didn't knit readme file

NAMESPACE

@@ -1,3 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
+export(seq_data)
 export(seq_smooth)
+importFrom(stats,approx)

R/seq_data_nl.R

@@ -0,0 +1,277 @@
+#' Interpolate a sequence of values with Easing or Stepping Given Data Points
+#'
+#' This function generates a sequence of values based on a specified easing or stepping function.
+#' It supports linear, polynomial, exponential, and other smooth transitions, as well as stepped transitions.
+#'
+#' @param data Numeric vector, matrix, data frame, or list. The input data to be used for generating the sequence.
+#' @param type Character string specifying the type of sequence. Supported types include:
+#'   \itemize{
+#'     \item `"linear"`: Linear interpolation.
+#'     \item `"quad"`: Quadratic easing.
+#'     \item `"cubic"`: Cubic easing.
+#'     \item `"quart"`: Quartic easing.
+#'     \item `"quint"`: Quintic easing.
+#'     \item `"exp"`: Exponential easing.
+#'     \item `"circle"`: Circular easing.
+#'     \item `"back"`: Back easing with overshoot.
+#'     \item `"elastic"`: Elastic easing with oscillation.
+#'     \item `"sine"`: Sine wave easing.
+#'     \item `"bounce"`: Bouncing easing.
+#'     \item `"step"`: Stepped transitions.
+#'   }
+#'   Defaults to `"linear"`.
+#' @param step_count Integer specifying the number of steps for the `"step"` type. Must be between 1 and the length of `data`. Defaults to `NULL`.
+#' @param ease Character string specifying the direction of easing. Supported values are:
+#'   \itemize{
+#'     \item `"in"`: Easing starts slow and accelerates.
+#'     \item `"out"`: Easing starts fast and decelerates.
+#'     \item `"in_out"`: Easing combines both behaviors.
+#'   }
+#'   Applicable only for non-linear types. Defaults to `NULL`.
+#'
+#' @return A numeric vector containing the generated sequence.
+#'   \itemize{
+#'     \item For `"linear"`, a smoothly interpolated sequence is returned.
+#'     \item For `"step"`, a sequence with distinct steps is generated.
+#'     \item For other easing types, the sequence follows the specified smooth transition curve.
+#'   }
+#'
+#' @details
+#' The `seq_data` function calculates a sequence of values based on the specified `type` and `ease`.
+#' The `data` input is used to determine the range (minimum and maximum) of the sequence to then be interpolated, and the resulting
+#' sequence is normalized between 0 and 1 before applying the specified easing or stepping function.
+#'
+#' For `"step"` type, the number of steps can be controlled using `step_count`. The `ease` parameter has no effect
+#' when `type` is `"linear"` or `"step"`.
+#'
+#' @examples
+#' # Generate a linear sequence
+#' seq_data(1:10, type = "linear")
+#'
+#' # Generate a quadratic easing sequence
+#' seq_data(rnorm(100,14,5), type = "quad", ease = "in_out")
+#'
+#' # Generate a stepped sequence with 5 steps
+#' seq_data(rpois(100,3), type = "step", step_count = 5)
+#'
+#' @note
+#' This function supports various easing functions commonly used in animations and graphics, as well as
+#' stepped sequences for discrete transitions. Invalid or unsupported inputs will result in informative
+#' error messages or warnings.
+#'
+#' @importFrom stats approx
+#' @export
+
+seq_data <- function(data,
+                            type = "linear",
+                            step_count = NULL,
+                            ease = NULL){
+
+  stopifnot(is.character(type),
+            is.numeric(data) || is.matrix(data) || is.data.frame(data) || is.list(data))
+
+  if(!is.null(ease))
+    if(!is.character(ease))
+      stop("Ease must be a characer string of length 1")
+
+  type <- match.arg(type,c("linear","quad","cubic","quart",
+                           "quint","exp","circle","back",
+                           "elastic","sine","bounce","step"))
+
+  # Compute normalized time (t) as the y-component
+  # Time could be any range, but it complicates comparison if
+  # time range is not bounded. However, you can always
+  # normalize it to be bounded from [0,1]
+  if (is.numeric(data)) {
+    n <- length(data)
+  } else if (is.data.frame(data) || is.matrix(data)) {
+    n <- nrow(data)
+  } else if (is.list(data)) {
+    n <- unique(lengths(data))
+  } else {
+    stop("Unsupported data type: data must be numeric, a data.frame, a matrix, or a list.")
+  }
+
+  from <- min(data)
+  to <- max(data)
+
+  t <- seq(0,1,length.out = n)
+
+  # Default sequence
+  if(type == "linear") {
+    seq <- from + t*(to-from)
+    return(seq)
+  }
+
+  # `in` curves it at the start
+  #`out` will curve the line at the end
+  #`in_out` will curve the line at both ends
+  # Keep in mind there are n - 1 critical points
+  # as polynomials of size n increases.
+
+  # Issue warning if 'ease' not set to NULL when type is linear
+  if (type != "linear" && type != "step") {
+    ease <- match.arg(ease, c("in", "out", "in_out"))
+  }
+
+  # Compute normalized time
+  t <- seq(0, 1, length.out = n)
+
+  # Linear sequence
+  if (type == "linear") {
+    seq <- from + t * (to - from)
+    return(seq)
+  }
+
+  # Step sequence
+  if (type == "step") {
+
+    # Handle null or invalid step_count
+    if (is.null(step_count)) {
+      warning("Step count is 'NULL'. Using default 'step_count' = 4.")
+      step_count <- 4
+    }
+
+    # Check step_count limits
+    if (step_count < 1) {
+      stop("Invalid 'step_count': Minimum number of steps is 1. Provided: ", step_count)
+    }
+    if (step_count > n) {
+      stop("Invalid 'step_count': Number of steps (", step_count,
+           ") cannot exceed the length of the numeric vector (n = ", n, ").")
+    }
+
