access the data inside the slots of S4 objects - r

basically I am using a package that gives me a S4 object with many slots. these objects are easily accessible with s4obj#portfolio but then I need to extract FROM this slot a vector.
more specifically, the package is fPortfolio and the function is portfolioFrontier(). in the pdf associated is at page 19.
(http://postimg.org/image/62oa8z7dv/)
in the pdf attached to the package it is specfied that such slot is
"a list, containing parameter specifications for the portfolio:
weights a numeric vector specifying the portfolio weights,
targetReturn a numeric value specifying the target return, "
I want to carve out "weights" but nothing worked so far (errors include that object is not subsettable, $ doens't work [meaning s4obj#portfolio$weights doesn't work], etc)
code at work:
frontier=portfolioFrontier(as.timeSeries(elements))
frontier#portfolio
frontier#portfolio$weights
Error in frontier#portfolio$weights :
$ operator not defined for this S4 class
p.s.: i hope this isn't already answered elsewhere but I found nothing on this forum or the web for that matter
p.s.2 : str() yields
> str(frontier#portfolio)
Formal class 'fPFOLIOVAL' [package "fPortfolio"] with 2 slots
..# portfolio:List of 7
.. ..$ weights : num [1:49, 1:14] 0.0805 0.161 0.2415 0.322 0.4025 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. ..$ covRiskBudgets : num [1:49, 1:14] -3.54e-05 -7.73e-05 -1.28e-04 -1.90e-04 -2.67e-04 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. ..$ targetReturn : num [1:49, 1:2] -0.00171 -0.00154 -0.00136 -0.00119 -0.00101 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : chr [1:2] "mean" "mu"
.. ..$ targetRisk : num [1:49, 1:4] 0.0394 0.0359 0.0325 0.029 0.0256 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : chr [1:4] "Cov" "Sigma" "CVaR" "VaR"
.. ..$ targetAlpha : num 0.05
.. ..$ minriskPortfolio:Formal class 'fPORTFOLIO' [package "fPortfolio"] with 7 slots
.. .. .. ..# call : language minriskPortfolio(data = data, spec = spec, constraints = constraints)
.. .. .. ..# data :Formal class 'fPFOLIODATA' [package "fPortfolio"] with 3 slots
.. .. .. .. .. ..# data :List of 3
.. .. .. .. .. .. ..$ series :Time Series:
Name: object
Data Matrix:
Dimension: 240 14
Column Names: MNEU OBGVEUBR OBGVEUML OBCPEU OBGVIN OBCPNOEU OBGLHGYD OBPSEM AZEU AZUS AZPC AZEM AZRE ATCOMM
Row Names: ...
Positions:
Start:
End:
With:
Format: counts
FinCenter:
Units: MNEU OBGVEUBR OBGVEUML OBCPEU OBGVIN OBCPNOEU OBGLHGYD OBPSEM AZEU AZUS AZPC AZEM AZRE ATCOMM
Title: Signal Series Object
Documentation: Wed Mar 18 12:18:11 2015
.. .. .. .. .. .. ..$ nAssets: int 14
.. .. .. .. .. .. ..$ names : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# statistics:List of 5
.. .. .. .. .. .. ..$ mean : Named num [1:14] 0.000277 0.0011 0.003337 0.002433 0.001609 ...
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. ..$ Cov : num [1:14, 1:14] 2.57e-08 1.60e-07 1.39e-07 -7.50e-08 4.11e-07 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. ..$ estimator: chr "covEstimator"
.. .. .. .. .. .. ..$ mu : Named num [1:14] 0.000277 0.0011 0.003337 0.002433 0.001609 ...
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. ..$ Sigma : num [1:14, 1:14] 2.57e-08 1.60e-07 1.39e-07 -7.50e-08 4.11e-07 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. .. .. ..$ : chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# tailRisk : list()
.. .. .. ..# spec :Formal class 'fPFOLIOSPEC' [package "fPortfolio"] with 5 slots
.. .. .. .. .. ..# model :List of 5
.. .. .. .. .. .. ..$ type : chr "MV"
.. .. .. .. .. .. ..$ optimize : chr "minRisk"
.. .. .. .. .. .. ..$ estimator: chr "covEstimator"
.. .. .. .. .. .. ..$ tailRisk : list()
.. .. .. .. .. .. ..$ params :List of 2
.. .. .. .. .. .. .. ..$ alpha: num 0.05
.. .. .. .. .. .. .. ..$ a : num 1
.. .. .. .. .. ..# portfolio:List of 6
.. .. .. .. .. .. ..$ weights : atomic [1:14] 0.999 0 0 0 0 ...
.. .. .. .. .. .. .. ..- attr(*, "invest")= num 1
.. .. .. .. .. .. ..$ targetRisk : num 0.000155
.. .. .. .. .. .. ..$ riskFreeRate : num 0
.. .. .. .. .. .. ..$ nFrontierPoints: num 50
.. .. .. .. .. .. ..$ status : num 0
.. .. .. .. .. .. ..$ targetReturn : Named num -1
.. .. .. .. .. .. .. ..- attr(*, "names")= chr ""
.. .. .. .. .. ..# optim :List of 5
.. .. .. .. .. .. ..$ solver : chr "solveRquadprog"
.. .. .. .. .. .. ..$ objective: chr [1:3] "portfolioObjective" "portfolioReturn" "portfolioRisk"
.. .. .. .. .. .. ..$ options :List of 1
.. .. .. .. .. .. .. ..$ meq: num 2
.. .. .. .. .. .. ..$ control : list()
.. .. .. .. .. .. ..$ trace : logi FALSE
.. .. .. .. .. ..# messages :List of 2
.. .. .. .. .. .. ..$ messages: logi FALSE
.. .. .. .. .. .. ..$ note : chr ""
.. .. .. .. .. ..# ampl :List of 5
.. .. .. .. .. .. ..$ ampl : logi FALSE
.. .. .. .. .. .. ..$ project : chr "ampl"
.. .. .. .. .. .. ..$ solver : chr "ipopt"
.. .. .. .. .. .. ..$ protocol: logi FALSE
.. .. .. .. .. .. ..$ trace : logi FALSE
.. .. .. ..# constraints:Formal class 'fPFOLIOCON' [package "fPortfolio"] with 16 slots
.. .. .. .. .. ..# stringConstraints : chr "LongOnly"
.. .. .. .. .. ..# minWConstraints : Named num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# maxWConstraints : Named num [1:14] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# eqsumWConstraints : num [1:2, 1:15] -1 -1 0.000277 -1 0.0011 ...
.. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. ..$ : chr [1:2] "Return" "Budget"
.. .. .. .. .. .. .. ..$ : chr [1:15] "ceq" "MNEU" "OBGVEUBR" "OBGVEUML" ...
.. .. .. .. .. ..# minsumWConstraints : logi [1, 1] NA
.. .. .. .. .. ..# maxsumWConstraints : logi [1, 1] NA
.. .. .. .. .. ..# minBConstraints : Named num [1:14] -Inf -Inf -Inf -Inf -Inf ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# maxBConstraints : Named num [1:14] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# listFConstraints : list()
.. .. .. .. .. ..# minFConstraints : num(0)
.. .. .. .. .. ..# maxFConstraints : num(0)
.. .. .. .. .. ..# minBuyinConstraints: Named num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# maxBuyinConstraints: Named num [1:14] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# nCardConstraints : int 14
.. .. .. .. .. ..# minCardConstraints : Named num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. ..# maxCardConstraints : Named num [1:14] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. ..# portfolio :Formal class 'fPFOLIOVAL' [package "fPortfolio"] with 2 slots
.. .. .. .. .. ..# portfolio:List of 6
.. .. .. .. .. .. ..$ weights : Named num [1:14] 0.999 0 0 0 0 ...
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. ..$ covRiskBudgets: Named num [1:14] 0.999 0 0 0 0 ...
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:14] "MNEU" "OBGVEUBR" "OBGVEUML" "OBCPEU" ...
.. .. .. .. .. .. ..$ targetReturn : Named num [1:2] 0.000279 0.000279
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:2] "mean" "mu"
.. .. .. .. .. .. ..$ targetRisk : Named num [1:4] 1.55e-04 1.55e-04 -1.22e-05 -3.15e-05
.. .. .. .. .. .. .. ..- attr(*, "names")= chr [1:4] "Cov" "Sigma" "CVaR" "VaR"
.. .. .. .. .. .. ..$ targetAlpha : num 0.05
.. .. .. .. .. .. ..$ status : num 0
.. .. .. .. .. ..# messages : list()
.. .. .. ..# title : chr "Minimum Variance Portfolio"
.. .. .. ..# description: chr "Wed Mar 18 12:18:11 2015 by user: dallaliberaf"
.. ..$ status : num 0
..# messages : list()


