philchodrow / compx Goto Github PK

library(compx)
library(tidyverse)    # data import, manipulation, and viz. Need dev version of ggplot2 for viz
library(maptools)     # plot the tracts
library(RColorBrewer) # for plots
library(ggthemes)     # for theme_map
library(viridis)      # for color scales
library(units)        # needed by sf
library(sf)           # for io and viz
library(scales)       # for ggplot2 viz

detroit_tracts <- st_read("data/shps/milano_int.shp")
detroit_tracts %>% 
    ggplot() + 
    geom_sf() + 
    theme_map()

detroit_race <- read_csv('data/prova.csv')
detroit_race$n <- detroit_race$n
head(detroit_race)

tracts_to_use <- detroit_race 

tracts <- detroit_tracts %>% 
    filter(GEOID %in% tracts_to_use$tract)

data  <- tracts_to_use %>%
    filter(tract %in% tracts$GEOID) %>%
    group_by(tract) %>%
    ungroup() 

metric_df <- compute_metric(tracts, 
                            data, 
                            km = T,
                            r_sigma = 10,
                            s_sigma = 1, 
                            smooth = F,
                            hessian = euc_)
metric_df %>% head()

library(compx)        # main analysis functions
library(tidyverse)    # for data manipulation and viz
library(igraph)       # for network analysis
library(RColorBrewer) # for plots
library(ggthemes)     # for theme_map
library(units)        # needed for sf
library(sf)           # needed for io and visualization in this vignette
library(scales)       # for ggplot2 viz
library(RSpectra)     # fast eigenvalues, also used within compx

detroit_tracts <- st_read("data/shps/milano_int.shp")
detroit_tracts <- st_transform(detroit_tracts, 4326)

race_2010 <- read_csv('data/prova.csv')
race_2010$n <- race_2010$n

tracts_to_use <- race_2010 

tracts <- detroit_tracts %>% 
    filter(GEOID %in% tracts_to_use$tract)

js_dist <- function(n,m){
    
    N <- sum(n)
    M <- sum(m)
    
    p <- n/N
    q <- m/M
    
    sqrt((N/(M+N))*DKL(p, p+q) + (M/(M+N))*DKL(q, p+q))
}

g <- construct_information_graph(tracts = tracts, 
                                 data = race_2010, 
                                 divergence = js_dist)
g

Error: vector memory exhausted (limit reached?)
Traceback:

1. construct_information_graph(tracts = tracts, data = race_2010, 
 .     divergence = js_dist)
2. adj %>% undirect() %>% add_data(data_df) %>% information_distances(divergence) %>% 
 .     make_graph() %>% add_n(data_df) %>% add_coordinates(tracts)
3. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
4. eval(quote(`_fseq`(`_lhs`)), env, env)
5. eval(quote(`_fseq`(`_lhs`)), env, env)
6. `_fseq`(`_lhs`)
7. freduce(value, `_function_list`)
8. function_list[[i]](value)
9. add_n(., data_df)
10. h %>% igraph::set.vertex.attribute("n", index = lookup$key, value = lookup$n)
11. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
12. eval(quote(`_fseq`(`_lhs`)), env, env)
13. eval(quote(`_fseq`(`_lhs`)), env, env)
14. `_fseq`(`_lhs`)
15. freduce(value, `_function_list`)
16. withVisible(function_list[[k]](value))
17. function_list[[k]](value)
18. igraph::set.vertex.attribute(., "n", index = lookup$key, value = lookup$n)
19. i_set_vertex_attr(graph = graph, name = name, index = index, 
  .     value = value)

h$name <- lookup$n