r - 在R中导入结构不良的数据

在一个文本文件中给了我数据，如下所示:

Measurement: mc
Loop:
var1=0, var2=-5, var3=1.8
values:
iteration     data
0             1.203
1             1.206
2             2.206
3             1.201
4             1.204
5             1.204
6             1.204
statistics:
max           1.206
min           1.201
mean          1.204
stddev        0.001
avgdev        0.001
failedtimes   0

Measurement: mc
Loop:
var1=10, var2=-5, var3=1.8
values:
iteration     data
0             1.203
1             1.206
2             2.206
3             1.201
statistics:
max           1.206
min           1.201
mean          1.204
stddev        0.001
avgdev        0.001
failedtimes   0

我希望以更普通的格式获取数据，例如:

var1, var2, var3, iteration,  data,
   0,   -5,  1.8,         0, 1.203,
   0,   -5,  1.8,         1, 1.206,
   ...
  10,   -5,  1.8,         0, 1.203,

我在尝试解析这样的数据时遇到问题。请帮助

最佳答案

一种方法是使用少量的简单正则表达式和readLines提取相关行。

您的资料

txt <-
  "Measurement: mc
Loop:
var1=0, var2=-5, var3=1.8
values:
iteration     data
0             1.203
1             1.206
2             2.206
3             1.201
4             1.204
5             1.204
6             1.204
statistics:
max           1.206
min           1.201
mean          1.204
stddev        0.001
avgdev        0.001
failedtimes   0

Measurement: mc
Loop:
var1=10, var2=-5, var3=1.8
values:
iteration     data
0             1.203
1             1.206
2             2.206
3             1.201
statistics:
max           1.206
min           1.201
mean          1.204
stddev        0.001
avgdev        0.001"

# Read in : you can pass the file path instead of textConnection
r = readLines(textConnection(txt))

# Find indices of relevant parts of string that you want to keep
id1 = grep("var", r)
id2 = grep("iteration", r)
id3 = grep("statistics", r)

# indices for iteration data
m = mapply( seq, id2, id3-1)

# Use read.table to parse the relevant rows
lst <- lapply(seq_along(m), function(x)
                     cbind(read.table(text=r[id1][x], sep=","), #var data
                           read.table(text=r[m[[x]]], header=TRUE))) # iteration data

dat <- do.call(rbind, lst)

# Remove the var= text and convert to numeric
dat[] <- lapply(dat, function(x) as.numeric(gsub("var\\d+=", "", x)))
dat
#    V1 V2  V3 iteration  data
# 1   0 -5 1.8         0 1.203
# 2   0 -5 1.8         1 1.206
# 3   0 -5 1.8         2 2.206
# 4   0 -5 1.8         3 1.201
# 5   0 -5 1.8         4 1.204
# 6   0 -5 1.8         5 1.204
# 7   0 -5 1.8         6 1.204
# 8  10 -5 1.8         0 1.203
# 9  10 -5 1.8         1 1.206
# 10 10 -5 1.8         2 2.206
# 11 10 -5 1.8         3 1.201

实际上可能更清晰一些，可以将数据拆分为多个部分，然后应用一个函数，即

sp <- split(r, cumsum(grepl("measure", r, TRUE)))

# Function to parse
fun <- function(x){
    id1 = grep("var", x)
    id2 = grep("iteration", x)
    id3 = grep("statistics", x)
    m = seq(id2, id3-1)

    cbind(read.table(text=x[id1], sep=","),
          read.table(text=x[m], header=TRUE))
}

lst <- lapply(sp, fun)

然后像以前一样继续

关于r - 在R中导入结构不良的数据，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/41904982/