绘图覆盖，所以在 R 循环结束时，所有元素都从列表的最后一个元素中提取。我究竟做错了什么？
Plots overwriting, so at the end of the R loop all have pulled from last element of list. What am I doing wrong?
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可以看出），但我想我已经接近修复它了，非常感谢我在堆栈上获得的帮助溢出。
我注意到在我的绘图中，每个绘图都使用列表中的最后一个元素 data_percentage_list[391]。我已经做了很多事情来试图阻止这种情况的发生，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_", i, sep ="")
assign(nam, 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，因此有 391 个地块，地块正在覆盖。因此，当我在 pdf 中打开一个图并刷新它时，它会更改为循环的最后一次迭代，直到最后它们都最终成为第 391 属性 的数据，并具有正确的酒店名称，因为它是从j.
有谁知道我需要如何修改我的代码才能让每个图对应正确的数据？意思是，breakdown_54 应该使用 data_percentage_list[54]，并保存为该数据的 pdf，breakdown_55 应该是 data_percentage_list[55]，等等？
谢谢！
编辑：在进一步处理之后跟进。
下面的代码制作了 391 个不同的图表，但是 391 个 pdf 文件中的每一个都包含所有 391 个图表，而不是像它们应该的那样只有它们各自的图表。
与修复上面的代码相比，在此代码中正确拆分这些 pdf 是否更容易？
# 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_", i, sep ="")
breakdown_list <- lapply(1:length(harps), function(i){
assign(nam, 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()
}}
再次感谢！
编辑 2：试图使其更具可重复性
Y 是 391 个数据帧的列表
下面的代码是 Y 中 391 个数据帧之一的输出。
structure(list(`Hotel (Q15 1)` = c("HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE"), `Metro Area State (Q10 1)` = c("OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS"), `Question ID` = c("Room Work Area", "Staff Knowledge",
"Add'tl Item Working Order", "Property Maintenance", "Property Appearance",
"Staff Knowledge", "Property Appearance", "Staff Interaction",
"Safety/Security", "Add'tl Item Working Order", "Room Work Area",
"Bed Quality", "Check In/Out", "Invoice Accuracy", "Staff Interaction",
"Safety/Security", "Bed Quality", "Invoice Accuracy", "Check In/Out",
"Safety/Security", "Invoice Accuracy", "Bed Quality", "Property Maintenance"
), `Question ID (group)` = c("Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items"), `Score Label` = c("7 Extremely Good", "7 Extremely Good",
"7 Extremely Good", "7 Extremely Good", "7 Extremely Good", "6 Quite Good",
"6 Quite Good", "6 Quite Good", "6 Quite Good", "6 Quite Good",
"6 Quite Good", "6 Quite Good", "6 Quite Good", "7 Extremely Good",
"7 Extremely Good", "5 Slightly Good", "7 Extremely Good", "6 Quite Good",
"7 Extremely Good", "7 Extremely Good", "3 Slightly Poor", "5 Slightly Good",
"6 Quite Good"), `Harp Number` = c("1111", "1111", "1111", "1111",
"1111", "1111", "1111", "1111", "1111", "1111", "1111", "1111",
"1111", "1111", "1111", "1111", "1111", "1111", "1111", "1111",
"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(harps)
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")
在 R 甚至编程中考虑以下一般技巧：
Variables：避免使用太多变量，而是直接与现有对象交互。这增强了环境变量的可维护性。冗余的一些示例包括：
titlenames <- c(harps)
nam <- paste("data_percentage_", i, sep ="")
data_percentage_list <- lapply(paste0("data_percentage_",1:length(harps)), get)
Names：为对象使用更具信息性的名称，因为 Y 不会在将来通知代码读者或您自己。它似乎是一个包含较大数据框 hotel_report 子集的列表。更多信息的名称，如 hotel_reports_df_list 可快速详细说明其内容和类型（即列表中的数据框）。
Indentation：始终在 for 循环中缩进代码（这可以在 RStudio 中使用键自动缩进：Ctrl/cmd + i) 甚至在 pdf、with 等上下文管理器内部。这增强了可读性和可维护性。
Assign/Get：避免R中通常不推荐的assign和get。而是直接保存您的对象作为列表中的项目。第一个循环可以绕过将子项目分配为单独变量的需要：
data_pct_matrix_list <- lapply(seq_along(harps), function(i) {
counts <- table(Y[[i]][[5]], Y[[i]][[3]])
pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE)})
return(pct_matrix)
})
另外最后assign绕barplot也可以重构：
plot_list <- lapply(data_percentage_matrix_list, function(mat) {
barplot(mat, 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))
})
循环：尽可能避免多重for或嵌套循环。在 R 中，lapply 是一个隐藏循环。 391 个 PDF 中每个 391 个图的问题可能是由于嵌套 lapply within 一个 for 循环。考虑以下步骤：
- 首先，考虑一下您对 one 数据框对象的处理。甚至将其概括为一个单独的函数。
- 然后，想想究竟有哪些变化可以迭代。
R 的应用系列不仅包括 apply 和 lapply，例如 mapply 可以 运行 元素循环来展平嵌套迭代或 by （object-oriented 包装到 tapply）可以按因子列对数据帧进行子集化，并对它们进行 运行 操作。
在没有看到示例数据的情况下，请考虑以下需要针对数据进行测试的方法。下面假设 Y 被 Harp Number.
