R/quake_grapher.R
In cowboy2718/QuakeExplorer: Earth Quake Explorer
# Tony Gojanovic
# Coursera "R Programming Capstone Project"
# Final project
# August 2018
#' Geom for plotting earthquake data as a time series.
#'
#' @description This function provides a time series interpretation of earthquakes over time expressed as a bubble plot.
#' @note Countries and data ranges are modified using dplyr prior to using the geom for time series plotting.
#' For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @export
#' @import ggplot2
#' @import ggplot2 grid
#' @importFrom dplyr mutate
#' @export
#' @references
#' @examples
#' \dontrun{
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' # Additional graph metadata can be added using standard ggplot syntax
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<-any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + ggtitle("NOAA Significant Earthquake Data")
#'
#' p1<-p1 + ylab("Country")+xlab("Date") + scale_size_continuous(name = 'Richter')
#'
#' p1<-p1 +scale_color_continuous(name = 'Mortality (deaths)') +theme(axis.text.x=element_text(angle=90,hjust=3))
#'
#' p1
#'
#' }
#'
#' @details geom_timeline provides a time series interpretation on filtered NOAA Significant Earthquake data.
#' @note Aesthetic considerations for the number of countries and data ranges should be taken into consideration as to not plot too many data points rendering the graph unreadable.
#' Use of dplyr and piping to filter the data set is suggested.
#' Additional graphical elements can be added using standard ggplot syntax as shown in the example.
#'
geom_timeline <- function(mapping = NULL, data = NULL, stat = "identity",
position = 'identity', na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE,...) {
ggplot2::layer(
geom = geomtimeline, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# The following is a subroutine used for generating data bubbles on the timeline.
bubble_subroutine <- function(data, params, size) {
grid::circleGrob(r = data$size/25, gp = grid::gpar(col = data$color, fill = alpha(data$fill, data$alpha),lwd = 0.1))
}
geomtimeline <- ggplot2::ggproto('geomtimeline', ggplot2::Geom,
required_aes = c('x'),
default_aes = ggplot2::aes(y = NULL, color = 'red',
fill = 'black', size = 5, alpha = 0.3),
draw_key = bubble_subroutine,
draw_group = function(data, panel_params, coord) {
coords <- coord$transform(data, panel_params)
grid::circleGrob(
coords$x,
coords$y,
r = coords$size / 225,
gp = grid::gpar(
col = coords$colour,
fill = coords$fill,
alpha = coords$alpha
)
)
}
)
#' Geom for labeling the time series plot.
#'
#' @description This function provides label meta data for the time series plot of earthquakes.
#' @note For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @importFrom dplyr mutate
#' @import ggplot2
#' @references The following are usefule references:
#' \url{https://ggplot2.tidyverse.org/reference/geom_text.html}
#' \url{https://ggplot2.tidyverse.org/articles/ggplot2-specs.html}
#'
#' @examples
#' \dontrun{
#'
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<- any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA" | COUNTRY=="GREECE") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot()
#'
#' p1<-p1 + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + geom_timeline_label(aes(x = datevalue, y = COUNTRY, magnitude = EQ_PRIMARY,label = location_name, max_labels= 5))
#'
#' p1<-p1 + ggtitle("NOAA Signficant Earthquakes Data with High Profile Events")
#'
#' p1
#'
#' }
geom_timeline_label <-
function(mapping = NULL,
data = NULL,
stat = "identity",
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
...) {
ggplot2::layer(
geom = auto_labeler,
mapping = mapping,
data = data,
stat = stat,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
auto_labeler <-
ggplot2::ggproto(
"auto_labeler",
ggplot2::Geom,required_aes = c('x', 'label', 'magnitude','max_labels'),
default_aes = ggplot2::aes(
max_labels = 3,y = 0,color = 'red',size = 0.25, linetype = 11,alpha = 0.5
),
draw_key = bubble_subroutine,
draw_panel = function(data, panel_scales, coord) {
max_labels <- data$max_labels[1]
data <- data %>% dplyr::group_by(group) %>%dplyr::top_n(max_labels, magnitude)
data$xend <- data$x
data$yend <- data$y + 0.25
panel <- ggplot2::GeomSegment$draw_panel(unique(data), panel_scales, coord)