+    # Warn if 'ease' is provided (not applicable for steps)
+    if (!is.null(ease) && !is.na(ease)) {
+      warning("'ease' has no effect on step functions. Step function is not continuous.")
+    }
+
+    # Compute step sequence
+    smooth_seq <- from + (to - from) * round(step_count * t) / step_count
+
+    return(smooth_seq)
+  }
+
+
+  # What type of sequence and direction to compute
+  smooth_fashion <- join_char(type,"_",ease)
+
+
+  smooth_fashion <- switch(
+    smooth_fashion,
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    # Notice how we use base '2' and exponentiate as
+    # polynomial increases by n. You can use any base you
+    # like, I chose 2 because that's what I've seen others
+    # do, and it's the standard as far as I know.
+    quad_in = t^2,
+    quad_out = 1-(1 - t)^2,
+    quad_in_out = ifelse(t < 0.5,
+                         2*t^2,
+                         1 - 0.5*(-2*t+2)^2),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    cubic_in = t^3,
+    cubic_out = 1 - (1-t)^3,
+    cubic_in_out = ifelse(t < 0.5,
+                          4*t^3,
+                          1- 0.5*(-2*t+2)^3),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    quart_in = t^4,
+    quart_out = 1 - (1-t)^4,
+    quart_in_out = ifelse(t < 0.5,
+                          8*t^4,
+                          1 - 0.5*(-2*t+2)^4),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    quint_in = t^5,
+    quint_out = 1 - (1-t)^5,
+    quint_in_out = ifelse(t < 0.5,
+                          16*t^5,
+                          1 - 0.5*(-2*t+2)^5),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    exp_in = 2^(10*t - 10),
+    exp_out = 1 - 2^(-10*t),
+    exp_in_out = ifelse(t == 0,0,
+                        ifelse(t == 1,1,
+                               ifelse(t < 0.5,
+                                      2^(20*t-10)/2,
+                                      (2 - 2^(-20*t+10))/2
+                               ))),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    circle_in = 1 - sqrt(1-t^2),
+    circle_out = sqrt(1 - (t - 1)^2),
+    circle_in_out = ifelse(t < 0.5,
+                           (1 - sqrt(1 - (2 * t)^2)) / 2,
+                           0.5 * (sqrt(1 - (-2 * t + 2)^2) + 1)),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    back_in = 2.70158*t^3 - 1.70158*t^2,
+    back_out = 1 + 2.70158* (t-1)^3 + 1.70158*(t-1)^2,
+    back_in_out = {
+      k <- 1.70158
+      k2 <- k * 1.525
+      ifelse(t < 0.5,
+             (2*t)^2 * ((k2 + 1) * 2 * t - k2) / 2,
+             ((2*t-2)^2 * ((k2 + 1) * (t * 2 - 2) + k2) + 2) / 2
+      )
+    },
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    elastic_in = ifelse(t == 0, 0,
+                        ifelse(t == 1,1,
+                               -(2 ^ (10 * t - 10)) * sin((t * 10 - 10.75) * 2 * pi / 3)
+                        )
+    ),
+    elastic_out = ifelse(t == 0,0,
+                         ifelse(t ==1,1,
+                                2^(-10*t)*sin((t*10-0.75)*2*pi/3)+1)
+    ),
+    elastic_in_out = ifelse(
+      t == 0, 0,
+      ifelse(t == 1, 1,
+             ifelse(t < 0.5,
+                    -(2^( 20*t - 10) * sin((20 * t - 11.125) * 2 * pi/4.5)) / 2,
+                    (2^(-20*t + 10) * sin((20 * t - 11.125) * 2 * pi/4.5)) / 2 + 1
+             ))
+    ),
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    sine_in = 1 - cos((t*pi)/2),
+    sine_out = sin((t*pi)/2),
+    sine_in_out = -(cos(t*pi)-1)/2,
+    #---- --- ---- --- ---- --- ---- --- ----- --- ----#
+    bounce_in = 1 - ifelse((1 - t) < 0.3636,
+                           7.5625 * (1 - t)^2,
+                           ifelse((1 - t) < 0.7273,
+                                  7.5625 * ((1 - t) - 1.5 / 2.75)^2 + 0.75,
+                                  ifelse((1 - t) < 0.9091,
+                                         7.5625 * ((1 - t) - 2.25 / 2.75)^2 + 0.9375,
+                                         7.5625 * ((1 - t) - 2.625 / 2.75)^2 + 0.984375))
+    ),
+    bounce_out = ifelse(t < 0.3636,
+                        7.5625 * t^2,
+                        ifelse(t < 0.7273,
+                               7.5625 * (t - 1.5 / 2.75)^2 + 0.75,
+                               ifelse(t < 0.9091,
+                                      7.5625 * (t - 2.25 / 2.75)^2 + 0.9375,
+                                      7.5625 * (t - 2.625 / 2.75)^2 + 0.984375))
+    )
+    ,
+    bounce_in_out = ifelse(t < 0.5,
+                           0.5 * ifelse(t * 2 < 0.3636,
+                                        7.5625 * (2 * t)^2,
+                                        ifelse(t * 2 < 0.7273,
+                                               7.5625 * ((2 * t) - 1.5 / 2.75)^2 + 0.75,
+                                               ifelse(t * 2 < 0.9091,
+                                                      7.5625 * ((2 * t) - 2.25 / 2.75)^2 + 0.9375,
+                                                      7.5625 * ((2 * t) - 2.625 / 2.75)^2 + 0.984375))),
+                           0.5 * ifelse((2 * t - 1) < 0.3636,
+                                        7.5625 * (2 * t - 1)^2,
+                                        ifelse((2 * t - 1) < 0.7273,
+                                               7.5625 * ((2 * t - 1) - 1.5 / 2.75)^2 + 0.75,
+                                               ifelse((2 * t - 1) < 0.9091,
+                                                      7.5625 * ((2 * t - 1) - 2.25 / 2.75)^2 + 0.9375,
+                                                      7.5625 * ((2 * t - 1) - 2.625 / 2.75)^2 + 0.984375))) + 0.5
+    )
+  )
+
+  smooth_seq <- from + smooth_fashion * (to-from)
+
+  return(smooth_seq)
+}