It really is just standard S4, so you need to pick element by element. Here is an example which I have actually wrapped in C++ as an RInside example:
suppressMessages(library(fPortfolio))
lppData <- 100 * LPP2005.RET[, 1:6]
ewSpec <- portfolioSpec()
nAssets <- ncol(lppData)
weightsvec <- c(0.5, rep(0.1, 5))
setWeights(ewSpec) <- weightsvec
ewPf <- feasiblePortfolio(data=lppData, spec=ewSpec, constraints="LongOnly")
print(ewPf)
vec <- getCovRiskBudgets(ewPf#portfolio)
Here, you simply read the str() output wrong and omitted a layer of #portfoio (Hint: you need two) when trying to get to weights:
R> ewPf#portfolio#portfolio$weights
SBI SPI SII LMI MPI ALT
0.5 0.1 0.1 0.1 0.1 0.1
R>
which are of course the same six values I gave it earlier in the example.
Edit: Your subsequent edits proves this. You have
> str(frontier#portfolio)
Formal class 'fPFOLIOVAL' [package "fPortfolio"] with 2 slots
..# portfolio:List of 7
.. ..$ weights : num [1:49, 1:14] 0.0805 0.161 0.2415 0.322 0.4025 ..
which is precisely the frontier#portfolio#portfolio$weights I show in my code (albeit applied to the variable named the way it is in your example).

Related

R testthat_result saving in file

I am trying to save unit tests result generated by "testthat" package result
How to save testthat result which is "testthat_result" object into a file(txt or csv file)
library(testthat)
result<-test_file('utils_test.R', reporter = "minimal")
outfile<-file("output.txt")
writeLines(c(result), outfile)
close(outfile)
But this is not working

