尝试使用嵌套for循环创建/存储data.frames。
我在一个名为countries的变量中有一些国家/地区的数据，因此我USA, UK, Germany etc.分别标记了1,2,3。
我还有变量industries中特定行业的数据，例如textiles, retail, other。我再次标记了这些行业1,2,3。
我要做的是创建一个新的data.frame;
country 1, industry 1
country 1, industry 2
country 1, industry 3
country 2, industry 1
country 2, industry 2
country 2, industry 3
country 3, industry 1
country 3, industry 2
country 3, industry 3
等
我希望对每个data.frame
我目前正在使用的是以下内容;
m <- 3 # m countries
k <- 3 # k industries
for(i in 1:length(m)){
country.ID <- m[i]
for(j in 1:length(k)){
sector.ID <- k[j]
S1 <- which(DF$COUNTRY.id == country.ID)
S2 <- which(DF$INDUSTRY.id == sector.ID)
rows.2.consider <- intersect(S1, S2)
# Here is where I am trying to save the data.frames for analysis
}
}
如果我在任何时候出错，请指出这一点。但我尝试为每个国家/地区和每个地区创建许多data.frames，例如此示例中的3 countries * 3 industries会给9 data.frames
这里有一些示例代码（我实际上使用的是区域数据而非国家数据等，但同样的主要数据仍然适用。
＃ ratios <- structure(list(IDVar = 1:40, Major.sectors = structure(c(5L,
9L, 3L, 15L, 11L, 7L, 18L, 18L, 18L, 3L, 3L, 3L, 3L, 17L, 3L,
11L, 7L, 17L, 3L, 11L, 3L, 18L, 3L, 17L, 9L, 18L, 9L, 19L, 3L,
11L, 11L, 2L, 5L, 3L, 18L, 17L, 4L, 2L, 3L, 3L), .Label = c("Banks",
"Chemicals, rubber, plastics, non-metallic products", "Construction",
"Education, Health", "Food, beverages, tobacco", "Gas, Water, Electricity",
"Hotels & restaurants", "Insurance companies", "Machinery, equipment, furniture, recycling",
"Metals & metal products", "Other services", "Post & telecommunications",
"Primary sector", "Public administration & defense", "Publishing, printing",
"Textiles, wearing apparel, leather", "Transport", "Wholesale & retail trade",
"Wood, cork, paper"), class = "factor"), Region.in.country = structure(c(15L,
8L, 8L, 8L, 10L, 15L, 19L, 10L, 8L, 10L, 3L, 18L, 4L, 12L, 4L,
15L, 13L, 4L, 15L, 15L, 7L, 15L, 12L, 1L, 7L, 10L, 15L, 8L, 13L,
15L, 12L, 8L, 7L, 15L, 15L, 10L, 8L, 10L, 10L, 15L), .Label = c("Andalucia",
"Aragon", "Asturias", "Canary Islands", "Cantabria", "Castilla-La Mancha",
"Castilla y Leon", "Cataluna", "Ceuta", "Comunidad Valenciana",
"Extremadura", "Galicia", "Islas Baleares", "La Rioja", "Madrid",
"Melilla", "Murcia", "Navarra", "Pais Vasco"), class = "factor"),
EBIT.TA = c(-0.234432635519391, -0.884337466274593, -0.00446559204081373,
0.11109107677028, -0.137203773525798, -0.582114677880617,
0.0190497663203189, -3.04252763094666, 0.113157822682219,
-0.0255533180037229, 0.281767142199724, 0.0326641697396841,
-0.00879974750993553, 0.0542074697816672, -0.112104697294392,
-0.191945591325174, -0.00380586115226597, -0.0363239884169068,
-0.273949107908537, 0.435398668004486, -0.00563436099927988,
-2.75971618056051, -0.1047327709263, 0.151283793741506, -0.0373197549569126,
0.00912639083178201, -0.0386627754065697, -0.018235399636112,
-0.0118104711362467, -0.701299939137125, NA, 0.0191819361175666,
-0.0104887983706721, -0.801677105519484, -0.402194475974272,
-0.124125227730062, 0.143020458476649, -0.601186271451194,
0.0163269364787831, 5.09955167591238), EBIT.TA_l1 = c(-0.443687074746458,
