我已經下載了txt。來自Kenneth R. French圖書館的文件,可通過鏈接http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_48_ind_port.html找到。如何在R中將一個數據幀轉換爲另一個數據幀?
我需要使用這些所謂的SIC代碼根據行業因素將我的樣本分爲不同的投資組合。下載的文件是這樣的:
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
我想要做的事情是創建數據幀,其中第一列給出了行業的域名(例如,食品,採礦和礦物等)和第二列中列出了與這個行業相關的所有SIC代碼(標準工業代碼)(因爲大多數SIC代碼是以5130-5139的方式給出的,這使得它更難一些)。
這個數據框會讓我的分析更容易實現。
任何建議將是非常可觀的。
我會考慮像谷歌瑞風(離線和免費的)真實數據預處理工具。 R並不適合這類任務,即使你可以用R來完成,但是會帶來更多的痛苦。 – ATN
我認爲使用其他程序來處理這個問題更好,因爲你的數據看起來不像數據框(你有像「4 Clths」之類的東西)。不是一種非常有效的方法,但是您可以手動執行此操作。我可以看到所有的SIC代碼都是以xxxx-xxxx的形式出現的,後面跟着一個空格。所以如果你使用sep =「」來讀取文件,那麼第一列應該是你的SIC代碼,第二列應該是你的行業名稱(我不確定是否所有的名字都是單個字符串,在你的例子中,他們是) ,剩下的就是他們賣的東西了? –