eiballots

# Work with local files - avoids specifying source= in every call, e.g.:
options(data_dir = "~/florida_2000/")

# Add this to ~/.Rprofile to persist across sessions

library(eiballots)

# Browse available races and counties
list_elections()
list_elections(level = "federal")
list_elections(type  = "referendum")
list_counties()

# Load raw microdata
lee <- get_county_data("Lee")
df  <- get_election_data(c("PRE", "USS"))

# --- Ecological inference summaries ---

# Single race: marginal distributions only (no joint array)
pres <- ei_summary("PRE")
summary(pres)

# Two races: margins + 3-D joint array [precinct x PRE x USS]
obj <- ei_summary(c("PRE", "USS"))
print(obj)
summary(obj)
dim(obj$joint_precinct)          # [n_precincts, opts_PRE, opts_USS]

# Three races: 4-D joint array
obj3 <- ei_summary(c("PRE", "USS", "HOS3"))
dim(obj3$joint_precinct)         # [n_precincts, opts_PRE, opts_USS, opts_HOS3]

# Extract a 2-race subset from an existing object (no data reload)
obj2 <- obj3[c("PRE", "USS")]

# Convert to eipack wide format
obj_wide <- to_eipack(obj)       # margins become a single wide data.frame
obj_back <- to_lphom(obj_wide)   # convert back to list format

# Provide data directly (useful for testing or custom subsets)
obj <- ei_summary(c("PRE", "USS"), data = my_data_frame)

ei_summary(c("PRE", "USS"))

# $margins        - named list of data.frames, one per race (lphom format)
#   $PRE:  precinct_id | county_id | n_voters | R | D | O | A | I
#   $USS:  precinct_id | county_id | n_voters | R | D | O | A | I
#   (n_voters is identical across races: shared intersection universe)
#
# $joint_precinct - integer array [n_precincts x opts_PRE x opts_USS]
# $joint_total    - integer array [opts_PRE x opts_USS]  (sum over precincts)
# $meta           - list: elections, counties, n_precincts, n_voters,
#                         opt_levels, format, precinct_info, created_at