定义为来自 hotel_report 数据框的子集列表
mapply / Map接近
在 equal-length 个对象 data_pct_matrix_list 和 harps 之间逐元素迭代。
data_pct_matrix_list <- lapply(seq_along(harps), function(i) {
counts <- table(Y[[i]]$`Score Label`, Y[[i]]$`Question ID`)
pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE) })
return(pct_matrix)
})
build_pdf <- function(data, harp) {
# For Hotel Name Subtitle
hotelname <- hotel_report$`Hotel (Q15 1)`[hotel_report$`Harp Number` == harp]
# Plot the Data
pdf(file = paste0(harp, ".pdf"), paper = "USr", width=8, height=7)
par(mar = c(5.1, 7, 4.1, 2.1))
hotel_plot <- barplot(data, 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()
return(hotel_plot)
}
plot_list <- Map(build_pdf, data_pct_matrix_list, harps)
# EQUIVALENTLY:
plot_list <- mapply(build_pdf, data_pct_matrix_list, harps, SIMPLIFY=FALSE)
by接近
通过唯一 Harp Number 对 hotel_report 数据框进行子集化，并在每个子集上迭代 运行 以构建 pct_matrix 和 hotel_plot。这种方法结合了矩阵构建和绘图步骤。
build_pdf <- function(sub_df) {
# Matrix build
counts <- table(sub_df$`Score Label`, sub_df$`Question ID`)
pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE) })
# For Hotel Name Subtitle
hotelname <- sub_df$`Hotel (Q15 1)`[1]
harp <- sub_df$`Harp Number`[1]
# Plot the Data
pdf(file = paste0(harp, ".pdf"), paper = "USr", width=8, height=7)
par(mar = c(5.1, 7, 4.1, 2.1))
hotel_plot <- barplot(pct_matrix, 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()
return(hotel_plot)
}
plot_list <- by(hotel_report, hotel_report$`Harp Number`, build_pdf)
# NEAR EQUIVALENT
plot_list <- lapply(split(hotel_report, hotel_report$`Harp Number`), build_pdf)
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可以看出），但我想我已经接近修复它了，非常感谢我在堆栈上获得的帮助溢出。
我注意到在我的绘图中，每个绘图都使用列表中的最后一个元素 data_percentage_list[391]。我已经做了很多事情来试图阻止这种情况的发生，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_", i, sep ="")
assign(nam, 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，因此有 391 个地块，地块正在覆盖。因此，当我在 pdf 中打开一个图并刷新它时，它会更改为循环的最后一次迭代，直到最后它们都最终成为第 391 属性 的数据，并具有正确的酒店名称，因为它是从j.
有谁知道我需要如何修改我的代码才能让每个图对应正确的数据？意思是，breakdown_54 应该使用 data_percentage_list[54]，并保存为该数据的 pdf，breakdown_55 应该是 data_percentage_list[55]，等等？
谢谢！
编辑：在进一步处理之后跟进。
下面的代码制作了 391 个不同的图表，但是 391 个 pdf 文件中的每一个都包含所有 391 个图表，而不是像它们应该的那样只有它们各自的图表。
与修复上面的代码相比，在此代码中正确拆分这些 pdf 是否更容易？
# 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_", i, sep ="")
breakdown_list <- lapply(1:length(harps), function(i){
assign(nam, 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()
}}
再次感谢！
编辑 2：试图使其更具可重复性
Y 是 391 个数据帧的列表
structure(list(`Hotel (Q15 1)` = c("HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE", "HILTON, SAN PEDRO, BELIZE",
"HILTON, SAN PEDRO, BELIZE"), `Metro Area State (Q10 1)` = c("OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS", "OCONUS",
"OCONUS"), `Question ID` = c("Room Work Area", "Staff Knowledge",
"Add'tl Item Working Order", "Property Maintenance", "Property Appearance",
"Staff Knowledge", "Property Appearance", "Staff Interaction",
"Safety/Security", "Add'tl Item Working Order", "Room Work Area",
"Bed Quality", "Check In/Out", "Invoice Accuracy", "Staff Interaction",
"Safety/Security", "Bed Quality", "Invoice Accuracy", "Check In/Out",
"Safety/Security", "Invoice Accuracy", "Bed Quality", "Property Maintenance"
), `Question ID (group)` = c("Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items", "Question 4 Items", "Question 4 Items", "Question 4 Items",
"Question 4 Items"), `Score Label` = c("7 Extremely Good", "7 Extremely Good",
