Something explainable to me is going on. I have a dataframe:
> head(df)
id lon lat temp month year hr prec ws
1 1 27.75 -22.25 295.35 9 2018 0.00007675205 401.1297 12.88135
2 2 28.25 -22.25 295.95 9 2018 0.00008084041 426.3411 12.89902
3 3 28.75 -22.25 296.25 9 2018 0.00008487972 449.7063 12.63242
4 4 29.25 -22.25 296.45 9 2018 0.00009112679 484.3495 12.59484
5 5 29.75 -22.25 296.65 9 2018 0.00009995372 533.0175 12.28485
6 6 30.25 -22.25 296.95 9 2018 0.00010895965 583.8255 11.80009
it looks like this:
> nrow(df)
[1] 607
> ncol(df)
[1] 9
when I do write.csv(df, /data/df.csv) it writes a humongous csv with tens of columns and thousands of rows. Has anyone experienced this kind of behavior? I rebooted my machine, restarted R, updated everything, and still persistently malicious, this keeps happening.
Output of dput(df):
https://drive.google.com/file/d/1AkGK9Svwi9mSAcB0G3Ecx7aDC6ccnYRg/view?usp=sharing
str(x) will help you figure out what's going on.
x <- dget("fupedCSV.txt")
str(x)
## 'data.frame': 607 obs. of 9 variables:
## <a bunch of normal columns> ...
## $ rh :'data.frame': 607 obs. of 1 variable:
## ..$ hr: num 7.68e-05 8.08e-05 8.49e-05 9.11e-05 1.00e-04 ...
## $ prec :'data.frame': 607 obs. of 1 variable:
## ..$ prec: num 401 426 450 484 533 ...
## $ ws :'data.frame': 607 obs. of 1 variable:
## ..$ ws: num 12.9 12.9 12.6 12.6 12.3 ...
Note the last three columns, which are actually data frames nested inside the data frame.
## ORIGINAL: y <- as.data.frame(lapply(x, function(x) if (is.list(x)) x[[1]] else x ))
y <- do.call(data.frame,x) ## thanks #akrun!
str(y)
## 'data.frame': 607 obs. of 9 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ lon : num 27.8 28.2 28.8 29.2 29.8 ...
## $ lat : num -22.2 -22.2 -22.2 -22.2 -22.2 ...
## $ temp : num 295 296 296 296 297 ...
## $ month: int 9 9 9 9 9 9 9 9 9 9 ...
## $ year : int 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
## $ rh : num 7.68e-05 8.08e-05 8.49e-05 9.11e-05 1.00e-04 ...
## $ prec : num 401 426 450 484 533 ...
## $ ws : num 12.9 12.9 12.6 12.6 12.3 ...
I haven't tested writing to a file, but I think this will clear up your problem.
The below does the trick.
do.call(data.frame,x) and
write.csv2(x,file="xxxx.csv", row.names=FALSE)
Related
I am trying to read in data from a URL however when I do run the following code:
x <- read.csv(url(myUrl), sep = '\t', head = FALSE)
print(x)
I get this
V1 V2
1 18.0 8 30.7 130.0 hello
2 32.0 6 23.5 121.5 bye
and I want this
V1 V2 V3 V4 V5
1 18.0 8.0 30.7 130.0 hello
2 32.0 6.0 23.5 121.5 bye
for some reason it is reading it as 2 columns instead of 5
Edit 1
Here is a snippet of the data file from the url:
Edit 2
Here is the url: https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data
Instead of \t, may be, use use ' ' or don't specify the delimiter
x <- read.table(url(myUrl), header = FALSE)
based on the url updated in the OP's post
x <- read.table("https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", header = FALSE)
str(x)
#'data.frame': 398 obs. of 9 variables:
# $ V1: num 18 15 18 16 17 15 14 14 14 15 ...
# $ V2: int 8 8 8 8 8 8 8 8 8 8 ...
# $ V3: num 307 350 318 304 302 429 454 440 455 390 ...
# $ V4: chr "130.0" "165.0" "150.0" "150.0" ...
# $ V5: num 3504 3693 3436 3433 3449 ...
# $ V6: num 12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
# $ V7: int 70 70 70 70 70 70 70 70 70 70 ...
# $ V8: int 1 1 1 1 1 1 1 1 1 1 ...
# $ V9: chr "chevrolet chevelle malibu" "buick skylark 320" "plymouth satellite" "amc rebel sst" ...
I'm trying to predict the values of test data set based on train data set, it is predicting the values (no errors) however the predictions deviate A LOT by the original values. Even predicting values around -356 although none of the original values exceeds 200 (and there are no negative values). The warning is bugging me as I think the values deviates a lot because of this warning.
