read.table、read.csv 或扫描以读取 R 中的文本文件？答案

【问题标题】：read.table, read.csv or scan for reading text file in R?read.table、read.csv 或扫描以读取 R 中的文本文件？
【发布时间】：2014-03-15 03:30:30
【问题描述】：

我很困惑我应该使用以下哪个？（实际上到目前为止，他们都给我错误）：

> beef = read.csv("beef.txt", header = TRUE)
Error in read.table(file = file, header = header, sep = sep, quote = quote,  : 
  more columns than column names
> beef = scan("beef.txt")
Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings,  : 
  scan() expected 'a real', got '%'
> beef=read.table("beef.txt", header = FALSE, sep = " ")
Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings,  : 
  line 1 did not have 8 elements
> beef=read.table("beef.txt", header = TRUE, sep = " ")
Error in read.table("beef.txt", header = TRUE, sep = " ") : 
  more columns than column names

这是beef.txt 文件的顶部，其余部分非常相似。

% http://lib.stat.cmu.edu/DASL/Datafiles/agecondat.html
% 
% Datafile Name: Agricultural Economics Studies
% Datafile Subjects: Agriculture , Economics , Consumer
% Story Names: Agricultural Economics Studies
% Reference: F.B. Waugh, Graphic Analysis in Agricultural Economics,
%   Agricultural Handbook No. 128, U.S. Department of Agriculture, 1957.
% Authorization: free use
% Description: Price and consumption per capita of beef and pork
%   annually from 1925 to 1941 together with other variables relevant to
%   an economic analysis of price and/or consumption of beef and pork
%   over the period.
% Number of cases: 17
% Variable Names:
% 
%   PBE = Price of beef (cents/lb)
%   CBE = Consumption of beef per capita (lbs)
%   PPO = Price of pork (cents/lb)
%   CPO = Consumption of pork per capita (lbs)
%   PFO = Retail food price index (1947-1949 = 100)
%   DINC = Disposable income per capita index (1947-1949 = 100)
%   CFO = Food consumption per capita index (1947-1949 = 100)
%   RDINC = Index of real disposable income per capita (1947-1949 = 100)
%   RFP = Retail food price index adjusted by the CPI (1947-1949 = 100)
% 
% The Data:
YEAR    PBE CBE PPO CPO PFO DINC    CFO RDINC   RFP
1925    59.7    58.6    60.5    65.8    65.8    51.4    90.9    68.5    877
1926    59.7    59.4    63.3    63.3    68  52.6    92.1    69.6    899
1927    63  53.7    59.9    66.8    65.5    52.1    90.9    70.2    883
1928    71  48.1    56.3    69.9    64.8    52.7    90.9    71.9    884
1929    71  49  55  68.7    65.6    55.1    91.1    75.2    895

当我使用 fread 时，数据的保存非常奇怪，如下所示，知道如何按预期格式化吗？

> library(data.table)
> beef=fread("beef.txt", header = T, sep = " ")
> beef
    YEAR V2 V3 V4
 1: 1925 NA NA NA
 2: 1926 NA NA NA
 3: 1927 NA NA NA
 4: 1928 NA NA NA
 5: 1929 NA NA NA
 6: 1930 NA NA NA
 7: 1931 NA NA NA
 8: 1932 NA NA NA
 9: 1933 NA NA NA
10: 1934 NA NA NA
11: 1935 NA NA NA
12: 1936 NA NA NA
13: 1937 NA NA NA
14: 1938 NA NA NA
15: 1939 NA NA NA
16: 1940 NA NA NA
17: 1941 NA NA NA
         PBE\tCBE\tPPO\tCPO\tPFO\tDINC\tCFO\tRDINC\tRFP
 1: 59.7\t58.6\t60.5\t65.8\t65.8\t51.4\t90.9\t68.5\t877
 2:   59.7\t59.4\t63.3\t63.3\t68\t52.6\t92.1\t69.6\t899
 3:   63\t53.7\t59.9\t66.8\t65.5\t52.1\t90.9\t70.2\t883
 4:   71\t48.1\t56.3\t69.9\t64.8\t52.7\t90.9\t71.9\t884
 5:       71\t49\t55\t68.7\t65.6\t55.1\t91.1\t75.2\t895
 6: 74.2\t48.2\t59.6\t66.1\t62.4\t48.8\t90.7\t68.3\t874
 7:       72.1\t47.9\t57\t67.4\t51.4\t41.5\t90\t64\t791
 8:     79\t46\t49.5\t69.7\t42.8\t31.4\t87.8\t53.9\t733
 9:   73.1\t50.8\t47.3\t68.7\t41.6\t29.4\t88\t53.2\t752
10:   70.2\t55.2\t56.6\t62.2\t46.4\t33.2\t89.1\t58\t811
11:   82.2\t52.2\t73.9\t47.7\t49.7\t37\t87.3\t63.2\t847
12: 68.4\t57.3\t64.4\t54.4\t50.1\t41.8\t90.5\t70.5\t845
13:     73\t54.4\t62.2\t55\t52.1\t44.5\t90.4\t72.5\t849
14: 70.2\t53.6\t59.9\t57.4\t48.4\t40.8\t90.6\t67.8\t803
15:   67.8\t53.9\t51\t63.9\t47.1\t43.5\t93.8\t73.2\t793
16: 63.4\t54.2\t41.5\t72.4\t47.8\t46.5\t95.5\t77.6\t798
17:     56\t60\t43.9\t67.4\t52.2\t56.3\t97.5\t89.5\t830

