Tables

sim_clean <- read_rds(here("data", "derived_data", "sim_final.rds"))
results <- read_rds(here("data", "raw_data", "final_data.rds"))

org_attributes <- tribble(
  ~`Issue area`, ~`Relationship with government`, ~`Funding`, ~`Funding sources`, ~`Organizational practices`, ~`Organization`,
  "Emergency response", "Friendly", "Small private donors", "Small private donors", "Financial transparency", "Amnesty International",
  "Environment", "Criticized", "Government grants", "Wealthy donors", "Accountability", "Greenpeace",
  "Human rights", "Crackdown", "", "Government grants", "", "Oxfam",
  "Refugee relief", "", "", "", "", "Red Cross"
)

persona_attributes <- tribble(
  ~`Demographics`, ~`Politics and public affairs`, ~`Social views`,
  "Higher income (> US median ($61,372)), high school graduate, frequent religious attendance", "Liberal (1), follows national and international news often, has traveled internationally", "High social trust: Trusts political institutions, trusts charities, thinks people should be more charitable, frequently volunteers, donates once a month, has a history of personal activism, is a member of an association",
  "Lower income (< US median), high school graduate, frequent religious attendance", "Conservative (7), follows news, has traveled", "Low social trust: Does not trust political institutions or charities, thinks people should be less charitable, does not volunteer or donate often, has no history of personal activism, is not a member of an association",
  "Higher income, college graduate, frequent religious attendance", "Liberal, does not follow news, has not traveled", "",
  "Lower income, college graduate, frequent religious attendance", "Conservative, does not follow news, has not traveled", "",
  "Higher income, high school graduate, rare religious attendance", "", "",
  "Lower income, high school graduate, rare religious attendance", "", "",
  "Higher income, college graduate, rare religious attendance", "", "",
  "Lower income, college graduate, rare religious attendance", "", ""
)

Organization attributes

All possible conjoint attributes

org_attributes %>% 
  select(Organization, `Issue area`, `Organizational practices`, `Funding sources`, `Relationship with government`) %>% 
  pandoc.table.return(caption = 'Organization attributes varied in the experiment {#tbl:organization-attributes-full}',
                      justify = "lllll", split.tables = Inf) %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-organization-attributes-full.md"))

Organization attributes varied in the experiment {#tbl:organization-attributes-full}

Organization	Issue area	Organizational practices	Funding sources	Relationship with government
Amnesty International	Emergency response	Financial transparency	Small private donors	Friendly
Greenpeace	Environment	Accountability	Wealthy donors	Criticized
Oxfam	Human rights		Government grants	Crackdown
Red Cross	Refugee relief

Attributes varied in simulation

org_attributes %>% 
  select(`Issue area`, `Relationship with government`, `Funding`) %>% 
  pandoc.table.return(caption = 'Organization attributes varied in the simulation, resulting in 24 hypothetical organizations {#tbl:organization-attributes}',
                      justify = "lll") %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-organization-attributes.md"))

Organization attributes varied in the simulation, resulting in 24 hypothetical organizations {#tbl:organization-attributes}

Issue area	Relationship with government	Funding
Emergency response	Friendly	Small private donors
Environment	Criticized	Government grants
Human rights	Crackdown
Refugee relief

Persona attributes

Attributes varied in simulation

persona_attributes %>% 
  pandoc.table.return(caption = 'Individual attributes varied in the simulation, resulting in 64 persona profiles {#tbl:persona-attributes}',
                      justify = "lll", split.tables = Inf) %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-persona-attributes.md"))

Individual attributes varied in the simulation, resulting in 64 persona profiles {#tbl:persona-attributes}

Demographics	Politics and public affairs	Social views
Higher income (> US median ($61,372)), high school graduate, frequent religious attendance	Liberal (1), follows national and international news often, has traveled internationally	High social trust: Trusts political institutions, trusts charities, thinks people should be more charitable, frequently volunteers, donates once a month, has a history of personal activism, is a member of an association
Lower income (< US median), high school graduate, frequent religious attendance	Conservative (7), follows news, has traveled	Low social trust: Does not trust political institutions or charities, thinks people should be less charitable, does not volunteer or donate often, has no history of personal activism, is not a member of an association
Higher income, college graduate, frequent religious attendance	Liberal, does not follow news, has not traveled
Lower income, college graduate, frequent religious attendance	Conservative, does not follow news, has not traveled
Higher income, high school graduate, rare religious attendance
Lower income, high school graduate, rare religious attendance
Higher income, college graduate, rare religious attendance
Lower income, college graduate, rare religious attendance

