How can I add zeros or NAs to each species that does not have a corresponding date in R?

I have a database what looks like this (although it has more species and dates):

I want each date to include each species at a count of 0 if there is origninally no species listed for that date. When there is no species listed, it just means that the species was not detected and therefore it should be a count of zero.

So for this example, I would want it to look like:

So I want all of my species listed for each date.

I'm wondering if anyone knows a way to accomplish this in R. I honestly have no idea how to tackle this problem, so would appreciate any tips!

Mostly a classic problem of filling in a series with missing dates. Create data expanding all values across all dates, and left outer join to this data.

# All combinations
full <- expand.grid(Date = unique(df$Date), Species = unique(df$Species))
# Filled new table
merge(full, df, by = c('Date', 'Species'), all.x = TRUE)
#        Date Species Count
#1 2022/06/01    AMAV    50
#2 2022/06/01    SESA    NA
#3 2022/06/05    AMAV    20
#4 2022/06/05    SESA    NA
#5 2022/06/07    AMAV     8
#6 2022/06/07    SESA    10

Afterwards you can replace NA values with zeros' using the standard df$Count[is.na(df$Count)] <- 0 if that is preferred.

A tidyverse solution:

Here we first create a helper column x,
by pivoting we get the column in shape,
then replacing x by 0 and
with arrange bringing rows in position to group and slice:

library(dplyr)
library(tidyr)

df %>% 
  mutate(x = ifelse(Species=="AMAV", "SESA", "AMAV")) %>% 
  pivot_longer(c(Species, x),
               names_to="name", 
               values_to = "Species") %>% 
  mutate(Count = ifelse(name=="x", 0, Count), .keep="unused") %>% 
  arrange(Date, -Count) %>%
  group_by(Date) %>% 
  slice(1:2) %>% 
  ungroup()

  Date       Count Species
  <chr>      <dbl> <chr>  
1 2022/06/01    50 AMAV   
2 2022/06/01     0 SESA   
3 2022/06/05    20 AMAV   
4 2022/06/05     0 SESA   
5 2022/06/07    10 SESA   
6 2022/06/07     8 AMAV