library(tidyverse)
library(fastDummies)
library(scales)
library(readxl)
library(here)

# Turn off grouping message
options(dplyr.summarise.inform = FALSE)

# Project-specific functions
source(here("R", "graphics.R"))

# General settings
source(here("analysis", "options.R"))

# Make all the randomness reproducible
set.seed(1234)
sim_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                      sheet = "Results", range = "O3:AU27")

orgs_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                       sheet = "Results", range = "N4:O27", 
                       col_names = c("organization", "org_details"))

orgs_clean <- orgs_raw %>% 
  separate(org_details, into = c("org_issue", "org_funding", "org_relationship"), sep = ", ")

personas_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                           sheet = "Results", range = "P2:AU3") %>% 
  pivot_longer(everything(), names_to = "persona", values_to = "persona_details")

personas_clean <- personas_raw %>% 
  separate(persona_details, into = c("demographics", "actions", "persona_trust"), sep = ", ") %>% 
  separate(demographics, into = c("persona_income", "persona_gender"), sep = " Income ") %>% 
  separate(actions, into = c("persona_ideology", "persona_experience"), sep = " that ") 

sim_long <- sim_raw %>% 
  rename(org_details = `...1`) %>% 
  pivot_longer(cols = -org_details, names_to = "persona_details", values_to = "share")

sim_excel_clean <- sim_long %>% 
  left_join(orgs_raw, by = "org_details") %>% 
  left_join(personas_raw, by = "persona_details") %>% 
  separate(org_details, into = c("org_issue", "org_funding", "org_relationship"), sep = ", ") %>% 
  separate(persona_details, into = c("demographics", "actions", "persona_trust"), sep = ", ") %>% 
  separate(demographics, into = c("persona_income", "persona_gender"), sep = " Income ") %>% 
  separate(actions, into = c("persona_ideology", "persona_experience"), sep = " that ") %>% 
  mutate(org_issue = recode(org_issue, "Refugess Relief" = "Refugee Relief"),
         org_funding = recode(org_funding, "Grants" = "Mostly funded by government grants",
                              "Small Donors" = "Mostly funded by small donors"),
         persona_income = paste(persona_income, "income"),
         persona_income = factor(persona_income, levels = c("Lower income", "Higher income"), ordered = TRUE),
         persona_trust = recode(persona_trust,
                                "Doesn't Trust or Donate" = "Less trusting; donates and volunteers less often",
                                "Trusts and Donates" = "More trusting; donates and volunteers often"),
         persona_experience = recode(persona_experience,
                                     "Doesn't Read or Travel" = "Doesn't follow news; has not travelled abroad",
                                     "Reads and Travels" = "Follows the news; has travelled abroad")) %>% 
  mutate_at(vars(organization, persona), ~fct_inorder(.))
# Gammas of the "everything" model
gammas <- read_rds(
  here('data', 'raw_data', 'posterior_draws', 'public_political_social_charity_demo.rds')
  ) %>% 
  group_by(i, j) %>% 
  summarize(gamma_mean = mean(Gamma)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = j, values_from = gamma_mean) %>% 
  select(-i) %>% 
  t()
organization_columns <- c("organization", "issue.area", "transparency", 
                          "accountability", "funding", "relationship")

organization_possibilities <- expand_grid(
  issue.area = c("Emergency response", "Environment", "Human rights", "Refugee relief"),
  relationship = c("Friendly", "Criticized", "Under crackdown"),
  funding = c("Small private donations", "Government grants")
) %>% 
  mutate(org_id = paste0("org", 1:n()))

organizations <- organization_possibilities %>% 
  mutate(
    organization = "Red Cross",
    transparency = "Yes",
    accountability = "Yes"
  ) %>% 
  select(org_id, all_of(organization_columns)) %>% 
  mutate(organization = factor(organization, 
                               levels = c("Amnesty International", "Greenpeace", 
                                          "Oxfam", "Red Cross")),
         issue.area = factor(issue.area, 
                            levels = c("Emergency response", "Environment", 
                                       "Human rights", "Refugee relief")),
         transparency = factor(transparency, levels = c("No", "Yes")),
         accountability = factor(accountability, levels = c("No", "Yes")),
         funding = factor(funding, levels = c("Small private donations", 
                                              "Handful of wealthy private donors", 
                                              "Government grants")),
         relationship = factor(relationship, levels = c("Friendly", "Criticized", 
                                                        "Under crackdown")))

org_table <- organizations %>% 
  dummy_columns(select_columns = organization_columns,
                remove_selected_columns = TRUE) %>% 
  pivot_longer(cols = -org_id, names_to = "name", ) %>% 
  pivot_wider(names_from = org_id, values_from = value) %>% 
  separate(name, into = c("attribute", "option"), sep = "_") %>% 
  filter(!(option %in% c("Emergency response", "No", "Small private donations", "Friendly")))
persona_columns <- c("national.news", "international.news", "medium.follow.tv", 
                     "medium.follow.print", "medium.follow.online", "medium.follow.social", 
                     "medium.follow.radio", "medium.follow.email", "medium.follow.app", 
                     "follow.govt", "travel", "ideology", "trust.institutions", 
                     "should.be.charitable", "religious.attendance", "religion.importance", 
                     "religion", "trust.charity.importance", "trust.charities", 
                     "donation.frequency", "donated.last.year", "volunteered", 
                     "volunteer.frequency", "history.activism.personal", "history.activism.family",
                     "membership.church", "membership.sport", "membership.art", "membership.union", 
                     "membership.party", "membership.environment", "membership.professional", 
                     "membership.charitable", "membership.consumer", "membership.other", 
                     "gender", "marital", "education", "income", "race", "age")

