fülle eine data.table basierend auf Wert in einer anderen data.table

Question

Ich bin sehr neu zu data.table aber möchte mein Problem damit lösen, da ich das Gefühl habe, es wäre 1000 mal schneller als mit "normalen" Daten .frames.

Hier ist mein Problem:

Was ich habe:

2 data.tables dt1 und dt2 wie so:

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)) 

Was ich will:

Füllen Sie dt1 mit dt2[, val] in der rechten Spalte, basierend auf dt2[, PID] und die richtigen Linien, basierend auf dt1[, Chromo] = dt2[, Chr] und dt1[, PP] zwischen dt2[, start] und dt2[, end].

Was ich jetzt tun:(nicht mich stolz macht, gelinde gesagt ...)

# 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"] 
} 

Die Eingabetabellen und gewünschten Ausgang (außer I‘ d wie ein 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