How to pivot data and create a list

I have a dataset (dummy data set below). I need to pivot it wider but also create a list with each level being by 'Block'

Block<-c("a1","a1","a1","a1","a1","b1","b1","b1","b1") #2 block
Species<-c("c","c","c","d","d","c","e","c","e")
date<-c("2021-03-29","2021-03-30","2021-03-28","2021-03-30","2021-03-29",
"2021-03-29","2021-03-29","2021-03-28","2021-03-28")
df<-data.frame(Block, Species, date)

Below is the output I am trying to obtain. I know how to do this if I pull each block out individually and then pivot wider and then combine each into a list. However, in reality I have a much larger dataset with more dates, species, and blocks so I don't really want to do this by hand. Is there a more efficient way to accomplish this? Thank you very much.

$a1
     2021-03-28 2021-03-29 2021-03-30 
c    1              1          1   
d    0              1          1   

$b1
     2021-03-28 2021-03-29 
c    1              1             
e    1              1

Solution

Assuming the numbers are counts of the Species per date, using lapply and data.frame to get the desired format with row names from Species

library(dplyr)
library(tidyr)

summarize(df, uID = length(Species), .by = c(Species, date, Block)) %>% 
  arrange(date) %>% 
  pivot_wider(names_from = date, values_from = uID, values_fill = 0) %>% 
  split(., ~ Block) %>% 
  lapply(., \(x) 
    data.frame(x[!grepl("Block|Species", colnames(x))], 
      row.names = x$Species, check.names = F))

output

$a1
  2021-03-28 2021-03-29 2021-03-30
c          1          1          2
d          0          1          1

$b1
  2021-03-28 2021-03-29 2021-03-30
c          1          1          0
e          1          1          0

or (depending on how many Blocks are created maybe less performant because pivot_wider has to be applied Block times)

library(dplyr)
library(tidyr)

split(df, ~ Block) %>% 
  lapply(., \(x) 
    arrange(x, date) %>% 
    pivot_wider(names_from = date, values_from = Block, values_fn = ~ length(.x), values_fill = 0) %>% 
    data.frame(., row.names = .$Species, check.names = F) %>%
    select(-Species))

output

$a1
  2021-03-28 2021-03-29 2021-03-30
c          1          1          2
d          0          1          1

$b1
  2021-03-28 2021-03-29
c          1          1
e          1          1

Data

(slightly modified to get counts > 1)

df <- structure(list(Block = c("a1", "a1", "a1", "a1", "a1", "a1", 
"b1", "b1", "b1", "b1"), Species = c("c", "c", "c", "c", "d", 
"d", "c", "e", "c", "e"), date = c("2021-03-29", "2021-03-30", 
"2021-03-30", "2021-03-28", "2021-03-30", "2021-03-29", "2021-03-29", 
"2021-03-29", "2021-03-28", "2021-03-28")), row.names = c("1", 
"2", "21", "3", "4", "5", "6", "7", "8", "9"), class = "data.frame")