", "359", "203",
"68", "33", "12", "6", "<2↓", "4", "<1↓"), C = c("20453",
"7699", "2499", "707.98", "412", "328", "156", "88", "39", "27",
"17", "<1↓", "<3↓", "<1↓"), D = c("7893", ">20000↑",
"1623", "685.64", "321", "644", "112", "65", "35", "29", "9",
"5", "<3↓", "<1↓"), E = c("320", "15444", "2049", "1065",
"389", "365", "145", "77", "38", "16", "9", "6", "<2↓", "<2↓"
), F = c("7438", ">21999↑", "3472", "1057", "563", "401", "167",
"89", "46", "19", "6", "<1↓", "<1↓", "<1↓"), G = c(7345,
9001, 2473, 1138, 516, 403, 134, 81, 37, 17, 8, 6, 4, 3), H = c("9004",
"3998", "2299", "964.88", "499", "341", "112", "88", "39", "32",
"<29↓", "<30↓", "<31↓", "<29↓"), I = c("8434", "8700",
"2217", "1263", "567", "352", "153", "80", "43", "18", "9", "2",
"3", "<1↓"), J = c("7734", "6733", "2092", "1115", "637", "332",
"155", "82", "37", "17", "10", "4", "1", "<1↓"), K = c(">3718↑",
">3000↑", "2118", "862.13", "426", "355", "143", "78", "44",
"22", "11", "<4↓", "<4↓", "<3↓"), L = c(6345, 7688, 2311,
1195, 647, 366, 177, 83, 41, 20, 8, 6, 3, 2), M = c("4222", ">25587↑",
"1846", "814.61", "422", "314", "154", "86", "41", "27", "21",
"<2↓", "<2↓", "<3↓"), N = c("6773", "8934", "2381", "1221",
"677", "356", "146", "89", "40", "17", "10", "5", "2", "<2↓"
), O = c(">2200↑", ">2133↑", ">2000↑", "564.5", "226",
"476", "111", "60", "32", "36", "18", "<10↓", "<1↓", "<2↓"
)), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L), spec = structure(list(cols = list(`Analyte Sample` = structure(list(), class = c("collector_double",
"collector")), A = structure(list(), class = c("collector_character",
"collector")), B = structure(list(), class = c("collector_character",
"collector")), C = structure(list(), class = c("collector_character",
"collector")), D = structure(list(), class = c("collector_character",
"collector")), E = structure(list(), class = c("collector_character",
"collector")), F = structure(list(), class = c("collector_character",
"collector")), G = structure(list(), class = c("collector_double",
"collector")), H = structure(list(), class = c("collector_character",
"collector")), I = structure(list(), class = c("collector_character",
"collector")), J = structure(list(), class = c("collector_character",
"collector")), K = structure(list(), class = c("collector_character",
"collector")), L = structure(list(), class = c("collector_double",
"collector")), M = structure(list(), class = c("collector_character",
"collector")), N = structure(list(), class = c("collector_character",
"collector")), O = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
You can use lapply() which returns a list and assign it back to df[]. [] is to keep the original attributes, i.e. a class of data.frame. You will see that df becomes what you want.
df[] <- lapply(df, gsub, pattern = "<|>", replacement = "")
I think in your case the best would be to use a regular expression. Using tidyverse:
df %>% mutate_at(vars(A:O), ~ as.numeric(gsub("[^0-9]*([0-9]*).*", "\\1", .)))
If you specifically want only to change values which start with a < or >, you do the following:
df %>% mutate_at(vars(A:O), ~ as.numeric(gsub("[<>]*([0-9]*).*", "\\1", .)))
Of course, you can also use apply... but mind the way apply changes the data frame into a matrix before applying the function (the columns which are numbers will have spaces prefixed, so we need to include space in the pattern):
apply(df, 2, function(x) gsub("[ <>]*([0-9]*).*", "\\1", x))
Explanation:
The pattern [0-9]* matches a digit any number of times. The pattern [^0-9] matches anything but a digit any number of times.