The problem is your result object, which is a list.
library(testthat)
path <- testthat_example("success")
result<-test_file(path, reporter = "minimal")
str(result)
You need to convert it to a character stream before saving it using writeLines.
str(result)
List of 4
$ :List of 7
..$ file : chr "test-success.R"
..$ context: NULL
..$ test : chr "one plus one is two"
..$ user : num 0.006
..$ system : num 0
..$ real : num 0.006
..$ results:List of 1
.. ..$ :List of 6
.. .. ..$ message : chr "1 + 1 not equal to 2.\nEqual"
.. .. ..$ srcref : 'srcref' int [1:8] 2 3 2 24 3 24 2 2
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 32
.. .. ..$ test : chr "one plus one is two"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_success" "expectation" "condition"
$ :List of 7
..$ file : chr "test-success.R"
..$ context: NULL
..$ test : chr "you can skip tests if needed"
..$ user : num 0.003
..$ system : num 0
..$ real : num 0.003
..$ results:List of 1
.. ..$ :List of 6
.. .. ..$ message : chr "Reason: This tests hasn't been written yet"
.. .. ..$ srcref : 'srcref' int [1:8] 6 3 6 44 3 44 6 6
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 31
.. .. ..$ test : chr "you can skip tests if needed"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_skip" "expectation" "condition"
$ :List of 7
..$ file : chr "test-success.R"
..$ context: NULL
..$ test : chr "some tests have warnings"
..$ user : num 0.006
..$ system : num 0
..$ real : num 0.007
..$ results:List of 2
.. ..$ :List of 6
.. .. ..$ message : chr "NaNs produced"
.. .. ..$ srcref : 'srcref' int [1:8] 10 3 10 28 3 28 10 10
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 33
.. .. ..$ test : chr "some tests have warnings"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_warning" "expectation" "condition"
.. ..$ :List of 6
.. .. ..$ message : chr "log(-1) not equal to NaN.\nEqual"
.. .. ..$ srcref : 'srcref' int [1:8] 10 3 10 28 3 28 10 10
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 32
.. .. ..$ test : chr "some tests have warnings"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_success" "expectation" "condition"
$ :List of 7
..$ file : chr "test-success.R"
..$ context: NULL
..$ test : chr "some more successes just to pad things out"
..$ user : num 0.003
..$ system : num 0
..$ real : num 0.003
..$ results:List of 2
.. ..$ :List of 6
.. .. ..$ message : chr "TRUE isn't true."
.. .. ..$ srcref : 'srcref' int [1:8] 14 3 14 19 3 19 14 14
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 32
.. .. ..$ test : chr "some more successes just to pad things out"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_success" "expectation" "condition"
.. ..$ :List of 6
.. .. ..$ message : chr "FALSE isn't false."
.. .. ..$ srcref : 'srcref' int [1:8] 15 3 15 21 3 21 15 15
.. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x5589bd9e6a20>
.. .. ..$ trace : NULL
.. .. ..$ start_frame: int 31
.. .. ..$ end_frame : num 32
.. .. ..$ test : chr "some more successes just to pad things out"
.. .. ..- attr(*, "class")= chr [1:3] "expectation_success" "expectation" "condition"
- attr(*, "class")= chr "testthat_results"