persona_demographics <- expand_grid(
  income = c("Less than median", "More than median"),
  education = c("High school graduate", "4 year degree"),
  religious.attendance = c("Rarely", "At least once a month")
) %>% 
  mutate(demographics_id = 1:n())

persona_politics <- tribble(
  ~ideology, ~national.news, ~international.news, ~travel,
  "Extremely liberal", "At least once a day",  "Always", "Yes",
  "Extremely conservative", "At least once a day", "Always", "Yes",
  "Extremely liberal", "Rarely", "Never", "No",
  "Extremely conservative", "Rarely", "Never", "No"
) %>% 
  mutate(politics_id = 1:n())

persona_social <- tibble(
  social_id = 1:2,
  trust.institutions = c("Complete trust", "No trust"),
  should.be.charitable = c("Strongly agree", "Strongly disagree"),
  trust.charities = c("Complete trust", "No trust"),
  volunteer.frequency = c("At least once a month", "Rarely"),
  donation.frequency = c("At least once a month", 
                         "More than once a month, less than once a year"),
  history.activism.personal = c("Involved", "Not involved"),
  membership.party = c("Active member", "Don't belong"),
  membership.charitable = c("Active member", "Don't belong")
)

persona_possibilities <- expand_grid(
  demographics_id = persona_demographics$demographics_id,
  politics_id = persona_politics$politics_id,
  social_id = persona_social$social_id
) %>% 
  left_join(persona_demographics, by = "demographics_id") %>% 
  left_join(persona_politics, by = "politics_id") %>% 
  left_join(persona_social, by = "social_id") %>% 
  select(-ends_with("_id")) %>% 
  mutate(persona_id = paste0("persona", 1:n()))

# Columns that should be held constant
# persona_columns %>% .[!(. %in% colnames(persona_possibilities))] %>% cat(sep = "\n")