You can try one of these options:
#Code 1
df <- apply(df, 1:2, function(x) gsub(pattern = "<|>", replacement = "",x))
#Code 2
df <- sapply(df,function(x) gsub(pattern = "<|>", replacement = "",x))
Just be careful that the output can be a matrix, so you will have to transform again to dataframe using as.data.frame().
I have a large dataset and I want to subtract specific columns from each other based on their position. I want to subtract column 2 from column 8, column 3 from column 9 and column 4 from column 10.
Thanks a lot
Magnus
structure(list(Stamp_summertime = structure(c(1546684744, 1546685858,
1546687004, 1547030061, 1547030835, 1547031816), tzone = "UTC", class = c("POSIXct",
"POSIXt")), X26.013 = c(0.138461, 0.138461, 0.138461, 0.144421,
0.144421, 0.144421), X27.024 = c(0.0752111, 0.0752111, 0.0752111,
0.0426819, 0.0426819, 0.0426819), X33.031 = c(3.75788, 3.75788,
3.75788, 3.12581, 3.12581, 3.12581), jar_camp = c("1_pf1.1",
"2_pf1.1", "3_pf1.1", "1_pf2.1", "2_pf2.1", "3_pf2.1"), jar = structure(c(1L,
12L, 23L, 1L, 12L, 23L), .Label = c("1", "10_blank", "11", "12",
"13", "14", "15", "16_blank", "17", "18", "19", "2", "20_blank",
"21", "22", "23", "24", "25", "26", "27", "28", "29", "3", "30_blank",
"31", "32", "33", "34", "35", "36", "37", "38_blank", "39", "4",
"40", "41", "42", "43", "44_blank", "45", "46", "47", "48", "49",
"5_blank", "blank_50", "51", "52", "53", "54", "55", "56", "57",
"6", "7", "8", "9", "X_blank"), class = "factor"), campaign = c("pf1.1",
"pf1.1", "pf1.1", "pf2.1", "pf2.1", "pf2.1"), i.X26.013 = c(0.144658,
0.21502, 0.458296, 0.191571, 0.0789067, 0.711814), i.X27.024 = c(0.0595547,
0.0651149, 0.146772, 0.0997815, 0.0539976, 0.185398), i.X33.031 = c(5.4066,
3.30406, 18.0479, 6.13854, 1.3028, 22.2226)), sorted = "Stamp_summertime", class = c("data.table",
"data.frame"), row.names = c(NA, -6L), .internal.selfref = <pointer: 0x00000237a3d91ef0>)
We can create 2 vectors of position and subtract the columns directly. Since you have data.table we use ..column_number to select columns by position.
library(data.table)
col1group <- 2:4
col2group <- 8:10
df[, ..col1group] - df[, ..col2group])
If you want to add them as new columns to original data you can rename them and cbind
cbind(df, setNames(df[, ..col1group] - df[, ..col2group],
paste0(names(df)[col1group], '_diff')))
Something like the following computes the subtractions in the question.
library(data.table)
nms <- names(df1)
iCols <- grep("^i\\.", nms, value = TRUE)
Cols <- sub("^i\\.", "", iCols)
df1[, lapply(seq_along(Cols), function(i) get(Cols[i]) - get(iCols[i]))]
# V1 V2 V3
#1: -0.0061970 0.0156564 -1.64872
#2: -0.0765590 0.0100962 0.45382
#3: -0.3198350 -0.0715609 -14.29002
#4: -0.0471500 -0.0570996 -3.01273
#5: 0.0655143 -0.0113157 1.82301
#6: -0.5673930 -0.1427161 -19.09679
Following Ronak Shah's answer I realized that the code below also works.
df1[, ..Cols] - df1[, ..iCols]
The numeric results are the same but the column names are the vector Cols.
