Wypełnij tabelę danych w oparciu o wartość w innym data.table

Question

Jestem bardzo nowy dla data.table, ale chciałbym rozwiązać mój problem z nim, ponieważ mam wrażenie, że byłby 1000 razy szybszy niż w przypadku "zwykłych" danych .frames.

Oto mój problem:

Co mam:

2 data.tables dt1 i dt2 tak:

dt1 <- data.table(SID=paste0("S", 1:15), Chromo=rep(1:3, e=5), PP=rep(1:5, 3), P1=0, P2=0, P3=0) 
set.seed(17) 
dt2 <- data.table(PID=rep(paste0("P", 1:3), c(2, 6, 3)), Chr=c(1, 3, 1, 1, 2, 3, 3, 3, 2, 2, 3), start= c(1, 1, 1, 4, 2, 1, 2, 4, 2, 4, 2), end=c(3, 4, 2, 5, 4, 1, 3, 5, 3, 5, 5), val=rnorm(11)) 

co chcę:

Wypełnij dt1 z dt2[, val] w prawej kolumnie, na podstawie dt2[, PID] i prawej linii, w oparciu o dt1[, Chromo] = dt2[, Chr] i dt1[, PP] między dt2[, start] i dt2[, end].

Co mam teraz zrobić:(co nie czyni mnie dumnym, co najmniej ...)

# preparing the tables, computing dt1 rows indices 
dt2[, numcol:=(1:ncol(dt1))[match(dt2[,PID], colnames(dt1))]] 
setkey(dt2, Chr, start, end) 
setkey(dt1, Chromo, PP) 
ind_start <- dt1[dt2[,.(Chr, start)], which=T] 
ind_end <- dt1[dt2[,.(Chr, end)], which=T] 
dt2[,c("ind_start", "ind_end"):=list(ind_start, ind_end)] 

# and feeling I'm that close but can't conclude with `data.table` so doing this "lame" `for` loop with `data.frames`....................... 
df1 <- as.data.frame(dt1) 
df2 <- as.data.frame(dt2) 
nr_seg <- nrow(df2) 
for(i in 1:nr_seg){ 
    df1[df2[i,"ind_start"]:df2[i,"ind_end"], df2[i,"numcol"]] <- df2[i, "val"] 
} 

Tabele wejściowe i sygnał wyjściowy (z wyjątkiem I d jak data.table):

dt1 
    # SID Chromo PP P1 P2 P3 
# 1: S1  1 1 0 0 0 
# 2: S2  1 2 0 0 0 
# 3: S3  1 3 0 0 0 
# 4: S4  1 4 0 0 0 
# 5: S5  1 5 0 0 0 
# 6: S6  2 1 0 0 0 
# 7: S7  2 2 0 0 0 
# 8: S8  2 3 0 0 0 
# 9: S9  2 4 0 0 0 
# 10: S10  2 5 0 0 0 
# 11: S11  3 1 0 0 0 
# 12: S12  3 2 0 0 0 
# 13: S13  3 3 0 0 0 
# 14: S14  3 4 0 0 0 
# 15: S15  3 5 0 0 0 

dt2 
    # PID Chr start end   val 
# 1: P2 1  1 2 -0.23298702 
# 2: P1 1  1 3 -1.01500872 
# 3: P2 1  4 5 -0.81726793 
# 4: P3 2  2 3 0.25523700 
# 5: P2 2  2 4 0.77209084 
# 6: P3 2  4 5 0.36658112 
# 7: P2 3  1 1 -0.16561194 
# 8: P1 3  1 4 -0.07963674 
# 9: P2 3  2 3 0.97287443 
# 10: P3 3  2 5 1.18078924 
# 11: P2 3  4 5 1.71653398 

df1 
    # SID Chromo PP   P1   P2  P3 
# 1 S1  1 1 -1.01500872 -0.2329870 0.0000000 
# 2 S2  1 2 -1.01500872 -0.2329870 0.0000000 
# 3 S3  1 3 -1.01500872 0.0000000 0.0000000 
# 4 S4  1 4 0.00000000 -0.8172679 0.0000000 
# 5 S5  1 5 0.00000000 -0.8172679 0.0000000 
# 6 S6  2 1 0.00000000 0.0000000 0.0000000 
# 7 S7  2 2 0.00000000 0.7720908 0.2552370 
# 8 S8  2 3 0.00000000 0.7720908 0.2552370 
# 9 S9  2 4 0.00000000 0.7720908 0.3665811 
# 10 S10  2 5 0.00000000 0.0000000 0.3665811 
# 11 S11  3 1 -0.07963674 -0.1656119 0.0000000 
# 12 S12  3 2 -0.07963674 0.9728744 1.1807892 
# 13 S13  3 3 -0.07963674 0.9728744 1.1807892 
# 14 S14  3 4 -0.07963674 1.7165340 1.1807892 
# 15 S15  3 5 0.00000000 1.7165340 1.1807892 

Answer 1

library(data.table) 
dt1 <- data.table(SID=paste0("S", 1:15), Chromo=rep(1:3, e=5), PP=rep(1:5, 3), P1=0, P2=0, P3=0) 
set.seed(17) 
dt2 <- data.table(PID=rep(paste0("P", 1:3), c(2, 6, 3)), Chr=c(1, 3, 1, 1, 2, 3, 3, 3, 2, 2, 3), start= c(1, 1, 1, 4, 2, 1, 2, 4, 2, 4, 2), end=c(3, 4, 2, 5, 4, 1, 3, 5, 3, 5, 5), val=rnorm(11)) 

dt1[, PP1 := PP] 
dt1[, c("P1", "P2", "P3") := NULL] 


setkey(dt2, Chr, start, end) 

setkey(dt1, Chromo, PP, PP1) 

res <- foverlaps(dt1, dt2, type="within") 
res[is.na(PID), PID := "P1"] #to ensure that dcast works if there is no match 
res <- dcast.data.table(res, SID + Chromo + PP ~ PID, value.var = "val") 
setkey(res, Chromo, PP) 

# SID Chromo PP   P1   P2  P3 
# 1: S1  1 1 -1.01500872 -0.2329870  NA 
# 2: S2  1 2 -1.01500872 -0.2329870  NA 
# 3: S3  1 3 -1.01500872   NA  NA 
# 4: S4  1 4   NA -0.8172679  NA 
# 5: S5  1 5   NA -0.8172679  NA 
# 6: S6  2 1   NA   NA  NA 
# 7: S7  2 2   NA 0.7720908 0.2552370 
# 8: S8  2 3   NA 0.7720908 0.2552370 
# 9: S9  2 4   NA 0.7720908 0.3665811 
#10: S10  2 5   NA   NA 0.3665811 
#11: S11  3 1 -0.07963674 -0.1656119  NA 
#12: S12  3 2 -0.07963674 0.9728744 1.1807892 
#13: S13  3 3 -0.07963674 0.9728744 1.1807892 
#14: S14  3 4 -0.07963674 1.7165340 1.1807892 
#15: S15  3 5   NA 1.7165340 1.1807892