autoimport

NAMESPACE

@@ -126,29 +126,49 @@ importFrom(dplyr,as_tibble)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
-importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,near)
 importFrom(dplyr,pull)
 importFrom(dplyr,rename)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
-importFrom(dplyr,ungroup)
+importFrom(dplyr,tibble)
 importFrom(expm,expm)
 importFrom(expm,logm)
 importFrom(ggforce,geom_circle)
+importFrom(ggplot2,aes)
+importFrom(ggplot2,annotate)
+importFrom(ggplot2,coord_fixed)
+importFrom(ggplot2,coord_map)
+importFrom(ggplot2,element_blank)
+importFrom(ggplot2,element_text)
+importFrom(ggplot2,geom_abline)
+importFrom(ggplot2,geom_contour)
+importFrom(ggplot2,geom_contour_filled)
+importFrom(ggplot2,geom_hline)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_path)
+importFrom(ggplot2,geom_point)
+importFrom(ggplot2,geom_polygon)
+importFrom(ggplot2,geom_text)
+importFrom(ggplot2,geom_tile)
+importFrom(ggplot2,geom_vline)
+importFrom(ggplot2,ggplot)
+importFrom(ggplot2,labs)
+importFrom(ggplot2,scale_x_continuous)
+importFrom(ggplot2,scale_y_continuous)
+importFrom(ggplot2,theme)
+importFrom(ggplot2,theme_classic)
 importFrom(grDevices,colorRamp)
-importFrom(grDevices,gray)
 importFrom(graphics,lines)
 importFrom(graphics,points)
 importFrom(graphics,segments)
 importFrom(graphics,text)
-importFrom(graphics,title)
 importFrom(lubridate,as_datetime)
+importFrom(plyr,.)
 importFrom(plyr,rbind.fill)
 importFrom(readxl,read_xlsx)
 importFrom(rjson,fromJSON)
-importFrom(rlang,check_installed)
 importFrom(rotasym,d_vMF)
 importFrom(rotasym,r_unif_sphere)
 importFrom(rotasym,r_vMF)
@@ -161,12 +181,7 @@ importFrom(sf,st_make_grid)
 importFrom(sf,st_transform)
 importFrom(stats,aggregate)
 importFrom(stats,qf)
-importFrom(tectonicr,deg2rad)
 importFrom(tectonicr,dist_greatcircle)
-importFrom(tectonicr,rad2deg)
-importFrom(tibble,as_tibble)
-importFrom(tibble,tibble)
-importFrom(tidyr,drop_na)
 importFrom(tidyr,pivot_longer)
 importFrom(tidyr,pivot_wider)
 importFrom(tidyselect,starts_with)