To create new columns, try
newCols <- paste(Cols, "diff", sep = "_")
df1[, (newCols) := lapply(seq_along(Cols), function(i) get(Cols[i]) - get(iCols[i]))]
Base R solution:
idx <- c(2, 3, 4)
jdx <- c(8, 9, 10)
Using lapply() and column binding the list:
setNames(do.call("cbind", lapply(seq_along(idx), function(i){
df[, jdx[i], drop = FALSE] - df[, idx[i], drop = FALSE]
}
)
), c(paste("x", jdx, idx, sep = "_")))
Using sapply() and coercing vectors to a data.frame:
setNames(data.frame(sapply(seq_along(idx), function(i){
df[, jdx[i], drop = FALSE] - df[, idx[i], drop = FALSE]
}
)
), c(paste("x", jdx, idx, sep = "_")))
Using Map() and Reduce() and column binding to original data.frame:
cbind(df, setNames(Reduce(cbind, Map(function(i){
df[, jdx[i], drop = FALSE] - df[, idx[i], drop = FALSE]
}, seq_along(idx))), c(paste("x", jdx, idx, sep = "_"))))
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I have a nested data.frame
dput(res)
structure(list(date = structure(list(pretty = "12:00 PM CDT on August 14, 2015",
year = "2015", mon = "08", mday = "14", hour = "12", min = "00",
tzname = "America/Chicago"), .Names = c("pretty", "year",
"mon", "mday", "hour", "min", "tzname"), class = "data.frame", row.names = 1L),
fog = "0", rain = "1", snow = "0", snowfallm = "0.00", snowfalli = "0.00",
monthtodatesnowfallm = "", monthtodatesnowfalli = "", since1julsnowfallm = "",
since1julsnowfalli = "", snowdepthm = "", snowdepthi = "",
hail = "0", thunder = "0", tornado = "0", meantempm = "26",
meantempi = "79", meandewptm = "17", meandewpti = "63", meanpressurem = "1019",
meanpressurei = "30.09", meanwindspdm = "11", meanwindspdi = "7",
meanwdire = "", meanwdird = "139", meanvism = "16", meanvisi = "10",
humidity = "", maxtempm = "32", maxtempi = "90", mintempm = "21",
mintempi = "69", maxhumidity = "86", minhumidity = "36",
maxdewptm = "18", maxdewpti = "65", mindewptm = "15", mindewpti = "59",
maxpressurem = "1021", maxpressurei = "30.15", minpressurem = "1017",
minpressurei = "30.04", maxwspdm = "19", maxwspdi = "12",
minwspdm = "0", minwspdi = "0", maxvism = "16", maxvisi = "10",
minvism = "16", minvisi = "10", gdegreedays = "29", heatingdegreedays = "0",
coolingdegreedays = "14", precipm = "0.00", precipi = "0.00",
precipsource = "", heatingdegreedaysnormal = "", monthtodateheatingdegreedays = "",
monthtodateheatingdegreedaysnormal = "", since1sepheatingdegreedays = "",
since1sepheatingdegreedaysnormal = "", since1julheatingdegreedays = "",
since1julheatingdegreedaysnormal = "", coolingdegreedaysnormal = "",
monthtodatecoolingdegreedays = "", monthtodatecoolingdegreedaysnormal = "",
since1sepcoolingdegreedays = "", since1sepcoolingdegreedaysnormal = "",
since1jancoolingdegreedays = "", since1jancoolingdegreedaysnormal = ""), .Names = c("date",
"fog", "rain", "snow", "snowfallm", "snowfalli", "monthtodatesnowfallm",
"monthtodatesnowfalli", "since1julsnowfallm", "since1julsnowfalli",
"snowdepthm", "snowdepthi", "hail", "thunder", "tornado", "meantempm",
"meantempi", "meandewptm", "meandewpti", "meanpressurem", "meanpressurei",
"meanwindspdm", "meanwindspdi", "meanwdire", "meanwdird", "meanvism",
"meanvisi", "humidity", "maxtempm", "maxtempi", "mintempm", "mintempi",
"maxhumidity", "minhumidity", "maxdewptm", "maxdewpti", "mindewptm",
"mindewpti", "maxpressurem", "maxpressurei", "minpressurem",
"minpressurei", "maxwspdm", "maxwspdi", "minwspdm", "minwspdi",
"maxvism", "maxvisi", "minvism", "minvisi", "gdegreedays", "heatingdegreedays",
"coolingdegreedays", "precipm", "precipi", "precipsource", "heatingdegreedaysnormal",
"monthtodateheatingdegreedays", "monthtodateheatingdegreedaysnormal",
"since1sepheatingdegreedays", "since1sepheatingdegreedaysnormal",
"since1julheatingdegreedays", "since1julheatingdegreedaysnormal",
"coolingdegreedaysnormal", "monthtodatecoolingdegreedays", "monthtodatecoolingdegreedaysnormal",
"since1sepcoolingdegreedays", "since1sepcoolingdegreedaysnormal",
"since1jancoolingdegreedays", "since1jancoolingdegreedaysnormal"
), class = "data.frame", row.names = 1L)
and I am using the following command to retrieve data from it
df <- data.frame()
df <- rbind(df, ldply(res, function(x) x[[1]]))
To use this data frame, I convert it into data table, using dt <- data.table(df) and now I know how to work with the data, for instance dt[.id=="fog"].
Is there a more elegant/efficient solution?
The problem was solved by #antoine-sac. It was not necessary to use the apply to get the data, it was only a question of "un-nest" the data.
Your problem is that your data is a data.frame and one of its column is date. But date is a data.frame. As you say it is a nested list. So let's "un-nest" it.
You can simply do (assuming your data is in data):
df.date <- data$date
# removing incorrectly formated date from data
data$date <- NULL
At this point, data is a normal data.frame and df.date is also a basic data.frame.
> df.date
pretty year mon mday hour min tzname
1 12:00 PM CDT on August 14, 2015 2015 08 14 12 00 America/Chicago
If you want to merge that with your existing data.frame:
# binding df.date with your data
data <- cbind(data, df.date)
No need for any kind of apply.
Now if you don't know how to access variables in a data.frame, that's another thing.
If you want, say, meantempm, you can simply do data$meantempm.
I refer you to beginner tutorial about R, there are plenty to choose from with a google request.
I want to plot procrustes rotations between to RDA-objects obtained by vegan with ggplot2.
library(vegan)
#perform two RDAs, do procrustes:
pro.test <- procrustes(rda.t1,rda.t2)
I extracted the x,y coordinates from list of class "procrustes" and added a factor "dates".
test <- data.frame(rda1=pro.test$Yrot[,1], rda2=pro.test$Yrot[,2])
test$dates <- c(rep("A", 8), rep("B",8), rep("C", 8))
test.2 <- data.frame(rda1=pro.test$X[,1], rda2=pro.test$X[,2])
test.2$dates <- c(rep("A", 8), rep("B",8), rep("C", 8))
Now the basic plot:
ggplot() +
geom_point(data=test, aes(x=rda1, y=rda2, color=dates)) +
geom_point(data=test.2,aes(x=rda1, y=rda2, color=dates))
The part i cannot do is the plotting of small lines between each corresponding point in test and test.2
Vegan does plot these rotations with arrows rather than connected points. However, vegan does not khow to color according to sampling groups/factors, which is important for me.
Having arrows in the ggplot would be extremely great - i know there is a geom_segment with the argument "arrow".
Could you help me?
the dput of pro.test is below.
