Overview of data

Balance of experimental conditions

Crackdown Issue Funding n
No crackdown Human rights Government 68
No crackdown Human rights Private 64
No crackdown Humanitarian assistance Government 68
No crackdown Humanitarian assistance Private 65
Crackdown Human rights Government 65
Crackdown Human rights Private 65
Crackdown Humanitarian assistance Government 68
Crackdown Humanitarian assistance Private 68
Total - - 531

Descriptive statistics table

vars_to_summarize <- tribble(
  ~variable, ~clean_name,
  "donate_likely", "Likelihood of donation",
  "donate_likely_bin", "Likelihood of donation (binary)",
  "amount_donate", "Amount hypothetically donated ($)",
  "gender", "Gender",
  "age", "Age",
  "income", "Income",
  "education", "Education",
  "religiosity", "Frequency of attending religious services",
  "ideology", "Political views",
  "political_knowledge", "Frequency of following public affairs",
  "give_charity", "Frequency of charitable donations",
  "volunteer", "Volunteered in past 12 months",
  "favor_humanitarian", "Prior favorability towards humanitarian NGOs",
  "favor_humanitarian_bin", "Prior favorability towards humanitarian NGOs (binary)",
  "favor_human_rights", "Prior favorability towards human rights NGOs",
  "favor_human_rights_bin", "Prior favorability towards human rights NGOs (binary)",
  "favor_development", "Prior favorability towards development NGOs",
  "favor_development_bin", "Prior favorability towards development NGOs (binary)",
  "check2", "Attention check 2"
)

results_summary_stats <- results %>% 
  select(one_of(vars_to_summarize$variable)) %>% 
  gather(variable, value) %>% 
  group_by(variable) %>% 
  nest() %>% 
  mutate(N = data %>% map_int(~ nrow(.)),
         summary = map2(.x = data, .y = variable, ~ md_summary_row(.x$value, .y, results))) %>% 
  left_join(vars_to_summarize, by = "variable") %>% 
  mutate(variable = factor(variable, levels = vars_to_summarize$variable, ordered = TRUE)) %>% 
  arrange(variable) %>% 
  select(-data, -variable) %>% 
  unnest(summary) %>% 
  select(Variable = clean_name, N, ` ` = spark, Details = summary)

results_summary_stats %>% 
  select(-N) %>% 
  pandoc.table.return(caption = "Descriptive statistics {#tbl:descriptive-stats}",
                      split.cell = 80, split.table = Inf) %T>% 
  cat(file = here("analysis", "output", "tables", "tbl-descriptive-stats.md")) %>%
  cat()
Descriptive statistics {#tbl:descriptive-stats}
Variable Details
Likelihood of donation Extremely unlikely (46; 8.7%) | Somewhat unlikely (110; 20.7%) | Neither likely nor unlikely (138; 26.0%) | Somewhat likely (192; 36.2%) | Extremely likely (45; 8.5%)
Likelihood of donation (binary) Not likely (294; 55.4%) | Likely (237; 44.6%)
Amount hypothetically donated ($) Median: 10 | Mean: 22.4 | Std. Dev.: 25.67
Gender Female (291; 54.8%) | Male (237; 44.6%) | Other (1; 0.2%) | Prefer not to say (2; 0.4%)
Age Under 18 (1; 0.2%) | 18 – 24 (43; 8.1%) | 25 – 34 (207; 39.0%) | 35 – 44 (129; 24.3%) | 45 – 54 (90; 16.9%) | 55 – 64 (44; 8.3%) | 65 – 74 (16; 3.0%) | 75 – 84 (1; 0.2%)
Income Less than $10,000 (32; 6.0%) | $10,000 – $19,999 (43; 8.1%) | $20,000 – $29,999 (50; 9.4%) | $30,000 – $39,999 (78; 14.7%) | $40,000 – $49,999 (58; 10.9%) | $50,000 – $59,999 (55; 10.4%) | $60,000 – $69,999 (43; 8.1%) | $70,000 – $79,999 (38; 7.2%) | $80,000 – $89,999 (25; 4.7%) | $90,000 – $99,999 (30; 5.6%) | $100,000 – $149,999 (45; 8.5%) | More than $150,000 (21; 4.0%) | Prefer not to say (13; 2.4%)
Education Less than high school (2; 0.4%) | High school graduate (47; 8.9%) | Some college (128; 24.1%) | 2 year degree (68; 12.8%) | 4 year degree (212; 39.9%) | Graduate or professional degree (66; 12.4%) | Doctorate (8; 1.5%)
Frequency of attending religious services More than once a week (19; 3.6%) | Once a week (74; 13.9%) | Once or twice a month (50; 9.4%) | A few times a year (71; 13.4%) | Seldom (98; 18.5%) | Never (215; 40.5%) | Don’t know (4; 0.8%)
Political views Strong liberal (76; 14.3%) | Liberal (150; 28.2%) | Independent, leaning liberal (82; 15.4%) | Independent (80; 15.1%) | Independent, leaning conservative (60; 11.3%) | Conservative (61; 11.5%) | Very conservative (22; 4.1%)
Frequency of following public affairs Most of the time (217; 40.9%) | Some of the time (213; 40.1%) | Only now and then (84; 15.8%) | Hardly at all (17; 3.2%)
Frequency of charitable donations Once a week (37; 7.0%) | Once a month (105; 19.8%) | Once every three months (105; 19.8%) | Once every six months (102; 19.2%) | Once a year (89; 16.8%) | Once every few years (56; 10.5%) | Never (37; 7.0%)
Volunteered in past 12 months No (288; 54.24%) | Yes (243; 45.76%)
Prior favorability towards humanitarian NGOs Very unfavorable (2; 0.4%) | Unfavorable (6; 1.1%) | Neutral (39; 7.3%) | Favorable (235; 44.3%) | Very favorable (249; 46.9%)
Prior favorability towards humanitarian NGOs (binary) Not favorable (47; 8.9%) | Favorable (484; 91.1%)
Prior favorability towards human rights NGOs Very unfavorable (5; 0.9%) | Unfavorable (12; 2.3%) | Neutral (61; 11.5%) | Favorable (226; 42.6%) | Very favorable (227; 42.7%)
Prior favorability towards human rights NGOs (binary) Not favorable (78; 14.7%) | Favorable (453; 85.3%)
Prior favorability towards development NGOs Very unfavorable (5; 0.9%) | Unfavorable (8; 1.5%) | Neutral (45; 8.5%) | Favorable (235; 44.3%) | Very favorable (238; 44.8%)
Prior favorability towards development NGOs (binary) Not favorable (58; 10.9%) | Favorable (473; 89.1%)
Attention check 2 Correct (531; 100%)

