library(tidyverse)
library(scales)
library(patchwork)
library(ggstance)
library(here)

# Wrap factor levels
# via Hadley: https://github.com/tidyverse/stringr/issues/107#issuecomment-233723948
str_wrap_factor <- function(x, ...) {
  levels(x) <- str_wrap(levels(x), ...)
  x
}

# 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_excel_clean <- read_rds(here("data", "derived_data", "sim_excel_final.rds"))
sim_clean <- read_rds(here("data", "derived_data", "sim_final.rds"))

Simulations

Income across issue area, funding, and relationship

plot_income_issue <- sim_clean %>% 
  group_by(org_issue, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)),
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002)),
                     breaks = seq(0, 0.06, 0.02)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), guide = FALSE) +
  coord_cartesian(xlim = c(0, 0.06)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_funding <- sim_clean %>% 
  group_by(org_funding, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_relationship <- sim_clean %>% 
  mutate(persona_income = fct_recode(persona_income,
                                     "< $61,372/year" = "Lower income",
                                     "> $61,372/year" = "Higher income")) %>% 
  group_by(org_relationship, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) +
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.003))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), 
                     guide = guide_legend(reverse = TRUE, nrow = 1,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 50))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_relationship_extreme <- sim_excel_clean %>% 
  mutate(persona_income = fct_recode(persona_income,
                                     "$50,000/year" = "Lower income",
                                     "$100,000/year" = "Higher income"),
         org_relationship = fct_recode(org_relationship,
                                       "Under crackdown" = "Crackdown")) %>% 
  group_by(org_relationship, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$pl_yellow, clrs_ngo$pl_purple_light), 
                     guide = guide_legend(reverse = TRUE, nrow = 1,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 50))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income <- ((plot_income_issue + labs(x = NULL)) + 
  (plot_income_funding + labs(x = NULL))) / 
  (plot_income_relationship + plot_income_relationship_extreme)

plot_income

ggsave(plot_income, filename = here("analysis", "output", "figures", "income-all.pdf"),
       width = 6, height = 4.5, units = "in", device = cairo_pdf)
ggsave(plot_income, filename = here("analysis", "output", "figures", "income-all.png"),
       width = 6, height = 4.5, units = "in", type = "cairo", dpi = 300)

Education across issue area, funding, and relationship

plot_education_issue <- sim_clean %>% 
  group_by(org_issue, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_funding <- sim_clean %>% 
  group_by(org_funding, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_relationship <- sim_clean %>% 
  group_by(org_relationship, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)),
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25)),
                     labels = label_wrap(15)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education <- plot_education_issue + plot_education_funding + 
  plot_education_relationship + guide_area() +
  plot_layout(guides = "collect", ncol = 4)
plot_education

Religiosity across issue area, funding, and relationship

plot_religion_issue <- sim_clean %>% 
  group_by(org_issue, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_funding <- sim_clean %>% 
  group_by(org_funding, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_relationship <- sim_clean %>% 
  group_by(org_relationship, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25)),
                     labels = label_wrap(20)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion <- plot_religion_issue + plot_religion_funding + 
  plot_religion_relationship + guide_area() +
  plot_layout(guides = "collect", ncol = 4)
plot_religion

Education and religion across issue area, funding, and relationship

plot_education_religion <- (plot_education_issue + labs(x = NULL)) + 
  (plot_education_funding + labs(x = NULL)) + 
  (plot_education_relationship + labs(x = NULL)) + 
  guide_area() +
  plot_religion_issue + 
  plot_religion_funding + 
  plot_religion_relationship +
  plot_layout(guides = "collect", ncol = 4)

plot_education_religion

ggsave(plot_education_religion, 
       filename = here("analysis", "output", "figures", "education-religion-all.pdf"),
       width = 6.5, height = 4.5, units = "in", device = cairo_pdf)
ggsave(plot_education_religion, 
       filename = here("analysis", "output", "figures", "education-religion-all.png"),
       width = 6.5, height = 4.5, units = "in", type = "cairo", dpi = 300)

Social trust across issue area

plot_issue_social <- sim_clean %>% 
  group_by(org_issue, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_issue_social

ggsave(plot_issue_social, filename = here("analysis", "output", "figures", "issue-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_issue_social, filename = here("analysis", "output", "figures", "issue-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)

Social trust across relationship

plot_relationship_social <- sim_clean %>% 
  group_by(org_relationship, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_relationship_social

ggsave(plot_relationship_social, filename = here("analysis", "output", "figures", "relationship-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_relationship_social, filename = here("analysis", "output", "figures", "relationship-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)

