# calculating column values based on other values

Hi everyone

I have this dataset

``````sector	    x 	         y
AA	 2,339,420,681,734 	99.8%
BB	 371,624,845,717 	99.0%
CC	 87,634,499,611 	99.6%
DD	 1,574,141,187,993 	100.0%
EE	 11,872,797,513,892 	99.2%
FF	 29,278,939,620 	99.3%
GG	 1,239,742,259,291 	99.7%
HH	 378,196,970,011 	99.2%
JJ	 1,112,863,452,542 	99.9%
KK	 11,094,449,154,530 	99.4%
LL	 132,484,420,106 	100.0%
MM	 414,378,771,482 	99.6%
NN	 407,184,157,752 	99.4%
OO	 504,904,229,961 	98.8%
PP	 5,073,887,827,567 	99.6%
QQ	 114,423,889,566 	100.0%
RR	 330,622,219,467 	100.0%
SS	 2,117,184,188,890 	99.0%
TT	 3,071,872,320,787 	99.8%
UU	 3,640,173,923,590 	99.7%
VV	 69,376,312,211 	100.0%
WW	 2,322,262,020,988 	99.8%
XX	 247,168,286,858 	99.9%
ZZ	 1,662,614,039,790 	100.0%
AAA	 349,885,446,127 	99.9%
BBB	 729,517,068,837 	98.9%
CCC	 93,011,710,959 	99.9%
``````

I need to have a new column (let say z) which are calculated from the multiplication of x and y (z ~ x . y). The condition is Σz > Σ(x . y), or Σz =52,143,500,004,971.10.

What should I do?

Sticking to `tidyverse` check out `rowwise` with `dplyr`. You could probably also do something like `dplyr::mutate(z = purrr::map2_*(x, y, \(x, y) ...))` -- where * would be the desired output type (int, double, etc.) -- if you don't want to go the `rowwise` route for some reason.

Thanks alot man.. I'll try to find out more about rowwise and purr functions..