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:

1. Checks if the input is a data frame or tibble; otherwise, it raises an error.

2. Checks if the data comes from a tidy_ distribution function; otherwise, it raises an error.

3. Converts the data into a time series format, grouping it by "sim_number" and transforming the "y" column into a time series.

4. 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.

See also

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  1.30 
#> 2 -0.739
#> 3  1.58 
#> 4 -2.11 
#> 5 -2.43 
#> 6 -0.151

# 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  1.02  
#> 2 -0.0722
#> 3  1.73  
#> 4  0.0478
#> 5 -0.713 
#> 6  0.470 

# Example 3: Convert data to time series format and return the time series data
x <- tidy_normal()
result <- convert_to_ts(x)
head(result)
#> Time Series:
#> Start = 1 
#> End = 6 
#> Frequency = 1 
#>               y
#> [1,] -0.4729851
#> [2,] -1.5943602
#> [3,] -2.0650183
#> [4,]  0.2766477
#> [5,]  0.1118095
#> [6,]  1.0223913