# Provisions for text formatting.
data$y <- data$yend
data$angle <- 60
data$fontface <- 'bold'
data$family <- 'sans'
data$size <- 3.5
data$fill<-'white'
data$colour <- 'red'
data$lineheight <- 4
data$hjust <- 'left'
data$vjust <- 'top'
meta_data <- ggplot2::GeomText$draw_panel(unique(data), panel_scales, coord)
ggplot2:::ggname('geom_timeline_label', grid::grobTree(panel, meta_data))
}
)
# Tony Gojanovic
# Coursera "R Programming Capstone Project"
# Final project
# August 2018
#' Geom for plotting earthquake data as a time series.
#'
#' @description This function provides a time series interpretation of earthquakes over time expressed as a bubble plot.
#' @note Countries and data ranges are modified using dplyr prior to using the geom for time series plotting.
#' For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @export
#' @import ggplot2
#' @import ggplot2 grid
#' @importFrom dplyr mutate
#' @export
#' @references
#' @examples
#' \dontrun{
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' # Additional graph metadata can be added using standard ggplot syntax
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<-any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + ggtitle("NOAA Significant Earthquake Data")
#'
#' p1<-p1 + ylab("Country")+xlab("Date") + scale_size_continuous(name = 'Richter')
#'
#' p1<-p1 +scale_color_continuous(name = 'Mortality (deaths)') +theme(axis.text.x=element_text(angle=90,hjust=3))
#'
#' p1
#'
#' }
#'
#' @details geom_timeline provides a time series interpretation on filtered NOAA Significant Earthquake data.
#' @note Aesthetic considerations for the number of countries and data ranges should be taken into consideration as to not plot too many data points rendering the graph unreadable.
#' Use of dplyr and piping to filter the data set is suggested.
#' Additional graphical elements can be added using standard ggplot syntax as shown in the example.
#'
geom_timeline <- function(mapping = NULL, data = NULL, stat = "identity",
position = 'identity', na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE,...) {
ggplot2::layer(
geom = geomtimeline, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# The following is a subroutine used for generating data bubbles on the timeline.
bubble_subroutine <- function(data, params, size) {
grid::circleGrob(r = data$size/25, gp = grid::gpar(col = data$color, fill = alpha(data$fill, data$alpha),lwd = 0.1))
}
geomtimeline <- ggplot2::ggproto('geomtimeline', ggplot2::Geom,
required_aes = c('x'),
default_aes = ggplot2::aes(y = NULL, color = 'red',
fill = 'black', size = 5, alpha = 0.3),
draw_key = bubble_subroutine,
draw_group = function(data, panel_params, coord) {
coords <- coord$transform(data, panel_params)
grid::circleGrob(coords$x,coords$y,r= coords$size / 225,gp = grid::gpar(alpha = coords$alpha,fill = coords$fill,col = coords$colour
)
)
}
)
#' Geom for labeling the time series plot.
#'
#' @description This function provides label meta data for the time series plot of earthquakes.
#' @note For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @importFrom dplyr mutate
#' @import ggplot2
#' @references The following are usefule references:
#' \url{https://ggplot2.tidyverse.org/reference/geom_text.html}
#' \url{https://ggplot2.tidyverse.org/articles/ggplot2-specs.html}
#'
#' @examples
#' \dontrun{
#'
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<- any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA" | COUNTRY=="GREECE") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot()
#'
#' p1<-p1 + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + geom_timeline_label(aes(x = datevalue, y = COUNTRY, magnitude = EQ_PRIMARY,label = location_name, max_labels= 5))
#'
#' p1<-p1 + ggtitle("NOAA Signficant Earthquakes Data with High Profile Events")
#'
#' p1
#'
#' }
geom_timeline_label <-
function(data = NULL,na.rm = FALSE,show.legend = NA,mapping = NULL, stat = "identity", position = "identity", inherit.aes = TRUE,...)