Social trust across funding

plot_funding_social <- sim_clean %>% 
  group_by(org_funding, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_funding_social

ggsave(plot_funding_social, filename = here("analysis", "output", "figures", "funding-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_funding_social, filename = here("analysis", "output", "figures", "funding-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)


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)
##  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)
##  farver        2.0.3   2020-01-16 [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)
##  ggstance    * 0.3.4   2020-04-02 [1] CRAN (R 4.0.0)
##  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)
##  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)
##  labeling      0.3     2014-08-23 [1] CRAN (R 4.0.0)
##  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)
##  patchwork   * 1.0.1   2020-06-22 [1] CRAN (R 4.0.2)
##  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)
##  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)
##  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: "Analysis and figures"
author: "Suparna Chaudhry, Marc Dotson, and Andrew Heiss"
date: "Last run: `r format(Sys.time(), '%F')`"
output: 
  html_document:
    code_folding: hide
    pandoc_args:
      - "--default-image-extension=png"
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.retina = 3, fig.align = "center")
```

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

# Wrap factor levels
# via Hadley: https://github.com/tidyverse/stringr/issues/107#issuecomment-233723948
str_wrap_factor <- function(x, ...) {
  levels(x) <- str_wrap(levels(x), ...)
  x
}

# 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 load-data}
sim_excel_clean <- read_rds(here("data", "derived_data", "sim_excel_final.rds"))
sim_clean <- read_rds(here("data", "derived_data", "sim_final.rds"))
```


# General trends in private philanthropy

Based on data from [Giving USA](https://theconversation.com/fewer-americans-are-giving-money-to-charity-but-total-donations-are-at-record-levels-anyway-98291), philanthropy in the United states continues to increase, both in aggregate and per capita.

```{r giving-usa-data, message=FALSE}
giving_aggregate_raw <- read_csv(here("data", "raw_data", "data-FTjUv.csv"))

giving_aggregate <- giving_aggregate_raw %>% 
  mutate(total = `Total donations` * 1000000000)

giving_per_capita_raw <- read_csv(here("data", "raw_data", "data-xextT.csv"))

giving_per_capita <- giving_per_capita_raw
```

## Total giving

```{r plot-total-giving, fig.width=4, fig.height=2.4, out.width="80%"}
ggplot(giving_aggregate, aes(x = Year, y = `Total donations`)) +
  geom_line(size = 1, color = clrs_ngo$vi_blue_dark) +
  scale_x_continuous(breaks = seq(1980, 2015, 5)) +
  scale_y_continuous(labels = dollar, breaks = seq(100, 450, 50)) + 
  coord_cartesian(ylim = c(100, 450)) +
  labs(x = NULL, y = "Billions of dollars") + 
  theme_ngo()

ggsave(here("analysis", "output", "figures", "giving_aggregate.pdf"), 
       width = 4, height = 2.4, units = "in", device = cairo_pdf)
ggsave(here("analysis", "output", "figures", "giving_aggregate.png"), 
       width = 4, height = 2.4, units = "in", type = "cairo", dpi = 300)
```

## Average per capita giving

```{r plot-per-capita-giving, fig.width=4, fig.height=2.4, out.width="80%"}
ggplot(giving_per_capita, aes(x = Year, y = `Average giving`)) +
  geom_line(size = 1, color = clrs_ngo$vi_blue_dark) +
  scale_x_continuous(breaks = seq(2000, 2014, 2)) +
  scale_y_continuous(labels = dollar, breaks = seq(1750, 2750, 250)) + 
  coord_cartesian(ylim = c(1750, 2750)) +
  labs(x = NULL, y = "Average annual donation") + 
  theme_ngo()
```


# Simulations

## Income across issue area, funding, and relationship

```{r income-issue-funding-relationship, fig.width=6, fig.height=4.5, out.width="100%"}
plot_income_issue <- sim_clean %>% 
  group_by(org_issue, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)),
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002)),
                     breaks = seq(0, 0.06, 0.02)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), guide = FALSE) +
  coord_cartesian(xlim = c(0, 0.06)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_funding <- sim_clean %>% 
  group_by(org_funding, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_relationship <- sim_clean %>% 
  mutate(persona_income = fct_recode(persona_income,
                                     "< $61,372/year" = "Lower income",
                                     "> $61,372/year" = "Higher income")) %>% 
  group_by(org_relationship, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) +
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.003))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$vi_turquoise, clrs_ngo$vi_purple), 
                     guide = guide_legend(reverse = TRUE, nrow = 1,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 50))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income_relationship_extreme <- sim_excel_clean %>% 
  mutate(persona_income = fct_recode(persona_income,
                                     "$50,000/year" = "Lower income",
                                     "$100,000/year" = "Higher income"),
         org_relationship = fct_recode(org_relationship,
                                       "Under crackdown" = "Crackdown")) %>% 
  group_by(org_relationship, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$pl_yellow, clrs_ngo$pl_purple_light), 
                     guide = guide_legend(reverse = TRUE, nrow = 1,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 50))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_income <- ((plot_income_issue + labs(x = NULL)) + 
  (plot_income_funding + labs(x = NULL))) / 
  (plot_income_relationship + plot_income_relationship_extreme)

plot_income
ggsave(plot_income, filename = here("analysis", "output", "figures", "income-all.pdf"),
       width = 6, height = 4.5, units = "in", device = cairo_pdf)
ggsave(plot_income, filename = here("analysis", "output", "figures", "income-all.png"),
       width = 6, height = 4.5, units = "in", type = "cairo", dpi = 300)
```

## Education across issue area, funding, and relationship

```{r edu-issue-funding-relationship, fig.width=8, fig.height=2.75, out.width="100%"}
plot_education_issue <- sim_clean %>% 
  group_by(org_issue, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_funding <- sim_clean %>% 
  group_by(org_funding, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_relationship <- sim_clean %>% 
  group_by(org_relationship, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)),
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$pl_purple_dark, clrs_ngo$pl_orange), 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25)),
                     labels = label_wrap(15)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education <- plot_education_issue + plot_education_funding + 
  plot_education_relationship + guide_area() +
  plot_layout(guides = "collect", ncol = 4)
plot_education
```

## Religiosity across issue area, funding, and relationship

```{r religion-issue-funding-relationship, fig.width=8, fig.height=2.75, out.width="100%"}
plot_religion_issue <- sim_clean %>% 
  group_by(org_issue, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_funding <- sim_clean %>% 
  group_by(org_funding, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_relationship <- sim_clean %>% 
  group_by(org_relationship, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = c(clrs_ngo$vi_blue_light, clrs_ngo$vi_yellow), 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25)),
                     labels = label_wrap(20)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion <- plot_religion_issue + plot_religion_funding + 
  plot_religion_relationship + guide_area() +
  plot_layout(guides = "collect", ncol = 4)
plot_religion
```

## Education and religion across issue area, funding, and relationship

```{r education-religion-issue-funding-relationship, fig.width=6, fig.height=4.5, out.width="100%"}
plot_education_religion <- (plot_education_issue + labs(x = NULL)) + 
  (plot_education_funding + labs(x = NULL)) + 
  (plot_education_relationship + labs(x = NULL)) + 
  guide_area() +
  plot_religion_issue + 
  plot_religion_funding + 
  plot_religion_relationship +
  plot_layout(guides = "collect", ncol = 4)

plot_education_religion
ggsave(plot_education_religion, 
       filename = here("analysis", "output", "figures", "education-religion-all.pdf"),
       width = 6.5, height = 4.5, units = "in", device = cairo_pdf)
ggsave(plot_education_religion, 
       filename = here("analysis", "output", "figures", "education-religion-all.png"),
       width = 6.5, height = 4.5, units = "in", type = "cairo", dpi = 300)
```


## Social trust across issue area

```{r trust-issues, fig.width=6, fig.height=4, out.width="100%"}
plot_issue_social <- sim_clean %>% 
  group_by(org_issue, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5, 
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_issue_social
ggsave(plot_issue_social, filename = here("analysis", "output", "figures", "issue-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_issue_social, filename = here("analysis", "output", "figures", "issue-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)
```

## Social trust across relationship

```{r trust-relationship, fig.width=6, fig.height=4, out.width="100%"}
plot_relationship_social <- sim_clean %>% 
  group_by(org_relationship, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_relationship_social
ggsave(plot_relationship_social, filename = here("analysis", "output", "figures", "relationship-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_relationship_social, filename = here("analysis", "output", "figures", "relationship-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)
```

## Social trust across funding

```{r trust-funding, fig.width=6, fig.height=4, out.width="100%"}
plot_funding_social <- sim_clean %>% 
  group_by(org_funding, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrangeh(size = 0.75, fatten = 1.5,
                   aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = c(clrs_ngo$pl_blue, clrs_ngo$pl_pink), 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_funding_social
ggsave(plot_funding_social, filename = here("analysis", "output", "figures", "funding-social.pdf"),
       width = 6, height = 4, units = "in", device = cairo_pdf)
ggsave(plot_funding_social, filename = here("analysis", "output", "figures", "funding-social.png"),
       width = 6, height = 4, units = "in", type = "cairo", dpi = 300)
```


\

# 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>