Warning message:
In predict.lm(fit2, data_test) :
prediction from a rank-deficient fit may be misleading
any way I can get rid of this warning? the code is simple
fit2 <- lm(runs~., data=train_data)
prediction<-predict(fit2, data_test)
prediction
I searched a lot but tbh I couldn't understand much about this error.
str of test and train data set in case someone needs them
> str(train_data)
'data.frame': 36 obs. of 28 variables:
$ matchid : int 57 58 55 56 53 54 51 52 45 46 ...
$ TeamName : chr "South Africa" "West Indies" "South Africa" "West Indies" ...
$ Opp_TeamName : chr "West Indies" "South Africa" "West Indies" "South Africa" ...
$ TeamRank : int 4 3 4 3 4 3 10 7 5 1 ...
$ Opp_TeamRank : int 3 4 3 4 3 4 7 10 1 5 ...
$ Team_Top10RankingBatsman : int 0 1 0 1 0 1 0 0 2 2 ...
$ Team_Top50RankingBatsman : int 4 6 4 6 4 6 3 5 4 3 ...
$ Team_Top100RankingBatsman: int 6 8 6 8 6 8 7 7 7 6 ...
$ Opp_Top10RankingBatsman : int 1 0 1 0 1 0 0 0 2 2 ...
$ Opp_Top50RankingBatsman : int 6 4 6 4 6 4 5 3 3 4 ...
$ Opp_Top100RankingBatsman : int 8 6 8 6 8 6 7 7 6 7 ...
$ InningType : chr "1st innings" "2nd innings" "1st innings" "2nd innings" ...
$ Runs_OverAll : num 361 705 348 630 347 ...
$ AVG_Overall : num 27.2 20 23.3 19.1 24 ...
$ SR_Overall : num 128 121 120 118 118 ...
$ Runs_Last10Matches : num 118.5 71 102.1 71 78.6 ...
$ AVG_Last10Matches : num 23.7 20.4 20.9 20.4 23.2 ...
$ SR_Last10Matches : num 120 106 114 106 116 ...
$ Runs_BatingFirst : num 236 459 230 394 203 ...
$ AVG_BatingFirst : num 30.6 23.2 24 21.2 27.1 ...
$ SR_BatingFirst : num 127 136 123 125 118 ...
$ Runs_BatingSecond : num 124 262 119 232 144 ...
$ AVG_BatingSecond : num 25.5 18.3 22.8 17.8 22.8 ...
$ SR_BatingSecond : num 125 118 112 117 114 ...
$ Runs_AgainstTeam2 : num 88.3 118.3 76.3 103.9 49.3 ...
$ AVG_AgainstTeam2 : num 28.2 23 24.7 22.1 16.4 ...
$ SR_AgainstTeam2 : num 139 127 131 128 111 ...
$ runs : int 165 168 231 236 195 126 143 141 191 135 ...
> str(data_test)
'data.frame': 34 obs. of 28 variables:
$ matchid : int 59 60 61 62 63 64 65 66 69 70 ...
$ TeamName : chr "India" "West Indies" "England" "New Zealand" ...
$ Opp_TeamName : chr "West Indies" "India" "New Zealand" "England" ...
$ TeamRank : int 2 3 5 1 4 8 6 2 10 1 ...
$ Opp_TeamRank : int 3 2 1 5 8 4 2 6 1 10 ...
$ Team_Top10RankingBatsman : int 1 1 2 2 0 0 1 1 0 2 ...
$ Team_Top50RankingBatsman : int 5 6 4 3 4 2 5 5 3 3 ...
$ Team_Top100RankingBatsman: int 7 8 7 6 6 5 7 7 7 6 ...
$ Opp_Top10RankingBatsman : int 1 1 2 2 0 0 1 1 2 0 ...
$ Opp_Top50RankingBatsman : int 6 5 3 4 2 4 5 5 3 3 ...
$ Opp_Top100RankingBatsman : int 8 7 6 7 5 6 7 7 6 7 ...
$ InningType : chr "1st innings" "2nd innings" "2nd innings" "1st innings" ...
$ Runs_OverAll : num 582 618 470 602 509 ...
$ AVG_Overall : num 25 21.8 20.3 20.7 19.6 ...
$ SR_Overall : num 113 120 123 120 112 ...