Appending Spatial Points Attributes to Spatial Lines Data Frame by Line ID

question for GIS fans out there. I have two sp objects. A spatial lines data frame (Taken as a clipped version of the OS Roads Vector) and a spatial points data frame (Data taken from the Police UK Data website). I used the snapPointsToLines function to create a third spatial point data frame with the new attribute for nearest_line_id.
How can I append the third dataframe back to my spatial lines data frame so I can use it as a color attribute in leaflet?
What I am trying to do overall is snap points to lines as a count. Then colour the lines based on the quantity of points in leaflet. So if there is another solution other than the one above to achieve that I am all ears.
Because I am using leaflet I am using a specific CRS
crs <- sp::CRS('+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0')
However to do the snapPointsToLines function I need to temporally convert the crs to cartesian coordinates and then back (I know, its a pain).
temp_crs <- sp::CRS("+proj=utm +zone=32 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
The structure is what is getting me for the two data sets. The Spatial Points Dataframe looks like this:
> str(Points)
Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
..# data :'data.frame': 191 obs. of 16 variables:
.. ..$ Crime type : chr [1:191] "Anti-social behaviour" "Anti-social behaviour" "Anti-social behaviour" "Anti-social behaviour" ...
.. ..$ Longitude : num [1:191] 1.3 1.31 1.34 1.35 1.37 ...
.. ..$ Latitude : num [1:191] 51.4 51.3 51.4 51.4 51.3 ...
.. ..$ n : int [1:191] 1 1 1 1 1 3 1 3 4 1 ...
.. ..$ objectid : Factor w/ 380 levels "1","10","100",..: 50 50 50 50 50 50 50 50 50 50 ...
.. ..$ lad17cd : Factor w/ 380 levels "E06000001","E06000002",..: 143 143 143 143 143 143 143 143 143 143 ...
.. ..$ lad17nm : Factor w/ 380 levels "Aberdeen City",..: 329 329 329 329 329 329 329 329 329 329 ...
.. ..$ lad17nmw : Factor w/ 22 levels "Abertawe","Blaenau Gwent",..: NA NA NA NA NA NA NA NA NA NA ...
.. ..$ bng_e : Factor w/ 380 levels "126473","199821",..: 376 376 376 376 376 376 376 376 376 376 ...
.. ..$ bng_n : Factor w/ 379 levels "1006584","101094",..: 63 63 63 63 63 63 63 63 63 63 ...
.. ..$ long : num [1:191] 1.33 1.33 1.33 1.33 1.33 ...
.. ..$ lat : num [1:191] 51.4 51.4 51.4 51.4 51.4 ...
.. ..$ st_areasha : num [1:191] 1.03e+08 1.03e+08 1.03e+08 1.03e+08 1.03e+08 ...
.. ..$ st_lengths : num [1:191] 56839 56839 56839 56839 56839 ...
.. ..$ nearest_line_id: Factor w/ 98 levels "1043","1107",..: 48 94 31 80 90 11 61 50 68 4 ...
.. ..$ snap_dist : num [1:191] 0.0317 0.0386 0.0317 0.0964 0.0826 ...
..# coords.nrs : num(0)
..# coords : num [1:191, 1:2] -35712 -35265 -32984 -31745 -31519 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:191] "142.4" "142.10" "142.17" "142.26" ...
.. .. ..$ : chr [1:2] "X" "Y"
..# bbox : num [1:2, 1:2] -38985 5714081 -25310 5720875
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:2] "X" "Y"
.. .. ..$ : chr [1:2] "min" "max"
..# proj4string:Formal class 'CRS' [package "sp"] with 1 slot
.. .. ..# projargs: chr "+proj=utm +zone=32 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0"
And the Spatial Lines DataFrame looks like this (had to cut it for size in this post):
> str(Clipped_Street)
Formal class 'SpatialLinesDataFrame' [package "sp"] with 4 slots
..# data :'data.frame': 6271 obs. of 29 variables:
.. ..$ fictitious: Factor w/ 1 level "false": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ identifier: Factor w/ 33642 levels "0002D55A-A91E-41D7-B8ED-973F7562D903",..: 6313 22851 21972 20186 15807 23558 25474 25983 26498 25490 ...
.. ..$ class : Factor w/ 7 levels "A Road","B Road",..: 2 3 2 6 3 6 3 3 6 6 ...
.. ..$ roadNumber: Factor w/ 52 levels "A2","A20","A2033",..: 32 NA 36 NA NA NA NA NA NA NA ...
.. ..$ name1 : Factor w/ 6320 levels "Abbey Close",..: 5586 2955 5720 1695 2955 99 2955 2955 2082 5720 ...
.. ..$ name1_lang: Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
.. ..$ name2 : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
.. ..$ name2_lang: Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
.. ..$ formOfWay : Factor w/ 6 levels "Collapsed Dual Carriageway",..: 5 5 5 5 5 5 5 5 5 5 ...
.. ..$ length : int [1:6271] 111 86 66 63 19 41 8 46 45 23 ...
.. ..$ primary : Factor w/ 2 levels "false","true": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ trunkRoad : Factor w/ 2 levels "false","true": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ loop : Factor w/ 2 levels "false","true": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ startNode : Factor w/ 21676 levels "00010DB2-DF39-455B-AA62-C3404539F18A",..: 1194 3350 8606 17475 6530 6530 5161 11442 3186 6852 ...
.. ..$ endNode : Factor w/ 24871 levels "00010DB2-DF39-455B-AA62-C3404539F18A",..: 5437 7429 10034 15538 5848 4003 13102 20036 13102 9098 ...
.. ..$ structure : Factor w/ 1 level "Road In Tunnel": NA NA NA NA NA NA NA NA NA NA ...
.. ..$ nameTOID : Factor w/ 7826 levels "osgb4000000029278481",..: 4067 4079 4071 6680 4079 7623 4079 4079 4084 4071 ...
.. ..$ numberTOID: Factor w/ 52 levels "osgb4000000030880859",..: 29 NA 43 NA NA NA NA NA NA NA ...
.. ..$ function. : Factor w/ 8 levels "A Road","B Road",..: 2 5 2 4 5 4 5 5 4 5 ...
.. ..$ objectid : Factor w/ 380 levels "1","10","100",..: 50 50 50 50 50 50 50 50 50 50 ...
.. ..$ lad17cd : Factor w/ 380 levels "E06000001","E06000002",..: 143 143 143 143 143 143 143 143 143 143 ...
.. ..$ lad17nm : Factor w/ 380 levels "Aberdeen City",..: 329 329 329 329 329 329 329 329 329 329 ...
.. ..$ lad17nmw : Factor w/ 22 levels "Abertawe","Blaenau Gwent",..: NA NA NA NA NA NA NA NA NA NA ...
.. ..$ bng_e : Factor w/ 380 levels "126473","199821",..: 376 376 376 376 376 376 376 376 376 376 ...
.. ..$ bng_n : Factor w/ 379 levels "1006584","101094",..: 63 63 63 63 63 63 63 63 63 63 ...
.. ..$ long : num [1:6271] 1.33 1.33 1.33 1.33 1.33 ...
.. ..$ lat : num [1:6271] 51.4 51.4 51.4 51.4 51.4 ...
.. ..$ st_areasha: num [1:6271] 1.03e+08 1.03e+08 1.03e+08 1.03e+08 1.03e+08 ...
.. ..$ st_lengths: num [1:6271] 56839 56839 56839 56839 56839 ...
..# lines :List of 6271
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
.. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
.. .. .. .. .. .. ..# coords: num [1:8, 1:2] 1.38 1.38 1.38 1.38 1.38 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
.. .. .. ..# ID : chr "1"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
.. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. .. .. ..# ID : chr "30"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
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.. .. .. ..# ID : chr "31"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
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.. .. .. .. .. .. ..# coords: num [1:5, 1:2] 1.38 1.38 1.38 1.38 1.38 ...
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.. .. .. ..# ID : chr "32"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
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.. .. .. ..# ID : chr "33"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
.. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
.. .. .. .. .. .. ..# coords: num [1:5, 1:2] 1.38 1.38 1.38 1.38 1.38 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
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.. .. .. ..# ID : chr "34"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
.. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
.. .. .. .. .. .. ..# coords: num [1:3, 1:2] 1.38 1.38 1.38 51.39 51.39 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
.. .. .. ..# ID : chr "35"
.. ..$ :Formal class 'Lines' [package "sp"] with 2 slots
.. .. .. ..# Lines:List of 1
.. .. .. .. ..$ :Formal class 'Line' [package "sp"] with 1 slot
.. .. .. .. .. .. ..# coords: num [1:3, 1:2] 1.38 1.38 1.38 51.39 51.39 ...
.. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
.. .. .. ..# ID : chr "36"
.. .. [list output truncated]
..# bbox : num [1:2, 1:2] 1.21 51.31 1.45 51.39
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:2] "x" "y"
.. .. ..$ : chr [1:2] "min" "max"
..# proj4string:Formal class 'CRS' [package "sp"] with 1 slot
.. .. ..# projargs: chr "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
How do I merge Points$nearest_line_id with Clipped_Streets#Lines. When one is a column and the other is a list within an object? Is there any way I can put the attributes in #data instead? Very grateful for any direction. Happy to provide more detail if necessary.

I think I cracked it. The key is to use sp::merge to ensure that attributes get written back to points. Then invoke the #data element. then it is just like a regular merge.
Points<-snapPointsToLines(Clipped_Points, Clipped_Street, maxDist=0.1, withAttrs = TRUE, idField="identifier")
Points_agg<-Points#data %>% group_by( nearest_line_id) %>% tally()
Clipped_Street#data<-sp::merge(Clipped_Street#data,Points_agg,by.x="identifier",by.y="nearest_line_id", all.x=TRUE )
The next problem is merging and splitting the dataset before or after a merge and keeping the spatial attributes, but I may put that in a new question.

