ESSD figures

[stephpenn1]

Two options for fig 1 (map):

Beginning skeleton for fig 2:

[codecov-io]

Codecov Report

Merging #180 into master will increase coverage by 0.52%. The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #180      +/-   ##
==========================================
+ Coverage   97.84%   98.36%   +0.52%     
==========================================
  Files           8        8              
  Lines         741      977     +236     
==========================================
+ Hits          725      961     +236     
  Misses         16       16              

Impacted Files	Coverage Δ
R/utils.R	100.00% <0.00%> (ø)
R/parser.R	100.00% <0.00%> (ø)

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[bpbond]

Here's my code for the leaflet map:

# library(ggplot2)
# library(ggmap)
library(dplyr)
# library(mapdata)

library(leaflet)

x <- read.csv("/Users/d3x290/Dropbox/Documents/Work/Data-ongoing/Soil respiration database/github/srdb/srdb-data.csv")

#mp <- get_map(maptype = "terrain-background")

# Load the library
library(leaflet)

# Note: if you do not already installed it, install it with:
# install.packages("leaflet")

# Background 1: NASA
# m <- leaflet() %>% 
#   addTiles() %>% 
#   setView( lng = 2.34, lat = 48.85, zoom = 5 ) %>% 
#   addProviderTiles("NASAGIBS.ViirsEarthAtNight2012")
# m

df <- data.frame(lon = x$Longitude, lat = x$Latitude, Rh_annual = x$Rh_annual)
df <- subset(df, !is.na(lon) & !is.na(lat))
df_rh <- subset(df, !is.na(Rh_annual))

library(cosore)
csr <- csr_datbase()

# Background 2: World Imagery
m <- leaflet() %>% 
  addProviderTiles("Esri.WorldImagery") %>% 
  addCircleMarkers(lng = df_rh$lon, lat = df_rh$lat, radius = 0.5, color = "red") %>% 
  addCircleMarkers(lng = csr$CSR_LONGITUDE, lat = csr$CSR_LATITUDE)

[bpbond]

By the way @stephpenn1 for figure 2 I was thinking something more like this

[stephpenn1]

@bpbond so, you would like more temporal coverage displayed?

[bpbond]

The plot I put up is data density within months over time. There are other ways to do it, and other things to visualize...not sure. Could have data density by latitude, for example.

[bpbond]

@stephpenn1 Can we touch base about this on Tuesday?

[stephpenn1]

Yes

[stephpenn1]

Other options to discuss Tuesday (these are rough):

I do like your idea by latitude

I do like the idea of showing the data by latitude, will come up with a graph for that as well

[bpbond]

Wow!!!!!!!!! That waffle chart is gorgeous. 😍

[stephpenn1]

Fig 3:

still need to reorder the y axis so [-35,-30] is at the bottom, but theres a comment in the code to go back to it later

[stephpenn1]

And updated Fig 1.

[stephpenn1]

Note: most recent updates have been pushed and ready to merge if accepted

[bpbond]

👏 those look great!

So, figure 1 the map; figure 2 the waffle plot; and figure 3 the latitude one? Sounds good.