当我按照 cmets 中的说明 read.table 时，我收到了奇怪的输出（我没有像预期的那样整齐地阅读它）：

> beef=read.table("beef.txt", header = TRUE, sep = " ", comment.char="%")
> beef
   YEAR  X X.1 X.2
1  1925 NA  NA  NA
2  1926 NA  NA  NA
3  1927 NA  NA  NA
4  1928 NA  NA  NA
5  1929 NA  NA  NA
6  1930 NA  NA  NA
7  1931 NA  NA  NA
8  1932 NA  NA  NA
9  1933 NA  NA  NA
10 1934 NA  NA  NA
11 1935 NA  NA  NA
12 1936 NA  NA  NA
13 1937 NA  NA  NA
14 1938 NA  NA  NA
15 1939 NA  NA  NA
16 1940 NA  NA  NA
17 1941 NA  NA  NA
                PBE.CBE.PPO.CPO.PFO.DINC.CFO.RDINC.RFP
1  59.7\t58.6\t60.5\t65.8\t65.8\t51.4\t90.9\t68.5\t877
2    59.7\t59.4\t63.3\t63.3\t68\t52.6\t92.1\t69.6\t899
3    63\t53.7\t59.9\t66.8\t65.5\t52.1\t90.9\t70.2\t883
4    71\t48.1\t56.3\t69.9\t64.8\t52.7\t90.9\t71.9\t884
5        71\t49\t55\t68.7\t65.6\t55.1\t91.1\t75.2\t895
6  74.2\t48.2\t59.6\t66.1\t62.4\t48.8\t90.7\t68.3\t874
7        72.1\t47.9\t57\t67.4\t51.4\t41.5\t90\t64\t791
8      79\t46\t49.5\t69.7\t42.8\t31.4\t87.8\t53.9\t733
9    73.1\t50.8\t47.3\t68.7\t41.6\t29.4\t88\t53.2\t752
10   70.2\t55.2\t56.6\t62.2\t46.4\t33.2\t89.1\t58\t811
11   82.2\t52.2\t73.9\t47.7\t49.7\t37\t87.3\t63.2\t847
12 68.4\t57.3\t64.4\t54.4\t50.1\t41.8\t90.5\t70.5\t845
13     73\t54.4\t62.2\t55\t52.1\t44.5\t90.4\t72.5\t849
14 70.2\t53.6\t59.9\t57.4\t48.4\t40.8\t90.6\t67.8\t803
15   67.8\t53.9\t51\t63.9\t47.1\t43.5\t93.8\t73.2\t793
16 63.4\t54.2\t41.5\t72.4\t47.8\t46.5\t95.5\t77.6\t798
17     56\t60\t43.9\t67.4\t52.2\t56.3\t97.5\t89.5\t830