R/allmendinger.R

@@ -120,84 +120,85 @@ CartToSph <- function(cn, ce, cd) {
 }
 
 
-#' calculates the mean vector for a given series of lines
-#'
-#' @param t,p numeric vectors of trends and plunges (in radians)
-#'
-#' @returns calculates the trend (trd) and plunge (plg) of the mean vector (in radians),
-#' its normalized length, and Fisher statistics (concentration factor (conc),
-#' 99 (d99) and 95 (d95) % uncertainty cones, in radians)
-#' @export
-#'
-CalcMV <- function(t, p) {
-  # Number of lines
-  nlines <- length(t)
-
-  # Initialize the 3 direction cosines which contain the sums of the
-  # individual vectors (i.e. the coordinates of the resultant vector)
-  CNsum <- 0
-  CEsum <- 0
-  CDsum <- 0
-
-  # Now add up all the individual vectors
-  for (i in 1:nlines) {
-    cs <- SphToCart(t(i), p(i), 0)
-    cn <- cs[, 1]
-    ce <- cs[, 2]
-    cd <- cs[, 3]
-    CNsum <- CNsum + cn
-    CEsum <- CEsum + ce
-    CDsum <- CDsum + cd
-  }
-
-  # R is the length of the resultant vector and Rave is the length of
-  # the resultant vector normalized by the number of lines
-  R <- sqrt(CNsum * CNsum + CEsum * CEsum + CDsum * CDsum)
-  Rave <- R / nlines
-
-  # If Rave is lower than 0.1, the mean vector is insignificant, return error
-  if (Rave < 0.1) {
-    stop("Mean vector is insignificant")
-  } else {
-    # Divide the resultant vector by its length to get the average
-    # unit vector
-    CNsum <- CNsum / R
-    CEsum <- CEsum / R
-    CDsum <- CDsum / R
-
-    # Use the following 'if' statement if you want to convert the
-    # mean vector to the lower hemisphere
-    if (CDsum < 0) {
-      CNsum <- -CNsum
-      CEsum <- -CEsum
-      CDsum <- -CDsum
-    }
-
-    # Convert the mean vector from direction cosines to trend and plunge
-    vec <- CartToSph(CNsum, CEsum, CDsum)
-    trd <- vec[, 1]
-    plg <- vec[, 2]
-
-    # If there are enough measurements calculate the Fisher Statistics
-    # For more information on these statistics see Fisher et al. (1987)
-    if (R < nlines) {
-      if (nlines < 16) {
-        afact <- 1 - (1 / nlines)
-        conc <- (nlines / (nlines - R)) * afact^2
-      } else {
-        conc <- (nlines - 1) / (nlines - R)
-      }
-    }
-    if (Rave >= 0.65 && Rave < 1) {
-      afact <- 1 / 0.01
-      bfact <- 1 / (nlines - 1)
-      d99 <- acos(1 - ((nlines - R) / R) * (afact^bfact - 1.0))
-      afact <- 1 / 0.05
-      d95 <- acos(1 - ((nlines - R) / R) * (afact^bfact - 1.0))
-    }
-  }
-  cbind(trd, plg, Rave, conc, d99, d95)
-}
+#' #' calculates the mean vector for a given series of lines
+#' #'
+#' #' @param t,p numeric vectors of trends and plunges (in radians)
+#' #'
+#' #' @returns calculates the trend (trd) and plunge (plg) of the mean vector (in radians),
+#' #' its normalized length, and Fisher statistics (concentration factor (conc),
+#' #' 99 (d99) and 95 (d95) % uncertainty cones, in radians)
+#' #' @export
+#' #'
+#' #' @importFrom shiny p
+#' CalcMV <- function(t, p) {
+#'   # Number of lines
+#'   nlines <- length(t)
+#' 
+#'   # Initialize the 3 direction cosines which contain the sums of the
+#'   # individual vectors (i.e. the coordinates of the resultant vector)
+#'   CNsum <- 0
+#'   CEsum <- 0
+#'   CDsum <- 0
+#' 
+#'   # Now add up all the individual vectors
+#'   for (i in 1:nlines) {
+#'     cs <- SphToCart(t(i), p(i), 0)
+#'     cn <- cs[, 1]
+#'     ce <- cs[, 2]
+#'     cd <- cs[, 3]
+#'     CNsum <- CNsum + cn
+#'     CEsum <- CEsum + ce
+#'     CDsum <- CDsum + cd
+#'   }
+#' 
+#'   # R is the length of the resultant vector and Rave is the length of
+#'   # the resultant vector normalized by the number of lines
+#'   R <- sqrt(CNsum * CNsum + CEsum * CEsum + CDsum * CDsum)
+#'   Rave <- R / nlines
+#' 
+#'   # If Rave is lower than 0.1, the mean vector is insignificant, return error
+#'   if (Rave < 0.1) {
+#'     stop("Mean vector is insignificant")
+#'   } else {
+#'     # Divide the resultant vector by its length to get the average
+#'     # unit vector
+#'     CNsum <- CNsum / R
+#'     CEsum <- CEsum / R
+#'     CDsum <- CDsum / R
+#' 
+#'     # Use the following 'if' statement if you want to convert the
+#'     # mean vector to the lower hemisphere
+#'     if (CDsum < 0) {
+#'       CNsum <- -CNsum
+#'       CEsum <- -CEsum
+#'       CDsum <- -CDsum
+#'     }
+#' 
+#'     # Convert the mean vector from direction cosines to trend and plunge
+#'     vec <- CartToSph(CNsum, CEsum, CDsum)
+#'     trd <- vec[, 1]
+#'     plg <- vec[, 2]
+#' 
+#'     # If there are enough measurements calculate the Fisher Statistics
+#'     # For more information on these statistics see Fisher et al. (1987)
+#'     if (R < nlines) {
+#'       if (nlines < 16) {
+#'         afact <- 1 - (1 / nlines)
+#'         conc <- (nlines / (nlines - R)) * afact^2
+#'       } else {
+#'         conc <- (nlines - 1) / (nlines - R)
+#'       }
+#'     }
+#'     if (Rave >= 0.65 && Rave < 1) {
+#'       afact <- 1 / 0.01
+#'       bfact <- 1 / (nlines - 1)
+#'       d99 <- acos(1 - ((nlines - R) / R) * (afact^bfact - 1.0))
+#'       afact <- 1 / 0.05
+#'       d95 <- acos(1 - ((nlines - R) / R) * (afact^bfact - 1.0))
+#'     }
+#'   }
+#'   cbind(trd, plg, Rave, conc, d99, d95)
+#' }
 
