CODEWITHSUNDEEP
rdplyrcorrelation
Ladislas Nalborczyk
Ladislas Nalborczyk

Reputation: 755

Reshape long dataframe to correlation matrix in R

I have a table with three columns containing my first variable, my second variable, and their estimated correlation (estimates coming from a brmsfit model).

As an example, let's consider the following table:

tibble::tribble(
     ~a,    ~b,                     ~est,
  "fro", "fcr",   "0.061 [-0.276, 0.38]",
  "fro", "nek",  "0.115 [-0.218, 0.421]",
  "nek", "fcr",  "0.154 [-0.169, 0.476]",
  "ooi", "fcr", "-0.022 [-0.352, 0.305]",
  "ooi", "fro",  "0.006 [-0.326, 0.323]",
  "ooi", "nek",  "0.173 [-0.151, 0.486]",
  "ooi", "zyg",   "0.039 [-0.29, 0.362]",
  "zyg", "fcr",  "-0.02 [-0.347, 0.317]",
  "zyg", "fro",  "0.092 [-0.245, 0.406]",
  "zyg", "nek",   "0.315 [0.011, 0.613]"
  )

I would like to reshape it in the form of a correlation matrix. I know some packages permits to do the exact inverse operation (e.g., the corrr::stretch() function), but I am not aware of an easy solution for reshaping a wide dataframe into a correlation matrix.

Any idea ?

Upvotes: 0

Views: 472

Answers (3)

Liang Zhang
Liang Zhang

Reputation: 819

There is an excellent way with the help of {igraph} package:

dat <- tibble::tribble(
  ~a,    ~b,                     ~est,
  "fro", "fcr",   "0.061 [-0.276, 0.38]",
  "fro", "nek",  "0.115 [-0.218, 0.421]",
  "nek", "fcr",  "0.154 [-0.169, 0.476]",
  "ooi", "fcr", "-0.022 [-0.352, 0.305]",
  "ooi", "fro",  "0.006 [-0.326, 0.323]",
  "ooi", "nek",  "0.173 [-0.151, 0.486]",
  "ooi", "zyg",   "0.039 [-0.29, 0.362]",
  "zyg", "fcr",  "-0.02 [-0.347, 0.317]",
  "zyg", "fro",  "0.092 [-0.245, 0.406]",
  "zyg", "nek",   "0.315 [0.011, 0.613]"
)
dat |> 
  igraph::graph_from_data_frame(directed = FALSE) |> 
  igraph::as_adjacency_matrix(attr = "est", sparse = FALSE)
#>     fro                     nek                     ooi                     
#> fro ""                      "0.115 [-0.218, 0.421]" "0.006 [-0.326, 0.323]" 
#> nek "0.115 [-0.218, 0.421]" ""                      "0.173 [-0.151, 0.486]" 
#> ooi "0.006 [-0.326, 0.323]" "0.173 [-0.151, 0.486]" ""                      
#> zyg "0.092 [-0.245, 0.406]" "0.315 [0.011, 0.613]"  "0.039 [-0.29, 0.362]"  
#> fcr "0.061 [-0.276, 0.38]"  "0.154 [-0.169, 0.476]" "-0.022 [-0.352, 0.305]"
#>     zyg                     fcr                     
#> fro "0.092 [-0.245, 0.406]" "0.061 [-0.276, 0.38]"  
#> nek "0.315 [0.011, 0.613]"  "0.154 [-0.169, 0.476]" 
#> ooi "0.039 [-0.29, 0.362]"  "-0.022 [-0.352, 0.305]"
#> zyg ""                      "-0.02 [-0.347, 0.317]" 
#> fcr "-0.02 [-0.347, 0.317]" ""

Created on 2023-10-19 with reprex v2.0.2

Upvotes: 0

Paweł Chabros
Paweł Chabros

Reputation: 2399

Is this what you are looking for?

library(tidyverse)
library(magrittr)

df %$%
  c(a, b) %>%
  crossing(a = ., b = .) %>%
  left_join(df) %>%
  mutate(est = if_else(a == b, '1', est)) %>%
  group_by(a) %>%
  mutate(rn = row_number() %>% rev()) %>%
  nest() %>%
  mutate(gn = row_number() %>% rev()) %>%
  unnest() %>%
  mutate(
    cond = gn > rn,
    a_temp = a,
    a = if_else(cond, b, a),
    b = if_else(cond, a_temp, b)
  ) %>%
  select(a, b, est) %>%
  filter(!is.na(est)) %>%
  spread(a, est)

Output:

  b     fcr   fro                  nek                   ooi                    zyg                  
  <chr> <chr> <chr>                <chr>                 <chr>                  <chr>                
1 fcr   1     0.061 [-0.276, 0.38] 0.154 [-0.169, 0.476] -0.022 [-0.352, 0.305] -0.02 [-0.347, 0.317]
2 fro   NA    1                    0.115 [-0.218, 0.421] 0.006 [-0.326, 0.323]  0.092 [-0.245, 0.406]
3 nek   NA    NA                   1                     0.173 [-0.151, 0.486]  0.315 [0.011, 0.613] 
4 ooi   NA    NA                   NA                    1                      0.039 [-0.29, 0.362] 
5 zyg   NA    NA                   NA                    NA                     1

Upvotes: 1

www
www

Reputation: 39154

A solution using tidyverse. dat2 is the final output with the first column as the variable name. The column header for other columns are also variable names.

library(tidyverse)

dat <- tibble::tribble(
  ~a,    ~b,                     ~est,
  "fro", "fcr",   "0.061 [-0.276, 0.38]",
  "fro", "nek",  "0.115 [-0.218, 0.421]",
  "nek", "fcr",  "0.154 [-0.169, 0.476]",
  "ooi", "fcr", "-0.022 [-0.352, 0.305]",
  "ooi", "fro",  "0.006 [-0.326, 0.323]",
  "ooi", "nek",  "0.173 [-0.151, 0.486]",
  "ooi", "zyg",   "0.039 [-0.29, 0.362]",
  "zyg", "fcr",  "-0.02 [-0.347, 0.317]",
  "zyg", "fro",  "0.092 [-0.245, 0.406]",
  "zyg", "nek",   "0.315 [0.011, 0.613]"
)

dat2 <- dat %>%
  separate(est, into = c("est", "range"), sep = 6, convert = TRUE) %>%
  select(-range) %>%
  spread(b, est)
dat2
# # A tibble: 4 x 5
#   a        fcr    fro    nek    zyg
#   <chr>  <dbl>  <dbl>  <dbl>  <dbl>
# 1 fro    0.061 NA      0.115 NA    
# 2 nek    0.154 NA     NA     NA    
# 3 ooi   -0.022  0.006  0.173  0.039
# 4 zyg   -0.02   0.092  0.315 NA

Upvotes: 0

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