### Q1: working directory: Observed #Only one file per Site.  Assuming this is the
### case for the full dataset, then I guess there is no need to average
dir.create("final")
lst1 <- split(list.files(pattern = ".csv"), gsub("\\_.*", "", list.files(pattern = ".csv")))

lst2 <- lapply(lst1, function(x1) lapply(x1, function(x2) {
    lines1 <- readLines(x2)
    header1 <- lines1[1:2]
    dat1 <- read.table(text = lines1, header = FALSE, sep = ",", stringsAsFactors = FALSE, 
        skip = 2)
    colnames(dat1) <- Reduce(paste, strsplit(header1, ","))
    dat1[-c(nrow(dat1), nrow(dat1) - 1), ]
}))

 lst3 <- lst2[sapply(seq_along(lst2),function(i){lstN <- sapply(lst2[[i]],function(x) is.integer(ncol(x)))})]
length(lst2)
#[1] 120
 length(lst3)
#[1] 119

library(plyr)
library(stringr)

lst4 <- setNames(lapply(seq_along(lst3), function(i) {
    lapply(lst3[[i]], function(x) {
        names(x)[-1] <- paste(names(x)[-1], names(lst1)[i], sep = "_")
        names(x) <- str_trim(names(x))
        x
    })[[1]]
}), names(lst3))
df1 <- join_all(lst4, by = "Year")
dim(df1)
# [1] 9 27311

dimCol <- sapply(split(names(df1)[-1], gsub(".*\\_", "", names(df1)[-1])), function(x) {
    df2 <- df1[, x]
    df3 <- data.frame(Percentiles = paste0(seq(0, 100, by = 1), "%"), numcolwise(function(y) quantile(y, 
        seq(0, 1, by = 0.01), na.rm = TRUE))(df2), stringsAsFactors = FALSE)
    ncol(df3)
})

lst5 <- split(names(df1)[-1], gsub(".*\\_", "", names(df1)[-1]))

lapply(seq_along(lst5), function(i) {
    df2 <- df1[, lst5[[i]]]
    df3 <- data.frame(Percentiles = paste0(seq(0, 100, by = 1), "%"), numcolwise(function(y) quantile(y, 
        seq(0, 1, by = 0.01), na.rm = TRUE))(df2), stringsAsFactors = FALSE)
     write.csv(df3, paste0(paste(getwd(), "final", paste(names(lst4)[[i]], "Quantile", 
     sep = "_"), sep = "/"), ".csv"), row.names = FALSE, quote = FALSE)
})

ReadOut1 <- lapply(list.files(recursive = TRUE)[grep("Quantile", list.files(recursive = TRUE))], 
    function(x) read.csv(x, header = TRUE, stringsAsFactors = FALSE))
dir.create("Indices")
 sapply(ReadOut1, dim)[,1:3]  ##different dimensions
#     [,1] [,2] [,3]
#[1,]  101  101  101
#[2,]  157  258  258

names1 <- unlist(lapply(ReadOut1, function(x) names(x)[-1]))
names2 <- gsub("\\_.*", "", names1)
names3 <- unique(gsub("[.]", " ", names2))

length(names3)
#[1] 264
#lstNew <- simplify2array(ReadOut1)  ###results you got
# nrow(lstNew)
#NULL####

ReadOut2 <-  lapply(seq_along(ReadOut1),function(i) {df2 <- ReadOut1[[i]]; df3 <-as.data.frame(matrix(NA,nrow=101,ncol=length(names3), dimnames=list(NULL, names3))); names(df2) <- gsub("[.]"," ", gsub("\\_.*","", names(df2))); df2 <- df2[,-1]; df3[,match(names(df2), names(df3))] <- df2; df3})

lstNew <- simplify2array(ReadOut2)
 nrow(lstNew)
#[1] 264

 lapply(1:nrow(lstNew), function(i) { dat1 <- data.frame(Percentiles = paste0(seq(0, 100, by = 1), "%"), do.call(cbind, lstNew[i, ]), stringsAsFactors = FALSE); colnames(dat1) <- c("Percentiles", paste(names(lst3), rep(rownames(lstNew)[i],length(lst3)),sep="_")); write.csv(dat1,paste0(paste(getwd(), "Indices", gsub(" ", "_",rownames(lstNew)[i]), sep="/"),".csv"),row.names=FALSE, quote=FALSE)})


## Output2:
ReadOut2 <- lapply(list.files(recursive = TRUE)[grep("Indices", list.files(recursive = TRUE))], 
    function(x) read.csv(x, header = TRUE, stringsAsFactors = FALSE))
length(ReadOut2)
#[1] 264

ReadOut2[[1]][1:3,1:3]
#  Percentiles G100_pav.ANN G101_pav.ANN
#1          0%     0.766900      0.96240
#2          1%     0.796132      0.96572
#3          2%     0.825364      0.96904