"7 Extremely Good", "7 Extremely Good", "7 Extremely Good", "6 Quite Good",
"6 Quite Good", "6 Quite Good", "6 Quite Good", "6 Quite Good",
"6 Quite Good", "6 Quite Good", "6 Quite Good", "7 Extremely Good",
"7 Extremely Good", "5 Slightly Good", "7 Extremely Good", "6 Quite Good",
"7 Extremely Good", "7 Extremely Good", "3 Slightly Poor", "5 Slightly Good",
"6 Quite Good"), `Harp Number` = c("1111", "1111", "1111", "1111",
"1111", "1111", "1111", "1111", "1111", "1111", "1111", "1111",
"1111", "1111", "1111", "1111", "1111", "1111", "1111", "1111",
"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(harps)
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")
在 R 甚至编程中考虑以下一般技巧：
Variables：避免使用太多变量，而是直接与现有对象交互。这增强了环境变量的可维护性。冗余的一些示例包括：
titlenames <- c(harps) nam <- paste("data_percentage_", i, sep ="") data_percentage_list <- lapply(paste0("data_percentage_",1:length(harps)), get)Names：为对象使用更具信息性的名称，因为
Y不会在将来通知代码读者或您自己。它似乎是一个包含较大数据框hotel_report子集的列表。更多信息的名称，如hotel_reports_df_list可快速详细说明其内容和类型（即列表中的数据框）。Indentation：始终在
for循环中缩进代码（这可以在 RStudio 中使用键自动缩进：Ctrl/cmd + i) 甚至在pdf、with等上下文管理器内部。这增强了可读性和可维护性。Assign/Get：避免R中通常不推荐的
assign和get。而是直接保存您的对象作为列表中的项目。第一个循环可以绕过将子项目分配为单独变量的需要：data_pct_matrix_list <- lapply(seq_along(harps), function(i) { counts <- table(Y[[i]][[5]], Y[[i]][[3]]) pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE)}) return(pct_matrix) })另外最后
assign绕barplot也可以重构：plot_list <- lapply(data_percentage_matrix_list, function(mat) { barplot(mat, 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)) })循环：尽可能避免多重
for或嵌套循环。在 R 中，lapply是一个隐藏循环。 391 个 PDF 中每个 391 个图的问题可能是由于嵌套lapplywithin 一个for循环。考虑以下步骤：- 首先，考虑一下您对 one 数据框对象的处理。甚至将其概括为一个单独的函数。
- 然后，想想究竟有哪些变化可以迭代。
R 的应用系列不仅包括
apply和lapply，例如mapply可以 运行 元素循环来展平嵌套迭代或by（object-oriented 包装到tapply）可以按因子列对数据帧进行子集化，并对它们进行 运行 操作。
在没有看到示例数据的情况下，请考虑以下需要针对数据进行测试的方法。下面假设 Y 被 Harp Number.
hotel_report 数据框的子集列表
mapply / Map接近
在 equal-length 个对象 data_pct_matrix_list 和 harps 之间逐元素迭代。
data_pct_matrix_list <- lapply(seq_along(harps), function(i) {
counts <- table(Y[[i]]$`Score Label`, Y[[i]]$`Question ID`)
pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE) })
return(pct_matrix)
})
build_pdf <- function(data, harp) {
# For Hotel Name Subtitle
hotelname <- hotel_report$`Hotel (Q15 1)`[hotel_report$`Harp Number` == harp]
# Plot the Data
pdf(file = paste0(harp, ".pdf"), paper = "USr", width=8, height=7)
par(mar = c(5.1, 7, 4.1, 2.1))
hotel_plot <- barplot(data, 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()
return(hotel_plot)
}
plot_list <- Map(build_pdf, data_pct_matrix_list, harps)
# EQUIVALENTLY:
plot_list <- mapply(build_pdf, data_pct_matrix_list, harps, SIMPLIFY=FALSE)
by接近
通过唯一 Harp Number 对 hotel_report 数据框进行子集化，并在每个子集上迭代 运行 以构建 pct_matrix 和 hotel_plot。这种方法结合了矩阵构建和绘图步骤。
build_pdf <- function(sub_df) {
# Matrix build
counts <- table(sub_df$`Score Label`, sub_df$`Question ID`)
pct_matrix <- apply(counts, 2, function(x) { x*100/sum(x, na.rm=TRUE) })
# For Hotel Name Subtitle
hotelname <- sub_df$`Hotel (Q15 1)`[1]
harp <- sub_df$`Harp Number`[1]
# Plot the Data
pdf(file = paste0(harp, ".pdf"), paper = "USr", width=8, height=7)
par(mar = c(5.1, 7, 4.1, 2.1))
hotel_plot <- barplot(pct_matrix, 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()
return(hotel_plot)
}
plot_list <- by(hotel_report, hotel_report$`Harp Number`, build_pdf)
# NEAR EQUIVALENT
plot_list <- lapply(split(hotel_report, hotel_report$`Harp Number`), build_pdf)