{
ggplot2::layer(
geom = auto_labeler,
mapping = mapping,
data = data,
stat = stat,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
auto_labeler <-
ggplot2::ggproto(
"auto_labeler",
ggplot2::Geom,required_aes = c('x', 'label', 'magnitude','max_labels'),
default_aes = ggplot2::aes(
max_labels = 3,y = 0,color = 'red',size = 0.25, linetype = 11,alpha = 0.5
),
draw_key = bubble_subroutine,
draw_panel = function(data, panel_scales, coord) {
max_labels <- data$max_labels[1]
data <- data %>% dplyr::group_by(group) %>%dplyr::top_n(max_labels, magnitude)
data$xend <- data$x
data$yend <- data$y + 0.25
panel <- ggplot2::GeomSegment$draw_panel(unique(data), panel_scales, coord)
# Provisions for text formatting for plot readability.
data$y <- data$yend
data$angle <- 60
data$size <- 3.5
data$fill<-'white'
data$colour <- 'red'
data$lineheight <- 4
data$fontface <- 'bold'
data$family <- 'sans'
data$vjust <- 'top'
data$hjust <- 'left'
meta_data <- ggplot2::GeomText$draw_panel(unique(data), panel_scales, coord)
ggplot2:::ggname('geom_timeline_label', grid::grobTree(panel, meta_data))
}
)
cowboy2718/QuakeExplorer documentation built on May 26, 2019, 4:41 p.m.
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# Tony Gojanovic
# Coursera "R Programming Capstone Project"
# Final project
# August 2018
#' Geom for plotting earthquake data as a time series.
#'
#' @description This function provides a time series interpretation of earthquakes over time expressed as a bubble plot.
#' @note Countries and data ranges are modified using dplyr prior to using the geom for time series plotting.
#' For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @export
#' @import ggplot2
#' @import ggplot2 grid
#' @importFrom dplyr mutate
#' @export
#' @references
#' @examples
#' \dontrun{
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' # Additional graph metadata can be added using standard ggplot syntax
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<-any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + ggtitle("NOAA Significant Earthquake Data")
#'
#' p1<-p1 + ylab("Country")+xlab("Date") + scale_size_continuous(name = 'Richter')
#'
#' p1<-p1 +scale_color_continuous(name = 'Mortality (deaths)') +theme(axis.text.x=element_text(angle=90,hjust=3))
#'
#' p1
#'
#' }
#'
#' @details geom_timeline provides a time series interpretation on filtered NOAA Significant Earthquake data.
#' @note Aesthetic considerations for the number of countries and data ranges should be taken into consideration as to not plot too many data points rendering the graph unreadable.
#' Use of dplyr and piping to filter the data set is suggested.
#' Additional graphical elements can be added using standard ggplot syntax as shown in the example.
#'
geom_timeline <- function(mapping = NULL, data = NULL, stat = "identity",
position = 'identity', na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE,...) {
ggplot2::layer(
geom = geomtimeline, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# The following is a subroutine used for generating data bubbles on the timeline.
bubble_subroutine <- function(data, params, size) {
grid::circleGrob(r = data$size/25, gp = grid::gpar(col = data$color, fill = alpha(data$fill, data$alpha),lwd = 0.1))
}
geomtimeline <- ggplot2::ggproto('geomtimeline', ggplot2::Geom,
required_aes = c('x'),
default_aes = ggplot2::aes(y = NULL, color = 'red',
fill = 'black', size = 5, alpha = 0.3),
draw_key = bubble_subroutine,
draw_group = function(data, panel_params, coord) {
coords <- coord$transform(data, panel_params)
grid::circleGrob(
coords$x,
coords$y,
r = coords$size / 225,
gp = grid::gpar(
col = coords$colour,
fill = coords$fill,
alpha = coords$alpha
)
)
}
)
#' Geom for labeling the time series plot.