personas <- persona_possibilities %>% 
  mutate(
    medium.follow.tv = "Follow",
    medium.follow.print = "Follow",
    medium.follow.online = "Don't follow",
    medium.follow.social = "Follow",
    medium.follow.radio = "Don't follow",
    medium.follow.email = "Don't follow",
    medium.follow.app = "Don't follow",
    follow.govt = "Often",
    religion.importance = "Important",
    religion = "Protestant",
    trust.charity.importance = "Somewhat important",
    donated.last.year = "$100-$499",
    volunteered = "No",
    history.activism.family = "Not involved",
    membership.church = "Don't belong",
    membership.sport = "Don't belong",
    membership.art = "Don't belong",
    membership.union = "Don't belong",
    membership.environment = "Don't belong",
    membership.professional = "Don't belong",
    membership.consumer = "Don't belong",
    membership.other = "Don't belong",
    gender = "Male",
    marital = "Married",
    race = "White",
    age = "Less than median"
  ) %>% 
  select(persona_id, all_of(persona_columns)) %>% 
  mutate(
    national.news = factor(national.news, levels = c("Rarely", "Once a week", "At least once a day")),
    international.news = factor(international.news, levels = c("Never", "Sometimes", "Always")),
    medium.follow.tv = factor(medium.follow.tv, levels = c("Don't follow", "Follow")),
    medium.follow.print = factor(medium.follow.print, levels = c("Don't follow", "Follow")),
    medium.follow.online = factor(medium.follow.online, levels = c("Don't follow", "Follow")),
    medium.follow.social = factor(medium.follow.social, levels = c("Don't follow", "Follow")),
    medium.follow.radio = factor(medium.follow.radio, levels = c("Don't follow", "Follow")),
    medium.follow.email = factor(medium.follow.email, levels = c("Don't follow", "Follow")),
    medium.follow.app = factor(medium.follow.app, levels = c("Don't follow", "Follow")),
    follow.govt = factor(follow.govt, levels = c("Not often", "Often")),
    travel = factor(travel, levels = c("No", "Yes")),
    ideology = factor(ideology, 
                      levels = c("Extremely liberal", "Somewhat liberal", 
                                 "Slightly liberal", "Moderate", "Slightly conservative", 
                                 "Somewhat conservative", "Extremely conservative")),
    trust.institutions = factor(trust.institutions,
                                levels = c("No trust", "Very little trust", "Little trust", 
                                           "Neutral", "Some trust", "A lot of trust", "Complete trust")),
    should.be.charitable = factor(should.be.charitable,
                                  levels = c("Strongly disagree", "Disagree", "Somewhat disagree", 
                                             "Neutral", "Somewhat agree", "Agree", "Strongly agree")),
    religious.attendance = factor(religious.attendance,
                                  levels = c("Not sure", "Rarely", "At least once a month")),
    religion.importance = factor(religion.importance,
                                 levels = c("Not important", "Important")),
    religion = factor(religion, 
                      levels = c("Catholic", "Protestant", "Christian Orthodox", "Jewish", 
                                 "Muslim", "Sikh", "Hindu", "Buddhist", "Atheist", "Other")),
    trust.charity.importance = factor(trust.charity.importance,
                                      levels = c("Not important", "Very unimportant", 
                                                 "Somewhat unimportant", "Neutral", 
                                                 "Somewhat important", "Very important", "Essential")),
    trust.charities = factor(trust.charities,
                             levels = c("No trust", "Very little trust", "Little trust", 
                                        "Neutral", "Some trust", "A lot of trust", "Complete trust")),
    donation.frequency = factor(donation.frequency,
                                levels = c("More than once a month, less than once a year", "At least once a month")),
    donated.last.year = factor(donated.last.year,
                               levels = c("$1-$49", "$50-$99", "$100-$499", "$500-$999", 
                                          "$1000-$4,999", "$5000-$9,999", "$10,000+")),
    volunteered = factor(volunteered, levels = c("No", "Yes")),
    volunteer.frequency = factor(volunteer.frequency,
                                 levels = c("Haven't volunteered in past 12 months", "Rarely", 
                                            "More than once a month, less than once a year", 
                                            "At least once a month")),
    history.activism.personal = factor(history.activism.personal,
                                       levels = c("Not involved", "Involved")),
    history.activism.family = factor(history.activism.family,
                                     levels = c("Not involved", "Involved")),
    membership.church = factor(membership.church,
                               levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.sport = factor(membership.sport,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.art = factor(membership.art,
                            levels = c("Don't belong", "Inactive member", "Active member")),
    membership.union = factor(membership.union,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.party = factor(membership.party,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.environment = factor(membership.environment,
                                    levels = c("Don't belong", "Inactive member", "Active member")),
    membership.professional = factor(membership.professional,
                                     levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.charitable = factor(membership.charitable,
                                   levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.consumer = factor(membership.consumer,
                                 levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.other = factor(membership.other,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    gender = factor(gender,
                    levels = c("Male", "Female", "Transgender", "Prefer not to say", "Other")),
    marital = factor(marital,
                     levels = c("Married", "Widowed", "Divorced", "Separated", "Never married")),
    education = factor(education,
                       levels = c("Less than high school", "High school graduate", "Some college", 
                                  "2 year degree", "4 year degree", "Graduate or professional degree", "Doctorate")),
    income = factor(income,
                    levels = c("Less than median", "More than median")),
    race = factor(race,
                  levels = c("White", "Black or African American", "American Indian or Alaska Native", 
                             "Asian", "Native Hawaiian or Pacific Islander", "Other")),
    age = factor(age,
                 levels = c("Less than median", "More than median"))
  )

persona_base_cases <- c(
  "national.news_Rarely",
  "international.news_Never",
  "follow.govt_Not often",
  "travel_No",
  "ideology_Extremely liberal",
  "trust.institutions_No trust",
  "should.be.charitable_Strongly disagree",
  "religious.attendance_Not sure",
  "religion.importance_Not important",
  "religion_Catholic",
  "trust.charity.importance_Not important",
  "trust.charities_No trust",
  "donation.frequency_More than once a month, less than once a year",
  "donated.last.year_$1-$49",
  "volunteered_No",
  "volunteer.frequency_Haven't volunteered in past 12 months",
  "history.activism.personal_Not involved",
  "history.activism.family_Not involved",
  "gender_Male",
  "marital_Married",
  "education_Less than high school",
  "income_Less than median",
  "age_Less than median"
)

persona_table <- personas %>% 
  dummy_columns(select_columns = persona_columns,
                remove_selected_columns = TRUE) %>% 
  pivot_longer(cols = -persona_id, names_to = "name", ) %>% 
  pivot_wider(names_from = persona_id, values_from = value) %>% 
  # Get rid of base cases
  filter(!(name %in% persona_base_cases)) %>% 
  filter(!str_detect(name, "Don't follow"),
         !str_detect(name, "Don't belong")) %>% 
  separate(name, into = c("attribute", "option"), sep = "_") %>% 
  # Add intercept
  add_row(attribute = "Intercept", option = "Intercept", .before = 1) %>% 
  mutate(across(everything(), ~replace_na(., 1)))
predicted_betas <- persona_table %>% 
  summarize(across(starts_with("persona"), ~as.matrix(gammas) %*% as.matrix(.)))

