Replace -inf, NaN and NA values with zero in a dataset in R

Question

I am trying to run some trading strategies in R. I have downloaded some stock prices and calculated returns. The new return dataset has a number of -inf, NaN, and NA values. I am reproducing a row of the dataset (log_ret). Its a zoo dataset.

library(zoo)
log_ret <- structure(
  c(0.234,-0.012,-Inf,NaN,0.454,Inf), .Dim = c(1L, 6L), 
  .Dimnames = list(NULL, c("x", "y", "z", "s", "p", "t")),
  index = structure(12784, class = "Date"),
  class = "zoo"
)

               x      y    z   s     p   t
2005-01-01 0.234 -0.012 -Inf NaN 0.454 Inf

How can I replace these unwanted values with 0?

Answer 1

Inf, NA and NaN are matched by !is.finite, for example

a <- c(1, Inf, NA, NaN)
a[!is.finite(a)] <- 0
# a is now [1, 0, 0, 0]

I don't know too much about manipulating zoo objects, but for the example above

log_ret[1, !is.finite(log_ret)] <- 0

works. In your actual data you will have to loop over all rows. There might be a zoo-specific way of doing this.

Edit: The zoo-specific way is log_ret[which(!is.finite(log_ret))] <- 0.

Answer 2

Another way to do it is (where df=your dataframe):

is.na(df)<-sapply(df, is.infinite)
df[is.na(df)]<-0

I don't know if this works for zoo objects, but it gets around the problem of is.infinite() only working on vectors.

Answer 3

Using mutate_all in dplyr:

library(dplyr)
fortify.zoo(log_ret) %>% mutate_all(function(x) ifelse(is.infinite(x), 0, x))  

Answer 4

Since the lifecycle for mutate_all has been superseded by the use of across:

library(dplyr)
fortify.zoo(log_ret) %>% mutate(across(.cols = everything(), ~ ifelse(is.infinite(.x), 0, .x)))