$ Runs_Last10Matches : num 182 107 117 167 140 ...
$ AVG_Last10Matches : num 37.1 43.8 21 24.9 27.3 ...
$ SR_Last10Matches : num 111 153 122 141 120 ...
$ Runs_BatingFirst : num 319 314 271 345 294 ...
$ AVG_BatingFirst : num 23.6 17.8 20.6 20.3 19.5 ...
$ SR_BatingFirst : num 116.9 98.5 118 124.3 115.8 ...
$ Runs_BatingSecond : num 264 282 304 256 186 ...
$ AVG_BatingSecond : num 28 23.7 31.9 21.6 16.5 ...
$ SR_BatingSecond : num 96.5 133.9 129.4 112 99.5 ...
$ Runs_AgainstTeam2 : num 98.2 95.2 106.9 75.4 88.5 ...
$ AVG_AgainstTeam2 : num 45.3 42.7 38.1 17.7 27.1 ...
$ SR_AgainstTeam2 : num 125 138 152 110 122 ...
$ runs : int 192 196 159 153 122 120 160 161 70 145 ...
In simple word, how can I get rid of this warning so that it doesn't effect my predictions?
(Intercept) matchid TeamNameBangladesh
1699.98232628 -0.06793787 59.29445330
TeamNameEngland TeamNameIndia TeamNameNew Zealand
347.33030177 -499.40074338 -179.19192936
TeamNamePakistan TeamNameSouth Africa TeamNameSri Lanka
-272.71610614 -3.54867488 -45.27920191
TeamNameWest Indies Opp_TeamNameBangladesh Opp_TeamNameEngland
-345.54349798 135.05901017 108.04227770
Opp_TeamNameIndia Opp_TeamNameNew Zealand Opp_TeamNamePakistan
-162.24418387 -60.55364436 -114.74599364
Opp_TeamNameSouth Africa Opp_TeamNameSri Lanka Opp_TeamNameWest Indies
196.90856999 150.70170068 -6.88997714
TeamRank Opp_TeamRank Team_Top10RankingBatsman
NA NA NA
Team_Top50RankingBatsman Team_Top100RankingBatsman Opp_Top10RankingBatsman
NA NA NA
Opp_Top50RankingBatsman Opp_Top100RankingBatsman InningType2nd innings
NA NA 24.24029455
Runs_OverAll AVG_Overall SR_Overall
-0.59935875 20.12721378 -13.60151334
Runs_Last10Matches AVG_Last10Matches SR_Last10Matches
-1.92526750 9.24182916 1.23914363
Runs_BatingFirst AVG_BatingFirst SR_BatingFirst
1.41001672 -9.88582744 -6.69780509
Runs_BatingSecond AVG_BatingSecond SR_BatingSecond
-0.90038727 -7.11580086 3.20915976
Runs_AgainstTeam2 AVG_AgainstTeam2 SR_AgainstTeam2
3.35936312 -5.90267210 2.36899131
You can have a look at this detailed discussion :
predict.lm() in a loop. warning: prediction from a rank-deficient fit may be misleading
In general, multi-collinearity can lead to a rank deficient matrix in logistic regression.
You can try applying PCA to tackle the multi-collinearity issue and then apply logistic regression afterwards.
Using matplot, I'm trying to plot the 2nd, 3rd and 4th columns of airquality data.frame after dividing these 3 columns by the first column of airquality.
However I'm getting an error
Error in ncol(xj) : object 'xj' not found
Why are we getting this error? The code below will reproduce this problem.
attach(airquality)
airquality[2:4] <- apply(airquality[2:4], 2, function(x) x /airquality[1])
matplot(x= airquality[,1], y= as.matrix(airquality[-1]))
You have managed to mangle your data in an interesting way. Starting with airquality before you mess with it. (And please don't attach() - it's unnecessary and sometimes dangerous/confusing.)
str(airquality)
'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
After you do
airquality[2:4] <- apply(airquality[2:4], 2,
function(x) x /airquality[1])
you get
'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R:'data.frame': 153 obs. of 1 variable:
..$ Ozone: num 4.63 3.28 12.42 17.39 NA ...
$ Wind :'data.frame': 153 obs. of 1 variable:
..$ Ozone: num 0.18 0.222 1.05 0.639 NA ...
