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 -0.0669   
#>  2     1     5 -0.128    
#>  3     2    10  0.000244 
#>  4     3    15  0.0100   
#>  5     4    20 -0.131    
#>  6     5    25 -0.161    
#>  7     6    30  0.0575   
#>  8     7    35  0.0127   
#>  9     8    40  0.166    
#> 10     9    45 -0.0199   
#> 11    10    50 -0.0209   
#> 12    11    55 -0.0108   
#> 13    12    60  0.0113   
#> 14    13    65 -0.0000138
#> 15    14    70 -0.00843  
#> 16    15    75  0.129    
#> 17    16    80  0.0124   
#> 18    17    85  0.0706   
#> 19    18    90  0.0656   
#> 20    19    95  0.0229   

# 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 -0.100 
#>  2     2   45.5 -0.0723
#>  3     3   75.5 -0.232 
#>  4     4  106.  -0.379 
#>  5     5  136.  -0.243 
#>  6     6  166.  -0.0330
#>  7     7  196.  -0.174 
#>  8     8  226.  -0.187 
#>  9     9  256.   0.0321
#> 10    10  286.  -0.0391
#> # ℹ 24 more rows