Summarizes random walk data by computing statistical measures.
Usage
summarize_walks(.data, .value, .group_var = NULL)
summarise_walks(.data, .value, .group_var = NULL)Value
A tibble containing the summarized statistics for each group, including mean, median, range, quantiles, variance, standard deviation, and more.
Details
This function requires that the input data frame contains a
column named 'walk_number' and that the value to summarize is provided.
It computes statistics such as mean, median, variance, and quantiles
for the specified value variable. Omit .group_var or set it to NULL
for an overall summary, ignoring any existing grouping on the input.
Supply a grouping column to summarize only by that column.
Dimension metadata is read from dimensions, falling back to the legacy
dimension attribute. If neither is present, dimensions is NA_integer_.
Examples
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# Example data frame
walk_data <- random_normal_walk(.initial_value = 100)
# Summarize by walk
summarize_walks(walk_data, cum_sum_y, walk_number) |>
glimpse()
#> Rows: 25
#> Columns: 18
#> $ walk_number <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ fns <chr> "random_normal_walk", "random_normal_walk", "random_nor…
#> $ fns_name <chr> "Random Normal Walk", "Random Normal Walk", "Random Nor…
#> $ dimensions <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ obs <int> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, …
#> $ mean_val <dbl> 100.28223, 100.07428, 99.60103, 100.92237, 100.52388, 9…
#> $ median <dbl> 100.38746, 100.03188, 99.46622, 100.95986, 100.59073, 9…
#> $ range <dbl> 1.4112366, 0.7933972, 1.8000254, 1.6746136, 1.2704141, …
#> $ quantile_lo <dbl> 99.56322, 99.78383, 99.01963, 100.08731, 99.92631, 97.7…
#> $ quantile_hi <dbl> 100.81114, 100.49322, 100.52730, 101.64598, 101.01788, …
#> $ variance <dbl> 0.16513629, 0.03658384, 0.21120318, 0.17645077, 0.11650…
#> $ sd <dbl> 0.4089335, 0.1924758, 0.4624680, 0.4227107, 0.3434863, …
#> $ min_val <dbl> 99.51558, 99.76337, 98.85992, 100.04337, 99.82228, 97.7…
#> $ max_val <dbl> 100.92682, 100.55677, 100.65995, 101.71798, 101.09270, …
#> $ harmonic_mean <dbl> 100.28058, 100.07391, 99.59892, 100.92062, 100.52272, 9…
#> $ geometric_mean <dbl> 100.28141, 100.07409, 99.59997, 100.92150, 100.52330, 9…
#> $ skewness <dbl> -0.32916650, 0.69240662, 0.69532128, -0.22420951, -0.29…
#> $ kurtosis <dbl> -1.25959602, -0.17700121, -0.64815886, -0.73025927, -1.…
# Overall summary (omitted grouping and explicit NULL are equivalent)
summarize_walks(walk_data, y) |>
glimpse()
#> Warning: There was 1 warning in `dplyr::summarize()`.
#> ℹ In argument: `geometric_mean = exp(mean(log(y)))`.
#> Caused by warning in `log()`:
#> ! NaNs produced
#> Rows: 1
#> Columns: 17
#> $ fns <chr> "random_normal_walk"
#> $ fns_name <chr> "Random Normal Walk"
#> $ dimensions <dbl> 1
#> $ obs <int> 100
#> $ mean_val <dbl> -0.001307085
#> $ median <dbl> -0.003502369
#> $ range <dbl> 0.6536899
#> $ quantile_lo <dbl> -0.2063977
#> $ quantile_hi <dbl> 0.1945617
#> $ variance <dbl> 0.009762381
#> $ sd <dbl> 0.09882947
#> $ min_val <dbl> -0.3146528
#> $ max_val <dbl> 0.3390371
#> $ harmonic_mean <dbl> 0.06668465
#> $ geometric_mean <dbl> NaN
#> $ skewness <dbl> 0.0008945441
#> $ kurtosis <dbl> 0.2597754
summarize_walks(walk_data, y, .group_var = NULL) |>
glimpse()
#> Warning: There was 1 warning in `dplyr::summarize()`.
#> ℹ In argument: `geometric_mean = exp(mean(log(y)))`.
#> Caused by warning in `log()`:
#> ! NaNs produced
#> Rows: 1
#> Columns: 17
#> $ fns <chr> "random_normal_walk"
#> $ fns_name <chr> "Random Normal Walk"
#> $ dimensions <dbl> 1
#> $ obs <int> 100
#> $ mean_val <dbl> -0.001307085
#> $ median <dbl> -0.003502369
#> $ range <dbl> 0.6536899
#> $ quantile_lo <dbl> -0.2063977
#> $ quantile_hi <dbl> 0.1945617
#> $ variance <dbl> 0.009762381
#> $ sd <dbl> 0.09882947
#> $ min_val <dbl> -0.3146528
#> $ max_val <dbl> 0.3390371
#> $ harmonic_mean <dbl> 0.06668465
#> $ geometric_mean <dbl> NaN
#> $ skewness <dbl> 0.0008945441
#> $ kurtosis <dbl> 0.2597754
# Example with missing value variable
# summarize_walks(walk_data, NULL, group) # This will trigger an error.