Reputation: 1683
First of all, I couldn't find a question related with my issue, apologies if this question was already answered.
I have a dataframe with some columns and I want to calculate a new value using a specific ecuation. I guess I have to use mutate()
from tidyverse, but I want to avoid rows/samples where there're one or more 0 values. I don't know how I can check if there's any 0 when I'm using mutate()
. Also, I don't know how I can apply my specific formula to create the new column.
I leave here a code to create a dataframe as an example of my issue.
set.seed(123)
df <- data.frame(
time = seq(now(), now()+hours(11),by='hours'),
a = sample(0:100,12),
b = sample(0:100,12),
c = sample((0:20)/1000,12))
df[1:3,]$a <- 0
df[3:5,]$b <- 0
df[3:4,]$c <- 0
# function: M = a*b+(1-e^(-c/2))
# if any 0 in the row -> M = NA
# else: apply function
The function could be written as
a*b*(1-exp(-c/2))
The final df should have 4 colums per each hour (row) (a,b,c and the new calculated M), but when a | b | c == 0, M = NA
.
I will be very grateful for every little help. Cheers!
EDIT: The real function is more complex that this example, so it will not be always true that if one term (a,b,c,...) is 0, the resulting M is 0. Sorry, I didn't realised this postulate is true for the simplified equation. But I want to avoid any 0 value because they are from monitoring physiological variables and I know if one value is 0 in a sample, then the sample is wrong, so NA.
Upvotes: 2
Views: 160
Reputation: 389325
If any of a
, b
or c
is 0 it returns M
as 0 which can be changed to NA
.
library(dplyr)
df %>%
mutate(M = a*b*(1-exp(-c/2)),
M = na_if(M, 0))
# time a b c M
#1 2021-10-18 19:41:56 0 90 0.013 NA
#2 2021-10-18 20:41:56 0 56 0.016 NA
#3 2021-10-18 21:41:56 0 0 0.000 NA
#4 2021-10-18 22:41:56 13 0 0.000 NA
#5 2021-10-18 23:41:56 66 0 0.011 NA
#6 2021-10-19 00:41:56 41 71 0.014 20.305847
#7 2021-10-19 01:41:56 49 25 0.009 5.500115
#8 2021-10-19 02:41:56 42 6 0.012 1.507473
#9 2021-10-19 03:41:56 97 41 0.017 33.661237
#10 2021-10-19 04:41:56 24 97 0.008 9.293401
#11 2021-10-19 05:41:56 89 82 0.019 69.002718
#12 2021-10-19 06:41:56 68 35 0.015 17.783230
Upvotes: 1