R how to get association rules (LHS, RHS, support, confidence, lift) from recommenderlab object?

i'm current construct product recommendation using R recommenderlab, after compute AR recommender, i'm hoping to understand the association rules, but i couldn't found any why to extract the complete association rules from the recommender object.
Below is the sample dataset
m <- matrix(sample(c(0,1), 50, replace=TRUE), nrow=5, ncol=10,
dimnames=list(users=paste("u", 1:5, sep=''),
items=paste("i", 1:10, sep='')))
Convert matrix into binaryRatingMatrix
b <- as(m, "binaryRatingMatrix")
create a user-based CF recommender using training data
r <- Recommender(getData(scheme, "train"), "AR")
Looking into the AR recommender object r#model$rule_base i found "rule_base"
Formal class 'Recommender' [package "recommenderlab"] with 5 slots
..# method : chr "AR"
..# dataType: chr "binaryRatingMatrix"
..# ntrain : int 5
..# model :List of 9
.. ..$ description : chr "AR: rule base"
.. ..$ rule_base :Formal class 'rules' [package "arules"] with 4 slots
.. .. .. ..# lhs :Formal class 'itemMatrix' [package "arules"] with 3 slots
.. .. .. .. .. ..# data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. .. .. .. .. .. ..# i : int [1:145] 1 1 1 1 2 0 0 0 5 5 ...
.. .. .. .. .. .. .. ..# p : int [1:80] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. .. .. .. .. ..# Dim : int [1:2] 10 79
.. .. .. .. .. .. .. ..# Dimnames:List of 2
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. ..# factors : list()
.. .. .. .. .. ..# itemInfo :'data.frame': 10 obs. of 1 variable:
.. .. .. .. .. .. ..$ labels: chr [1:10] "i1" "i2" "i3" "i4" ...
.. .. .. .. .. ..# itemsetInfo:'data.frame': 0 obs. of 0 variables
.. .. .. ..# rhs :Formal class 'itemMatrix' [package "arules"] with 3 slots
.. .. .. .. .. ..# data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
.. .. .. .. .. .. .. ..# i : int [1:79] 6 4 9 3 8 4 9 3 6 3 ...
.. .. .. .. .. .. .. ..# p : int [1:80] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. .. .. .. .. ..# Dim : int [1:2] 10 79
.. .. .. .. .. .. .. ..# Dimnames:List of 2
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. .. ..$ : NULL
.. .. .. .. .. .. .. ..# factors : list()
.. .. .. .. .. ..# itemInfo :'data.frame': 10 obs. of 1 variable:
.. .. .. .. .. .. ..$ labels: chr [1:10] "i1" "i2" "i3" "i4" ...
.. .. .. .. .. ..# itemsetInfo:'data.frame': 0 obs. of 0 variables
.. .. .. ..# quality:'data.frame': 79 obs. of 4 variables:
.. .. .. .. ..$ support : num [1:79] 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.4 ...
.. .. .. .. ..$ confidence: num [1:79] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. ..$ lift : num [1:79] 1.67 1.25 1.25 1.25 1.67 ...
.. .. .. .. ..$ count : num [1:79] 1 1 1 1 2 2 2 2 2 2 ...
.. .. .. ..# info :List of 4
.. .. .. .. ..$ data : symbol data
.. .. .. .. ..$ ntransactions: int 5
.. .. .. .. ..$ support : num 0.1
.. .. .. .. ..$ confidence : num 0.8
.. ..$ support : num 0.1
.. ..$ confidence : num 0.8
.. ..$ maxlen : num 3
.. ..$ sort_measure : chr "confidence"
.. ..$ sort_decreasing: logi TRUE
.. ..$ apriori_control:List of 1
.. .. ..$ verbose: logi FALSE
.. ..$ verbose : logi FALSE
..# predict :function (model, newdata, n = 10, data = NULL, type = c("topNList", "ratings", "ratingMatrix"), ...)
Question: how can i extract the association rules from recommender object as data frame?
Get association rules dataframe with columns (LHS, RHS, support, confidence, lift, count)

you can use
#Convert rules into data frame
rules3 = as(rules, "data.frame")