#'
#' @description This function provides label meta data for the time series plot of earthquakes.
#' @note For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @importFrom dplyr mutate
#' @import ggplot2
#' @references The following are usefule references:
#' \url{https://ggplot2.tidyverse.org/reference/geom_text.html}
#' \url{https://ggplot2.tidyverse.org/articles/ggplot2-specs.html}
#'
#' @examples
#' \dontrun{
#'
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<- any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA" | COUNTRY=="GREECE") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot()
#'
#' p1<-p1 + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + geom_timeline_label(aes(x = datevalue, y = COUNTRY, magnitude = EQ_PRIMARY,label = location_name, max_labels= 5))
#'
#' p1<-p1 + ggtitle("NOAA Signficant Earthquakes Data with High Profile Events")
#'
#' p1
#'
#' }
geom_timeline_label <-
function(mapping = NULL,
data = NULL,
stat = "identity",
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
...) {
ggplot2::layer(
geom = auto_labeler,
mapping = mapping,
data = data,
stat = stat,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
auto_labeler <-
ggplot2::ggproto(
"auto_labeler",
ggplot2::Geom,required_aes = c('x', 'label', 'magnitude','max_labels'),
default_aes = ggplot2::aes(
max_labels = 3,y = 0,color = 'red',size = 0.25, linetype = 11,alpha = 0.5
),
draw_key = bubble_subroutine,
draw_panel = function(data, panel_scales, coord) {
max_labels <- data$max_labels[1]
data <- data %>% dplyr::group_by(group) %>%dplyr::top_n(max_labels, magnitude)
data$xend <- data$x
data$yend <- data$y + 0.25
panel <- ggplot2::GeomSegment$draw_panel(unique(data), panel_scales, coord)
# Provisions for text formatting.
data$y <- data$yend
data$angle <- 60
data$fontface <- 'bold'
data$family <- 'sans'
data$size <- 3.5
data$fill<-'white'
data$colour <- 'red'
data$lineheight <- 4
data$hjust <- 'left'
data$vjust <- 'top'
meta_data <- ggplot2::GeomText$draw_panel(unique(data), panel_scales, coord)
ggplot2:::ggname('geom_timeline_label', grid::grobTree(panel, meta_data))
}
)
# Tony Gojanovic
# Coursera "R Programming Capstone Project"
# Final project
# August 2018
#' Geom for plotting earthquake data as a time series.
#'
#' @description This function provides a time series interpretation of earthquakes over time expressed as a bubble plot.
#' @note Countries and data ranges are modified using dplyr prior to using the geom for time series plotting.
#' For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @export
#' @import ggplot2
#' @import ggplot2 grid
#' @importFrom dplyr mutate
#' @export
#' @references
#' @examples
#' \dontrun{
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' # Additional graph metadata can be added using standard ggplot syntax
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<-any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot() + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + ggtitle("NOAA Significant Earthquake Data")
#'
#' p1<-p1 + ylab("Country")+xlab("Date") + scale_size_continuous(name = 'Richter')
#'
#' p1<-p1 +scale_color_continuous(name = 'Mortality (deaths)') +theme(axis.text.x=element_text(angle=90,hjust=3))
#'
#' p1
#'
#' }
#'
#' @details geom_timeline provides a time series interpretation on filtered NOAA Significant Earthquake data.
#' @note Aesthetic considerations for the number of countries and data ranges should be taken into consideration as to not plot too many data points rendering the graph unreadable.
#' Use of dplyr and piping to filter the data set is suggested.
#' Additional graphical elements can be added using standard ggplot syntax as shown in the example.
