Convert Data to Time Series Format

This function converts data in a data frame or tibble into a time series format. It is designed to work with data generated from tidy_ distribution functions. The function can return time series data, pivot it into long format, or both.

Usage

convert_to_ts(.data, .return_ts = TRUE, .pivot_longer = FALSE)

Arguments

.data: A data frame or tibble to be converted into a time series format.
.return_ts: A logical value indicating whether to return the time series data. Default is TRUE.
.pivot_longer: A logical value indicating whether to pivot the data into long format. Default is FALSE.

Value

The function returns the processed data based on the chosen options:

If ret_ts is set to TRUE, it returns time series data.
If pivot_longer is set to TRUE, it returns the data in long format.
If both options are set to FALSE, it returns the data as a tibble.

Details

The function takes a data frame or tibble as input and processes it based on the specified options. It performs the following actions:

Checks if the input is a data frame or tibble; otherwise, it raises an error.
Checks if the data comes from a tidy_ distribution function; otherwise, it raises an error.
Converts the data into a time series format, grouping it by "sim_number" and transforming the "y" column into a time series.
Returns the result based on the chosen options:
- If ret_ts is set to TRUE, it returns the time series data.
- If pivot_longer is set to TRUE, it pivots the data into long format.
- If both options are set to FALSE, it returns the data as a tibble.

Other Utility: check_duplicate_rows(), quantile_normalize(), tidy_mcmc_sampling(), util_beta_aic(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_f_aic(), util_gamma_aic(), util_generalized_beta_aic(), util_generalized_pareto_aic(), util_geometric_aic(), util_hypergeometric_aic(), util_inverse_burr_aic(), util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), util_normal_aic(), util_paralogistic_aic(), util_pareto1_aic(), util_pareto_aic(), util_poisson_aic(), util_t_aic(), util_triangular_aic(), util_uniform_aic(), util_weibull_aic(), util_zero_truncated_binomial_aic(), util_zero_truncated_geometric_aic(), util_zero_truncated_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Author

Steven P. Sanderson II, MPH

Examples

# Example 1: Convert data to time series format without returning time series data
x <- tidy_normal()
result <- convert_to_ts(x, FALSE)
head(result)
#> # A tibble: 6 × 1
#>        y
#>    <dbl>
#> 1  1.99 
#> 2  0.416
#> 3 -0.362
#> 4 -0.282
#> 5  0.404
#> 6 -0.694

# Example 2: Convert data to time series format and pivot it into long format
x <- tidy_normal()
result <- convert_to_ts(x, FALSE, TRUE)
head(result)
#> # A tibble: 6 × 1
#>        y
#>    <dbl>
#> 1 -0.912
#> 2 -0.732
#> 3 -0.582
#> 4  0.204
#> 5 -0.661
#> 6 -2.18 

# Example 3: Convert data to time series format and return the time series data
x <- tidy_normal()
result <- convert_to_ts(x)
head(result)
#>               y
#> [1,] -0.1348973
#> [2,]  0.6769697
#> [3,] -0.5048327
#> [4,] -0.8381438
#> [5,] -2.9578102
#> [6,]  1.1051425