$ Temp :'data.frame': 153 obs. of 1 variable:
..$ Ozone: num 1.63 2 6.17 3.44 NA ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
or
sapply(airquality,class)
## Ozone Solar.R Wind Temp Month Day
## "integer" "data.frame" "data.frame" "data.frame" "integer" "integer"
that is, you have data frames embedded within your data frame!
rm(airquality) ## clean up
Now change one character and divide by the column airquality[,1] rather than airquality[1] (divide by a vector, not a list of length one ...)
airquality[,2:4] <- apply(airquality[,2:4], 2,
function(x) x/airquality[,1])
matplot(x= airquality[,1], y= as.matrix(airquality[,-1]))
In general it's safer to use [, ...] indexing rather than [] indexing to refer to columns of a data frame unless you really know what you're doing ...
I try to make KDA (Kernel discriminant analysis) for carc data, but when I call command X<-data.frame(scale(X)); r shows error:
"Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric"
I tried to use as.numeric(as.matrix(carc)) and carc<-na.omit(carc), but it does not help either
library(ks);library(MASS);library(klaR);library(FSelector)
install.packages("klaR")
install.packages("FSelector")
library(ks);library(MASS);library(klaR);library(FSelector)
attach("carc.rda")
data<-load("carc.rda")
data
carc<-na.omit(carc)
head(carc)
class(carc) # check for its class
class(as.matrix(carc)) # change class, and
as.numeric(as.matrix(carc))
XX<-carc
X<-XX[,1:12];X.class<-XX[,13];
X<-data.frame(scale(X));
fit.pc<-princomp(X,scores=TRUE);
plot(fit.pc,type="line")
X.new<-fit.pc$scores[,1:5]; X.new<-data.frame(X.new);
cfs(X.class~.,cbind(X.new,X.class))
X.new<-fit.pc$scores[,c(1,4)]; X.new<-data.frame(X.new);
fit.kda1<-Hkda(x=X.new,x.group=X.class,pilot="samse",
bw="plugin",pre="sphere")
kda.fit1 <- kda(x=X.new, x.group=X.class, Hs=fit.kda1)
Can you help to resolve this problem and make this analysis?
Added:The car data set( Chambers, kleveland, Kleiner & Tukey 1983)
> head(carc)
P M R78 R77 H R Tr W L T D G C
AMC_Concord 4099 22 3 2 2.5 27.5 11 2930 186 40 121 3.58 US
AMC_Pacer 4749 17 3 1 3.0 25.5 11 3350 173 40 258 2.53 US
AMC_Spirit 3799 22 . . 3.0 18.5 12 2640 168 35 121 3.08 US
Audi_5000 9690 17 5 2 3.0 27.0 15 2830 189 37 131 3.20 Europe
Audi_Fox 6295 23 3 3 2.5 28.0 11 2070 174 36 97 3.70 Europe
Here is a small dataset with similar characteristics to what you describe
in order to answer this error:
"Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric"
carc <- data.frame(type1=rep(c('1','2'), each=5),
type2=rep(c('5','6'), each=5),
x = rnorm(10,1,2)/10, y = rnorm(10))
This should be similar to your data.frame
str(carc)
# 'data.frame': 10 obs. of 3 variables:
# $ type1: Factor w/ 2 levels "1","2": 1 1 1 1 1 2 2 2 2 2
# $ type2: Factor w/ 2 levels "5","6": 1 1 1 1 1 2 2 2 2 2
# $ x : num -0.1177 0.3443 0.1351 0.0443 0.4702 ...
# $ y : num -0.355 0.149 -0.208 -1.202 -1.495 ...
scale(carc)
# Similar error
# Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric
Using set()
require(data.table)
DT <- data.table(carc)
cols_fix <- c("type1", "type2")
for (col in cols_fix) set(DT, j=col, value = as.numeric(as.character(DT[[col]])))
str(DT)
# Classes ‘data.table’ and 'data.frame': 10 obs. of 4 variables:
# $ type1: num 1 1 1 1 1 2 2 2 2 2
# $ type2: num 5 5 5 5 5 6 6 6 6 6
# $ x : num 0.0465 0.1712 0.1582 0.1684 0.1183 ...
# $ y : num 0.155 -0.977 -0.291 -0.766 -1.02 ...
# - attr(*, ".internal.selfref")=<externalptr>
The first column(s) of your data set may be factors. Taking the data from corrgram:
library(corrgram)
carc <- auto
str(carc)