error while assigning value to list of list

I have an R code that looks like this:
r <- rowData(sce_list[[1]])
c <- colData(sce_list[[1]])
t <- sce_list[[1]][r$use,c$use]
Till here all runs smoothly. However, when I try to assign the value of t to another object like the following I run into error.
sce_list[[1]]$qc = t
Error in `[[<-`(`*tmp*`, name, value = <S4 object of class "SingleCellExperiment">) :
1842 elements in value to replace 5586 elements
where sce_list is a list of sce.
EDIT:
output of str(t)
Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 10 slots
..# int_elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. ..# rownames : NULL
.. .. ..# nrows : int 1842
.. .. ..# listData :List of 2
.. .. .. ..$ is_spike_MT: logi [1:1842] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. ..$ is_spike : logi [1:1842] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# int_colData :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. ..# rownames : NULL
.. .. ..# nrows : int 3973
.. .. ..# listData : Named list()
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# int_metadata :List of 2
.. ..$ version :Classes 'package_version', 'numeric_version' hidden list of 1
.. .. ..$ : int [1:3] 1 0 0
.. ..$ spike_names: chr "MT"
..# reducedDims :Formal class 'SimpleList' [package "S4Vectors"] with 4 slots
.. .. ..# listData : Named list()
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# rowRanges :Formal class 'GRangesList' [package "GenomicRanges"] with 5 slots
.. .. ..# unlistData :Formal class 'GRanges' [package "GenomicRanges"] with 6 slots
.. .. .. .. ..# seqnames :Formal class 'Rle' [package "S4Vectors"] with 4 slots
.. .. .. .. .. .. ..# values : Factor w/ 0 levels:
.. .. .. .. .. .. ..# lengths : int(0)
.. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. ..# metadata : list()
.. .. .. .. ..# ranges :Formal class 'IRanges' [package "IRanges"] with 6 slots
.. .. .. .. .. .. ..# start : int(0)
.. .. .. .. .. .. ..# width : int(0)
.. .. .. .. .. .. ..# NAMES : NULL
.. .. .. .. .. .. ..# elementType : chr "integer"
.. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. ..# metadata : list()
.. .. .. .. ..# strand :Formal class 'Rle' [package "S4Vectors"] with 4 slots
.. .. .. .. .. .. ..# values : Factor w/ 3 levels "+","-","*":
.. .. .. .. .. .. ..# lengths : int(0)
.. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. ..# metadata : list()
.. .. .. .. ..# elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. .. .. .. ..# rownames : NULL
.. .. .. .. .. .. ..# nrows : int 0
.. .. .. .. .. .. ..# listData : Named list()
.. .. .. .. .. .. ..# elementType : chr "ANY"
.. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. ..# metadata : list()
.. .. .. .. ..# seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots
.. .. .. .. .. .. ..# seqnames : chr(0)
.. .. .. .. .. .. ..# seqlengths : int(0)
.. .. .. .. .. .. ..# is_circular: logi(0)
.. .. .. .. .. .. ..# genome : chr(0)
.. .. .. .. ..# metadata : list()
.. .. ..# elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. .. ..# rownames : NULL
.. .. .. .. ..# nrows : int 1842
.. .. .. .. ..# listData :List of 12
.. .. .. .. .. ..$ id : chr [1:1842] "ENSG00000187608" "ENSG00000186891" "ENSG00000186827" "ENSG00000175756" ...
.. .. .. .. .. ..$ symbol : chr [1:1842] "ISG15" "TNFRSF18" "TNFRSF4" "AURKAIP1" ...
.. .. .. .. .. ..$ is_feature_control : logi [1:1842] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. .. .. ..$ is_feature_control_MT: logi [1:1842] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. .. .. ..$ mean_counts : num [1:1842] 5.067 0.166 0.162 0.639 0.719 ...
.. .. .. .. .. ..$ log10_mean_counts : num [1:1842] 0.783 0.0669 0.0651 0.2146 0.2353 ...
.. .. .. .. .. ..$ rank_counts : num [1:1842] 33536 30766 30710 32857 32945 ...
.. .. .. .. .. ..$ n_cells_counts : int [1:1842] 4580 516 458 2376 2578 2153 2277 5388 3604 4131 ...
.. .. .. .. .. ..$ pct_dropout_counts : num [1:1842] 18 90.8 91.8 57.5 53.8 ...
.. .. .. .. .. ..$ total_counts : num [1:1842] 28305 930 904 3569 4017 ...
.. .. .. .. .. ..$ log10_total_counts : num [1:1842] 4.45 2.97 2.96 3.55 3.6 ...
.. .. .. .. .. ..$ use : Named logi [1:1842] TRUE TRUE TRUE TRUE TRUE TRUE ...
.. .. .. .. .. .. ..- attr(*, "names")= chr [1:1842] "ENSG00000187608" "ENSG00000186891" "ENSG00000186827" "ENSG00000175756" ...
.. .. .. .. ..# elementType : chr "ANY"
.. .. .. .. ..# elementMetadata: NULL
.. .. .. .. ..# metadata : list()
.. .. ..# partitioning :Formal class 'PartitioningByEnd' [package "IRanges"] with 5 slots
.. .. .. .. ..# end : int [1:1842] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..# NAMES : chr [1:1842] "ENSG00000187608" "ENSG00000186891" "ENSG00000186827" "ENSG00000175756" ...
.. .. .. .. ..# elementType : chr "integer"
.. .. .. .. ..# elementMetadata: NULL
.. .. .. .. ..# metadata : list()
.. .. ..# elementType : chr "GRanges"
.. .. ..# metadata : list()
..# colData :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. ..# rownames : chr [1:3973] "AAACCTGAGAAGGTTT-1" "AAACCTGAGCGTTCCG-1" "AAACCTGAGTACGTAA-1" "AAACCTGGTCCATGAT-1" ...
.. .. ..# nrows : int 3973
.. .. ..# listData :List of 32
.. .. .. ..$ dataset : int [1:3973] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. ..$ barcode : chr [1:3973] "AAACCTGAGAAGGTTT-1" "AAACCTGAGCGTTCCG-1" "AAACCTGAGTACGTAA-1" "AAACCTGGTCCATGAT-1" ...
.. .. .. ..$ total_features : int [1:3973] 1373 1837 2259 2601 1967 1236 1642 1153 1078 2682 ...
.. .. .. ..$ log10_total_features : num [1:3973] 3.14 3.26 3.35 3.42 3.29 ...
.. .. .. ..$ total_counts : num [1:3973] 3380 7332 10099 12184 7352 ...
.. .. .. ..$ log10_total_counts : num [1:3973] 3.53 3.87 4 4.09 3.87 ...
.. .. .. ..$ pct_counts_top_50_features : num [1:3973] 33.1 38.9 42.5 42.2 42.8 ...
.. .. .. ..$ pct_counts_top_100_features : num [1:3973] 45 54.8 59.2 53.5 52.4 ...
.. .. .. ..$ pct_counts_top_200_features : num [1:3973] 57.5 66.9 69 64.7 63.5 ...
.. .. .. ..$ pct_counts_top_500_features : num [1:3973] 74.2 79.2 79.4 76.8 76.6 ...
.. .. .. ..$ total_features_endogenous : int [1:3973] 1373 1837 2259 2601 1967 1236 1642 1153 1078 2682 ...
.. .. .. ..$ log10_total_features_endogenous : num [1:3973] 3.14 3.26 3.35 3.42 3.29 ...
.. .. .. ..$ total_counts_endogenous : num [1:3973] 3380 7332 10099 12184 7352 ...
.. .. .. ..$ log10_total_counts_endogenous : num [1:3973] 3.53 3.87 4 4.09 3.87 ...
.. .. .. ..$ pct_counts_endogenous : num [1:3973] 100 100 100 100 100 100 100 100 100 100 ...
.. .. .. ..$ pct_counts_top_50_features_endogenous : num [1:3973] 33.1 38.9 42.5 42.2 42.8 ...
.. .. .. ..$ pct_counts_top_100_features_endogenous: num [1:3973] 45 54.8 59.2 53.5 52.4 ...
.. .. .. ..$ pct_counts_top_200_features_endogenous: num [1:3973] 57.5 66.9 69 64.7 63.5 ...
.. .. .. ..$ pct_counts_top_500_features_endogenous: num [1:3973] 74.2 79.2 79.4 76.8 76.6 ...
.. .. .. ..$ total_features_feature_control : int [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ log10_total_features_feature_control : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ total_counts_feature_control : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ log10_total_counts_feature_control : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ pct_counts_feature_control : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ total_features_MT : int [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ log10_total_features_MT : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ total_counts_MT : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ log10_total_counts_MT : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ pct_counts_MT : num [1:3973] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. ..$ is_cell_control : logi [1:3973] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. ..$ use : logi [1:3973] TRUE TRUE TRUE TRUE TRUE TRUE ...
.. .. .. ..$ outlier : logi [1:3973] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# assays :Reference class 'ShallowSimpleListAssays' [package "SummarizedExperiment"] with 1 field
.. ..$ data: NULL
.. ..and 14 methods.
..# NAMES : NULL
..# elementMetadata :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. ..# rownames : NULL
.. .. ..# nrows : int 1842
.. .. ..# listData : Named list()
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# metadata :List of 1
.. ..$ name: chr "iMates-1"
and of
class(sce_list[[1]])
[1] "SingleCellExperiment"
attr(,"package")
[1] "SingleCellExperiment"
Could anyone of the R gurus here help me in solving this problem.