#'
geom_timeline <- function(mapping = NULL, data = NULL, stat = "identity",
position = 'identity', na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE,...) {
ggplot2::layer(
geom = geomtimeline, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# The following is a subroutine used for generating data bubbles on the timeline.
bubble_subroutine <- function(data, params, size) {
grid::circleGrob(r = data$size/25, gp = grid::gpar(col = data$color, fill = alpha(data$fill, data$alpha),lwd = 0.1))
}
geomtimeline <- ggplot2::ggproto('geomtimeline', ggplot2::Geom,
required_aes = c('x'),
default_aes = ggplot2::aes(y = NULL, color = 'red',
fill = 'black', size = 5, alpha = 0.3),
draw_key = bubble_subroutine,
draw_group = function(data, panel_params, coord) {
coords <- coord$transform(data, panel_params)
grid::circleGrob(coords$x,coords$y,r= coords$size / 225,gp = grid::gpar(alpha = coords$alpha,fill = coords$fill,col = coords$colour
)
)
}
)
#' Geom for labeling the time series plot.
#'
#' @description This function provides label meta data for the time series plot of earthquakes.
#' @note For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#' @importFrom dplyr mutate
#' @import ggplot2
#' @references The following are usefule references:
#' \url{https://ggplot2.tidyverse.org/reference/geom_text.html}
#' \url{https://ggplot2.tidyverse.org/articles/ggplot2-specs.html}
#'
#' @examples
#' \dontrun{
#'
#' # For BC date ranges, use a one sided inequality e.g. datevalue < '0000-01-01'
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' any_name_df<-eq_clean_data(results)
#'
#' p1<- any_name_df %>% filter(COUNTRY =="CHINA" | COUNTRY=="USA" | COUNTRY=="GREECE") %>%
#' filter(datevalue > '1900-01-01', datevalue < '1950-01-01') %>%
#' ggplot()
#'
#' p1<-p1 + geom_timeline(aes(x = datevalue, y = COUNTRY, color = TOTAL_DEATHS, size = EQ_PRIMARY))
#'
#' p1<-p1 + geom_timeline_label(aes(x = datevalue, y = COUNTRY, magnitude = EQ_PRIMARY,label = location_name, max_labels= 5))
#'
#' p1<-p1 + ggtitle("NOAA Signficant Earthquakes Data with High Profile Events")
#'
#' p1
#'
#' }
geom_timeline_label <-
function(data = NULL,na.rm = FALSE,show.legend = NA,mapping = NULL, stat = "identity", position = "identity", inherit.aes = TRUE,...)
{
ggplot2::layer(
geom = auto_labeler,
mapping = mapping,
data = data,
stat = stat,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
auto_labeler <-
ggplot2::ggproto(
"auto_labeler",
ggplot2::Geom,required_aes = c('x', 'label', 'magnitude','max_labels'),
default_aes = ggplot2::aes(
max_labels = 3,y = 0,color = 'red',size = 0.25, linetype = 11,alpha = 0.5
),
draw_key = bubble_subroutine,
draw_panel = function(data, panel_scales, coord) {
max_labels <- data$max_labels[1]
data <- data %>% dplyr::group_by(group) %>%dplyr::top_n(max_labels, magnitude)
data$xend <- data$x
data$yend <- data$y + 0.25
panel <- ggplot2::GeomSegment$draw_panel(unique(data), panel_scales, coord)
# Provisions for text formatting for plot readability.
data$y <- data$yend
data$angle <- 60
data$size <- 3.5
data$fill<-'white'
data$colour <- 'red'
data$lineheight <- 4
data$fontface <- 'bold'
data$family <- 'sans'
data$vjust <- 'top'
data$hjust <- 'left'
meta_data <- ggplot2::GeomText$draw_panel(unique(data), panel_scales, coord)
ggplot2:::ggname('geom_timeline_label', grid::grobTree(panel, meta_data))
}
)
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