如何从 R 中的 CSV 读取特定列并将结果逐行写入文件？

Question

我们有一个这种格式的 CSV 文件：

id1, id2, id3, id4, id5
23,24,23,25,23
25,46,23,756,34
23,54,73,83,74
...

我们准备好一个 R 脚本，它可以读取数据、处理与每一列相关的数据（进行预测和预测），然后将结果写入文件。问题是，目前它只需要 CSV 中的 1 列/id。我们最近在 CSV 中引入了多个列（如上所示）。我们希望脚本读取每一列，对其进行处理，然后将预测存储在输出 CSV 中，然后对其他列执行相同的操作。我们的脚本是：

library("forecast")
data = read.csv("data.csv")
seasonal_per <- msts(data,seasonal.periods=c(24,168))
best_model <- tbats(seasonal_per)
fcst <- forecast.tbats(best_model,h=24,level=90)
dfForec <- print(fcst)
result <- cbind(0:23,dfForec[, 1])
write.csv(result, file="out.csv")

生成的“out.csv”包含：

"","V1","V2"
"1",0,5080.64143403622
"2",1,5024.80341301731
"3",2,4697.62476220884
"4",3,4419.10506083084
"5",4,4262.78237536907
"6",5,4187.62903442766
"7",6,4349.19557668607
"8",7,4484.10807151227
"9",8,4247.8575479654
"10",9,3851.37930582024
"11",10,3575.95149262212
"12",11,3494.94340348126
"13",12,3501.35397669752
"14",13,3445.56274629188
"15",14,3362.23686727733
"16",15,3365.56431618894
"17",16,3573.96314478735
"18",17,3945.43879134651
"19",18,4278.44501871782
"20",19,4499.11200729996
"21",20,4574.2023320236
"22",21,4555.22528793877
"23",22,4550.89877322609
"24",23,4517.26727161547

（基于 1 个 id 的 24 个预测）

新脚本将逐一读取每一列（不引用硬编码的列名）、处理、预测，然后以这种格式存储结果：

"id","V1","V2"
"id1",0,5080.64143403622
"id1",1,5024.80341301731
"id1",2,4697.62476220884
"id1",3,4419.10506083084
"id1",4,4262.78237536907
"id1",5,4187.62903442766
"id1",6,4349.19557668607
"id1",7,4484.10807151227
"id1",8,4247.8575479654
"id1",9,3851.37930582024
"id1",10,3575.95149262212
"id1",11,3494.94340348126
"id1",12,3501.35397669752
"id1",13,3445.56274629188
"id1",14,3362.23686727733
"id1",15,3365.56431618894
"id1",16,3573.96314478735
"id1",17,3945.43879134651
"id1",18,4278.44501871782
"id1",19,4499.11200729996
"id1",20,4574.2023320236
"id1",21,4555.22528793877
"id1",22,4550.89877322609
"id1",23,4517.26727161547
"id2",0,5080.64143403622    <-- id2 predictions begin
"id2",1,5024.80341301731    <-- id2 predictions begin

更新：尝试了@Parfait 建议的代码，但它给出了以下错误：

Error in file(file, ifelse(append, "a", "w")) : 
  cannot open the connection
In addition: There were 26 warnings (use warnings() to see them)

另外，finaldf 数据框是空的：

> finaldf
      <NA> V1 V2
 [1,] NULL 0  1 
 [2,] NULL 0  1 
 [3,] NULL 0  1 
 [4,] NULL 0  1 
 [5,] NULL 0  1 
 [6,] NULL 0  1 
 [7,] NULL 0  1 
 [8,] NULL 0  1 
 [9,] NULL 0  1 
[10,] NULL 0  1 
[11,] NULL 0  1 
[12,] NULL 0  1 
[13,] NULL 0  1 
[14,] NULL 0  1 
[15,] NULL 0  1 
[16,] NULL 0  1 
[17,] NULL 0  1 
[18,] NULL 0  1 
[19,] NULL 0  1 
[20,] NULL 0  1 
[21,] NULL 0  1 
[22,] NULL 0  1 
[23,] NULL 0  1 
[24,] NULL 0  1 

Answer 1

这是使用plyr 包中的lapply 和ldply 的方法：

# Make replicable example with fake data and function
d <- as.data.frame(replicate(5, rnorm(100)))
names(d) <- paste0("id", 1:5)
get_numbers <- function(x) {
  data.frame(V1 = 1:10, V2 = sample(x, 10))
}

out <- lapply(d, get_numbers)
out <- plyr::ldply(out)
out    
#    .id V1           V2
# 1  id1  1 -0.462111424
# 2  id1  2  0.431549655
# 3  id1  3  1.360865990
# ..snip..
# 9  id1  9 -0.110209805
# 10 id1 10 -0.335578166
# 11 id2  1 -0.246840243
# 12 id2  2 -0.881482663
# 13 id2  3  0.352842837
# ..snip..

要为您的任务修改此示例，您可能希望将处理步骤包装到一个函数中，例如

process <- function(x) {
  seasonal_per <- msts(x, seasonal.periods=c(24,168))
  best_model <- tbats(seasonal_per)
  fcst <- forecast.tbats(best_model,h=24,level=90)
  dfForec <- print(fcst)
  result <- cbind(0:23, dfForec[, 1])
}

然后做，例如lapply(d, process).

Answer 2

考虑使用lapply() 和seq_along() 来检索每列的数字索引。这样的索引将用于检索列的 data 和 name。总之，这将产生预测值的数据帧列表。迭代完成后，运行do.call() 以堆叠列表的各个数据帧：

data <- read.csv("data.csv")
data[is.na(data)] <- 0

dfList <- lapply(seq_along(data), function(i) {
    seasonal_per <- msts(data[, i], seasonal.periods=c(24,168))
    best_model <- tbats(seasonal_per)
    fcst <- forecast.tbats(best_model, h=24, level=90)
    dfForec <- print(fcst)
    result <- cbind(0:23, dfForec[, 1])
    result$id <- names(data)[i]
    names(result)[1:2] <- c("V1", "V2")

    return(result[c("id", "V1", "V2")])
})

finaldf <- do.call(rbind, dfList)
write.csv(finaldf, file = "out.csv", row.names = FALSE)