R ""object '.' not found" error when replacing all NAs in a dataframe with zero

I'm trying to add a "total" column to my dataframe that sums the row values for specific columns, but first I need to change NAs to zero.

My data is a monthly file that has variables for every hour of every day in the month. The first 4 columns of the data are explanatory and won't be included in the total column; the full dataset has 44 variables:

library(tidyverse)

df <- structure(list(Flowday = structure(c(19417, 19417, 19417, 19417, 19417), class = "Date"),
               Interval = c("01:00", "02:00", "03:00", "04:00", "05:00"), 
               Interval_int = 1:5, Sequence = c(14, 14, 14, 14, 14), 
               DA_RC_AMT = c(18.3, 12.0, 5.6, 8.3, 11.5), 
               DA_ASSET_EN = c(20.4, 14.6, 6.6, 3.0, 15.9), 
               RT_MVP_DIST = c(NA_real_, NA_real_, NA_real_, NA_real_,   NA_real_),
               RT_RAA = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_)), 
          row.names = c(NA, -5L), class = c("tbl_df", "tbl",  "data.frame"))

Here's my code to replace NAs with zero:

df <- df  |>
  replace(is.na(.), 0)

which returns this error message:

Error in [<-.tbl_df(*tmp*, list, value = 0) : object '.' not found

Here's my code to create the total column, which works as expected when I exclude the columns with NA values:

df <- df |> 
  rowwise() |> 
  mutate(total = sum(c_across(5:8)))

How can I replace the NAs with 0 so that my total works?
Also, is it better to use the column index or column name in the total code?

Thanks for the help!
David

Try:

library(tidyverse)
library(dplyr)

df <- df %>%
  replace(is.na(.), 0) %>%
  rowwise(.) %>%
  mutate(total = sum(c_across(contains("DA_")), na.rm = TRUE))

I got the result as:

# A tibble: 5 × 9
# Rowwise: 
  Flowday    Interval Interval_int Sequence DA_RC_AMT DA_ASSET_EN RT_MVP_DIST RT_RAA total
  <date>     <chr>           <int>    <dbl>     <dbl>       <dbl>       <dbl>  <dbl> <dbl>
1 2023-03-01 01:00               1       14      18.3        20.4           0      0  38.7
2 2023-03-01 02:00               2       14      12          14.6           0      0  26.6
3 2023-03-01 03:00               3       14       5.6         6.6           0      0  12.2
4 2023-03-01 04:00               4       14       8.3         3             0      0  11.3
5 2023-03-01 05:00               5       14      11.5        15.9           0      0  27.4

Or:

df <- df %>%
  replace(is.na(.), 0) %>%
  rowwise(.) %>%
  mutate(total = sum(c_across(contains(c("DA_", "RT_"))), na.rm = TRUE))

which would return:

# A tibble: 5 × 9
# Rowwise: 
  Flowday    Interval Interval_int Sequence DA_RC_AMT DA_ASSET_EN RT_MVP_DIST RT_RAA total
  <date>     <chr>           <int>    <dbl>     <dbl>       <dbl>       <dbl>  <dbl> <dbl>
1 2023-03-01 01:00               1       14      18.3        20.4           0      0  38.7
2 2023-03-01 02:00               2       14      12          14.6           0      0  26.6
3 2023-03-01 03:00               3       14       5.6         6.6           0      0  12.2
4 2023-03-01 04:00               4       14       8.3         3             0      0  11.3
5 2023-03-01 05:00               5       14      11.5        15.9           0      0  27.4

We don't need to replace NA with 0 as there is already na.rm argument in either sum or the vectorized rowSums

library(dplyr) #version >= 1.1.0
df %>%
  mutate(total = rowSums(pick(5:8), na.rm = TRUE))

-output

# A tibble: 5 × 9
  Flowday    Interval Interval_int Sequence DA_RC_AMT DA_ASSET_EN RT_MVP_DIST RT_RAA total
  <date>     <chr>           <int>    <dbl>     <dbl>       <dbl>       <dbl>  <dbl> <dbl>
1 2023-03-01 01:00               1       14      18.3        20.4          NA     NA  38.7
2 2023-03-01 02:00               2       14      12          14.6          NA     NA  26.6
3 2023-03-01 03:00               3       14       5.6         6.6          NA     NA  12.2
4 2023-03-01 04:00               4       14       8.3         3            NA     NA  11.3
5 2023-03-01 05:00               5       14      11.5        15.9          NA     NA  27.4