In amcrisan/GEViTRec: GEViTRec

devtools::load_all() #temporary once things are done

Loading data

start_time <- Sys.time()
set.seed(416)

library(mincombinr) # for displaying the resulting and loading the data
devtools::load_all() #loading gevitrec

#Table data
tab_dat<-input_data(file = system.file("./inst/extdata/", "ebov_metadata.csv", package = "gevitRec"),dataType = "table")

#Tree data
tree_dat<-input_data(file = system.file("./inst/extdata/", "ebov_tree.nwk", package = "gevitRec"),dataType = "tree")

#Genomic data
genomic_dat<-input_data(file = system.file("./inst/extdata/", "ebov_GIN_genomic.fasta", package = "gevitRec"),dataType = "dna")

#Shape files
#Shape files require that .shp,.shx,and .prj files at a minimun to be in the same directory
#to add metadata to the shape file, you can also add .dbf files
gin_shape_dat<-input_data(file = system.file("./inst/extdata/", "gin_admbnda_adm1_ocha_itos.shp", package = "gevitRec"),dataType = "spatial")
lbr_shape_dat<-input_data(file = system.file("./inst/extdata/", "lbr_admbnda_adm1_ocha.shp", package = "gevitRec"),dataType = "spatial")
sle_shape_dat<-input_data(file = system.file("extdata/", "sle_admbnda_adm1_1m_gov_ocha_20161017.shp",package = "gevitRec"),dataType = "spatial")

Modify the spatial objections to that they're more interesting to work with

#join the spatial files
all_spatial<-join_spatial_data(gin_shape_dat,lbr_shape_dat,sle_shape_dat,obj_names = c("GIN","LBR","SLE"))

#GEViTRec does not perform any aggregation or detailed analysis on the data, so here I will modify and clean up the metadata
#To get it into some shape that I actually care about. Users don't have to do this step, but it can be helpful
#to derive additional information.

Data Harmonization

harmon_obj<-data_harmonization(tab_dat,tree_dat,genomic_dat,all_spatial)
view_entity_graph(harmon_obj[["entityGraph"]])

Now let's change the metadata up a bit, so that the program can link it more directly to the tabular data. The code below adds the tabular data site.id variable to the spatial metadata this will create more exact linkages.

meta_tmp<-data.frame(point_id = tab_dat@data$table$site_id,stringsAsFactors = FALSE)
all_spatial@data$metadata<-meta_tmp

harmon_obj<-data_harmonization(tab_dat,tree_dat,genomic_dat,all_spatial)
view_entity_graph(harmon_obj[["entityGraph"]])

It's also possible to filter according to the strength of the jaccard distance strength. A strength of 1 means that two variables in different datasets are effectively the same (have set equality), whereas 0 means that they have nothing in common.

view_entity_graph(subset_graph(harmon_obj[["entityGraph"]],1))

Chart Recommendations

With the data harmonized, it is possible to generate specifications for different charts. It possible to use the whole graph too.

** Note there are different recursive depths depending on the degree of connectivity

component_specs<-get_spec_list(harmon_obj)

#the messy code to make on figure
#IT WORKS IT BLOODY WORKS!

#But this runs into a bug ...

plot_view(component_specs,view_num=1)

plot_view(component_specs,view_num=2)

plot_view(component_specs,view_num=3)

plot_view(component_specs,view_num=4)

plot_view(component_specs,view_num=5)

end_time <- Sys.time()

print(end_time - start_time)

harmon_obj_sub<-harmon_obj
harmon_obj_sub[["entityGraph"]]<-subset_graph(harmon_obj_sub[["entityGraph"]],1)
tmp<-get_spec_list(harmon_obj_sub,usrChoices = c("country","year"))