Replace all 0 values to NA

by

Replacing all zeroes to NA:

df[df == 0] <- NA

Explanation

1. It is not NULL what you should want to replace zeroes with. As it says in ?'NULL',

NULL represents the null object in R

which is unique and, I guess, can be seen as the most uninformative and empty object.1 Then it becomes not so surprising that

data.frame(x = c(1, NULL, 2))
#   x
# 1 1
# 2 2

That is, R does not reserve any space for this null object.2 Meanwhile, looking at ?'NA' we see that

NA is a logical constant of length 1 which contains a missing value
indicator. NA can be coerced to any other vector type except raw.

Importantly, NA is of length 1 so that R reserves some space for it. E.g.,

data.frame(x = c(1, NA, 2))
#    x
# 1  1
# 2 NA
# 3  2

Also, the data frame structure requires all the columns to have the same number of elements so that there can be no “holes” (i.e., NULL values).

Now you could replace zeroes by NULL in a data frame in the sense of completely removing all the rows containing at least one zero. When using, e.g., var, cov, or cor, that is actually equivalent to first replacing zeroes with NA and setting the value of use as "complete.obs". Typically, however, this is unsatisfactory as it leads to extra information loss.

2. Instead of running some sort of loop, in the solution I use df == 0 vectorization. df == 0 returns (try it) a matrix of the same size as df, with the entries TRUE and FALSE. Further, we are also allowed to pass this matrix to the subsetting [...] (see ?'['). Lastly, while the result of df[df == 0] is perfectly intuitive, it may seem strange that df[df == 0] <- NA gives the desired effect. The assignment operator <- is indeed not always so smart and does not work in this way with some other objects, but it does so with data frames; see ?'<-'.

1 The empty set in the set theory feels somehow related.
2 Another similarity with the set theory: the empty set is a subset of every set, but we do not reserve any space for it.

More Related Contents:

  1. Reshaping data.frame from wide to long format
  2. ggplot with 2 y axes on each side and different scales
  3. Split data frame string column into multiple columns
  4. How to import multiple .csv files at once?
  5. Quickly reading very large tables as dataframes
  6. Combine two data frames by rows (rbind) when they have different sets of columns
  7. Convert continuous numeric values to discrete categories defined by intervals
  8. Count number of rows per group and add result to original data frame
  9. Why does summarize or mutate not work with group_by when I load `plyr` after `dplyr`?
  10. Counting unique / distinct values by group in a data frame
  11. What are the differences between “=” and “
  12. Formatting Decimal places in R
  13. Determine path of the executing script
  14. Reshape multiple value columns to wide format
  15. How to generate permutations or combinations of object in R?
  16. Numeric comparison difficulty in R
  17. Scheduling R Script
  18. Remove duplicated rows
  19. Use of ~ (tilde) in R programming Language
  20. Combine two or more columns in a dataframe into a new column with a new name
  21. Conditional merge/replacement in R
  22. Subset rows in a data frame based on a vector of values
  23. How to plot a function curve in R
  24. Index values from a matrix using row, col indices
  25. What are the main differences between R data files?
  26. Get filename without extension in R
  27. ending “+” prompt in R
  28. R round to nearest .5 or .1
  29. Display exact value of a variable in R
  30. Is there anything wrong with using T & F instead of TRUE & FALSE?

Leave a Comment