Question

我已经下载了txt。来自Kenneth R. French library的文件，可以通过链接http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_48_ind_port.html找到。

我需要使用这些所谓的SIC代码，根据行业因素将我的样本分成不同的投资组合。下载的文件如下所示：

      1 Food  
      0100-0199 Agric production - crops
      0200-0299 Agric production - livestock
      0700-0799 Agricultural services
      0900-0999 Fishing, hunting & trapping
      2000-2009 Food and kindred products
      2010-2019 Meat products
      2020-2029 Dairy products
      2030-2039 Canned-preserved fruits-vegs
      2040-2046 Flour and other grain mill products
      2047-2047 Dog and cat food
      2048-2048 Prepared feeds for animals
      2050-2059 Bakery products
      2060-2063 Sugar and confectionery products
      2064-2068 Candy and other confectionery
      2070-2079 Fats and oils
      2080-2080 Beverages
      2082-2082 Malt beverages
      2083-2083 Malt
      2084-2084 Wine
      2085-2085 Distilled and blended liquors
      2086-2086 Bottled-canned soft drinks
      2087-2087 Flavoring syrup
      2090-2092 Misc food preps
      2095-2095 Roasted coffee
      2096-2096 Potato chips
      2097-2097 Manufactured ice
      2098-2099 Misc food preparations
      5140-5149 Wholesale - groceries & related prods
      5150-5159 Wholesale - farm products
      5180-5182 Wholesale - beer, wine
      5191-5191 Wholesale - farm supplies

      2 Mines 
      1000-1009 Metal mining
      1010-1019 Iron ores
      1020-1029 Copper ores
      1030-1039 Lead and zinc ores
      1040-1049 Gold & silver ores
      1060-1069 Ferroalloy ores
      1080-1089 Mining services
      1090-1099 Misc metal ores
      1200-1299 Bituminous coal
      1400-1499 Mining and quarrying non-metalic minerals
      5050-5052 Wholesale - metals and minerals

      3 Oil
      1300-1300 Oil and gas extraction
      1310-1319 Crude petroleum & natural gas
      1320-1329 Natural gas liquids
      1380-1380 Oil and gas field services
      1381-1381 Drilling oil & gas wells
      1382-1382 Oil-gas field exploration
      1389-1389 Oil and gas field services
      2900-2912 Petroleum refining
      5170-5172 Wholesale - petroleum and petro prods

      4 Clths 
      2200-2269 Textile mill products
      2270-2279 Floor covering mills
      2280-2284 Yarn and thread mills
      2290-2295 Misc textile goods
      2296-2296 Tire cord and fabric
      2297-2297 Nonwoven fabrics
      2298-2298 Cordage and twine
      2299-2299 Misc textile products
      2300-2390 Apparel and other finished products
      2391-2392 Curtains, home furnishings
      2393-2395 Textile bags, canvas products
      2396-2396 Auto trim
      2397-2399 Misc textile products
      3020-3021 Rubber and plastics footwear
      3100-3111 Leather tanning and finishing
      3130-3131 Boot, shoe cut stock, findings
      3140-3149 Footware except rubber
      3150-3151 Leather gloves and mittens
      3963-3965 Fasteners, buttons, needles, pins
      5130-5139 Wholesale - apparel

我想要做的是创建数据框，其中第一列给出行业名称（例如，食品，采矿和矿物等），第二列是所有与之相关的SIC代码（标准工业代码）这个行业（因为大多数SIC代码是以5130-5139的方式给出的，这使得这样做有点困难）。

此数据框将使我的分析更容易实现。

任何建议都会非常值得注意。

Answer 1

这将生成一个2列数据框df.new，其中包含第2列中逗号分隔的代码：

df <- read.fwf("Siccodes48.txt", widths=c(3, 7, 60), stringsAsFactors=FALSE)
df <- df[!is.na(df$V3), ]
library(zoo)
df$V1 <- na.locf(df$V1)
l <- split(df, df$V1)
l <- setNames(lapply(l, function(x) {
  m <- regexec("([0-9]{4})-([0-9]{4}) .*", x$V3[-1]) # omit headline
  r <- regmatches(x$V3[-1], m)
  fromTo <- t(sapply(r, "[", 2:3))
  paste(sprintf("%04d", unlist(mapply(":", fromTo[, 1], fromTo[, 2]))), collapse=", ")
}), sapply(l, "[", 1, 3))
df.new <- data.frame(name=names(l), sic=unlist(l))

Answer 2

这个怎么样？

df<-readLines("Siccodes48.txt")
df<-data.frame(t=df[df!=""])              # delete blanks and make data frame
df$prefix<-c(substr(df$t,1,10))           # break out the prefix (first 10 char)
df$index<-cumsum(df$prefix!="          ") # make an index
ind<-df[df$prefix!="          ",]         # make an index table
ind$desc<-substring(ind$t,11,100)         # parse descriptions
final<-merge(ind[,c("index","desc")],     # merge the index table
             df[df$prefix=="          ",c("index","t")],  # with all non-title rows of the list
             by="index")                                  # by index

head(final,10)

   index          desc                                                       t
1      1   Agriculture                      0100-0199 Agric production - crops
2      1   Agriculture                  0200-0299 Agric production - livestock
3      1   Agriculture                         0700-0799 Agricultural services
4      1   Agriculture                            0910-0919 Commercial fishing
5      1   Agriculture                    2048-2048 Prepared feeds for animals
6      2 Food Products                     2000-2009 Food and kindred products
7      2 Food Products                                 2010-2019 Meat products
8      2 Food Products                                2020-2029 Dairy products
9      2 Food Products                  2030-2039 Canned-preserved fruits-vegs
10     2 Food Products           2040-2046 Flour and other grain mill products

您还可以添加此代码以将代码分成单独的列：

final$codes<-substr(gsub("          "," ",final$t),2,10)

如何在R中将一个数据帧转换为另一个数据帧？

2 个答案: