创建索引，以其他列中的值为条件;随着时间的推移差异

Question

我正在努力解决以下问题： 下面的数据框包含了各种ID随时间推移的值的发展。我试图得到的是这些价值根据事件发生的一年中的价值增加/减少。一个ID中可能会发生多个事件，因此新事件将成为该ID的新基准年。 为了让事情更清晰，我还加我想下面

结果我有什么

id value year event 
a 100  1950 NA 
a 101  1951 NA 
a 102  1952 NA 
a 103  1953 NA 
a 104  1954 NA 
a 105  1955 X 
a 106  1956 NA 
a 107  1957 NA 
a 108  1958 NA 
a 107  1959 Y 
a 106  1960 NA 
a 105  1961 NA 
a 104.8 1962 NA 
a 104.2 1963 NA 
b 70  1970 NA 
b 75  1971 NA 
b 80  1972 NA 
b 85  1973 NA 
b 90  1974 NA 
b 60  1975 Z 
b 59  1976 NA 
b 58  1977 NA 
b 57  1978 NA 
b 56  1979 NA 
b 55  1980 W 
b 54  1981 NA 
b 53  1982 NA 
b 52  1983 NA 
b 51  1984 NA 

什么我找

id value year event index growth 
a 100  1950 NA  0 
a 101  1951 NA  0 
a 102  1952 NA  0 
a 103  1953 NA  0 
a 104  1954 NA  0 
a 105  1955 X   1  1 
a 106  1956 NA  2  1.00952381 
a 107  1957 NA  3  1.019047619 
a 108  1958 NA  4  1.028571429 
a 107  1959 Y   1  1     #new baseline year 
a 106  1960 NA  2  0.990654206 
a 105  1961 NA  3  0.981308411 
a 104.8 1962 NA  4  0.979439252 
a 104.2 1963 NA  5  0.973831776 
b 70  1970 NA  6 
b 75  1971 NA  7 
b 80  1972 NA  8 
b 85  1973 NA  9 
b 90  1974 NA  10 
b 60  1975 Z   1  1 
b 59  1976 NA  2  0.983333333 
b 58  1977 NA  3  0.966666667 
b 57  1978 NA  4  0.95 
b 56  1979 NA  5  0.933333333 
b 55  1980 W   1  1    #new baseline year 
b 54  1981 NA  2  0.981818182 
b 53  1982 NA  3  0.963636364 
b 52  1983 NA  4  0.945454545 
b 51  1984 NA  5  0.927272727 

我试过

This和this帖子相当有帮助，我设法创造了这些年份之间的差异，但是，当有新事件发生时，我无法重置基准年（索引）。此外，我怀疑我的方法是否确实是最高效/最优秀的方法。似乎有点笨拙...

x <- ddply(x, .(id), transform, year.min=min(year[!is.na(event)])) #identifies first event year 
x1 <- ddply(x[x$year>=x$year.min,], .(id), transform, index=seq_along(id)) #creates counter years following first event; prior years are removed 
x1 <- x1[order(x1$id, x1$year),] #sort 
x1 <- ddply(x1, .(id), transform, growth=100*(value/value[1])) #calculate difference, however, based on first event year; this is wrong. 

library(Interact) #i then merge the df with the years prior to first event which have been removed in the begining 
x$id.year <- interaction(x$id,x$year) 
x1$id.year <- interaction(x1$id,x1$year) 
x$index <- x$growth <- NA 
y <- rbind(x[x$year<x$year.min,],x1) 
y <- y[order(y$id,y$year),] 

非常感谢您的任何意见。

Answer 1

# Create a tag to indicate the start of each new event by id or 
# when id changes 
dat$tag <- with(dat, ave(as.character(event), as.character(id), 
            FUN=function(i) cumsum(!is.na(i)))) 

# Calculate the growth by id and tag 
# this will also produce results for each id before an event has happened 
dat$growth <- with(dat, ave(value, tag, id, FUN=function(i) i/i[1])) 

# remove growth prior to an event (this will be when tag equals zero as no 
# event have occurred) 
dat$growth[dat$tag==0] <- NA 

Answer 2

尝试：

ddf$index=0 
ddf$growth=0 
baseline =0 
r=1; start=FALSE 
for(r in 1:nrow(ddf)){ 
    if(is.na(ddf$event[r])){ 
     if(start) { 
      ddf$index[r] = ddf$index[r-1]+1 
      ddf$growth[r] = ddf$value[r]/baseline 
     } 
     else {ddf$index[r] = 0; 
     } 
    } 
    else{ 
     start=T 
     ddf$index[r] = 1 
     ddf$growth[r]=1 
     baseline = ddf$value[r] 
    } 
} 

ddf 
    id value year event index growth 
1 a 100.0 1950 <NA>  0 0.0000000 
2 a 101.0 1951 <NA>  0 0.0000000 
3 a 102.0 1952 <NA>  0 0.0000000 
4 a 103.0 1953 <NA>  0 0.0000000 
5 a 104.0 1954 <NA>  0 0.0000000 
6 a 105.0 1955  X  1 1.0000000 
7 a 106.0 1956 <NA>  2 1.0095238 
8 a 107.0 1957 <NA>  3 1.0190476 
9 a 108.0 1958 <NA>  4 1.0285714 
10 a 107.0 1959  Y  1 1.0000000 
11 a 106.0 1960 <NA>  2 0.9906542 
12 a 105.0 1961 <NA>  3 0.9813084 
13 a 104.8 1962 <NA>  4 0.9794393 
14 a 104.2 1963 <NA>  5 0.9738318 
15 b 70.0 1970 <NA>  6 0.6542056 
16 b 75.0 1971 <NA>  7 0.7009346 
17 b 80.0 1972 <NA>  8 0.7476636 
18 b 85.0 1973 <NA>  9 0.7943925 
19 b 90.0 1974 <NA> 10 0.8411215 
20 b 60.0 1975  Z  1 1.0000000 
21 b 59.0 1976 <NA>  2 0.9833333 
22 b 58.0 1977 <NA>  3 0.9666667 
23 b 57.0 1978 <NA>  4 0.9500000 
24 b 56.0 1979 <NA>  5 0.9333333 
25 b 55.0 1980  W  1 1.0000000 
26 b 54.0 1981 <NA>  2 0.9818182 
27 b 53.0 1982 <NA>  3 0.9636364 
28 b 52.0 1983 <NA>  4 0.9454545 
29 b 51.0 1984 <NA>  5 0.9272727 

Answer 3

这里是dplyr的解决方案。

ana <- group_by(mydf, id) %>% 
     do(na.locf(., na.rm = FALSE)) %>% 
     mutate(value = as.numeric(value)) %>% 
     group_by(id, event) %>% 
     mutate(growth = value/value[1]) %>% 
     mutate(index = row_number(event)) 

ana$growth[is.na(ana$event)] <- 0 

    id value year event growth index 
1 a 100.0 1950 NA 0.0000000  1 
2 a 101.0 1951 NA 0.0000000  2 
3 a 102.0 1952 NA 0.0000000  3 
4 a 103.0 1953 NA 0.0000000  4 
5 a 104.0 1954 NA 0.0000000  5 
6 a 105.0 1955  X 1.0000000  1 
7 a 106.0 1956  X 1.0095238  2 
8 a 107.0 1957  X 1.0190476  3 
9 a 108.0 1958  X 1.0285714  4 
10 a 107.0 1959  Y 1.0000000  1 
11 a 106.0 1960  Y 0.9906542  2 
12 a 105.0 1961  Y 0.9813084  3 
13 a 104.8 1962  Y 0.9794393  4 
14 a 104.2 1963  Y 0.9738318  5 
15 b 70.0 1970 NA 0.0000000  1 
16 b 75.0 1971 NA 0.0000000  2 
17 b 80.0 1972 NA 0.0000000  3 
18 b 85.0 1973 NA 0.0000000  4 
19 b 90.0 1974 NA 0.0000000  5 
20 b 60.0 1975  Z 1.0000000  1 
21 b 59.0 1976  Z 0.9833333  2 
22 b 58.0 1977  Z 0.9666667  3 
23 b 57.0 1978  Z 0.9500000  4 
24 b 56.0 1979  Z 0.9333333  5 
25 b 55.0 1980  W 1.0000000  1 
26 b 54.0 1981  W 0.9818182  2 
27 b 53.0 1982  W 0.9636364  3 
28 b 52.0 1983  W 0.9454545  4