@@ -28,17 +28,17 @@
# ' hemisphere latitudes. For determination of afternoon values, the presented
# ' tabulated values are symmetric about noon.
# '
# ' The solar zenith angle (SZA) is one measure that helps to describe the
# ' sun's path across the sky. It's defined as the angle of the sun relative
# ' to a line perpendicular to the earth's surface. It is useful to calculate
# ' the SZA in relation to the true solar time. True solar time relates to
# ' the position of the sun with respect to the observer, which is different
# ' depending on the exact longitude. For example, two hours before the sun
# ' crosses the meridian (the highest point it would reach that day)
# ' corresponds to a true solar time of 10 a.m. The SZA has a strong
# ' dependence on the observer's latitude. For example, at a latitude of 50
# ' deg N at the start of January, the noontime SZA is 73.0 but a different
# ' observer at 20 deg N would measure the noontime SZA to be 43.0 degrees.
# ' The solar zenith angle (SZA) is one measure that helps to describe the sun's
# ' path across the sky. It's defined as the angle of the sun relative to a line
# ' perpendicular to the earth's surface. It is useful to calculate the SZA in
# ' relation to the true solar time. True solar time relates to the position of
# ' the sun with respect to the observer, which is different depending on the
# ' exact longitude. For example, two hours before the sun crosses the meridian
# ' (the highest point it would reach that day) corresponds to a true solar time
# ' of 10 a.m. The SZA has a strong dependence on the observer's latitude. For
# ' example, at a latitude of 50 deg N at the start of January, the noontime SZA
# ' is 73.0 but a different observer at 20 deg N would measure the noontime SZA
# ' to be 43.0 degrees.
# '
# ' @format A tibble with 816 rows and 4 variables:
# ' \describe{
@@ -91,10 +91,9 @@
# ' Daily S&P 500 Index data from 1950 to 2015
# '
# ' This dataset provides daily price indicators for the S&P 500 index
# ' from the beginning of 1950 to the end of 2015. The index includes 500
# ' leading companies and captures about 80% coverage of available market
# ' capitalization.
# ' This dataset provides daily price indicators for the S&P 500 index from the
# ' beginning of 1950 to the end of 2015. The index includes 500 leading
# ' companies and captures about 80% coverage of available market capitalization.
# '
# ' @format A tibble with 16607 rows and 7 variables:
# ' \describe{
@@ -110,17 +109,16 @@
# ' A year of pizza sales from a pizza place
# '
# ' A synthetic dataset that describes pizza sales for a pizza place
# ' somewhere in the US. While the contents are artificial, the
# ' ingredients used to make the pizzas are far from it. There are 32
# ' different pizzas that fall into 4 different categories: \code{classic}
# ' (classic pizzas: 'You probably had one like it before, but never like
# ' this!'), \code{chicken} (pizzas with chicken as a major ingredient: 'Try
# ' the Southwest Chicken Pizza! You'll love it!'), \code{supreme} (pizzas
# ' that try a little harder: 'My Soppressata pizza uses only the finest
# ' salami from my personal salumist!'), and, \code{veggie} (pizzas without
# ' any meats whatsoever: 'My Five Cheese pizza has so many cheeses, I can
# ' only offer it in Large Size!').
# ' A synthetic dataset that describes pizza sales for a pizza place somewhere in
# ' the US. While the contents are artificial, the ingredients used to make the
# ' pizzas are far from it. There are 32 different pizzas that fall into 4
# ' different categories: \code{classic} (classic pizzas: 'You probably had one
# ' like it before, but never like this!'), \code{chicken} (pizzas with chicken
# ' as a major ingredient: 'Try the Southwest Chicken Pizza! You'll love it!'),
# ' \code{supreme} (pizzas that try a little harder: 'My Soppressata pizza uses
# ' only the finest salami from my personal salumist!'), and, \code{veggie}
# ' (pizzas without any meats whatsoever: 'My Five Cheese pizza has so many
# ' cheeses, I can only offer it in Large Size!').
# '
# ' @format A tibble with 49574 rows and 7 variables:
# ' \describe{
@@ -142,3 +140,34 @@
# ' (in USD)}
# ' }
" pizzaplace"
# ' A toy example tibble for testing with gt: exibble
# '
# ' This tibble contains data of a few different classes, which makes it
# ' well-suited for quick experimentation with the functions in this package. It
# ' contains only eight rows with numeric, character, and factor columns. The
# ' last 4 rows contain \code{NA} values in the majority of the tibbles's columns
# ' (1 missing value per column). The \code{date}, \code{time}, and
# ' \code{datetime} columns are character-based dates/times in the familiar ISO
# ' 8601 format. The \code{row} and \code{group} columns provide for unique
# ' rownames and two groups (\code{grp_a} and \code{grp_b}) for experimenting
# ' with the \code{\link{gt}()} function's \code{rowname_col} and
# ' \code{groupname_col} arguments.
# '
# ' @format A tibble with 8 rows and 9 variables:
# ' \describe{
# ' \item{num}{a numeric column ordered with increasingly larger values}
# ' \item{char}{a character column composed of names of fruits from \code{a} to
# ' \code{h}}
# ' \item{fctr}{a factor column with numbers from 1 to 8, written out}
# ' \item{date, time, datetime}{character columns with dates, times, and
# ' datetimes}
# ' \item{currency}{a numeric column that is useful for testing currency-based
# ' formatting}
# ' \item{row}{a character column in the format \code{row_X} which can be
# ' useful for testing with row captions in a table stub}
# ' \item{group}{a character column with four \code{grp_a} values and four
# ' \code{grp_b} values which can be useful for testing tables that contain
# ' row groups}
# ' }
" exibble"
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