I figured out a way around to solve this problem. It is not the most elegant answer but it does solve the problem.So here it is:
for (i in seq_along(sce_list))
{
r <- rowData(sce_list[[i]])$use
c <- colData(sce_list[[i]])$use
tt=(sce_list[[i]])[r,c]
assign(paste0("sce_",i,".qc"),tt)
}
Yes, I have put this in a loop as I want this to run in a loop. :)
If someone has an elegant way of doing this please post it and I will try it out.

to get rid of warning and error: non-language object in R

I am coding in R and I have a small chunk that I need to loop it over.
the part of the code looks like this:
sc_1$ue <- cF(sc_1, 2500, 1000, 5)
sc_2$ue <- cF(sc_2, 2500, 1000, 5)
sc_3$ue <- cF(sc_3, 2500, 1000, 5)
sc_4$ue <- cF(sc_4, 3000, 1000, 5)
where cF is a function I am calling. This code in its present form works without any error.
However, when I try to loop it like the following:
sc_list=mget(sc)
for (i in 1:5)
sc_list[i]$ue <- cF(sc_list[i], 2500, 1000, 5)
where sc contains sc_1 to sc_n objects.
The output is not an immediate error but a warning.
## Warning in sc_list[i] <- `*vtmp*`: number of items to replace is not a
## multiple of replacement length
## Warning in sc_list[i] <- `*vtmp*`: number of items to replace is not a
## multiple of replacement length
## Warning in sc_list[i] <- `*vtmp*`: number of items to replace is not a
## multiple of replacement length
## Warning in sc_list[i] <- `*vtmp*`: number of items to replace is not a
## multiple of replacement length
because of this following code in the program gives me an error.
How can I fix this?
What I have tried is:
sc_list=mget(sc)
for (i in 1:length(paths))
paste0("sc_",i)$ue <- cF(sc_list[i], 2500, 1000, 5)
This throws up an error.
Quitting from lines 209-217 (prototype.Rmd)
Error in paste0("sc_", i)$use <- cF(sc_list[i], 2500, 1000, :
target of assignment expands to non-language object
Calls: <Anonymous> ... handle -> withCallingHandlers -> withVisible -> eval -> eval
Execution halted
Edit: output of
> str(sc_list)
List of 4
$ sc_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 10 slots
.. ..# int_elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. ..# rownames : NULL
.. .. .. ..# nrows : int 33694
.. .. .. ..# listData :List of 2
.. .. .. .. ..$ is_spike_MT: logi [1:33694] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. .. ..$ is_spike : logi [1:33694] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..# int_colData :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. ..# rownames : NULL
.. .. .. ..# nrows : int 5586
.. .. .. ..# listData : Named list()
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..# int_metadata :List of 2
.. .. ..$ version :Classes 'package_version', 'numeric_version' hidden list of 1
.. .. .. ..$ : int [1:3] 1 0 0
.. .. ..$ spike_names: chr "MT"
.. ..# reducedDims :Formal class 'SimpleList' [package "S4Vectors"] with 4 slots
.. .. .. ..# listData : list()
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..# rowRanges :Formal class 'GRangesList' [package "GenomicRanges"] with 5 slots
.. .. .. ..# unlistData :Formal class 'GRanges' [package "GenomicRanges"] with 6 slots
.. .. .. .. .. ..# seqnames :Formal class 'Rle' [package "S4Vectors"] with 4 slots
.. .. .. .. .. .. .. ..# values : Factor w/ 0 levels:
.. .. .. .. .. .. .. ..# lengths : int(0)
.. .. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. .. ..# metadata : list()
.. .. .. .. .. ..# ranges :Formal class 'IRanges' [package "IRanges"] with 6 slots
.. .. .. .. .. .. .. ..# start : int(0)
.. .. .. .. .. .. .. ..# width : int(0)
.. .. .. .. .. .. .. ..# NAMES : NULL
.. .. .. .. .. .. .. ..# elementType : chr "integer"
.. .. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. .. ..# metadata : list()
.. .. .. .. .. ..# strand :Formal class 'Rle' [package "S4Vectors"] with 4 slots
.. .. .. .. .. .. .. ..# values : Factor w/ 3 levels "+","-","*":
.. .. .. .. .. .. .. ..# lengths : int(0)
.. .. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. .. ..# metadata : list()
.. .. .. .. .. ..# elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. .. .. .. .. ..# rownames : NULL
.. .. .. .. .. .. .. ..# nrows : int 0
.. .. .. .. .. .. .. ..# listData : Named list()
.. .. .. .. .. .. .. ..# elementType : chr "ANY"
.. .. .. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. .. .. ..# metadata : list()
.. .. .. .. .. ..# seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots
.. .. .. .. .. .. .. ..# seqnames : chr(0)
.. .. .. .. .. .. .. ..# seqlengths : int(0)
.. .. .. .. .. .. .. ..# is_circular: logi(0)
.. .. .. .. .. .. .. ..# genome : chr(0)
.. .. .. .. .. ..# metadata : list()
.. .. .. ..# elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. .. .. ..# rownames : NULL
.. .. .. .. .. ..# nrows : int 33694
.. .. .. .. .. ..# listData :List of 11
.. .. .. .. .. .. ..$ id : chr [1:33694] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009" ...
.. .. .. .. .. .. ..$ symbol : chr [1:33694] "RP11-34P13.3" "FAM138A" "OR4F5" "RP11-34P13.7" ...