Example simulation output

example_personas <- c("persona2", "persona63")

example_persona_details <- sim_clean %>%
  filter(persona_id %in% example_personas) %>% 
  select(starts_with("persona")) %>% 
  slice(1:2)

example_persona_details %>% 
  select(-persona_id) %>% 
  pivot_longer(cols = !persona) %>% 
  pivot_wider(names_from = "persona", values_from = "value") %>% 
  select(-name) %>% 
  pandoc.table(justify = "ll")

Persona 2	Persona 63
Lower income	Higher income
High school graduate	College graduate
Rarely attends religious services	Attends at least monthly
Liberal	Conservative
Follows the news; has travelled abroad	Doesn’t follow news; has not travelled abroad
Less trusting; donates and volunteers less often	More trusting; donates and volunteers often

example_persona_results <- sim_clean %>% 
  filter(persona_id %in% example_personas) %>% 
  mutate(org_funding = str_to_sentence(str_remove(org_funding, "Mostly funded by "))) %>% 
  mutate(org_clean = glue("{organization}: {org_issue}, {org_funding}, {org_relationship}")) %>% 
  mutate(persona_desc = recode(
    persona_id,
    "persona2" = "Lower income high school graduate who rarely attends religious services; liberal who reads and travels; doesn't trust or donate",
    "persona63" = "Higher income college graduate who attends religious services; conservative who doesn't read or travel; trusts and donates")
  ) %>%
  mutate(persona_clean = glue("{persona}: {persona_desc}")) %>% 
  select(persona_clean, share, org_clean) %>% 
  pivot_wider(names_from = "persona_clean", values_from = "share") %>% 
  adorn_totals(where = "row", name = "**Total**")

example_persona_results_small <- example_persona_results %>% 
  slice(c(1, 2, 3, 7, 8, 9, 16, 17, 25)) %>% 
  mutate(across(where(is.numeric), ~ percent_format(accuracy = 0.1)(.))) %>% 
  add_row(org_clean = "…", .after = 3) %>% 
  add_row(org_clean = "…", .after = 7) %>% 
  add_row(org_clean = "…", .after = 10) %>% 
  mutate(across(everything(), ~replace_na(., "…"))) %>% 
  rename(Organization = org_clean)

example_persona_results_small %>% 
  pandoc.table.return(caption = 'Sample simulation output {#tbl:sim-output}',
                      justify = "lcc", split.tables = Inf) %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-sim-output.md"))

Sample simulation output {#tbl:sim-output}

Organization	Persona 2: Lower income high school graduate who rarely attends religious services; liberal who reads and travels; doesn’t trust or donate	Persona 63: Higher income college graduate who attends religious services; conservative who doesn’t read or travel; trusts and donates
Org 1: Emergency response, Small donors, Friendly	11.4%	3.3%
Org 2: Emergency response, Government grants, Friendly	7.2%	11.1%
Org 3: Emergency response, Small donors, Criticized	1.1%	1.3%
…	…	…
Org 7: Environment, Small donors, Friendly	10.2%	1.6%
Org 8: Environment, Government grants, Friendly	6.5%	5.2%
Org 9: Environment, Small donors, Criticized	1.0%	0.6%
…	…	…
Org 16: Human rights, Government grants, Criticized	0.7%	6.8%
Org 17: Human rights, Small donors, Under crackdown	0.9%	2.0%
…	…	…
Total	100.0%	100.0%

Sample details

vars_to_summarize <- tribble(
  ~category, ~variable, ~clean_name,
  "Demographics", "Q5.12", "Gender",
  "Demographics", "Q5.17", "Age",
  "Demographics", "Q5.13", "Marital status",
  "Demographics", "Q5.14", "Education",
  "Demographics", "Q5.15", "Income",
  "Attitudes toward charity", "Q2.5", "Frequency of donating to charity",
  "Attitudes toward charity", "Q2.6", "Amount of donations to charity last year",
  "Attitudes toward charity", "Q2.7_f", "Importance of trusting charities",
  "Attitudes toward charity", "Q2.8_f", "Level of trust in charities",
  "Attitudes toward charity", "Q2.10", "Frequency of volunteering",
  "Politics, ideology, and religion", "Q2.1", "Frequency of following national news",
  "Politics, ideology, and religion", "Q5.7", "Traveled to a developing country",
  "Politics, ideology, and religion", "Q5.1", "Voted in last election",
  "Politics, ideology, and religion", "Q5.6_f", "Trust in political institutions and the state",
  "Politics, ideology, and religion", "Q5.2_f", "Political ideology",
  "Politics, ideology, and religion", "Q5.4", "Involvement in activist causes",
  "Politics, ideology, and religion", "Q5.8", "Frequency of attending religious services",
  "Politics, ideology, and religion", "Q5.9", "Importance of religion"
)