dput(pro.test)
structure(list(Yrot = structure(c(0.126093537705143, 0.196350569855869,
-0.0513472841582749, -0.304416713452662, 0.210682972922012, -0.0219477831881197,
-0.24519038499101, 0.338357488742126, -0.399739151138497, -0.366424716631558,
0.0321561053701086, 0.565794811541598, 0.606054432756139, -0.0122819831669951,
-0.00403199420346042, -0.0448308879361932, 0.0631101371381566,
-0.150820933315408, -0.018216051372273, -0.68513841544701, -0.117446131920294,
-0.450735018917557, 0.25749869839177, 0.47646869541639, -0.211447138648954,
-0.236584149111598, -0.0316882271224907, -0.281680981927695,
-0.182346139754316, -0.366221121187894, -0.263915986724565, -0.203160918536977,
0.209888424862468, 0.219400450315756, 0.143569801341895, 0.258388604988749,
0.542334722496036, 0.465147580652753, 0.294835945722885, 0.523372408452242,
0.0739580893460179, 0.242768571724456, 0.0409877673276456, -0.0942111509903291,
-0.193072299067071, -0.38889179801965, -0.352882980509932, -0.208549475629433
), .Dim = c(24L, 2L), .Dimnames = list(c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "12", "13", "14", "15", "16",
"17", "18", "19", "20", "21", "22", "23", "24", "25"), NULL)),
X = structure(c(0.0860177119127241, 0.217144585357183, -0.0301829830202831,
-0.246142550516987, 0.230574651598493, 0.00485065775494245,
-0.225907453854864, 0.371465194869491, -0.395330365511425,
-0.359255005182027, -0.00775013746753128, 0.47442649486468,
0.519983070801763, -0.0146878517934982, 0.0377018407084686,
-0.0885829362985767, 0.0935962405791314, -0.186192083265912,
0.00247095461296341, -0.655467761687806, -0.0966978065526177,
-0.398672122636169, 0.275589258531376, 0.39104839619648,
-0.273098318897548, -0.237373845171625, -0.0351119316278201,
-0.279271270040404, -0.171188235636994, -0.342350443283954,
-0.297148604541773, -0.21965804713297, 0.269830887309913,
0.268669489120665, 0.143826114581508, 0.229549645414776,
0.531869658831067, 0.479136042616735, 0.380638462867711,
0.548249030471031, 0.161449266776772, 0.282765937749097,
0.0756433464279055, 0.00516171212969907, -0.195519622624857,
-0.568932423412245, -0.381681091857682, -0.375455760069009
), .Dim = c(24L, 2L), const = 1.30375728392289, .Dimnames = list(
c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
"12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "23", "24", "25"), c("RDA1", "RDA2")), "`scaled:center`" = structure(c(1.3588667228485e-17,
4.69820941410385e-18), .Names = c("RDA1", "RDA2"))), ss = 0.146265769408323,
rotation = structure(c(-0.958883328045618, 0.283800569407742,
0.283800569407742, 0.958883328045619), .Dim = c(2L, 2L)),
translation = structure(c(-9.76742606822348e-18, 1.35523649355013e-17
), .Dim = 1:2), scale = 0.918742698883168, xmean = structure(c(1.3586408473959e-17,
4.71176194125992e-18), .Names = c("RDA1", "RDA2")), symmetric = FALSE,
call = procrustes(X = rda.t1, Y = rda.t2), svd = structure(list(
d = c(2.51563498111738, 2.16729713036852), u = structure(c(0.743008138366833,
0.669282381600362, 0.669282381600362, -0.743008138366833
), .Dim = c(2L, 2L)), v = structure(c(-0.522515395489416,
0.852629850214347, -0.852629850214347, -0.522515395489416
), .Dim = c(2L, 2L))), .Names = c("d", "u", "v"))), .Names = c("Yrot",
"X", "ss", "rotation", "translation", "scale", "xmean", "symmetric",
"call", "svd"), class = "procrustes")
Does this do the job?
library(ggplot2)
library(grid)
ctest <- data.frame(rda1=pro.test$Yrot[,1],
rda2=pro.test$Yrot[,2],xrda1=pro.test$X[,1],
xrda2=pro.test$X[,2],dates=rep(c("A","B","C"),each=8))
ggplot(ctest) +
geom_point(aes(x=rda1, y=rda2, colour=dates)) +
geom_point(aes(x=xrda1, y=xrda2, colour=dates)) +
geom_segment(aes(x=rda1,y=rda2,xend=xrda1,yend=xrda2,colour=dates),arrow=arrow(length=unit(0.2,"cm")))