Group every N observations together

Sometimes your sampling rate is too high; group_every allows you to down-sample by creating "bins" which can subsequently be summarised on. When using n, data needs to be regularly sampled; if there are gaps in time, the bin duration will differ. Works well with calculate_summary() for movement data.

Usage

group_every(data, seconds = NULL, n = NULL)

Arguments

data

Input data frame

seconds

Number of seconds to bin together

n

Number of observations to include in each bin/group

Value

Grouped data frame, with new "bin" variable.

Examples

## Group by every 5 seconds
df_time <- data.frame(
  time = seq(from = 0.02, to = 100, by = 1/30), # time at 30Hz, slightly offset
  y = rnorm(3000)) # random numbers

df_time |>
  group_every(seconds = 5) |> # group for every 5 seconds
  dplyr::summarise(time = min(time), # summarise for time and y
                   mean_y = mean(y)) |>
  dplyr::mutate(time = floor(time)) # floor to get the round second number
#> # A tibble: 20 × 3
#>      bin  time   mean_y
#>    <dbl> <dbl>    <dbl>
#>  1     0     0  0.0554 
#>  2     1     5  0.0484 
#>  3     2    10  0.0538 
#>  4     3    15 -0.0803 
#>  5     4    20 -0.126  
#>  6     5    25 -0.0140 
#>  7     6    30  0.0443 
#>  8     7    35 -0.189  
#>  9     8    40 -0.0825 
#> 10     9    45  0.120  
#> 11    10    50  0.0276 
#> 12    11    55  0.124  
#> 13    12    60 -0.0545 
#> 14    13    65  0.0156 
#> 15    14    70 -0.0550 
#> 16    15    75  0.0348 
#> 17    16    80  0.0331 
#> 18    17    85  0.0543 
#> 19    18    90  0.00880
#> 20    19    95  0.158  

# Group every n observations
df <- data.frame(
  x = seq(1:1000),
  y = rnorm(1000))

df |>
  group_every(n = 30) |> # group every 30 observations together
  dplyr::summarise(mean_x = mean(x),
                   mean_y = mean(y))
#> # A tibble: 34 × 3
#>      bin mean_x   mean_y
#>    <dbl>  <dbl>    <dbl>
#>  1     1   15.5  0.0405 
#>  2     2   45.5 -0.226  
#>  3     3   75.5 -0.0563 
#>  4     4  106.   0.189  
#>  5     5  136.  -0.0854 
#>  6     6  166.   0.0903 
#>  7     7  196.   0.0856 
#>  8     8  226.  -0.136  
#>  9     9  256.  -0.0732 
#> 10    10  286.   0.00451
#> # ℹ 24 more rows