# 'data.frame': 74 obs. of 14 variables:
# $ Model : Factor w/ 74 levels "AMC Concord ",..: 1 2 3 4 5 6 7 8 9 10 ...
# $ Origin: Factor w/ 3 levels "A","E","J": 1 1 1 2 2 2 1 1 1 1 ...
# $ Price : int 4099 4749 3799 9690 6295 9735 4816 7827 5788 4453 ...
# $ MPG : int 22 17 22 17 23 25 20 15 18 26 ...
# $ Rep78 : num 3 3 NA 5 3 4 3 4 3 NA ...
# $ Rep77 : num 2 1 NA 2 3 4 3 4 4 NA ...
# $ Hroom : num 2.5 3 3 3 2.5 2.5 4.5 4 4 3 ...
# $ Rseat : num 27.5 25.5 18.5 27 28 26 29 31.5 30.5 24 ...
# $ Trunk : int 11 11 12 15 11 12 16 20 21 10 ...
# $ Weight: int 2930 3350 2640 2830 2070 2650 3250 4080 3670 2230 ...
# $ Length: int 186 173 168 189 174 177 196 222 218 170 ...
# $ Turn : int 40 40 35 37 36 34 40 43 43 34 ...
# $ Displa: int 121 258 121 131 97 121 196 350 231 304 ...
# $ Gratio: num 3.58 2.53 3.08 3.2 3.7 3.64 2.93 2.41 2.73 2.87 ...
So exclude them by trying this:
X<-XX[,3:14]
or this
X<-XX[,-(1:2)]
I'd like to split a dataframe in 4 equals parts, because I'd like to use the 4 cores of my computer.
I did this :
df2 <- split(df, 1:4)
unsplit(df2, f=1:4)
and that
df2 <- split(df, 1:4)
unsplit(df2, f=c('1','2','3','4')
But the unsplit function did not work, I have these warnings messages
1: In split.default(seq_along(x), f, drop = drop, ...) :
data length is not a multiple of split variable
...
Do you have an idea of the reason ?
How many rows in df? You will get that warning if the number of rows in your table is not divisible by 4. I think you are using the split factor f incorrectly, unless what you want to do is put each subsequent row into a different split data.frame.
If you really want to split your data into 4 dataframes. one row after the other then make your splitting factor the same size as the number of rows in your dataframe using rep_len like this:
## Split like this:
split(df , f = rep_len(1:4, nrow(df) ) )
## Unsplit like this:
unsplit( split(df , f = rep_len(1:4, nrow(df) ) ) , f = rep_len(1:4,nrow(df) ) )
Hopefully this example illustrates why the error occurs and how to avoid it (i.e. use a proper splitting factor!).
## Want to split our data.frame into two halves, but rows not divisible by 2
df <- data.frame( x = runif(5) )
df
## Splitting still works but...
## We get a warning because the split factor 'f' was not recycled as a multiple of it's length
split( df , f = 1:2 )
#$`1`
# x
#1 0.6970968
#3 0.5614762
#5 0.5910995
#$`2`
# x
#2 0.6206521
#4 0.1798006
Warning message:
In split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) :
data length is not a multiple of split variable
## Instead let's use the same split levels (1:2)...
## but make it equal to the length of the rows in the table:
splt <- rep_len( 1:2 , nrow(df) )
splt
#[1] 1 2 1 2 1
## Split works, and f is not recycled because there are
## the same number of values in 'f' as rows in the table
split( df , f = splt )
#$`1`
# x
#1 0.6970968
#3 0.5614762
#5 0.5910995
#$`2`
# x
#2 0.6206521
#4 0.1798006
## And unsplitting then works as expected and reconstructs our original data.frame
unsplit( split( df , f = splt ) , f = splt )
# x
#1 0.6970968
#2 0.6206521
#3 0.5614762
#4 0.1798006
#5 0.5910995
In the R language 'split' example . . .
aq <- airquality
g <- aq$Month
l <- split(aq,g)
After the 'scale' function is executed
l <- lapply(l, transform, Ozone = scale(Ozone))
I am guessing that at one time in R history
the function 'scale' did not add extra attributes
to the column it is modifying.
..$ Ozone : num ...
.. ..- attr(*, "scaled:center")= num 29.4
.. ..- attr(*, "scaled:scale")= num 18.2
As seen in here . . .
> str(l)
List of 5
$ 5:'data.frame': 31 obs. of 6 variables:
..$ Ozone : num [1:31, 1] 0.782 0.557 -0.523 -0.253 NA ...
.. ..- attr(*, "scaled:center")= num 23.6
.. ..- attr(*, "scaled:scale")= num 22.2
..$ Solar.R: int [1:31] 190 118 149 313 NA NA 299 99 19 194 ...
