The following code combines a vector with a dataframe:
newrow = c(1:4) existingDF = rbind(existingDF,newrow) However this code always inserts the new row at the end of the dataframe.
How can I insert the row at a specified point within the dataframe? For example, lets say the dataframe has 20 rows, how can I insert the new row between rows 10 and 11?
Here's a solution that avoids the (often slow) rbind call:
existingDF <- as.data.frame(matrix(seq(20),nrow=5,ncol=4)) r <- 3 newrow <- seq(4) insertRow <- function(existingDF, newrow, r) { existingDF[seq(r+1,nrow(existingDF)+1),] <- existingDF[seq(r,nrow(existingDF)),] existingDF[r,] <- newrow existingDF } > insertRow(existingDF, newrow, r) V1 V2 V3 V4 1 1 6 11 16 2 2 7 12 17 3 1 2 3 4 4 3 8 13 18 5 4 9 14 19 6 5 10 15 20 If speed is less important than clarity, then @Simon's solution works well:
existingDF <- rbind(existingDF[1:r,],newrow,existingDF[-(1:r),]) > existingDF V1 V2 V3 V4 1 1 6 11 16 2 2 7 12 17 3 3 8 13 18 4 1 2 3 4 41 4 9 14 19 5 5 10 15 20 (Note we index r differently).
And finally, benchmarks:
library(microbenchmark) microbenchmark( rbind(existingDF[1:r,],newrow,existingDF[-(1:r),]), insertRow(existingDF,newrow,r) ) Unit: microseconds expr min lq median uq max 1 insertRow(existingDF, newrow, r) 660.131 678.3675 695.5515 725.2775 928.299 2 rbind(existingDF[1:r, ], newrow, existingDF[-(1:r), ]) 801.161 831.7730 854.6320 881.6560 10641.417 Benchmarks
As @MatthewDowle always points out to me, benchmarks need to be examined for the scaling as the size of the problem increases. Here we go then:
benchmarkInsertionSolutions <- function(nrow=5,ncol=4) { existingDF <- as.data.frame(matrix(seq(nrow*ncol),nrow=nrow,ncol=ncol)) r <- 3 # Row to insert into newrow <- seq(ncol) m <- microbenchmark( rbind(existingDF[1:r,],newrow,existingDF[-(1:r),]), insertRow(existingDF,newrow,r), insertRow2(existingDF,newrow,r) ) # Now return the median times mediansBy <- by(m$time,m$expr, FUN=median) res <- as.numeric(mediansBy) names(res) <- names(mediansBy) res } nrows <- 5*10^(0:5) benchmarks <- sapply(nrows,benchmarkInsertionSolutions) colnames(benchmarks) <- as.character(nrows) ggplot( melt(benchmarks), aes(x=Var2,y=value,colour=Var1) ) + geom_line() + scale_x_log10() + scale_y_log10() @Roland's solution scales quite well, even with the call to rbind:
5 50 500 5000 50000 5e+05 insertRow2(existingDF, newrow, r) 549861.5 579579.0 789452 2512926 46994560 414790214 insertRow(existingDF, newrow, r) 895401.0 905318.5 1168201 2603926 39765358 392904851 rbind(existingDF[1:r, ], newrow, existingDF[-(1:r), ]) 787218.0 814979.0 1263886 5591880 63351247 829650894 Plotted on a linear scale:
And a log-log scale:
df with columns a,b,c,d and I want to add the row 1,2,3,4. How do I do that?insertRow2 <- function(existingDF, newrow, r) { existingDF <- rbind(existingDF,newrow) existingDF <- existingDF[order(c(1:(nrow(existingDF)-1),r-0.5)),] row.names(existingDF) <- 1:nrow(existingDF) return(existingDF) } insertRow2(existingDF,newrow,r) V1 V2 V3 V4 1 1 6 11 16 2 2 7 12 17 3 1 2 3 4 4 3 8 13 18 5 4 9 14 19 6 5 10 15 20 microbenchmark( + rbind(existingDF[1:r,],newrow,existingDF[-(1:r),]), + insertRow(existingDF,newrow,r), + insertRow2(existingDF,newrow,r) + ) Unit: microseconds expr min lq median uq max 1 insertRow(existingDF, newrow, r) 513.157 525.6730 531.8715 544.4575 1409.553 2 insertRow2(existingDF, newrow, r) 430.664 443.9010 450.0570 461.3415 499.988 3 rbind(existingDF[1:r, ], newrow, existingDF[-(1:r), ]) 606.822 625.2485 633.3710 653.1500 1489.216 
rbind, but I'm intrigued.
The .before argument in tibble::add_row() can be used to specify the row.
tibble::add_row( cars, speed = 0, dist = 0, .before = 3 ) #> speed dist #> 1 4 2 #> 2 4 10 #> 3 0 0 #> 4 7 4 #> 5 7 22 #> 6 8 16 #> ... 
add_row is imported into dplyr from the tibble packageYou should try dplyr package
library(dplyr) a <- data.frame(A = c(1, 2, 3, 4), B = c(11, 12, 13, 14)) system.time({ for (i in 50:1000) { b <- data.frame(A = i, B = i * i) a <- bind_rows(a, b) } }) Output
user system elapsed 0.25 0.00 0.25 In contrast with using rbind function
a <- data.frame(A = c(1, 2, 3, 4), B = c(11, 12, 13, 14)) system.time({ for (i in 50:1000) { b <- data.frame(A = i, B = i * i) a <- rbind(a, b) } }) Output
user system elapsed 0.49 0.00 0.49 There is some performance gain.
To insert a row after row 10 in a dataframe we can use use:
library(berryFunctions) newrow = c(1:4) df <- insertRows(df, 10, new = newrow) Data frames are actually lists of vectors. Hence we could deploy base::append—that adds elements to a vector—in Map, which is pretty fast. Here a raw version:
appendRow <- \(data, x, before) { as.data.frame(Map(\(v, y, ...) append(v, y, before - 1L), data, x)) } > appendRow(data=mtcars, x=rep_len(-9, ncol(mtcars)), before=3) |> head() mpg cyl disp hp drat wt qsec vs am gear carb 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 3 -9.0 -9 -9 -9 -9.00 -9.000 -9.00 -9 -9 -9 -9 4 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 5 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 6 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Checked for equivalence, using Rscript --vanilla.
Based on 10M rows of mtcars:
Unit: milliseconds expr min lq mean median uq max neval cld appendRow 384.0279 388.8217 715.8154 667.6382 893.8432 1324.993 10 a insertRow2 1930.2843 2163.2117 2215.9483 2217.6469 2224.6168 2540.156 10 b dplyr::add_row 3320.5795 3326.2406 3948.6887 3646.0097 4489.3242 5014.286 10 c Based on 10 to 10M rows of mtcars:
existingDF = rbind(existingDF[1:10,],newrow,existingDF[-(1:10),])newdataframe[nrow(newdataframe)+1,] <- existingdataframe[i,]