所以感谢 cmets 原来分隔的不是空格而是制表符。正确答案如下：

> beef=read.table("beef.txt", header = TRUE, sep = "\t", comment.char="%")
> beef
    YEAR....PBE  CBE  PPO  CPO  PFO DINC  CFO RDINC RFP
1  1925    59.7 58.6 60.5 65.8 65.8 51.4 90.9  68.5 877
2  1926    59.7 59.4 63.3 63.3 68.0 52.6 92.1  69.6 899
3    1927    63 53.7 59.9 66.8 65.5 52.1 90.9  70.2 883
4    1928    71 48.1 56.3 69.9 64.8 52.7 90.9  71.9 884
5    1929    71 49.0 55.0 68.7 65.6 55.1 91.1  75.2 895
6  1930    74.2 48.2 59.6 66.1 62.4 48.8 90.7  68.3 874
7  1931    72.1 47.9 57.0 67.4 51.4 41.5 90.0  64.0 791
8    1932    79 46.0 49.5 69.7 42.8 31.4 87.8  53.9 733
9  1933    73.1 50.8 47.3 68.7 41.6 29.4 88.0  53.2 752
10 1934    70.2 55.2 56.6 62.2 46.4 33.2 89.1  58.0 811
11 1935    82.2 52.2 73.9 47.7 49.7 37.0 87.3  63.2 847
12 1936    68.4 57.3 64.4 54.4 50.1 41.8 90.5  70.5 845
13   1937    73 54.4 62.2 55.0 52.1 44.5 90.4  72.5 849
14 1938    70.2 53.6 59.9 57.4 48.4 40.8 90.6  67.8 803
15 1939    67.8 53.9 51.0 63.9 47.1 43.5 93.8  73.2 793
16 1940    63.4 54.2 41.5 72.4 47.8 46.5 95.5  77.6 798
17   1941    56 60.0 43.9 67.4 52.2 56.3 97.5  89.5 830

【问题讨论】：

寻找read.table的comment.char参数。（read.csv 只是 read.table 的包装；scan 类似，但您必须自己指定字段类型。）
read.table 是“只是”scan 的包装器。
如果你有一个格式良好的.csv 不要想：去read.csv()。如果您有一个fwf 文件（固定宽度格式，在气象学中很常见），请使用read.fwf()。否则，请使用正确的参数尝试 read.table() 或 scan()。

标签： r file csv

【解决方案1】：

beef=read.table("beef.txt", header = TRUE, sep = " ", comment.char="%")

beef=read.table("beef.txt", header = TRUE, sep = "\t", comment.char="%") #after update

【讨论】：

请查看更新后的问题。我用了你的方法，但是读取的文件不整齐！
所以当我们使用 read.table 读取文件时，最终结果是 data.frame 吗？如果不是，我应该如何将其更改为 data.frame？
@MonaJalal 阅读help("read.table")。文档可以回答这些问题。
它是data.frame，您可以通过class(beef) 进行检查，不过有一个诚实的建议，似乎最近您正在探索R 很多，而对于少数人，我已经回答了，所以是获得答案的好方法，但是这些问题大部分都是非常基础的，如果你想更好地探索 R，我认为你应该阅读一些 R 初学者书籍，例如 (cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf)。
@Ananta 我认为读取文件有问题，因为它使 PBE 为空：> m3=lm(PBE ~ CBE + PBO + CPO + PFO +DINC + CFO+RDINC+RFP+YEAR, data = beef) Error in eval(expr, envir, enclos) : object 'PBE' not found > beef$PBE NULL

【解决方案2】：

最近有一篇关于它的博客文章显示fread 是最快的，其余的都是一样的。链接：http://statcompute.wordpress.com/2014/02/11/efficiency-of-importing-large-csv-files-in-r/

在你的情况下，这并不重要，使用你觉得最舒服的那个。

使用fread 的示例如下（假设TAB 分隔符）：

library(data.table)
a = fread("data.csv", skip=26)
a
   YEAR  PBE  CBE  PPO  CPO  PFO DINC  CFO RDINC RFP
1: 1925 59.7 58.6 60.5 65.8 65.8 51.4 90.9  68.5 877
2: 1926 59.7 59.4 63.3 63.3 68.0 52.6 92.1  69.6 899
3: 1927 63.0 53.7 59.9 66.8 65.5 52.1 90.9  70.2 883
4: 1928 71.0 48.1 56.3 69.9 64.8 52.7 90.9  71.9 884
5: 1929 71.0 49.0 55.0 68.7 65.6 55.1 91.1  75.2 895

【讨论】：

我用了fread，但是读取的数据保存很奇怪！！有什么提示吗？
我没有投反对票，但我猜有人认为这太过于仅提供链接的答案，而且该博客是 our faq 的糟糕版本。您应该改进您的答案并展示如何使用fread 处理此类文件。
我实际上投了赞成票，因为很高兴知道还有其他更快的方法（即使在我的情况下，阅读时间将被计算为零）
@Roland：对不起，好点子，我会用一个例子来更新我的答案
@LauriK 感谢您的示例。但在你的情况下是一样的：> a$PBE NULL

【解决方案3】：

正确答案如下：

beef=read.table("beef.txt", header = TRUE, sep = "", comment.char="%")