.. .. .. .. .. .. ..$ is_feature_control : logi [1:33694] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. .. .. .. ..$ is_feature_control_MT: logi [1:33694] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. .. .. .. ..$ mean_counts : num [1:33694] 0 0 0 0.000895 0.000179 ...
.. .. .. .. .. .. ..$ log10_mean_counts : num [1:33694] 0.00 0.00 0.00 3.89e-04 7.77e-05 ...
.. .. .. .. .. .. ..$ rank_counts : num [1:33694] 6741 6741 6741 18054 14520 ...
.. .. .. .. .. .. ..$ n_cells_counts : int [1:33694] 0 0 0 5 1 0 0 0 611 542 ...
.. .. .. .. .. .. ..$ pct_dropout_counts : num [1:33694] 100 100 100 99.9 100 ...
.. .. .. .. .. .. ..$ total_counts : num [1:33694] 0 0 0 5 1 0 0 0 661 617 ...
.. .. .. .. .. .. ..$ log10_total_counts : num [1:33694] 0 0 0 0.778 0.301 ...
.. .. .. .. .. ..# elementType : chr "ANY"
.. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. ..# metadata : list()
.. .. .. ..# partitioning :Formal class 'PartitioningByEnd' [package "IRanges"] with 5 slots
.. .. .. .. .. ..# end : int [1:33694] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. .. ..# NAMES : chr [1:33694] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009" ...
.. .. .. .. .. ..# elementType : chr "integer"
.. .. .. .. .. ..# elementMetadata: NULL
.. .. .. .. .. ..# metadata : list()
.. .. .. ..# elementType : chr "GRanges"
.. .. .. ..# metadata : list()
.. ..# colData :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. ..# rownames : chr [1:5586] "AAACCTGAGAAGGTTT-1" "AAACCTGAGCGTTCCG-1" "AAACCTGAGTACGTAA-1" "AAACCTGGTAAACACA-1" ...
.. .. .. ..# nrows : int 5586
.. .. .. ..# listData :List of 30
.. .. .. .. ..$ dataset : int [1:5586] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. .. ..$ barcode : chr [1:5586] "AAACCTGAGAAGGTTT-1" "AAACCTGAGCGTTCCG-1" "AAACCTGAGTACGTAA-1" "AAACCTGGTAAACACA-1" ...
.. .. .. .. ..$ total_features : int [1:5586] 1373 1837 2259 802 2601 1967 1236 1642 1153 1078 ...
.. .. .. .. ..$ log10_total_features : num [1:5586] 3.14 3.26 3.35 2.9 3.42 ...
.. .. .. .. ..$ total_counts : num [1:5586] 3380 7332 10099 1833 12184 ...
.. .. .. .. ..$ log10_total_counts : num [1:5586] 3.53 3.87 4 3.26 4.09 ...
.. .. .. .. ..$ pct_counts_top_50_features : num [1:5586] 33.1 38.9 42.5 40.9 42.2 ...
.. .. .. .. ..$ pct_counts_top_100_features : num [1:5586] 45 54.8 59.2 53.7 53.5 ...
.. .. .. .. ..$ pct_counts_top_200_features : num [1:5586] 57.5 66.9 69 66.4 64.7 ...
.. .. .. .. ..$ pct_counts_top_500_features : num [1:5586] 74.2 79.2 79.4 83.5 76.8 ...
.. .. .. .. ..$ total_features_endogenous : int [1:5586] 1373 1837 2259 802 2601 1967 1236 1642 1153 1078 ...
.. .. .. .. ..$ log10_total_features_endogenous : num [1:5586] 3.14 3.26 3.35 2.9 3.42 ...
.. .. .. .. ..$ total_counts_endogenous : num [1:5586] 3380 7332 10099 1833 12184 ...
.. .. .. .. ..$ log10_total_counts_endogenous : num [1:5586] 3.53 3.87 4 3.26 4.09 ...
.. .. .. .. ..$ pct_counts_endogenous : num [1:5586] 100 100 100 100 100 100 100 100 100 100 ...
.. .. .. .. ..$ pct_counts_top_50_features_endogenous : num [1:5586] 33.1 38.9 42.5 40.9 42.2 ...
.. .. .. .. ..$ pct_counts_top_100_features_endogenous: num [1:5586] 45 54.8 59.2 53.7 53.5 ...
.. .. .. .. ..$ pct_counts_top_200_features_endogenous: num [1:5586] 57.5 66.9 69 66.4 64.7 ...
.. .. .. .. ..$ pct_counts_top_500_features_endogenous: num [1:5586] 74.2 79.2 79.4 83.5 76.8 ...
.. .. .. .. ..$ total_features_feature_control : int [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ log10_total_features_feature_control : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ total_counts_feature_control : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ log10_total_counts_feature_control : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ pct_counts_feature_control : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ total_features_MT : int [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ log10_total_features_MT : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ total_counts_MT : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ log10_total_counts_MT : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ pct_counts_MT : num [1:5586] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. .. ..$ is_cell_control : logi [1:5586] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..# assays :Reference class 'ShallowSimpleListAssays' [package "SummarizedExperiment"] with 1 field
.. .. ..$ data: NULL
.. .. ..and 14 methods.
.. ..# NAMES : NULL
.. ..# elementMetadata :Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. .. ..# rownames : NULL
.. .. .. ..# nrows : int 33694
.. .. .. ..# listData : Named list()
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..# metadata :List of 1
.. .. ..$ name: chr "iMates-1"
Here is one of the four objects.
Could anyone of you help me.
Thank you

I'm assuming sc_list is a list of lists, right?
If so, can you check if the following code works?
sc_list=mget(sc)
for (i in 1:5)
sc_list[[i]]$ue <- cF(sc_list[[i]], 2500, 1000, 5)

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