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I need some help to find a baseR way of adding a single empty row into a dataframe after every 'x' number of rows. does anybody know how I could do this? any help would be most appreciated.. Thanks.

eg if x = 64, add an empty row after every 64 rows in the data frame

Ash_23S
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2 Answers2

5
#DATA
df1 = mtcars
row.names(df1) = NULL

#Number of empty rows to insert
N = 3

#Every N rows after which empty rows should be inserted
after_rows = 4

do.call(rbind, lapply(split(df1, ceiling(1:NROW(df1)/after_rows)),
                      function(a) rbind(a, replace(a[1:N,], TRUE, ""))))
d.b
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  • Oops, saw you delete yours so I added my answer. Only real difference is `NA` vs `""` and your flexibility in number of rows to insert. – Gregor Thomas Oct 18 '17 at 15:54
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    @Gregor, I just realized `NA` might be better because it doesn't change class – d.b Oct 18 '17 at 15:56
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It mangles the row names a little bit, but hopefully that doesn't matter. It will always put a row on the end - if you need to modify so that it only adds a row to the end if the last group is of size n, I'll leave that to you. (I would recommend testing if needed, and if so head(result, -1).)

n = 5
x = split(mtcars, f = seq(nrow(mtcars)) %/% n)
do.call(rbind, lapply(x, rbind, NA))
#                        mpg cyl  disp  hp drat    wt  qsec vs am gear carb
# 0.Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
# 0.Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
# 0.Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
# 0.Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
# 0.5                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 1.Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
# 1.Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
# 1.Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
# 1.Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
# 1.Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
# 1.6                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 2.Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
# 2.Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
# 2.Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
# 2.Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
# 2.Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
# 2.6                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 3.Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
# 3.Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
# 3.Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
# 3.Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
# 3.Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
# 3.6                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 4.Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
# 4.Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
# 4.Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
# 4.AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
# 4.Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
# 4.6                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 5.Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
# 5.Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
# 5.Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
# 5.Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
# 5.Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
# 5.6                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
# 6.Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
# 6.Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
# 6.Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
# 6.4                     NA  NA    NA  NA   NA    NA    NA NA NA   NA   NA
Gregor Thomas
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