Bdtyktl

I have a very large (too big to open in Excel) biological dataset that looks something like this

    year <- c(1990, 1980, 1985, 1980, 1990, 1990, 1980, 1985, 1985,1990, 

              1980, 1985, 1980, 1990, 1990, 1980, 1985, 1985,

              1990, 1980, 1985, 1980, 1990, 1990, 1980, 1985, 1985)

    species <- c('A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'A','A', 'A', 

                 'B', 'B', 'B', 'C', 'C', 'C', 'A', 'A', 'A', 'B', 'B', 'B', 

                 'C', 'C', 'C', 'A')

    region <- c(1, 1, 1, 3, 2, 3, 3, 2, 1, 1, 3, 3, 3, 2, 2, 1, 1, 1,1, 3, 3, 

                3, 2, 2, 1, 1, 1)

    df <- data.frame(year, species, region)



    df

    year species region

 1  1990       A      1

 2  1980       A      1

 3  1985       B      1

 4  1980       B      3

 5  1990       B      2

 6  1990       C      3

 7  1980       C      3

 8  1985       C      2

 9  1985       A      1

 10 1990       A      1

 11 1980       A      3

 12 1985       B      3

 13 1980       B      3

 14 1990       B      2

 15 1990       C      2

 16 1980       C      1

 17 1985       C      1

 18 1985       A      1

 19 1990       A      1

 20 1980       A      3

 21 1985       B      3

 22 1980       B      3

 23 1990       B      2

 24 1990       C      2

 25 1980       C      1

 26 1985       C      1

 27 1985       A      1

What I am looking to do is figure out how many of each species (A, B, or C) exist in each region (1, 2, or 3) in each of the three years I have (1980, 1985, or 1990).

I'm looking to end up with a dataset that looks something along the lines of this,

      region A_1980 B_1980 C_1980 A_1985 B_1985 C_1985 A_1990 B_1990 C_1990

 1      1      0      0      0      0      0      0      0      0      0

 2      2      1      1      1      1      1      1      1      1      1

 3      3      2      2      2      2      2      2      2      2      2

such that each row represents a region, and each column represents the count of each species, in a particular year. I've tried to do this using the spread function in conjunction with the group_by dplyr function, but I couldn't get it to do anything close to what I want.

Does anyone have any suggestions?

Something like this?

library(dplyr)



df2 <- df %>% 

  mutate(sp_year = paste(species, year, sep = "_")) %>%

  group_by(region) %>% 

  count(sp_year) %>% 

  spread(sp_year,n)



df2

Which gives this:

# A tibble: 3 x 10

# Groups:   region [3]

  region A_1980 A_1985 A_1990 B_1980 B_1985 B_1990 C_1980 C_1985 C_1990

   <dbl>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>

1      1      1      3      3     NA      1     NA      2      2     NA

2      2     NA     NA     NA     NA     NA      3     NA      1      2

3      3      2     NA     NA      3      2     NA      1     NA      1

Similar to wl1234's answer but more concise. We can use unite to combine columns. We can also use count without group_by the variable. Finally, we can set fill = 0 in the spread function to replace NA with 0.

library(tidyverse)



df2 <- df %>%

  unite(sp_year, species, year, sep = "_") %>%

  count(sp_year, region) %>%

  spread(sp_year, n, fill = 0)

df2

# # A tibble: 3 x 10

#   region A_1980 A_1985 A_1990 B_1980 B_1985 B_1990 C_1980 C_1985 C_1990

#    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>

# 1      1      1      3      3      0      1      0      2      2      0

# 2      2      0      0      0      0      0      3      0      1      2

# 3      3      2      0      0      3      2      0      1      0      1