benmarwick

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


## call required packages

benmarwick

# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {

benmarwick

library(rvest)
library(httr)
library(tidyverse)

# get list of regions in the W7W association
# via API
sota_regions_w7w <- "https://api2.sota.org.uk/api/associations/w7w"
r <- GET(sota_regions_w7w)
dat <- 
  jsonlite::fromJSON(content(r, as = "text")) %>% 

benmarwick

library(tidyverse)

# using expression() for the text formatting:
ggplot(mtcars,
       aes(disp,
           mpg)) +
geom_point() +
# ~ for spaces, and * for no-space between (unquoted) expressions
ylab(expression(Anthropogenic~SO[4]^{"2-"}~(ngm^-3))) + 
xlab(expression(italic(delta)^13*C[ap]*"‰")) + 

benmarwick

# Adapted from https://stackoverflow.com/a/7267364/1036500 by Andrie de Vries

# This is it: theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

library(ggplot2)

td <- expand.grid(
  hjust=c(0, 0.5, 1),
  vjust=c(0, 0.5, 1),
  angle=c(0, 45, 90),

benmarwick

#'  Bagplot
#' 
#' The bag geom is useful for graphical summaries of scatterplots. It 
#' is effective at showing the  location, spread, skewness, and 
#' outliers of a data set.
#'  
#' A bagplot is a bivariate generalization of the well known boxplot. It
#' was proposed by Rousseeuw, Ruts, and Tukey. This geom plots bagplots that 
#' are very similar to the one described in Rousseeuw et al. and
#' uses code from their bagplot functions in the aplpack pacakge. 

benmarwick

library(raster)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(tidyverse)
library(ggrepel)
library(ggspatial)

# get all summits via the GeoJSON download

benmarwick

##' Function readDFile
##' 
##' Function readDFile
##' @param pathname       the pathname of the directory containing the data to import
##' @return outData       Output is a matrix of ion counts with rows as scantime and 
##' columns as mass, and the respective values as labels
##' @export
readDFile<-function(pathname){
  
  filename<-file.path(pathname,'DATA.MS')

benmarwick

library(tidyverse) # you may need to install.packages("tidyverse") in the console
library(googlesheets4) # ditto

# change to your own UW email
gs4_auth("bmarwick@uw.edu")

# Get our google sheet of information about the location of each point
ms_data <- 
  read_sheet("https://docs.google.com/spreadsheets/d/1rDyfJQ6OnWJktIWMZfnLe8YRtCiW1PVrP9nKW1kERN4/edit?pli=1#gid=0") 

benmarwick

# another method
# remotes::install_github("ropenscilabs/reviewer")
browseURL(reviewer::diff_rmd("analysis/paper/paper.qmd", 
                             # this gets the sha of the previous commit
                             git2r::commits(n=2)[[2]]$sha)$raw)