【讨论】：

【解决方案4】：

这是使用readLines 的基础替代方案。这种方法要复杂得多，但会返回可供分析的数字数据。但是，您必须手动计算原始数据文件中的列数，然后再重新分配列名。

编辑

在底部我添加了一个通用版本，不需要手动计算列或手动添加列名。

请注意，无论数据是用空格还是制表符分隔，任何一个版本都可以工作。

这是原始版本的代码：

my.data <- readLines('c:/users/mmiller21/simple R programs/beef.txt')

ncols <- 10

header.info <- ifelse(substr(my.data, 1, 1) == '%', 1, 0)

my.data2 <- my.data[header.info==0]

my.data3 <- data.frame(matrix(unlist(strsplit(my.data2[-1], "[^0-9,.]+")), ncol=ncols, byrow=TRUE), stringsAsFactors = FALSE)

my.data4 <- as.data.frame(apply(my.data3, 2, function(x) as.numeric(x)))

colnames(my.data4) <- c('YEAR', 'PBE', 'CBE', 'PPO', 'CPO', 'PFO', 'DINC', 'CFO', 'RDINC', 'RFP')

> my.data4
     YEAR  PBE  CBE  PPO  CPO  PFO DINC  CFO RDINC RFP
[1,] 1925 59.7 58.6 60.5 65.8 65.8 51.4 90.9  68.5 877
[2,] 1926 59.7 59.4 63.3 63.3 68.0 52.6 92.1  69.6 899
[3,] 1927 63.0 53.7 59.9 66.8 65.5 52.1 90.9  70.2 883
[4,] 1928 71.0 48.1 56.3 69.9 64.8 52.7 90.9  71.9 884
[5,] 1929 71.0 49.0 55.0 68.7 65.6 55.1 91.1  75.2 895

以下是原始数据文件的内容：

% http://lib.stat.cmu.edu/DASL/Datafiles/agecondat.html
% 
% Datafile Name: Agricultural Economics Studies
% Datafile Subjects: Agriculture , Economics , Consumer
% Story Names: Agricultural Economics Studies
% Reference: F.B. Waugh, Graphic Analysis in Agricultural Economics,
%   Agricultural Handbook No. 128, U.S. Department of Agriculture, 1957.
% Authorization: free use
% Description: Price and consumption per capita of beef and pork
%   annually from 1925 to 1941 together with other variables relevant to
%   an economic analysis of price and/or consumption of beef and pork
%   over the period.
% Number of cases: 17
% Variable Names:
% 
%   PBE = Price of beef (cents/lb)
%   CBE = Consumption of beef per capita (lbs)
%   PPO = Price of pork (cents/lb)
%   CPO = Consumption of pork per capita (lbs)
%   PFO = Retail food price index (1947-1949 = 100)
%   DINC = Disposable income per capita index (1947-1949 = 100)
%   CFO = Food consumption per capita index (1947-1949 = 100)
%   RDINC = Index of real disposable income per capita (1947-1949 = 100)
%   RFP = Retail food price index adjusted by the CPI (1947-1949 = 100)
% 
% The Data:
YEAR    PBE CBE PPO CPO PFO DINC    CFO RDINC   RFP
1925    59.7    58.6    60.5    65.8    65.8    51.4    90.9    68.5    877
1926    59.7    59.4    63.3    63.3    68  52.6    92.1    69.6    899
1927    63  53.7    59.9    66.8    65.5    52.1    90.9    70.2    883
1928    71  48.1    56.3    69.9    64.8    52.7    90.9    71.9    884
1929    71  49  55  68.7    65.6    55.1    91.1    75.2    895

这是通用版本的代码：

my.data <- readLines('c:/users/mmiller21/simple R programs/beef.txt')

header.info <- ifelse(substr(my.data, 1, 1) == '%', 1, 0)

my.data2 <- my.data[header.info==0]

ncols <- length(read.table(textConnection(my.data2[1])))

my.data3 <- data.frame(matrix(unlist(strsplit(my.data2[-1], "[^0-9,.]+")), ncol=ncols, byrow=TRUE), stringsAsFactors = FALSE)

my.data4 <- as.data.frame(apply(my.data3, 2, function(x) as.numeric(x)))

#colnames(my.data4) <- c('YEAR', 'PBE', 'CBE', 'PPO', 'CPO', 'PFO', 'DINC', 'CFO', 'RDINC', 'RFP')
#my.data4

colnames(my.data4) <- read.table(textConnection(my.data2[1]), colClasses = c('character'))
my.data4

colSums(my.data4)

sum(my.data4$PPO)

【讨论】：