Treat NA as zero only when adding a number
You can define your own function to act as you want
plus <- function(x) {
if(all(is.na(x))){
c(x[0],NA)} else {
sum(x,na.rm = TRUE)}
}
rbind(dt1, dt2)[,lapply(.SD, plus), by = Name]
R programming - treat NA in a file as 0
Something like this?
sum(arch1, na.rm = T)/length(arch1)
Set NA to 0 in R
You can just use the output of is.na
to replace directly with subsetting:
bothbeams.data[is.na(bothbeams.data)] <- 0
Or with a reproducible example:
dfr <- data.frame(x=c(1:3,NA),y=c(NA,4:6))
dfr[is.na(dfr)] <- 0
dfr
x y
1 1 0
2 2 4
3 3 5
4 0 6
However, be careful using this method on a data frame containing factors that also have missing values:
> d <- data.frame(x = c(NA,2,3),y = c("a",NA,"c"))
> d[is.na(d)] <- 0
Warning message:
In `[<-.factor`(`*tmp*`, thisvar, value = 0) :
invalid factor level, NA generated
It "works":
> d
x y
1 0 a
2 2 <NA>
3 3 c
...but you likely will want to specifically alter only the numeric columns in this case, rather than the whole data frame. See, eg, the answer below using dplyr::mutate_if
.
Sum to 0 if vector includes NAs and 0?
The checks in this answer can be extended to
plus <- function(...) {
if(all(is.na(do.call(c, list(...))))){
NA} else {
sum(...,na.rm = TRUE)}
}
plus(c(NA, 3), 1:5)
# [1] 18
How to Sum NA (text NA) values in excel with numbers by treating NA as 1
By default COUNTIF
counts specific cells and returns a number. This gives you the ability to type in D1 (or D2)
:
=COUNTIF(A3:C3,"NA")+SUM(A3:C3)
Which counts the number of cells that contain "NA" and then adds the sum of the numbers.
You can then drag the formula down to apply the same logic to all rows.
R aggregation with sum function gives the value 0 when it sums NA
all you need is to remove the na.rm=T
agg2<- aggregate(y ~ x, data = df, sum, na.action=na.pass)
and this is the result :
> agg2
x y
1 a 66
2 b NA
3 c 98
How do I replace NA values with zeros in an R dataframe?
See my comment in @gsk3 answer. A simple example:
> m <- matrix(sample(c(NA, 1:10), 100, replace = TRUE), 10)
> d <- as.data.frame(m)
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
1 4 3 NA 3 7 6 6 10 6 5
2 9 8 9 5 10 NA 2 1 7 2
3 1 1 6 3 6 NA 1 4 1 6
4 NA 4 NA 7 10 2 NA 4 1 8
5 1 2 4 NA 2 6 2 6 7 4
6 NA 3 NA NA 10 2 1 10 8 4
7 4 4 9 10 9 8 9 4 10 NA
8 5 8 3 2 1 4 5 9 4 7
9 3 9 10 1 9 9 10 5 3 3
10 4 2 2 5 NA 9 7 2 5 5
> d[is.na(d)] <- 0
> d
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
1 4 3 0 3 7 6 6 10 6 5
2 9 8 9 5 10 0 2 1 7 2
3 1 1 6 3 6 0 1 4 1 6
4 0 4 0 7 10 2 0 4 1 8
5 1 2 4 0 2 6 2 6 7 4
6 0 3 0 0 10 2 1 10 8 4
7 4 4 9 10 9 8 9 4 10 0
8 5 8 3 2 1 4 5 9 4 7
9 3 9 10 1 9 9 10 5 3 3
10 4 2 2 5 0 9 7 2 5 5
There's no need to apply apply
. =)
EDIT
You should also take a look at norm
package. It has a lot of nice features for missing data analysis. =)
Sum of two Columns of Data Frame with NA Values
dat$e <- rowSums(dat[,c("b", "c")], na.rm=TRUE)
dat
# a b c d e
# 1 1 2 3 4 5
# 2 5 NA 7 8 7
Replace all 0 values to NA
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.
Related Topics
How to Ddply() Without Sorting
Insert Images Using Knitr::Include_Graphics in a for Loop
Combine Lists While Overriding Values with Same Name in R
If_Else() 'False' Must Be Type Double, Not Integer - in R
R: Adding Alpha Bags to a 2D or 3D Scatterplot
Reshape Data from Long to Wide, with Time in New Wide Variable Name
Subset Data Based on Partial Match of Column Names
Access Data Frame Column Using Variable
R - How to Add Row Index to a Data Frame, Based on Combination of Factors
How to Select All Unique Combinations of Two Columns in an R Data Frame
Ellipse Containing Percentage of Given Points in R
Align Axis Label on the Right with Ggplot2
Error in Unserialize(Socklist[[N]]):Error Reading from Connection on Unix
How to Increase the Space Between Grouped Bars in Ggplot2
Clustered Standard Errors in R Using Plm (With Fixed Effects)
Offline Installation of R Packages