Combines two data frames or matrices by rows even when they do
not share the same columns. Columns present in one input but absent in the
other are added and filled with NA before binding. Row names from
both inputs are preserved unless they would produce duplicates, in which
case default integer row names are used.
Value
A data frame containing all rows from df1 followed by all
rows from df2, with the union of both column sets. Cells where a
column did not exist in the original input are NA.
Examples
df1 <- generate_correlation_matrix(n = 10)
df2 <- generate_correlation_matrix(n = 10)
names(df2)[4] <- "X11"
rbind_all(df1 = df1, df2 = df2)
#> X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
#> 1 -1.2302 -1.44044 1.00694 1.1640 -0.117938 -0.54010 -0.12828 0.53458 2.083889 0.006756 NA
#> 2 1.5449 1.46931 0.59323 1.8123 0.002193 -0.75066 -1.49667 1.33014 -1.807998 -1.015929 NA
#> 3 1.6422 1.04344 0.76423 -0.8823 2.812118 -0.64477 -0.67503 0.90787 0.418324 -0.112648 NA
#> 4 -1.1403 -1.40126 -0.82031 -1.7621 -0.378538 0.13740 -1.87370 0.37844 0.471128 -0.219776 NA
#> 5 -1.6193 0.38372 0.08117 -0.8926 -0.264401 -0.41152 1.48348 -0.41415 -0.031112 -0.727691 NA
#> 6 -0.4876 0.25287 -0.26111 -0.5170 -1.054339 -0.42325 0.45420 -1.33966 1.523717 -0.125981 NA
#> 7 0.1999 1.36043 0.17759 0.9590 -1.468812 0.56001 2.55258 0.66378 0.504439 -1.463682 NA
#> 8 -0.9117 -0.13997 -0.49730 0.9871 0.759596 0.08916 1.44350 0.47399 -0.037169 -0.369100 NA
#> 9 0.5435 -0.44830 0.78827 0.3708 1.386392 0.97267 1.07456 -0.14076 -0.168658 0.256216 NA
#> 10 -1.4361 -0.76972 0.04076 0.8646 0.718422 0.06174 -1.74121 0.48963 -0.009437 -0.173754 NA
#> 11 0.7109 1.33234 0.04390 NA 1.594708 0.07943 0.26931 0.70335 -0.893242 -0.451990 -0.3346
#> 12 -0.6346 -0.76191 0.44462 NA 0.017036 0.90498 -0.13189 -0.45032 -1.096836 0.246160 -2.1658
#> 13 -1.5139 0.01207 -0.99944 NA 0.549158 0.30899 0.08178 -0.67261 0.897815 0.738328 1.3649
#> 14 -0.4283 -0.75317 -0.29605 NA -1.055438 0.01891 -2.25635 -0.89035 -1.064214 0.094039 0.2717
#> 15 0.5223 0.06197 -0.38339 NA -0.007801 0.13711 1.27275 0.51160 -0.126197 1.805744 1.2011
#> 16 -0.9181 -0.17917 1.20849 NA 2.269799 -0.01723 -0.29134 -1.13833 2.369882 -0.472317 1.7183
#> 17 0.3803 0.64014 1.10313 NA -1.939014 0.97570 0.58624 0.53118 -0.538031 1.094108 0.6459
#> 18 1.4240 0.62470 0.53229 NA 0.678750 0.66930 -1.15561 1.75538 0.816988 0.805324 1.0644
#> 19 2.0767 -0.09844 1.12352 NA 0.204884 0.15273 -0.87619 0.74374 -2.278520 0.861011 0.7846
#> 20 0.2929 -0.05698 -1.43152 NA 0.859434 -0.46135 0.21240 -0.08201 0.353768 -0.978242 -1.1692
row.names(df1) <- 21:30
rbind_all(df1 = df1, df2 = df2)
#> X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
#> 21 -1.2302 -1.44044 1.00694 1.1640 -0.117938 -0.54010 -0.12828 0.53458 2.083889 0.006756 NA
#> 22 1.5449 1.46931 0.59323 1.8123 0.002193 -0.75066 -1.49667 1.33014 -1.807998 -1.015929 NA
#> 23 1.6422 1.04344 0.76423 -0.8823 2.812118 -0.64477 -0.67503 0.90787 0.418324 -0.112648 NA
#> 24 -1.1403 -1.40126 -0.82031 -1.7621 -0.378538 0.13740 -1.87370 0.37844 0.471128 -0.219776 NA
#> 25 -1.6193 0.38372 0.08117 -0.8926 -0.264401 -0.41152 1.48348 -0.41415 -0.031112 -0.727691 NA
#> 26 -0.4876 0.25287 -0.26111 -0.5170 -1.054339 -0.42325 0.45420 -1.33966 1.523717 -0.125981 NA
#> 27 0.1999 1.36043 0.17759 0.9590 -1.468812 0.56001 2.55258 0.66378 0.504439 -1.463682 NA
#> 28 -0.9117 -0.13997 -0.49730 0.9871 0.759596 0.08916 1.44350 0.47399 -0.037169 -0.369100 NA
#> 29 0.5435 -0.44830 0.78827 0.3708 1.386392 0.97267 1.07456 -0.14076 -0.168658 0.256216 NA
#> 30 -1.4361 -0.76972 0.04076 0.8646 0.718422 0.06174 -1.74121 0.48963 -0.009437 -0.173754 NA
#> 1 0.7109 1.33234 0.04390 NA 1.594708 0.07943 0.26931 0.70335 -0.893242 -0.451990 -0.3346
#> 2 -0.6346 -0.76191 0.44462 NA 0.017036 0.90498 -0.13189 -0.45032 -1.096836 0.246160 -2.1658
#> 3 -1.5139 0.01207 -0.99944 NA 0.549158 0.30899 0.08178 -0.67261 0.897815 0.738328 1.3649
#> 4 -0.4283 -0.75317 -0.29605 NA -1.055438 0.01891 -2.25635 -0.89035 -1.064214 0.094039 0.2717
#> 5 0.5223 0.06197 -0.38339 NA -0.007801 0.13711 1.27275 0.51160 -0.126197 1.805744 1.2011
#> 6 -0.9181 -0.17917 1.20849 NA 2.269799 -0.01723 -0.29134 -1.13833 2.369882 -0.472317 1.7183
#> 7 0.3803 0.64014 1.10313 NA -1.939014 0.97570 0.58624 0.53118 -0.538031 1.094108 0.6459
#> 8 1.4240 0.62470 0.53229 NA 0.678750 0.66930 -1.15561 1.75538 0.816988 0.805324 1.0644
#> 9 2.0767 -0.09844 1.12352 NA 0.204884 0.15273 -0.87619 0.74374 -2.278520 0.861011 0.7846
#> 10 0.2929 -0.05698 -1.43152 NA 0.859434 -0.46135 0.21240 -0.08201 0.353768 -0.978242 -1.1692