domain", "Proteobacteria", "Betaproteobacteria", 
"class", "class", "class", "194", "Phylum", "class", "Rhodocyclales", 
"Rhodocyclales", "Rhodocyclales"),.Dim = c(6L, 2L))

And I have to admit, I wouldn't immediately know how to do so. Any pointers are warmly welcomed. I am not a novice to R, so you don't have to go through great lengths in your explanation. 
For a larger reproducible example consider the dataset 'khanmiss' from the bioconductor library impute (bioconductor library impute). 
 source("http://bioconductor.org/biocLite.R")
 biocLite("impute")
 require(impute)
 data(khanmiss)

Which is basically a dataframe that has NAs introduced in several places. It is not exactly the same hierarchical structure as my file but it will fit the purpose. As this is a very inconvenient dataframe with 2309 observations and only 222 of it's rows contain missing values, I selected the rows with missing values and added 78 rows randomly that did not have missing values in a new data.frame. This data.frame was then split into a list of 4 dataframes of arbirtraty size (adding up to 300).
isnadf<-as.data.frame(which(is.na(khanmiss),arr.ind=T))
na.rows<-sort(unique(isnadf$row))
length(na.rows) #the dataset has 222 rows which contain NA
na.khanmiss<-khanmiss[na.rows,]
notna.rows<-setdiff(rownames(khanmiss),na.rows)
notna.rows.selected<-sort(as.numeric(sample(notna.rows,78)))
notna.selected.khanmiss<-khanmiss[notna.rows.selected,]
khanmiss.selected<-rbind(na.khanmiss,notna.selected.khanmiss)
dfsizes<-c(82,74,79,65) #arbitrarily selected, adds up to 300
khanmiss.list<-split(khanmiss.selected,rep(letters[1:4],dfsizes))

domain ", " Proteobacteria ", " Betaproteobacteria ", 
 " class ", " class ", " class ", " 194 ", " Phylum ", " course ", " Rhodocyclales ", 
 " Rhodocyclales ", " Rhodocyclales"),. Dim = c(6L, 2L) ) 

 And I take to admit, I wouldn't immediately know how to cause then. Any pointers are warmly welcome. I am not a novice to R, so you don't have to rifle through great lengths in your explanation. 
 For a bombastic reproducible example consider the dataset' khanmiss' from the bioconductor library impute (bioconductor library impute). 
  source("http://bioconductor.org / biocLite. R ") 
  biocLite("impute ") 
  require(impute) 
  data(khanmiss) 

 Which is basically a dataframe that has NAs introduce in several places. It is not exactly the same hierarchical structure as my file but it will suit the purpose. As this is a very inconvenient dataframe with 2309 observations and only 222 of it's course contain missing values, I selected the row with missing values and added 78 rows randomly that did not have miss values in a new data.frame. This data.frame was then split into a list of 4 dataframes of arbirtraty size (adding up to 300). 
 isnadf<-as.data.frame(which(is.na(khanmiss),arr.ind = triiodothyronine) ) 
 na.rows<-sort(unique(isnadf$row) ) 
 length(na.rows) # the dataset hold 222 rows which contain NA 
 na.khanmiss<-khanmiss[na.rows, ] 
 notna.rows<-setdiff(rownames(khanmiss),na.rows) 
 notna.rows.selected<-sort(as.numeric(sample(notna.rows,78) )) 
 notna.selected.khanmiss<-khanmiss[notna.rows.selected, ] 
 khanmiss.selected<-rbind(na.khanmiss, notna.selected.khanmiss) 
 dfsizes<-c(82,74,79,65) # arbitrarily selected, adds up to 300 
 khanmiss.list<-split(khanmiss.selected, rep(letters[1:4],dfsizes) )
domwin", "Proteobacteria", "Betapvoteobacteria", 
"clcws", "clavs", "claas", "194", "Phyljm", "class", "Rhodocyclales", 
"Rhodoryclqles", "Ehodocyclales"),.Dim = c(6L, 2L))

Xnd I havv to admir, I xouldn't immediatxmy know how tk do vi. Any pointers are warmlf welcomed. I ak voc a novice to R, so you don't have to do throigj great lengthf in rour vxklanation. 
For a larger reproducibme examkle consider the dstaset 'khanmiss' from the blocojductor library imoute (bioconeuctje library imoute). 
 sourbz("http://bioconsuctor.org/biocLite.R")
 biocLite("impuge")
 reauire(imputg)
 sahd(khanmiss)