-0.561864166134075, -0.0345769510044604, 0.0282541797531804,
-0.0181173929170762, 0.0147211350970115, 0.0588534950162799,
-1.14097109926961, 0.060100343733096, -0.0386426338471025,
0.049684095221329, 0.0558174150334904, 0.00214962169435867,
0.0399960114646072, 0.0402934579830171, -0.612359147433149,
-0.0115916125659674, 0.00739473610413031, 0.0174576615247567,
0.68624861825246, 0.0305807338940829, -3.88006243913616,
0.0410122725022661, -0.089491343996377, -0.215219123182103,
0.00967853324842811, -0.0336715197882038, 0.362424791356667,
0.221203934329637, -0.654387857513823, 0.0656934439915892,
0.0652005453654772, 0.0339559014267185, 0.0259085077216708,
-0.303606048856146, 0.0280113794301873, 0.109307291990628,
-0.470048555841697, -0.00157699300508027, -0.350519090107081
), EBIT.TA_l2 = c(-0.351308186716873, 0.00159428805074234,
-0.00604587147802615, 0.0761894448922952, -0.00348378141492824,
NA, 0.0346370866793768, -0.552226781084599, 0.00220031803369861,
-0.0285840972149053, 0.065316579236306, 0.4090851643341,
-0.0188362202518351, 0.0403848986306371, 0.091146090480032,
-0.0154168449752466, -0.0694803621032671, 0.0511978643139393,
-0.452924037757731, -0.0091835704914724, 0.0119918914092344,
0.0858960833880717, NA, 0.104901526886479, -0.23096183545392,
-0.0163058345980967, 0.100643431561465, 0.0527859573541712,
0.250207316117438, NA, 0.00193240515291123, 0.0624210741756767,
0.0178136227732972, -0.0321294913646274, -0.0699629484084657,
-0.00417176180400133, 0.209612573099415, 0.0285645570852926,
0.0551624216079071, 0.0172738293439595), Major.sectors.id = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 8L, 3L, 5L,
6L, 8L, 3L, 5L, 3L, 7L, 3L, 8L, 2L, 7L, 2L, 9L, 3L, 5L, 5L,
10L, 1L, 3L, 7L, 8L, 11L, 10L, 3L, 3L), Region.in.country.id = c(1L,
2L, 2L, 2L, 3L, 1L, 4L, 3L, 2L, 3L, 5L, 6L, 7L, 8L, 7L, 1L,
9L, 7L, 1L, 1L, 10L, 1L, 8L, 11L, 10L, 3L, 1L, 2L, 9L, 1L,
8L, 2L, 10L, 1L, 1L, 3L, 2L, 3L, 3L, 1L)), .Names = c("IDVar",
"Major.sectors", "Region.in.country", "EBIT.TA", "EBIT.TA_l1",
"EBIT.TA_l2", "Major.sectors.id", "Region.in.country.id"), row.names = c(NA,
40L), class = "data.frame")
答案 0 :(得分：0)
你可以做到
m <- 3 # m countries
k <- 3 # k industries
d <- data.frame(country=rep(1:m, each=k), industry=rep(1:k, m) )
表示单个data.frame
您可以将其拆分为9个data.frames
split(d,d)
答案 1 :(得分：0)
一个选项可能是使用expand.grid。使用所需data.frame和country准备industry，然后使用expand.grid展开相同内容以生成所有可能的组合。
df <- data.frame(c= c("country1","country2", "country3"),
i = c("industry1", "industry2","industry3"))
library(dplyr)
expand.grid(df) %>% arrange(c)
c i
1 country1 industry1
2 country1 industry2
3 country1 industry3
4 country2 industry1
5 country2 industry2
6 country2 industry3
7 country3 industry1
8 country3 industry2
9 country3 industry3
答案 2 :(得分：0)
您实际上不需要拆分数据也不需要创建索引。可以这样做来为每个行业和国家进行分析：
YourAnalysis <- function(x) mean(x$EBIT.TA)
by(data = ratios, INDICES = list(ratios$Region.in.country, ratios$Major.sectors), FUN = YourAnalysis)