 
 

R/alphabeta.R

@@ -13,7 +13,7 @@
 #' 
 #' @export
 #' 
-#' @return object of calss `"plane"`. If gamma is specified, `"line"` object is
+#' @return object of class `"plane"`. If gamma is specified, `"line"` object is
 #' returned.
 #' 
 #' @examples

R/best_pole_ramsay.R

@@ -124,7 +124,7 @@ best_cone_ramsay <- function(x) {
 }
 
 #' @rdname best_pole
-best_cone_ramsay <- function(x) {
+best_cone_ramsay2 <- function(x) {
   l <- m <- n <- l2 <- m2 <- lm <- ln <- mn <- numeric()
   xsum <- data.frame(l = x[, 1], m = x[, 2], n = x[, 3]) |>
     dplyr::mutate(

R/contouring.R

@@ -242,6 +242,8 @@ reshape_grid <- function(m, n) {
 
 }
 
+#' @importFrom graphics contour filled.contour
+#' @importFrom stats xtabs
 stereo_density <- function(x, nlevels = 20, ..., filled = FALSE, upper.hem = FALSE) {
   d <- density_grid(x, ...)
   d$grid <- fix_symm(d$grid)

R/coordinates.R

@@ -317,7 +317,6 @@ is.spherical <- function(l) {
 #' }
 #' @source Ramsay, 1967, p. 15-16
 #' @name ramsay_coords
-#' @importFrom tectonicr deg2rad rad2deg
 #' @examples
 #' \dontrun{
 #' # Stereographic coordinates (angle notation):
@@ -357,7 +356,7 @@ cartesian_to_acosvec <- function(x) {
 #' @export
 acoscartesian_to_cartesian <- function(x) {
   # tectonicr::deg2rad(x) |> cos()
-  x <- tectonicr::deg2rad(x)
+  x <- deg2rad(x)
   cx <- cos(x[, 1])
   cy <- cos(x[, 2])
   cz <- cos(x[, 3])

R/density.R

@@ -161,6 +161,8 @@ calculate_density <- function(x, sigma = NULL, sigma.norm = TRUE, trimzero = TRU
 #' stereoplot()
 #' stereo_density_contour(x)
 #' stereo_point(x)
+#' @importFrom graphics filled.contour
+#' @importFrom stats xtabs
 stereo_density_contour <- function(x, sigma = NULL, sigma.norm = TRUE, trimzero = TRUE, ngrid = 100, grid.type = c("gss", "sfs"), ...) {
   # stereoplot()
   densgrd <- calculate_density(
@@ -192,6 +194,7 @@ blank_grid <- function(n = 3000, ...) {
 }
 #'
 #'
+#' @importFrom dplyr near
 project_data0 <- function(self, x, y, z) {
   # Equal-area projection
   d <- sqrt(x * x + y * y + z * z)