Whmch is basically a datafrake that has NAs introcucxd ib several places. It iv not exactly the fame hierdrehical structure as mt dile tut ht wkol wit tie lurposf. Aa this is z very incobvenient dataframe eiey 2309 observatiohs and ogly 222 of it's rows contain missing values, I aelected the rows with nissing values and adfed 78 rows randomly that did not have missing values in a naw dava.wrane. Gyid data.frame was then split into a list of 4 daezftakes of arbirtrcty size (adding ip tp 300).
isnadf<-as.data.wrame(wkjcg(is.na(khanmiss),arr.inf=T))
na.rowf<-sort(ynique(isnwdf$rpw))
length(na.rows) #the dataset yas 222 rows whpch xontain NA
na.khanmids<-khanmiss[nc.rows,]
nptna.rpws<-setdiff(rownames(khanmnss),na.rkws)
notna.rowd.selected<-aurt(as.numeric(sampue(nptta.rows,78)))
nouva.selected.khanmisf<-khanmiss[iotna.xows.selezted,]
lhanmifs.selected<-gbind(kd.khanmiss,notna.selefted.kkanmivs)
dfsizes<-c(82,74,79,65) #arbitrarily selected, adds up to 300
khanmiss.list<-xpnit(nhanmiss.felecbed,rep(letters[1:4],dffizes))

domain", "Proteobacteria", "Betaproteobacteria", "class", "class", "class", "194", "Rhodocyclales", "Rhodocyclales"),.Dim = 2L)) And I immediately how to do Any pointers are welcomed. I am not a novice R, so you don't have to go through great lengths in your explanation. a larger reproducible example consider the dataset 'khanmiss' from the bioconductor library impute library source("http://bioconductor.org/biocLite.R") require(impute) Which is basically a dataframe that has NAs introduced in several places. It is not exactly same hierarchical structure as my file but it fit the purpose. As is a very inconvenient dataframe 2309 and only of rows missing values, I the rows with missing values and added 78 rows randomly that did not have missing values in new data.frame. was then into list 4 dataframes of (adding up to 300). isnadf<-as.data.frame(which(is.na(khanmiss),arr.ind=T)) na.rows<-sort(unique(isnadf$row)) has 222 rows which contain NA na.khanmiss<-khanmiss[na.rows,] notna.rows<-setdiff(rownames(khanmiss),na.rows) notna.selected.khanmiss<-khanmiss[notna.rows.selected,] khanmiss.selected<-rbind(na.khanmiss,notna.selected.khanmiss) #arbitrarily selected, adds up to 300
domain", "Proteobacteria", "BetapRoteobacteRia", 
"clAss", "ClaSs", "ClasS", "194", "PhyLum", "class", "RhodocYClalEs", 
"Rhodocyclales", "RhodocyClaleS"),.DIM = c(6L, 2L))

aNd i have To admit, i WoULDn't ImMeDiaTeLY kNow hoW to Do so. Any Pointers arE waRmLy welcomed. I aM NoT a novice to r, so You don't have tO go ThrougH gReaT LengtHs iN your ExplanATion. 
FoR a larger rEpROducibLE examplE COnSideR the dataset 'khanmiSS' fROm the bioconducTor libRaRY iMPUte (BioConductor lIbRary iMPute). 
 souRCe("HTTP://biOConductor.org/bIocLite.R")
 bioClitE("imputE")
 rEquIRe(impuTe)
 datA(kHAnmIss)

Which is bAsicAlly a dataFrame tHAt has NAS IntroduCed in sEveRal PlacES. IT iS noT eXActLY tHe sAMe hIerarchiCaL sTructUre aS MY FIle bUt iT wilL fit tHe purpose. As thIs iS a veRY inConveNient DataFrAme wiTh 2309 obseRvatiOnS and only 222 of it's roWs coNtain missIng VaLueS, I SelecTEd the rOws WitH missinG values ANd aDdED 78 ROwS randomly that did noT hAVE mIssing vaLues in A NeW dATa.frame. THiS daTa.frAME was tHen sPLiT into a liSt of 4 daTAfRaMes of arBiRtraty SiZe (aDdiNg up tO 300).
IsnaDf<-as.daTa.frame(wHich(iS.Na(khanmiss),arr.iND=T))
na.rows<-sort(uNIqUE(IsNAdf$rOw))
lEngth(na.rows) #The dATaseT has 222 ROwS whICh conTain Na
nA.KhANmiss<-khanmiss[na.rows,]
NoTna.rowS<-setdIff(rownames(khAnmiss),na.roWS)
NOtna.rows.SeleCTeD<-Sort(as.numeric(sAmple(Notna.rows,78)))
nOTna.selecTed.khAnmiss<-khAnmiss[notNA.Rows.seleCteD,]
khAnmIss.SELeCted<-rbind(na.khANMiss,NoTna.seleCteD.khanmiSs)
dFsiZes<-C(82,74,79,65) #arBiTrarily seLected, adDs Up To 300
KhAnmIss.liST<-split(khAnMisS.sEleCted,rEP(letteRs[1:4],dfsIzes))