expected_utilities <- exp(t(org_table[,3:ncol(org_table)]) %*% 
                            as.matrix(predicted_betas[,1:ncol(predicted_betas)])) %>% 
  as_tibble(rownames = "org")

prop_expected_utilities <- expected_utilities %>% 
  mutate(across(starts_with("persona"), ~. / sum(.)))
org_details <- organizations %>% 
  mutate(organization = str_replace(org_id, "org", "Org "),
         funding = fct_recode(funding, 
                              "Mostly funded by small donors" = "Small private donations",
                              "Mostly funded by government grants" = "Government grants"),
         relationship = fct_rev(relationship)) %>%
  select(org_id, organization, org_issue = issue.area, 
         org_funding = funding, org_relationship = relationship)

persona_details <- personas %>% 
  mutate(persona_income = fct_recode(income, 
                                     "Lower income" = "Less than median", 
                                     "Higher income" = "More than median"),
         persona_education = fct_recode(education, "College graduate" = "4 year degree"),
         persona_religion = fct_recode(religious.attendance, 
                                       "Rarely attends religious services" = "Rarely",
                                       "Attends at least monthly" = "At least once a month"),
         persona_ideology = fct_recode(ideology, 
                                       "Conservative" = "Extremely conservative",
                                       "Liberal" = "Extremely liberal"),
         persona_experience = ifelse(travel == "Yes", 
                                     "Follows the news; has travelled abroad", 
                                     "Doesn't follow news; has not travelled abroad"),
         persona_trust = ifelse(trust.charities == "Complete trust", 
                                "More trusting; donates and volunteers often", 
                                "Less trusting; donates and volunteers less often")) %>% 
  mutate(persona = str_replace(persona_id, "persona", "Persona ")) %>% 
  select(persona, starts_with("persona"))

sim_clean <- prop_expected_utilities %>% 
  pivot_longer(cols = -org, names_to = "persona_id", values_to = "share") %>% 
  left_join(org_details, by = c("org" = "org_id")) %>% 
  left_join(persona_details, by = "persona_id")
write_rds(sim_excel_clean, here("data", "derived_data", "sim_excel_final.rds"))
write_rds(sim_clean, here("data", "derived_data", "sim_final.rds"))


Original computing environment

devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.2 (2020-06-22)
##  os       macOS Catalina 10.15.6      
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2020-10-01                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date       lib source        
##  assertthat    0.2.1   2019-03-21 [1] CRAN (R 4.0.0)
##  backports     1.1.9   2020-08-24 [1] CRAN (R 4.0.2)
##  base64enc     0.1-3   2015-07-28 [1] CRAN (R 4.0.0)
##  blob          1.2.1   2020-01-20 [1] CRAN (R 4.0.0)
##  broom         0.7.0   2020-07-09 [1] CRAN (R 4.0.2)
##  callr         3.4.3   2020-03-28 [1] CRAN (R 4.0.0)
##  cellranger    1.1.0   2016-07-27 [1] CRAN (R 4.0.0)
##  cli           2.0.2   2020-02-28 [1] CRAN (R 4.0.0)
##  colorspace    1.4-1   2019-03-18 [1] CRAN (R 4.0.0)
##  crayon        1.3.4   2017-09-16 [1] CRAN (R 4.0.0)
##  data.table    1.13.0  2020-07-24 [1] CRAN (R 4.0.2)
##  DBI           1.1.0   2019-12-15 [1] CRAN (R 4.0.0)
##  dbplyr        1.4.4   2020-05-27 [1] CRAN (R 4.0.2)
##  desc          1.2.0   2018-05-01 [1] CRAN (R 4.0.0)
##  devtools      2.3.1   2020-07-21 [1] CRAN (R 4.0.2)
##  digest        0.6.25  2020-02-23 [1] CRAN (R 4.0.0)
##  dplyr       * 1.0.2   2020-08-18 [1] CRAN (R 4.0.2)
##  ellipsis      0.3.1   2020-05-15 [1] CRAN (R 4.0.0)
##  evaluate      0.14    2019-05-28 [1] CRAN (R 4.0.0)
##  fansi         0.4.1   2020-01-08 [1] CRAN (R 4.0.0)
##  fastDummies * 1.6.2   2020-09-16 [1] CRAN (R 4.0.2)
##  forcats     * 0.5.0   2020-03-01 [1] CRAN (R 4.0.0)
##  fs            1.5.0   2020-07-31 [1] CRAN (R 4.0.2)
##  generics      0.0.2   2018-11-29 [1] CRAN (R 4.0.0)
##  ggplot2     * 3.3.2   2020-06-19 [1] CRAN (R 4.0.2)
##  glue          1.4.2   2020-08-27 [1] CRAN (R 4.0.2)
##  gtable        0.3.0   2019-03-25 [1] CRAN (R 4.0.0)
##  haven         2.3.1   2020-06-01 [1] CRAN (R 4.0.2)
##  here        * 0.1     2017-05-28 [1] CRAN (R 4.0.0)
##  hms           0.5.3   2020-01-08 [1] CRAN (R 4.0.0)
##  htmltools     0.5.0   2020-06-16 [1] CRAN (R 4.0.0)
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##  jsonlite      1.7.0   2020-06-25 [1] CRAN (R 4.0.2)
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##  magrittr      1.5     2014-11-22 [1] CRAN (R 4.0.0)
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##  munsell       0.5.0   2018-06-12 [1] CRAN (R 4.0.0)
##  pander        0.6.3   2018-11-06 [1] CRAN (R 4.0.0)
##  pillar        1.4.6   2020-07-10 [1] CRAN (R 4.0.2)
##  pkgbuild      1.1.0   2020-07-13 [1] CRAN (R 4.0.2)
##  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.0.0)
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##  rvest         0.3.6   2020-07-25 [1] CRAN (R 4.0.2)
##  scales      * 1.1.1   2020-05-11 [1] CRAN (R 4.0.0)
##  sessioninfo   1.1.1   2018-11-05 [1] CRAN (R 4.0.0)
##  stringi       1.4.6   2020-02-17 [1] CRAN (R 4.0.0)
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##  testthat      2.3.2   2020-03-02 [1] CRAN (R 4.0.0)
##  tibble      * 3.0.3   2020-07-10 [1] CRAN (R 4.0.2)
##  tidyr       * 1.1.2   2020-08-27 [1] CRAN (R 4.0.2)
##  tidyselect    1.1.0   2020-05-11 [1] CRAN (R 4.0.0)
##  tidyverse   * 1.3.0   2019-11-21 [1] CRAN (R 4.0.0)
##  usethis       1.6.1   2020-04-29 [1] CRAN (R 4.0.0)
##  vctrs         0.3.4   2020-08-29 [1] CRAN (R 4.0.2)
##  viridisLite   0.3.0   2018-02-01 [1] CRAN (R 4.0.0)
##  withr         2.2.0   2020-04-20 [1] CRAN (R 4.0.0)
##  xfun          0.16    2020-07-24 [1] CRAN (R 4.0.2)
##  xml2          1.3.2   2020-04-23 [1] CRAN (R 4.0.0)
##  yaml          2.2.1   2020-02-01 [1] CRAN (R 4.0.0)
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
writeLines(readLines(file.path(Sys.getenv("HOME"), ".R/Makevars")))
## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
## 
## CXX14FLAGS=-O3 -march=native -mtune=native -fPIC
---
title: "Generate and clean simulated data"
author: "Suparna Chaudhry, Marc Dotson, and Andrew Heiss"
date: "Last run: `r format(Sys.time(), '%F')`"
output: 
  html_document:
    code_folding: show
    pandoc_args:
      - "--default-image-extension=png"
editor_options: 
  chunk_output_type: console
---

```{r load-packages, warning=FALSE, message=FALSE}
library(tidyverse)
library(fastDummies)
library(scales)
library(readxl)
library(here)

# Turn off grouping message
options(dplyr.summarise.inform = FALSE)

# Project-specific functions
source(here("R", "graphics.R"))

# General settings
source(here("analysis", "options.R"))

# Make all the randomness reproducible
set.seed(1234)
```

```{r simpler-excel-based-model, message=FALSE}
sim_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                      sheet = "Results", range = "O3:AU27")

orgs_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                       sheet = "Results", range = "N4:O27", 
                       col_names = c("organization", "org_details"))

orgs_clean <- orgs_raw %>% 
  separate(org_details, into = c("org_issue", "org_funding", "org_relationship"), sep = ", ")

personas_raw <- read_excel(here("data", "raw_data", "Market Simulator Version 01.xlsx"),
                           sheet = "Results", range = "P2:AU3") %>% 
  pivot_longer(everything(), names_to = "persona", values_to = "persona_details")

personas_clean <- personas_raw %>% 
  separate(persona_details, into = c("demographics", "actions", "persona_trust"), sep = ", ") %>% 
  separate(demographics, into = c("persona_income", "persona_gender"), sep = " Income ") %>% 
  separate(actions, into = c("persona_ideology", "persona_experience"), sep = " that ") 

sim_long <- sim_raw %>% 
  rename(org_details = `...1`) %>% 
  pivot_longer(cols = -org_details, names_to = "persona_details", values_to = "share")

sim_excel_clean <- sim_long %>% 
  left_join(orgs_raw, by = "org_details") %>% 
  left_join(personas_raw, by = "persona_details") %>% 
  separate(org_details, into = c("org_issue", "org_funding", "org_relationship"), sep = ", ") %>% 
  separate(persona_details, into = c("demographics", "actions", "persona_trust"), sep = ", ") %>% 
  separate(demographics, into = c("persona_income", "persona_gender"), sep = " Income ") %>% 
  separate(actions, into = c("persona_ideology", "persona_experience"), sep = " that ") %>% 
  mutate(org_issue = recode(org_issue, "Refugess Relief" = "Refugee Relief"),
         org_funding = recode(org_funding, "Grants" = "Mostly funded by government grants",
                              "Small Donors" = "Mostly funded by small donors"),
         persona_income = paste(persona_income, "income"),
         persona_income = factor(persona_income, levels = c("Lower income", "Higher income"), ordered = TRUE),
         persona_trust = recode(persona_trust,
                                "Doesn't Trust or Donate" = "Less trusting; donates and volunteers less often",
                                "Trusts and Donates" = "More trusting; donates and volunteers often"),
         persona_experience = recode(persona_experience,
                                     "Doesn't Read or Travel" = "Doesn't follow news; has not travelled abroad",
                                     "Reads and Travels" = "Follows the news; has travelled abroad")) %>% 
  mutate_at(vars(organization, persona), ~fct_inorder(.))
```

```{r load-data, message=FALSE}
# Gammas of the "everything" model
gammas <- read_rds(
  here('data', 'raw_data', 'posterior_draws', 'public_political_social_charity_demo.rds')
  ) %>% 
  group_by(i, j) %>% 
  summarize(gamma_mean = mean(Gamma)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = j, values_from = gamma_mean) %>% 
  select(-i) %>% 
  t()
```

```{r possible-organizations}
organization_columns <- c("organization", "issue.area", "transparency", 
                          "accountability", "funding", "relationship")

organization_possibilities <- expand_grid(
  issue.area = c("Emergency response", "Environment", "Human rights", "Refugee relief"),
  relationship = c("Friendly", "Criticized", "Under crackdown"),
  funding = c("Small private donations", "Government grants")
) %>% 
  mutate(org_id = paste0("org", 1:n()))

organizations <- organization_possibilities %>% 
  mutate(
    organization = "Red Cross",
    transparency = "Yes",
    accountability = "Yes"
  ) %>% 
  select(org_id, all_of(organization_columns)) %>% 
  mutate(organization = factor(organization, 
                               levels = c("Amnesty International", "Greenpeace", 
                                          "Oxfam", "Red Cross")),
         issue.area = factor(issue.area, 
                            levels = c("Emergency response", "Environment", 
                                       "Human rights", "Refugee relief")),
         transparency = factor(transparency, levels = c("No", "Yes")),
         accountability = factor(accountability, levels = c("No", "Yes")),
         funding = factor(funding, levels = c("Small private donations", 
                                              "Handful of wealthy private donors", 
                                              "Government grants")),
         relationship = factor(relationship, levels = c("Friendly", "Criticized", 
                                                        "Under crackdown")))

org_table <- organizations %>% 
  dummy_columns(select_columns = organization_columns,
                remove_selected_columns = TRUE) %>% 
  pivot_longer(cols = -org_id, names_to = "name", ) %>% 
  pivot_wider(names_from = org_id, values_from = value) %>% 
  separate(name, into = c("attribute", "option"), sep = "_") %>% 
  filter(!(option %in% c("Emergency response", "No", "Small private donations", "Friendly")))
```

```{r possible-personas}
persona_columns <- c("national.news", "international.news", "medium.follow.tv", 
                     "medium.follow.print", "medium.follow.online", "medium.follow.social", 
                     "medium.follow.radio", "medium.follow.email", "medium.follow.app", 
                     "follow.govt", "travel", "ideology", "trust.institutions", 
                     "should.be.charitable", "religious.attendance", "religion.importance", 
                     "religion", "trust.charity.importance", "trust.charities", 
                     "donation.frequency", "donated.last.year", "volunteered", 
                     "volunteer.frequency", "history.activism.personal", "history.activism.family",
                     "membership.church", "membership.sport", "membership.art", "membership.union", 
                     "membership.party", "membership.environment", "membership.professional", 
                     "membership.charitable", "membership.consumer", "membership.other", 
                     "gender", "marital", "education", "income", "race", "age")

persona_demographics <- expand_grid(
  income = c("Less than median", "More than median"),
  education = c("High school graduate", "4 year degree"),
  religious.attendance = c("Rarely", "At least once a month")
) %>% 
  mutate(demographics_id = 1:n())

persona_politics <- tribble(
  ~ideology, ~national.news, ~international.news, ~travel,
  "Extremely liberal", "At least once a day",  "Always", "Yes",
  "Extremely conservative", "At least once a day", "Always", "Yes",
  "Extremely liberal", "Rarely", "Never", "No",
  "Extremely conservative", "Rarely", "Never", "No"
) %>% 
  mutate(politics_id = 1:n())

persona_social <- tibble(
  social_id = 1:2,
  trust.institutions = c("Complete trust", "No trust"),
  should.be.charitable = c("Strongly agree", "Strongly disagree"),
  trust.charities = c("Complete trust", "No trust"),
  volunteer.frequency = c("At least once a month", "Rarely"),
  donation.frequency = c("At least once a month", 
                         "More than once a month, less than once a year"),
  history.activism.personal = c("Involved", "Not involved"),
  membership.party = c("Active member", "Don't belong"),
  membership.charitable = c("Active member", "Don't belong")
)

persona_possibilities <- expand_grid(
  demographics_id = persona_demographics$demographics_id,
  politics_id = persona_politics$politics_id,
  social_id = persona_social$social_id
) %>% 
  left_join(persona_demographics, by = "demographics_id") %>% 
  left_join(persona_politics, by = "politics_id") %>% 
  left_join(persona_social, by = "social_id") %>% 
  select(-ends_with("_id")) %>% 
  mutate(persona_id = paste0("persona", 1:n()))

# Columns that should be held constant
# persona_columns %>% .[!(. %in% colnames(persona_possibilities))] %>% cat(sep = "\n")

personas <- persona_possibilities %>% 
  mutate(
    medium.follow.tv = "Follow",
    medium.follow.print = "Follow",
    medium.follow.online = "Don't follow",
    medium.follow.social = "Follow",
    medium.follow.radio = "Don't follow",
    medium.follow.email = "Don't follow",
    medium.follow.app = "Don't follow",
    follow.govt = "Often",
    religion.importance = "Important",
    religion = "Protestant",
    trust.charity.importance = "Somewhat important",
    donated.last.year = "$100-$499",
    volunteered = "No",
    history.activism.family = "Not involved",
    membership.church = "Don't belong",
    membership.sport = "Don't belong",
    membership.art = "Don't belong",
    membership.union = "Don't belong",
    membership.environment = "Don't belong",
    membership.professional = "Don't belong",
    membership.consumer = "Don't belong",
    membership.other = "Don't belong",
    gender = "Male",
    marital = "Married",
    race = "White",
    age = "Less than median"
  ) %>% 
  select(persona_id, all_of(persona_columns)) %>% 
  mutate(
    national.news = factor(national.news, levels = c("Rarely", "Once a week", "At least once a day")),
    international.news = factor(international.news, levels = c("Never", "Sometimes", "Always")),
    medium.follow.tv = factor(medium.follow.tv, levels = c("Don't follow", "Follow")),
    medium.follow.print = factor(medium.follow.print, levels = c("Don't follow", "Follow")),
    medium.follow.online = factor(medium.follow.online, levels = c("Don't follow", "Follow")),
    medium.follow.social = factor(medium.follow.social, levels = c("Don't follow", "Follow")),
    medium.follow.radio = factor(medium.follow.radio, levels = c("Don't follow", "Follow")),
    medium.follow.email = factor(medium.follow.email, levels = c("Don't follow", "Follow")),
    medium.follow.app = factor(medium.follow.app, levels = c("Don't follow", "Follow")),
    follow.govt = factor(follow.govt, levels = c("Not often", "Often")),
    travel = factor(travel, levels = c("No", "Yes")),
    ideology = factor(ideology, 
                      levels = c("Extremely liberal", "Somewhat liberal", 
                                 "Slightly liberal", "Moderate", "Slightly conservative", 
                                 "Somewhat conservative", "Extremely conservative")),
    trust.institutions = factor(trust.institutions,
                                levels = c("No trust", "Very little trust", "Little trust", 
                                           "Neutral", "Some trust", "A lot of trust", "Complete trust")),
    should.be.charitable = factor(should.be.charitable,
                                  levels = c("Strongly disagree", "Disagree", "Somewhat disagree", 
                                             "Neutral", "Somewhat agree", "Agree", "Strongly agree")),
    religious.attendance = factor(religious.attendance,
                                  levels = c("Not sure", "Rarely", "At least once a month")),
    religion.importance = factor(religion.importance,
                                 levels = c("Not important", "Important")),
    religion = factor(religion, 
                      levels = c("Catholic", "Protestant", "Christian Orthodox", "Jewish", 
                                 "Muslim", "Sikh", "Hindu", "Buddhist", "Atheist", "Other")),
    trust.charity.importance = factor(trust.charity.importance,
                                      levels = c("Not important", "Very unimportant", 
                                                 "Somewhat unimportant", "Neutral", 
                                                 "Somewhat important", "Very important", "Essential")),
    trust.charities = factor(trust.charities,
                             levels = c("No trust", "Very little trust", "Little trust", 
                                        "Neutral", "Some trust", "A lot of trust", "Complete trust")),
    donation.frequency = factor(donation.frequency,
                                levels = c("More than once a month, less than once a year", "At least once a month")),
    donated.last.year = factor(donated.last.year,
                               levels = c("$1-$49", "$50-$99", "$100-$499", "$500-$999", 
                                          "$1000-$4,999", "$5000-$9,999", "$10,000+")),
    volunteered = factor(volunteered, levels = c("No", "Yes")),
    volunteer.frequency = factor(volunteer.frequency,
                                 levels = c("Haven't volunteered in past 12 months", "Rarely", 
                                            "More than once a month, less than once a year", 
                                            "At least once a month")),
    history.activism.personal = factor(history.activism.personal,
                                       levels = c("Not involved", "Involved")),
    history.activism.family = factor(history.activism.family,
                                     levels = c("Not involved", "Involved")),
    membership.church = factor(membership.church,
                               levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.sport = factor(membership.sport,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.art = factor(membership.art,
                            levels = c("Don't belong", "Inactive member", "Active member")),
    membership.union = factor(membership.union,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.party = factor(membership.party,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    membership.environment = factor(membership.environment,
                                    levels = c("Don't belong", "Inactive member", "Active member")),
