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Create Data Frame With Histogram Values

Source: R/data_processing.R
create_histogram_df.Rd

Generates a data.frame of binned values, binning starts at the first stimulus, e.g. 0 ms, and ends at window_size.

Usage

create_histogram_df(
  df,
  time_of_stim,
  condition_names,
  bin_size = 1,
  window_size,
  first_bin_on_stim = FALSE,
  one_event_per_bin = FALSE
)

Arguments

df

Data Frame or Tibble with the following columns:

  • time_ms

  • sweep

  • condition

  • stimulus

  • event_index

time_of_stim

List, with event times in milliseconds.

condition_names

Character, vector with condition titles.

bin_size

Integer, histogram bin width size in milliseconds. Default is '1'.

window_size

Integer, in milliseconds the size of the window to use for the histogram, the start of the window will be at the first stimulus.

first_bin_on_stim

Logical, if TRUE the first bin center will occur on the first stimulus, i.e. 0. Default is FALSE.

one_event_per_bin

Logical, if TRUE only a single event will be counted for each bin. Default is FALSE.

Value

Tibble with the following columns:

  • condition - character

  • count_events - integer

  • count_sweeps - integer

  • h_counts - nested list of integers

  • h_density - nested list of doubles

  • h_bin_centers - nested list of doubles

  • probability - nested list of doubles

  • h_count_norm_max - nested list of doubles

  • h_prob_norm_max - nested list of doubles

Note

This only returns a tibble. Histogram related columns are nested, which facilitates aggregating the output into a tibble for future comparison across experiments. See code example using dplyr::select and tidyr::unnest for working with this functions output.

See also

Examples

library(magrittr, include.only = "%>%")

simple_df <- data.frame(
  time_ms = rep(c(417, 427), 10),
  sweep = rep(c(1:10), each = 2),
  condition = rep("control", 10),
  stimulus = rep(c(1, 2), 10),
  event_index = 1
)

df_output <-
  create_histogram_df(
    df = simple_df,
    time_of_stim = c(415.5, 425.5),
    condition_names = c("control", "drug1"),
    bin_size = 1,
    window_size = 20,
    first_bin_on_stim = FALSE,
    one_event_per_bin = FALSE
  )

df_output %>%
  dplyr::select(
    condition, h_counts, h_density, h_bin_centers, probability, h_count_norm_max, h_prob_norm_max
  ) %>%
  tidyr::unnest(c(
    h_counts, h_density, h_bin_centers, probability, h_count_norm_max, h_prob_norm_max
  ))
#> # A tibble: 20 × 7
#> # Groups:   condition [1]
#>    condition h_counts h_density h_bin_centers probability h_count_norm_max
#>    <chr>        <int>     <dbl>         <dbl>       <dbl>            <dbl>
#>  1 control          0       0             0.5           0                0
#>  2 control         10       0.5           1.5           1                1
#>  3 control          0       0             2.5           0                0
#>  4 control          0       0             3.5           0                0
#>  5 control          0       0             4.5           0                0
#>  6 control          0       0             5.5           0                0
#>  7 control          0       0             6.5           0                0
#>  8 control          0       0             7.5           0                0
#>  9 control          0       0             8.5           0                0
#> 10 control          0       0             9.5           0                0
#> 11 control          0       0            10.5           0                0
#> 12 control         10       0.5          11.5           1                1
#> 13 control          0       0            12.5           0                0
#> 14 control          0       0            13.5           0                0
#> 15 control          0       0            14.5           0                0
#> 16 control          0       0            15.5           0                0
#> 17 control          0       0            16.5           0                0
#> 18 control          0       0            17.5           0                0
#> 19 control          0       0            18.5           0                0
#> 20 control          0       0            19.5           0                0
#> # ℹ 1 more variable: h_prob_norm_max <dbl>