Calculating what proportion of a variable is in a given discrete state on a given date in an R dataf-CodePudding

I have a dataframe of time series data which is tracking the status of several species as discrete states. I want to calculate what proportion of each species is in each state for each day in the time series.

So my data is something like this:

example.df <- data.frame(date = rep(seq(ymd("2022-3-6"), ymd("2022-3-8"), by = "days"),
                          each = 8),
               species = rep(c("a", "b"), each = 4),
               state = sample(0:2, replace = TRUE, size = 24))

print(example.df)

             date species state
1  2022-03-06       a     0
2  2022-03-06       a     2
3  2022-03-06       a     1
4  2022-03-06       a     0
5  2022-03-06       b     2
6  2022-03-06       b     1
7  2022-03-06       b     0
8  2022-03-06       b     1
9  2022-03-07       a     0
10 2022-03-07       a     1
11 2022-03-07       a     0
12 2022-03-07       a     0
13 2022-03-07       b     1
14 2022-03-07       b     1
15 2022-03-07       b     1
16 2022-03-07       b     2
17 2022-03-08       a     1
18 2022-03-08       a     0
19 2022-03-08       a     1
20 2022-03-08       a     1
21 2022-03-08       b     1
22 2022-03-08       b     2
23 2022-03-08       b     2
24 2022-03-08       b     2

I want to produce something like this:

            date species proportion.0 proportion.1 proportion.2
1 2022-03-06       a         0.25         0.25         0.50
2 2022-03-06       b         0.25         0.50         0.25
3 2022-03-07       a         0.25         0.50         0.25
4 2022-03-07       b         0.25         0.50         0.25
5 2022-03-08       a         0.00         1.00         0.00
6 2022-03-08       b         0.00         0.75         0.25

I've been trying to do this in dplyr using group_by() and summarise() but I cannot figure out a generic term to get the proportion for every day/ species combination. Any help would be greatly appreciated, staying inside the tidyverse if possible.

CodePudding user response：

You Could do something like this:

library(tidyverse)
library(lubridate)

example.df |>
  count(date, species, state) |>
  group_by(date, species) |>
  mutate(f = n/sum(n),
         state = paste0("proportion.", state)) |>
  select(-n) |>
  ungroup()|>
  pivot_wider(names_from = state, values_from = f, values_fill = 0)
#> # A tibble: 6 x 5
#>   date       species proportion.0 proportion.1 proportion.2
#>   <date>     <chr>          <dbl>        <dbl>        <dbl>
#> 1 2022-03-06 a               0.25         0.5          0.25
#> 2 2022-03-06 b               0.25         0.5          0.25
#> 3 2022-03-07 a               0.75         0            0.25
#> 4 2022-03-07 b               0.5          0.25         0.25
#> 5 2022-03-08 a               0.5          0            0.5 
#> 6 2022-03-08 b               0.5          0            0.5

First we count the number of species in each state in each day. Then we calculate the proportion of each state (i.e., f) for each date and species. Lastly, we pivot_wider to go from long to wide format.

EDIT:

I thought of another option:

example.df |>
  mutate(v = 1) |>
  pivot_wider(names_prefix = "proportion.", 
              names_from = state, 
              values_from = v,
              values_fn = list(v = sum),
              values_fill = 0) |>
  (\(d) mutate(d, across(contains("proportion."), \(x) x/rowSums(d[,3:5]))))()
#> # A tibble: 6 x 5
#>   date       species proportion.1 proportion.0 proportion.2
#>   <date>     <chr>          <dbl>        <dbl>        <dbl>
#> 1 2022-03-06 a               0.75         0.25         0   
#> 2 2022-03-06 b               0.25         0.25         0.5 
#> 3 2022-03-07 a               0.25         0.25         0.5 
#> 4 2022-03-07 b               0.5          0.5          0   
#> 5 2022-03-08 a               0.25         0.5          0.25
#> 6 2022-03-08 b               0            0            1

Here we set up a placeholder column called v, then we pivot wider and calculate the number of states per species per date. Then we calculate the proportions using rowSums.