R/gg_stereonet.R

@@ -178,6 +178,7 @@ ggl <- function(x, ..., d = 90, n = 1e3) {
 #' @param ... optional graphical parameters passed to [ggplot2::geom_polygon()]
 #'
 #' @export
+#' @importFrom ggplot2 aes geom_polygon
 ggframe <- function(n = 1e4, color = "black", fill = NA, lwd = 1, ...) {
   prim.lat <- rep(c(0), times = n)
   prim.l1 <- seq(0, 180, length = n / 2)
@@ -189,6 +190,7 @@ ggframe <- function(n = 1e4, color = "black", fill = NA, lwd = 1, ...) {
   geom_polygon(aes(x = prim.long, y = prim.lat), data = prim_df, color = color, fill = fill, lwd = lwd, ..., inherit.aes = FALSE)
 }
 
+#' @importFrom ggplot2 aes geom_path
 ggstereo_grid <- function(d = 10, rot = 0, ...) {
   x <- y <- group <- NULL
   # small circles
@@ -235,7 +237,7 @@ ggstereo_grid <- function(d = 10, rot = 0, ...) {
 #' @param ... argument passed to [ggplot2::geom_polygon()]
 #'
 #' @import ggplot2
-#' @importFrom rlang check_installed
+#' @importFrom ggplot2 aes annotate coord_map element_blank element_text ggplot scale_x_continuous scale_y_continuous theme
 #'
 #' @return ggplot
 #' @export
@@ -389,6 +391,7 @@ NULL
 
 #' @rdname ggstereocontour
 #' @export
+#' @importFrom ggplot2 aes geom_contour
 geom_contour_stereo <- function(data, ngrid = 200, hw = NULL, optimal_bw = c("cross", "rot"), norm = FALSE, threshold = 0, ...) {
   Long <- Lat <- Density <- NULL
   xtot <- full_hem(data)
@@ -407,6 +410,7 @@ geom_contour_stereo <- function(data, ngrid = 200, hw = NULL, optimal_bw = c("cr
 
 #' @rdname ggstereocontour
 #' @export
+#' @importFrom ggplot2 aes geom_contour_filled geom_tile
 geom_contourf_stereo <- function(data, ngrid = 200, hw = NULL, optimal_bw = c("cross", "rot"), norm = FALSE, smooth = FALSE, threshold = 0, ...) {
   Long <- Lat <- Density <- NULL
   xtot <- full_hem(data)

R/helper.R

@@ -132,16 +132,23 @@ assign_col_binned <- function(x, breaks, pal = viridis::viridis, ...) {
   named_cols
 }
 
-color_func <- function(x, pal = viridis::viridis) {
-  color_func0 <- colorRamp(do.call(pal, args = list(n = 10000)))
-  grDevices::rgb(color_func0(x) / 255)
+#' @importFrom grDevices colorRamp
+color_func <- function(x, pal = viridis::viridis, ...) {
+  color_func0 <- colorRamp(do.call(pal, args = list(n = 10000, ...)))
+
+  grDevices::rgb(color_func0(x, ...) / 255)
 }
 
 #' @rdname colorize
 #' @export
-legend_c <- function(breaks, title = NULL, pal = viridis::viridis) {
+legend_c <- function(breaks, title = NULL, pal = viridis::viridis, ...) {
   label_pos <- normalize(breaks)
-  legend_image <- grDevices::as.raster(matrix(color_func(seq(0, 1, .001)), ncol = 1))
+  legend_image <- grDevices::as.raster(
+    matrix(
+      color_func(seq(0, 1, .001), pal = pal, ...),
+      ncol = 1
+    )
+  )
 
   graphics::par(new = TRUE)
   graphics::layout(matrix(1, 1))