R/seq_nl.R

@@ -211,10 +211,11 @@ stopifnot(is.character(type))
              )
     },
     #---- --- ---- --- ---- --- ---- --- ----- --- ----#
-    elastic_in = ifelse(t == 0,0,
-                        ifelse(t == 1,1
-                               -(2^(10*t-10))*sin((t*10-10.75)*2*pi/3))
-                        ),
+    elastic_in =  ifelse(t == 0, 0,
+                         ifelse(t == 1,1,
+                                -(2 ^ (10 * t - 10)) * sin((t * 10 - 10.75) * 2 * pi / 3)
+                         )
+    ),
     elastic_out = ifelse(t == 0,0,
                          ifelse(t ==1,1,
                                 2^(-10*t)*sin((t*10-0.75)*2*pi/3)+1)

R/utils.R

@@ -25,3 +25,19 @@ join_char <- function(first,
 
   return(new_string)
 }
+
+get_mode <- function(.x) {
+  .x <- unique(.x)
+  x_not_na <- .x[which(!is.na(.x))]
+  if(length(x_not_na) > 0) {
+    tab <- tabulate(match(.x, x_not_na))
+    candidates <- x_not_na[tab == max(tab)]
+    if (is.logical(.x)) {
+      any(candidates) # return TRUE if any true. max returns an integer
+    } else {
+      max(candidates) # return highest (ie max) value
+    }
+  } else {
+    .x[NA_integer_]
+  }
+}

README.md

@@ -9,7 +9,7 @@
 status](https://www.r-pkg.org/badges/version/sequentially)](https://CRAN.R-project.org/package=sequentially)
 [![stability-wip](https://img.shields.io/badge/stability-wip-lightgrey.svg)](https://github.com/mkenney/software-guides/blob/master/STABILITY-BADGES.md#work-in-progress)
 [![Codecov test
-coverage](https://codecov.io/gh/jpmonteagudo28/sequentially/graph/badge.svg)](https://app.codecov.io/gh/jpmonteagudo28/sequentially)
+coverage](https://codecov.io/gh/jpmonteagudo28/sequentially/branch/main/graph/badge.svg)](https://app.codecov.io/gh/jpmonteagudo28/sequentially?branch=main)
 
 <!-- badges: end -->