summarize_factor <- function(x) {
  output <- table(x) %>% 
    as_tibble() %>% 
    magrittr::set_colnames(., c("level", "count")) %>% 
    mutate(level = factor(level, levels = levels(x))) %>%
    mutate(prop = count / sum(count),
           nice_prop = scales::percent(prop))
  
  return(list(output))
}

participant_summary <- results %>% 
  select(one_of(vars_to_summarize$variable)) %>% 
  summarize_all(summarize_factor) %>% 
  pivot_longer(cols = everything(), names_to = "variable", values_to = "details") %>% 
  left_join(vars_to_summarize, by = "variable") %>% 
  unnest(details) %>% 
  mutate(level = as.character(level)) %>% 
  mutate(level = case_when(
    variable == "Q2.7_f" & level == "1" ~ "1 (not important)",
    variable == "Q2.7_f" & level == "7" ~ "7 (important)",
    variable == "Q2.8_f" & level == "1" ~ "1 (no trust)",
    variable == "Q2.8_f" & level == "7" ~ "7 (complete trust)",
    variable == "Q5.6_f" & level == "1" ~ "1 (no trust)",
    variable == "Q5.6_f" & level == "7" ~ "7 (complete trust)",
    variable == "Q5.2_f" & level == "1" ~ "1 (extremely liberal)",
    variable == "Q5.2_f" & level == "7" ~ "7 (extremely conservative)",
    variable == "Q5.15" & level == "Less than median" ~ "Less than 2017 national median ($61,372)",
    variable == "Q5.17" & level == "Less than median" ~ "Less than 2017 national median (36)",
    TRUE ~ level
  )) %>% 
  mutate(clean_name_shrunk = ifelse(clean_name == lag(clean_name), "", clean_name),
         clean_name_shrunk = ifelse(is.na(clean_name_shrunk), 
                                    clean_name[1], 
                                    clean_name_shrunk),
         category_shrunk = ifelse(category == lag(category), "", category),
         category_shrunk = ifelse(is.na(category_shrunk), 
                                    category[1], 
                                    category_shrunk))

participant_summary %>% 
  select("Category" = category_shrunk, "Question" = clean_name_shrunk, 
         "Response" = level, "N" = count, "%" = nice_prop) %>% 
  pandoc.table.return(caption = 'Summary of individual respondent characteristics {#tbl:sample-details}',
                      justify = "lllcc", split.tables = Inf) %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-sample-details.md"))