Average likelihood and amount donated across conditions

Crackdown condition Issue condition Funding condition % likely to donate Amount donated (mean) Amount donated (sd) N
No crackdown Human rights Government 47.1% 22.4 24.8 68
Private 39.1% 19.3 22.4 64
Total 43.2% 20.9 23.6 132
Humanitarian assistance Government 44.1% 17.9 21.4 68
Private 40.0% 21.9 26.9 65
Total 42.1% 19.9 24.2 133
Total 42.6% 20.4 23.9 265
Crackdown Human rights Government 29.2% 19.4 25.8 65
Private 58.5% 28 26.4 65
Total 43.8% 23.7 26.3 130
Humanitarian assistance Government 51.5% 30.4 32.7 68
Private 47.1% 19.8 21.5 68
Total 49.3% 25.1 28.1 136
Total 46.6% 24.4 27.2 266
Total 44.6% 22.4 25.7 531


Visualize important variables

Amount donated


Treatment effects: Likelihood of donation

This time around, we’re not using interactionful regression models to calculate all these differences in groups. Instead we use real live Stan code to estimate the differences in group means and proportions!

Priors and models

We estimate the proportion of people responding that they’d be likely to donate to the organization with a binomial distribution, with a prior \(\theta\) distribution of \(\text{Beta}(5, 5)\). We build the following model in Stan:

\[ \begin{aligned} n_{\text{group 1, group 2}} &\sim \text{Binomial}(n_{\text{total in group}}, \theta_{\text{group}}) &\text{[likelihood]}\\ \text{Difference} &= n_{\text{group 2}} - n_{\text{group 1}} &\text{[difference in proportions]} \\ n &: \text{Number likely to donate} \\ \\ \theta_{\text{group 1, group 2}} &\sim \text{Beta}(5, 5) &\text{[prior prob. of being likely to donate]} \end{aligned} \]

Differences

tidied_diffs_likely <- all_models_likely %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "theta_diff") %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  mutate(title = str_remove(title, " \\| Crackdown"))

level1_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown −\nNo crackdown",
                        Issue = "Humanitarian\nassistance −\nHuman rights",
                        Funding = "Private −\nGovernment\nfunding")) %>% 
  mutate(title = fct_inorder(title))