..$ Wind : num [1:31] 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
..$ Temp : int [1:31] 67 72 74 62 56 66 65 59 61 69 ...
..$ Month : int [1:31] 5 5 5 5 5 5 5 5 5 5 ...
..$ Day : int [1:31] 1 2 3 4 5 6 7 8 9 10 ...
$ 6:'data.frame': 30 obs. of 6 variables:
..$ Ozone : num [1:30, 1] NA NA NA NA NA ...
.. ..- attr(*, "scaled:center")= num 29.4
.. ..- attr(*, "scaled:scale")= num 18.2
..$ Solar.R: int [1:30] 286 287 242 186 220 264 127 273 291 323 ...
..$ Wind : num [1:30] 8.6 9.7 16.1 9.2 8.6 14.3 9.7 6.9 13.8 11.5 ...
..$ Temp : int [1:30] 78 74 67 84 85 79 82 87 90 87 ...
..$ Month : int [1:30] 6 6 6 6 6 6 6 6 6 6 ...
..$ Day : int [1:30] 1 2 3 4 5 6 7 8 9 10 ...
$ 7:'data.frame': 31 obs. of 6 variables:
..$ Ozone : num [1:31, 1] 2.399 -0.32 -0.857 NA 0.154 ...
.. ..- attr(*, "scaled:center")= num 59.1
.. ..- attr(*, "scaled:scale")= num 31.6
..$ Solar.R: int [1:31] 269 248 236 101 175 314 276 267 272 175 ...
..$ Wind : num [1:31] 4.1 9.2 9.2 10.9 4.6 10.9 5.1 6.3 5.7 7.4 ...
..$ Temp : int [1:31] 84 85 81 84 83 83 88 92 92 89 ...
..$ Month : int [1:31] 7 7 7 7 7 7 7 7 7 7 ...
..$ Day : int [1:31] 1 2 3 4 5 6 7 8 9 10 ...
$ 8:'data.frame': 31 obs. of 6 variables:
..$ Ozone : num [1:31, 1] -0.528 -1.284 -1.108 0.455 -0.629 ...
.. ..- attr(*, "scaled:center")= num 60
.. ..- attr(*, "scaled:scale")= num 39.7
..$ Solar.R: int [1:31] 83 24 77 NA NA NA 255 229 207 222 ...
..$ Wind : num [1:31] 6.9 13.8 7.4 6.9 7.4 4.6 4 10.3 8 8.6 ...
..$ Temp : int [1:31] 81 81 82 86 85 87 89 90 90 92 ...
..$ Month : int [1:31] 8 8 8 8 8 8 8 8 8 8 ...
..$ Day : int [1:31] 1 2 3 4 5 6 7 8 9 10 ...
$ 9:'data.frame': 30 obs. of 6 variables:
..$ Ozone : num [1:30, 1] 2.674 1.928 1.721 2.467 0.644 ...
.. ..- attr(*, "scaled:center")= num 31.4
.. ..- attr(*, "scaled:scale")= num 24.1
..$ Solar.R: int [1:30] 167 197 183 189 95 92 252 220 230 259 ...
..$ Wind : num [1:30] 6.9 5.1 2.8 4.6 7.4 15.5 10.9 10.3 10.9 9.7 ...
..$ Temp : int [1:30] 91 92 93 93 87 84 80 78 75 73 ...
..$ Month : int [1:30] 9 9 9 9 9 9 9 9 9 9 ...
..$ Day : int [1:30] 1 2 3 4 5 6 7 8 9 10 ...
But now it does add those attributes
..$ Ozone : num ...
.. ..- attr(*, "scaled:center")= num 29.4
.. ..- attr(*, "scaled:scale")= num 18.2
and the very simple 'unsplit' function is not programmed to handle those attributes.
> unsplit(l,g)
Error in xj[i, , drop = FALSE] : (subscript) logical subscript too long
The (direct and simple) solution is to get rid of those attributes.
attributes(l[[1]]$Ozone) <- NULL
attributes(l[[2]]$Ozone) <- NULL
attributes(l[[3]]$Ozone) <- NULL
attributes(l[[4]]$Ozone) <- NULL
attributes(l[[5]]$Ozone) <- NULL
Then try to unsplit again.
str( unsplit(l,g) )
> str( unsplit(l,g) )
'data.frame': 153 obs. of 6 variables:
$ Ozone : num 0.782 0.557 -0.523 -0.253 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
So, now it works.
Andre Mikulec