在 R 中使用从宽到长的 Reshape [英] Using Reshape from wide to long in R
问题描述
我正在尝试在 R 中将数据从宽变长.
I am trying to reshape Data from wide to long in R.
我的宽格式数据如下所示:
my data in wide format looks like this:
我有以下数据矩阵:在行中我有不同的公司，在列中有不同年份的不同变量.(earnings_2012、earnings_2011、earnings_2010、...、tot_assets_2012、tot_assets_2011，等等.
I have the following Data-Matrix: in the rows i have the different companies, and in the columns in have different variables from different years. (earnings_2012, earnings_2011, earnings_2010,...,tot_assets_2012,tot_assets_2011, and so on.
我想将其重新排列为长格式:
I would like to rearrange this into a long format:
只有这些列:公司、年份、变量(收入、tot_assets、..)
having only these columns: company, year, variables (earnings,tot_assets,..)
我已经尝试了好几天了.我还咨询了http://www.ats.ucla.edu/stat/r/faq/reshape.htm 寻求帮助，但我无法理解他们如何重塑数据以及如何分离年份.
I've been trying for days now. I also consulted http://www.ats.ucla.edu/stat/r/faq/reshape.htm for help but i could not grasp how they reshaped their data and how to detach the year.
预先感谢您的帮助.
问候
格里蒂
PS:由于我是 R 的新手，并且 R 本身提供的帮助似乎相当技术性，我感谢任何关于非常简单介绍的提示.
PS: Since I am new to R and the provided help in R itself seems to be rather technical I appreciate any hints for a very simple introduction.
推荐答案
以下是三个示例(以及一些我认为代表您所描述内容的示例数据).
Here are three examples (along with some sample data that I think is representative of what you described).
这是示例数据:
set.seed(1)
mydf <- data.frame(
company = LETTERS[1:4],
earnings_2012 = runif(4),
earnings_2011 = runif(4),
earnings_2010 = runif(4),
assets_2012 = runif(4),
assets_2011 = runif(4),
assets_2010 = runif(4)
)
mydf
# company earnings_2012 earnings_2011 earnings_2010 assets_2012 assets_2011 assets_2010
# 1 A 0.2655087 0.2016819 0.62911404 0.6870228 0.7176185 0.9347052
# 2 B 0.3721239 0.8983897 0.06178627 0.3841037 0.9919061 0.2121425
# 3 C 0.5728534 0.9446753 0.20597457 0.7698414 0.3800352 0.6516738
# 4 D 0.9082078 0.6607978 0.17655675 0.4976992 0.7774452 0.1255551
选项 1:重塑
一个限制是它不能处理不平衡"的数据集(例如，如果您的数据中没有assets_2010"，这将不起作用).
reshape(mydf, direction = "long", idvar="company",
varying = 2:ncol(mydf), sep = "_")
# company time earnings assets
# A.2012 A 2012 0.26550866 0.6870228
# B.2012 B 2012 0.37212390 0.3841037
# C.2012 C 2012 0.57285336 0.7698414
# D.2012 D 2012 0.90820779 0.4976992
# A.2011 A 2011 0.20168193 0.7176185
# B.2011 B 2011 0.89838968 0.9919061
# C.2011 C 2011 0.94467527 0.3800352
# D.2011 D 2011 0.66079779 0.7774452
# A.2010 A 2010 0.62911404 0.9347052
# B.2010 B 2010 0.06178627 0.2121425
# C.2010 C 2010 0.20597457 0.6516738
# D.2010 D 2010 0.17655675 0.1255551
选项 2:reshape2"包
因其语法而广受欢迎.在它可以工作之前需要一些处理，因为需要拆分列名以便我们获得这种双宽"类型的数据.能够处理不平衡的数据，但如果您的不同列具有不同的列类型(数字、字符、因子)，则不会是最好的.
library(reshape2)
dfL <- melt(mydf, id.vars="company")
dfL <- cbind(dfL, colsplit(dfL$variable, "_", c("var", "year")))
dcast(dfL, company + year ~ var, value.var="value")
# company year assets earnings
# 1 A 2010 0.9347052 0.62911404
# 2 A 2011 0.7176185 0.20168193
# 3 A 2012 0.6870228 0.26550866
# 4 B 2010 0.2121425 0.06178627
# 5 B 2011 0.9919061 0.89838968
# 6 B 2012 0.3841037 0.37212390
# 7 C 2010 0.6516738 0.20597457
# 8 C 2011 0.3800352 0.94467527
# 9 C 2012 0.7698414 0.57285336
# 10 D 2010 0.1255551 0.17655675
# 11 D 2011 0.7774452 0.66079779
# 12 D 2012 0.4976992 0.90820779
选项 3:merged.stack 来自splitstackshape"
merged.stack 来自我的splitstackshape"包的语法非常简单，如果您需要以这种双宽"类型的结构结束，应该会非常快.它的创建是为了能够处理不平衡的数据，并且由于它单独处理列，因此在转换列类型时不会出现问题.
Option 3: merged.stack from "splitstackshape"
merged.stack from my "splitstackshape" package has pretty straightforward syntax and should be pretty fast if you need to end up with this "double-wide" type of structure. It was created to be able to handle unbalanced data and since it treats columns separately, won't have problems with converting column types.
library(splitstackshape)
merged.stack(mydf, id.vars="company",
var.stubs=c("earnings", "assets"), sep = "_")
# company .time_1 earnings assets
# 1: A 2010 0.62911404 0.9347052
# 2: A 2011 0.20168193 0.7176185
# 3: A 2012 0.26550866 0.6870228
# 4: B 2010 0.06178627 0.2121425
# 5: B 2011 0.89838968 0.9919061
# 6: B 2012 0.37212390 0.3841037
# 7: C 2010 0.20597457 0.6516738
# 8: C 2011 0.94467527 0.3800352
# 9: C 2012 0.57285336 0.7698414
# 10: D 2010 0.17655675 0.1255551
# 11: D 2011 0.66079779 0.7774452
# 12: D 2012 0.90820779 0.4976992
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