wide_summary: Wide-formatted summary data
In jcoliver/weathercommand: Summary Statistics for Weather Data

Description Usage Arguments Details Value See Also Examples

wide_summary converts long-formatted summary data to wide-format. Season-specific statistics are characterized by column names ending in "_YYYY", where "YYYY" is the four-digit year the season corresponds to. Long-term, season-independent statistics lack this suffix.

1	wide_summary(x, id_col, year_col = "season_year", long_term_cols = "")

`x`	summary data in pseudo-wide format
`id_col`	name of column with id value
`year_col`	name of column with year value
`long_term_cols`	vector of names of columns not to be transformed to separate values for each year

The function generally works from long-formatted data, within one of the summarize_* functions. When transforming to wide, season-specific statistics (e.g. mean total rainfall for a season) are represented in a separate column for each season, with column names appended with the year of the season in "_YYYY" format. See specific summarize_* functions (below) for more details about output. The long-formatted data may include statistics that are not specific to a particular season (e.g. the mean total seasonal rainfall across all seasons, calculated by summarize_rainfall). Even though the long-formatted data will represent those values in multiple rows (i.e. one row for each year/site combination), the values are identical and do not require separate columns in wide-formatted data. Thus each season-independent statistic is represented by a single column in the resultant wide-formatted data.

wide-formatted tibble, where summary statistics for individual years are in separate columns

summarize_rainfall, summarize_temperature

## Not run: 
# An example, although this function should not be called on its own; rather,
when using any of the \code{summarize_} or \code{par_summarize_} functions,
pass \code{wide = FALSE}.
# Generate "long" format summary statistics
temperature_summary_long <- summarize_temperature(temperature = temperature_2yr,
                                                  start_month = 3,
                                                  end_month = 11,
                                                  wide = FALSE)
# Identify those columns that do not have year_specific values
long_term_cols <- c("mean_gdd", "sd_gdd")
# Identify the column that has the site unique id
id_column_name <- colnames(temperature_summary_long)[2]
# Convert to "wide" format
temperature_summary <- wxsumR:::wide_summary(x = temperature_summary_long,
                                                     id_col = id_column_name,
                                                     long_term_cols = long_term_cols)

## End(Not run)