Summary of individual respondent characteristics {#tbl:sample-details}

Category	Question	Response	N	%
Demographics	Gender	Male	517	50.89%
		Female	485	47.74%
		Transgender	8	0.79%
		Prefer not to say	3	0.30%
		Other	3	0.30%
	Age	Less than 2017 national median (36)	179	18%
		More than median	837	82%
	Marital status	Married	403	39.7%
		Widowed	21	2.1%
		Divorced	104	10.2%
		Separated	35	3.4%
		Never married	453	44.6%
	Education	Less than high school	25	2.5%
		High school graduate	270	26.6%
		Some college	287	28.2%
		2 year degree	138	13.6%
		4 year degree	206	20.3%
		Graduate or professional degree	82	8.1%
		Doctorate	8	0.8%
	Income	Less than 2017 national median ($61,372)	585	58%
		More than median	431	42%
Attitudes toward charity	Frequency of donating to charity	More than once a month, less than once a year	566	56%
		At least once a month	450	44%
	Amount of donations to charity last year	$1-$49	337	33.17%
		$50-$99	245	24.11%
		$100-$499	233	22.93%
		$500-$999	107	10.53%
		$1000-$4,999	65	6.40%
		$5000-$9,999	18	1.77%
		$10,000+	11	1.08%
	Importance of trusting charities	1 (not important)	7	0.69%
		2	9	0.89%
		3	21	2.07%
		4	98	9.65%
		5	168	16.54%
		6	157	15.45%
		7 (important)	556	54.72%
	Level of trust in charities	1 (no trust)	14	1.38%
		2	20	1.97%
		3	68	6.69%
		4	257	25.30%
		5	328	32.28%
		6	169	16.63%
		7 (complete trust)	160	15.75%
	Frequency of volunteering	Haven’t volunteered in past 12 months	423	41.6%
		Rarely	20	2.0%
		More than once a month, less than once a year	322	31.7%
		At least once a month	251	24.7%
Politics, ideology, and religion	Frequency of following national news	Rarely	88	9%
		Once a week	216	21%
		At least once a day	712	70%
	Traveled to a developing country	Yes	250	25%
		No	766	75%
	Voted in last election	Yes	742	73%
		No	274	27%
	Trust in political institutions and the state	1 (no trust)	123	12.11%
		2	155	15.26%
		3	207	20.37%
		4	276	27.17%
		5	151	14.86%
		6	49	4.82%
		7 (complete trust)	55	5.41%
	Political ideology	1 (extremely liberal)	87	8.56%
		2	87	8.56%
		3	112	11.02%
		4	363	35.73%
		5	175	17.22%
		6	80	7.87%
		7 (extremely conservative)	112	11.02%
	Involvement in activist causes	Not involved	569	56%
		Involved	447	44%
	Frequency of attending religious services	Not sure	11	1%
		Rarely	600	59%
		At least once a month	405	40%
	Importance of religion	Not important	338	33%
		Important	678	67%

Original computing environment

Here’s what we used the last time we built this page

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)
##  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)
##  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)
##  httr          1.4.2   2020-07-20 [1] CRAN (R 4.0.2)
##  janitor     * 2.0.1   2020-04-12 [1] CRAN (R 4.0.0)
##  jsonlite      1.7.0   2020-06-25 [1] CRAN (R 4.0.2)
##  knitr         1.29    2020-06-23 [1] CRAN (R 4.0.2)
##  lifecycle     0.2.0   2020-03-06 [1] CRAN (R 4.0.0)
##  lubridate     1.7.9   2020-06-08 [1] CRAN (R 4.0.2)
##  magrittr    * 1.5     2014-11-22 [1] CRAN (R 4.0.0)
##  memoise       1.1.0   2017-04-21 [1] CRAN (R 4.0.0)
##  modelr        0.1.8   2020-05-19 [1] CRAN (R 4.0.2)
##  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)
##  pkgload       1.1.0   2020-05-29 [1] CRAN (R 4.0.2)
##  prettyunits   1.1.1   2020-01-24 [1] CRAN (R 4.0.0)
##  processx      3.4.3   2020-07-05 [1] CRAN (R 4.0.0)
##  ps            1.3.4   2020-08-11 [1] CRAN (R 4.0.2)
##  purrr       * 0.3.4   2020-04-17 [1] CRAN (R 4.0.0)
##  R6            2.4.1   2019-11-12 [1] CRAN (R 4.0.0)
##  Rcpp          1.0.5   2020-07-06 [1] CRAN (R 4.0.2)
##  readr       * 1.3.1   2018-12-21 [1] CRAN (R 4.0.0)
##  readxl        1.3.1   2019-03-13 [1] CRAN (R 4.0.0)
##  remotes       2.2.0   2020-07-21 [1] CRAN (R 4.0.2)
##  reprex        0.3.0   2019-05-16 [1] CRAN (R 4.0.0)
##  rlang         0.4.7   2020-07-09 [1] CRAN (R 4.0.2)
##  rmarkdown     2.3     2020-06-18 [1] CRAN (R 4.0.2)
##  rprojroot     1.3-2   2018-01-03 [1] CRAN (R 4.0.0)
##  rstudioapi    0.11    2020-02-07 [1] CRAN (R 4.0.0)
##  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)
##  snakecase     0.11.0  2019-05-25 [1] CRAN (R 4.0.0)
##  stringi       1.4.6   2020-02-17 [1] CRAN (R 4.0.0)
##  stringr     * 1.4.0   2019-02-10 [1] CRAN (R 4.0.0)
##  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)
##  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

Copyright © 2020 Suparna Chaudhry and Andrew Heiss and Marc Dotson. Licensed under the MIT License. Code available on GitHub.