    membership.professional = factor(membership.professional,
                                     levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.charitable = factor(membership.charitable,
                                   levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.consumer = factor(membership.consumer,
                                 levels  = c("Don't belong", "Inactive member", "Active member")),
    membership.other = factor(membership.other,
                              levels = c("Don't belong", "Inactive member", "Active member")),
    gender = factor(gender,
                    levels = c("Male", "Female", "Transgender", "Prefer not to say", "Other")),
    marital = factor(marital,
                     levels = c("Married", "Widowed", "Divorced", "Separated", "Never married")),
    education = factor(education,
                       levels = c("Less than high school", "High school graduate", "Some college", 
                                  "2 year degree", "4 year degree", "Graduate or professional degree", "Doctorate")),
    income = factor(income,
                    levels = c("Less than median", "More than median")),
    race = factor(race,
                  levels = c("White", "Black or African American", "American Indian or Alaska Native", 
                             "Asian", "Native Hawaiian or Pacific Islander", "Other")),
    age = factor(age,
                 levels = c("Less than median", "More than median"))
  )

persona_base_cases <- c(
  "national.news_Rarely",
  "international.news_Never",
  "follow.govt_Not often",
  "travel_No",
  "ideology_Extremely liberal",
  "trust.institutions_No trust",
  "should.be.charitable_Strongly disagree",
  "religious.attendance_Not sure",
  "religion.importance_Not important",
  "religion_Catholic",
  "trust.charity.importance_Not important",
  "trust.charities_No trust",
  "donation.frequency_More than once a month, less than once a year",
  "donated.last.year_$1-$49",
  "volunteered_No",
  "volunteer.frequency_Haven't volunteered in past 12 months",
  "history.activism.personal_Not involved",
  "history.activism.family_Not involved",
  "gender_Male",
  "marital_Married",
  "education_Less than high school",
  "income_Less than median",
  "age_Less than median"
)

persona_table <- personas %>% 
  dummy_columns(select_columns = persona_columns,
                remove_selected_columns = TRUE) %>% 
  pivot_longer(cols = -persona_id, names_to = "name", ) %>% 
  pivot_wider(names_from = persona_id, values_from = value) %>% 
  # Get rid of base cases
  filter(!(name %in% persona_base_cases)) %>% 
  filter(!str_detect(name, "Don't follow"),
         !str_detect(name, "Don't belong")) %>% 
  separate(name, into = c("attribute", "option"), sep = "_") %>% 
  # Add intercept
  add_row(attribute = "Intercept", option = "Intercept", .before = 1) %>% 
  mutate(across(everything(), ~replace_na(., 1)))
```

```{r predicted-market-shares}
predicted_betas <- persona_table %>% 
  summarize(across(starts_with("persona"), ~as.matrix(gammas) %*% as.matrix(.)))

expected_utilities <- exp(t(org_table[,3:ncol(org_table)]) %*% 
                            as.matrix(predicted_betas[,1:ncol(predicted_betas)])) %>% 
  as_tibble(rownames = "org")

prop_expected_utilities <- expected_utilities %>% 
  mutate(across(starts_with("persona"), ~. / sum(.)))
```

```{r finalize-data}
org_details <- organizations %>% 
  mutate(organization = str_replace(org_id, "org", "Org "),
         funding = fct_recode(funding, 
                              "Mostly funded by small donors" = "Small private donations",
                              "Mostly funded by government grants" = "Government grants"),
         relationship = fct_rev(relationship)) %>%
  select(org_id, organization, org_issue = issue.area, 
         org_funding = funding, org_relationship = relationship)

persona_details <- personas %>% 
  mutate(persona_income = fct_recode(income, 
                                     "Lower income" = "Less than median", 
                                     "Higher income" = "More than median"),
         persona_education = fct_recode(education, "College graduate" = "4 year degree"),
         persona_religion = fct_recode(religious.attendance, 
                                       "Rarely attends religious services" = "Rarely",
                                       "Attends at least monthly" = "At least once a month"),
         persona_ideology = fct_recode(ideology, 
                                       "Conservative" = "Extremely conservative",
                                       "Liberal" = "Extremely liberal"),
         persona_experience = ifelse(travel == "Yes", 
                                     "Follows the news; has travelled abroad", 
                                     "Doesn't follow news; has not travelled abroad"),
         persona_trust = ifelse(trust.charities == "Complete trust", 
                                "More trusting; donates and volunteers often", 
                                "Less trusting; donates and volunteers less often")) %>% 
  mutate(persona = str_replace(persona_id, "persona", "Persona ")) %>% 
  select(persona, starts_with("persona"))

sim_clean <- prop_expected_utilities %>% 
  pivot_longer(cols = -org, names_to = "persona_id", values_to = "share") %>% 
  left_join(org_details, by = c("org" = "org_id")) %>% 
  left_join(persona_details, by = "persona_id")
```

```{r save-clean-data}
write_rds(sim_excel_clean, here("data", "derived_data", "sim_excel_final.rds"))
write_rds(sim_clean, here("data", "derived_data", "sim_final.rds"))
```


\

# Original computing environment

<button data-toggle="collapse" data-target="#sessioninfo" class="btn btn-primary btn-md btn-info">Here's what we used the last time we built this page</button>

<div id="sessioninfo" class="collapse">

```{r show-session-info, echo=TRUE, width=100}
devtools::session_info()

writeLines(readLines(file.path(Sys.getenv("HOME"), ".R/Makevars")))
```

</div>
