问题描述
我已经在这个循环上停留了一段时间（从我的问题历史记录中可以看出），但是我想我已经快要解决它了，这要感谢我对堆栈溢出的帮助。
我注意到在我的绘图中，每个绘图都使用data_percentage_list [391]，这是列表中的最后一个元素。为了防止这种情况的发生，我做了很多事情，但是使用下面的代码：
# Create graphs in list
# Create titles for plots
titlenames <- c(harps)
for (i in 1:length(harps)){
counts <- table(Y[[i]][[5]],Y[[i]][[3]])
nam <- paste("data_percentage_",i,sep ="")
assign(nam,apply(counts,2,function(x){x*100/sum(x,na.rm=T)}))
}
data_percentage_list <- lapply(paste0("data_percentage_",1:length(harps)),get)
# Create pdf of score breakdown
for (i in 1:length(harps)){ for(j in titlenames) {
# For Hotel Name Subtitle
hotelname <- hotel_report$`Hotel (Q15 1)`[hotel_report$`Harp Number`==j]
# Plot the Data
pdf(file = paste0(j,".pdf"),paper = "USr",width=8,height=7)
par(mar = c(5.1,7,4.1,2.1))
nam <- paste("breakdown_",barplot(data_percentage_list[[i]],main = "Breakdown of Property Score Distribution",sub = hotelname,col = coul,las = 1,cex.names = .6,horiz = TRUE,yaxs="i",xlab = "Percentage",cex.axis = .8,cex.lab = .8,cex.main = .8,cex.sub = .8))
dev.off()
}}
其中长度（竖琴）为391，所以有391个图，这些图在进行时被覆盖。因此，当我打开pdf格式的图并刷新时，它会变为循环的最后一次迭代，直到最后它们都成为第391个属性的数据，并带有正确的酒店名称，因为该名称是从j中提取的。
有人知道我需要如何更改我的代码以使每个图对应正确的数据吗？意思是，downdown_54应该使用data_percentage_list [54]，并另存为该数据的pdf，击穿_55应该是data_percentage_list [55]，以此类推？
谢谢！
编辑：进行更多操作后继续进行跟踪。
下面的代码制作了391个不同的图形，但是391个pdf中的每一个都具有全部391个图形，而不是像应该的那样只是它们各自的图形。
与固定上面的代码相比，在此代码中正确拆分这些pdf容易吗？
# Create graphs in list
# Create titles for plots
titlenames <- c(harps)
for (i in 1:length(harps)){
counts <- table(Y[[i]][[5]],sep ="")
breakdown_list <- lapply(1:length(harps),function(i){
assign(nam,cex.sub = .8))})
dev.off()
}}
再次感谢！
编辑2：尝试使其更具可重复性
Y是391个数据帧的列表
structure(list(`Hotel (Q15 1)` = c("HILTON,SAN PEDRO,BELIZE","HILTON,BELIZE"),`Metro Area State (Q10 1)` = c("OCONUS","OCONUS","OCONUS"),`Question ID` = c("Room Work Area","Staff Knowledge","Add'tl Item Working Order","Property Maintenance","Property Appearance","Staff Interaction","Safety/Security","Room Work Area","Bed Quality","Check In/Out","Invoice Accuracy","Property Maintenance"
),`Question ID (group)` = c("Question 4 Items","Question 4 Items","Question 4 Items"),`Score Label` = c("7 Extremely Good","7 Extremely Good","6 Quite Good","5 Slightly Good","3 Slightly Poor","6 Quite Good"),`Harp Number` = c("1111","1111","1111")),row.names = c(9380L,9381L,9383L,9384L,9385L,9387L,9388L,9389L,9390L,9391L,9392L,9393L,9394L,9395L,9396L,9399L,9402L,9403L,9404L,9405L,9407L,9408L,9411L),class = "data.frame")
下面是dput（竖琴）
dput(harps)
c("1111","1696","3279","5646","5724","5938","6887","8859","9368","9508","11569","11644","18661","21418","22460","23317","25755","26076","26336","28917","29497","29498","30465","30619","30629","32784","35578","35588","40390","40866","47493","47677","47866","48064","48294","50432","50667","50773","51857","52125","52146","52383","52432","52451","52755","53589","53620","56939","57784","59571","61276","61283","62329","62666","66058","66553","66741","66763","67092","67169","67214","67373","67840","69494","71343","73906","74550","75285","76253","76335","76361","76393","76396","76898","76949","78501","78800","80079","81035","81620","85043","87026","87219","87304","88683","89650","92759","94380","94427","95043","95255","96061","96677","97269","100135","109591","109743","109971","110414","110856","110884","110899","110926","111032","111384","111605","123136","123411","124380","124753","124848","127565","135185","135999","136005","138251","140027","140074","140091","140095","140159","145523","148284","149639","153676","154790","157239","158213","158259","159248","159343","159401","159842","161219","161725","163154","163653","167172","170199","171936","172095","172272","172273","172340","172868","173429","173816","175033","177012","177150","177361","177383","177692","177892","177965","179887","180495","182189","182979","183174","183717","183879","184076","185191","185341","185675","185961","189276","190279","190896","192388","192984","193387","193441","193526","193534","193605","193613","193614","194274","194794","196133","196546","197075","197647","198115","200996","201627","202124","202992","205802","206405","206880","206990","207423","207483","207723","208210","208943","209614","210006","211605","211985","212714","213707","213803","213842","215961","216533","217963","218029","218348","218376","221745","222179","222299","222399","222736","222882","224539","224624","225339","225346","225368","225553","225565","225572","225573","226003","228325","229582","229614","230871","231228","231402","235196","235538","239409","241353","244587","244654","245353","246093","246311","247209","251084","253732","254388","256996","258464","260958","261655","262754","263192","263444","265835","269872","270285","271683","271687","272664","275922","276312","279909","287731","291167","291988","296004","297975","298318","298401","300962","301940","302250","302702","304896","308049","311490","312027","313227","313603","315536","319957","320049","320270","320352","327521","330319","331054","332070","332426","334213","341876","345820","346263","346723","347340","352596","354486","396465","445549","473263","482701","496665","503123","503365","528259","538396","539834","540896","546228","546290","546652","546922","548916","550479","552466","709416","714793","714861","716337","719021","728913","731082","732346","733242","735165","735348","735473","749296","757777","761782","762104","770251","808540","809896","809951","812527","816275","837926","842678","843836","847737","857277","864044","864495","865468","865951","866108","866502","866547","867803","867809","868374","868420","868593","868793","869746","869748","870953","872490","872579","875200","875288","878016","878858","879328","879640","882643","882781","883894","886067","886876","888522","888560","888820","889693","890261","890264","891171","894931","896794","896840","899485","901218","903465","904381","912517","913354","918968","921083")
解决方法
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