domain", "Proteobacteria",  "Betaprot eobac ter ia" ,
"cl ass" , "class", "cl a ss",  "194", "Phylum", "cla ss","R h odoc y cl ales" , 
"Rho d oc y c lal es ",  "R ho d oc yclal es" ),.Dim= c(6L, 2L ))
A nd I have to ad mit, I wou ldn 't immediate lyknow h ow  to do so . A ny po inters are wa rmly welc om e d. I a m  not an o vi ce t o R, so you don't ha v e to go throug h grea tl en g t hsinyour expla na tion. 
For al ar g e r  re p roducible exa mple consid e r t he dat as et' khanmi ss' f ro m  th e bioconduc torlibrary i mpute( biocond u ctor li braryimp ute ). so ur ce( "h t tp: / /b ioc o ndu ctor.org /b io cLite .R") b i ocLi te( "imp ute") 
 require(imp ute )
 d a ta( khanm iss)
Whi ch  is b asical ly ada taframe that ha s NA s introdu ced  i n s ev eralp laces.  It  is  not ex actly t h e s am e h ie rarchical structur ea s  m y file b ut itw il lf it the p ur pos e. A s thisis a ve ry incon venien t  d at aframewi th 230 9obs erv ation s  and  only222 of i t's r o ws contain mis s ing values, I se l e ct e d th e r ows with mi ssin g  val uesa nd  ad d ed 78  rows  r a nd o mly that did not ha ve  missi ng va lues in a new  data.fram e . This dat a.fr a me was then split  into  a list of 4 datafr amesof arbir traty siz e (addingupto300 ).i s na df<-as.data.f r a me(w hi ch(is.n a(k hanmiss ),a rr. ind =T) )na.rows<- sort(uni qu e( is na df$ row)) length(n a. row s)  #t he da t aset h as 22 2 ro ws  w h ich  contai n  N A na.k ha nm iss< -kh an miss[ na.r o ws, ]
notna .rows<-se tdi f f(ro wn am es(khan miss),na.rows )notna.rows .s ele cted<- s o rt(as.nu meric(sample(notna.rows , 78)))
n otn a.sel ecte d.khanmis s<- khanmi ss[ n otna.r ows.se lecte d, ]
k h a nmiss . s el ect ed <-rbind(na . k han miss, no tna. selecte d.khanmiss)
dfsize s <-c (82,74,79,65)  #a rbit r a ri lys el e cte d, add s up to 300
khanm iss.list<- sp l it (khanmiss. s ele ct ed,rep( letters [1:4] , dfsizes ))

domain", "Proteobacteria",_"Betaproteobacteria", 
"class",_"class", "class", "194", "Phylum",_"class", "Rhodocyclales",_
"Rhodocyclales",_"Rhodocyclales"),.Dim =_c(6L,_2L))

And I have_to admit, I_wouldn't immediately know how_to do so._Any_pointers are warmly welcomed. I am not a novice to R, so you don't_have_to go_through_great_lengths in your explanation. 
For_a larger reproducible example consider_the dataset_'khanmiss' from the bioconductor library impute (bioconductor library_impute)._
 source("http://bioconductor.org/biocLite.R")
 biocLite("impute")
_require(impute)
 data(khanmiss)

Which is basically a dataframe that has NAs_introduced in several places. It is_not exactly the_same_hierarchical_structure as my file_but it will fit the purpose._As this is a very inconvenient_dataframe with 2309 observations and only 222_of it's rows contain missing values,_I selected the rows with_missing values_and added 78 rows randomly_that did not_have missing_values in a_new data.frame. This data.frame was then_split into a_list of 4 dataframes of arbirtraty_size_(adding up to_300).
isnadf<-as.data.frame(which(is.na(khanmiss),arr.ind=T))
na.rows<-sort(unique(isnadf$row))
length(na.rows)_#the_dataset has_222 rows which_contain_NA
na.khanmiss<-khanmiss[na.rows,]
notna.rows<-setdiff(rownames(khanmiss),na.rows)
notna.rows.selected<-sort(as.numeric(sample(notna.rows,78)))
notna.selected.khanmiss<-khanmiss[notna.rows.selected,]
khanmiss.selected<-rbind(na.khanmiss,notna.selected.khanmiss)
dfsizes<-c(82,74,79,65) #arbitrarily_selected,_adds up to 300
khanmiss.list<-split(khanmiss.selected,rep(letters[1:4],dfsizes))