plot_diff_likely_a <- ggplot(level1_likely, aes(x = .value, y = fct_rev(title), fill = title)) +
  geom_halfeyeh(.width = c(0.8, 0.95)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_manual(values = ngo_cols(c("blue", "red", "orange"), name = FALSE), guide = FALSE) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "A") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level2_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 2") %>%
  mutate(condition = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ "Issue",
    title %in% c("Government", "Private") ~ "Funding"
  )) %>% 
  mutate(facet_title = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ paste(title, "issues"),
    title %in% c("Government", "Private") ~ paste(title, "funding")
  )) %>% 
  mutate(title = case_when(
    title %in% c("Humanitarian assistance", "Private") ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_likely_b <- ggplot(filter(level2_likely, condition == "Issue"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("red", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = NULL, y = NULL, tag = "B") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diff_likely_c <- ggplot(filter(level2_likely, condition == "Funding"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("orange", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "C") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level3_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 3") %>%
  separate(title, c("issue", "funding"), sep = " \\| ") %>%
  mutate(issue = paste(issue, "issues"),
         funding = paste(funding, "funding")) %>% 
  mutate(facet_title = paste0(issue, "\n", funding)) %>% 
  mutate(title = case_when(
    funding == "Private funding" ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_likely_d <- ggplot(level3_likely, aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("green", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "D",
       caption = "Point shows posterior median; thick black lines show 80% credible interval;\nthin black lines show 95% credible interval") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diffs_likely_all <- plot_diff_likely_a / 
  (plot_diff_likely_b / plot_diff_likely_c) / 
  plot_diff_likely_d

plot_diffs_likely_all

Likelihood of donation and differences in proportions in “crackdown” (treatment) and “no crackdown” (control) conditions; values represent posterior medians {#tbl:likely-diffs}
H1a % likelyTreatment % likelyControl \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Crackdown − No crackdown 46.8% 42.8% 3.9% 9.2% 0.83
Private − Government funding 46.4% 43.3% 3.1% 7.1% 0.76
Humanitarian assistance − Human rights 45.9% 43.8% 2.1% 4.7% 0.7
H2a and H3a % likelyCrackdown % likelyNo crackdown \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Human rights issues 44.2% 43.7% 0.6% 1.5% 0.54
Humanitarian assistance issues 49.4% 42.7% 6.8% 15.9% 0.88
Government funding 41.3% 45.9% -4.6% -10.1% 0.79
Private funding 52.5% 40.2% 12.2% 30.3% 0.98
H2a and H3a (nested) % likelyCrackdown % likelyNo crackdown \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Human rights issues, Government funding 31.9% 47.5% -15.4% -32.6% 0.98
Human rights issues, Private funding 57.5% 40.5% 17.0% 41.7% 0.98
Humanitarian assistance issues, Government funding 51.2% 44.8% 6.4% 14.4% 0.8
Humanitarian assistance issues, Private funding 47.5% 41.2% 6.2% 15.2% 0.79


Treatment effects: Amount donated

Priors and models

Following John Kruschke’s “Bayesian Estimation Supersedes the t-test (BEST)” procedure, we estimate means for each group with a t-distribution. We use the following priors for the distribution parameters:

\[ \begin{aligned} x_{\text{group 1, group 2}} &\sim \text{Student } t(\nu, \mu, \sigma) &\text{[likelihood]}\\ \text{Difference} &= x_{\text{group 2}} - x_{\text{group 1}} &\text{[difference in means]} \\ x &: \text{Mean amount donated} \\ \\ \nu &\sim \text{Exponential}(1 / 29) &\text{[prior normality]} \\ \mu_{\text{group 1, group 2}} &\sim \mathcal{N}(\bar{x}_{\text{group 1, group 2}}, 10) &\text{[prior donation mean per group]}\\ \sigma_{\text{group 1, group 2}} &\sim \text{Cauchy}(0, 1)&\text{[prior donation sd per group]} \end{aligned} \]

Differences

# This will generate a warning because unnest() puts the crackdown values into
# one column and we cheated by renaming issue and funding as crackdown, so it
# gets mad when combining the factors in crackdown with those in issue and
# funding. But because we only care about the diffs, we don't actually need the
# category labels here, so it's all good.
tidied_diffs_amount <- all_models_amount %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "mu_diff") %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  mutate(title = str_remove(title, " \\| Crackdown"))

level1_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown −\nNo crackdown",
                        Issue = "Humanitarian\nassistance −\nHuman rights",
                        Funding = "Private −\nGovernment\nfunding")) %>% 
  mutate(title = fct_inorder(title))

plot_diff_amount_a <- ggplot(level1_amount, aes(x = .value, y = fct_rev(title), fill = title)) +
  geom_vline(xintercept = 0) +
  geom_halfeyeh(.width = c(0.8, 0.95)) +
  scale_x_continuous(labels = dollar) +
  scale_fill_manual(values = ngo_cols(c("blue", "red", "orange"), name = FALSE), guide = FALSE) +
  labs(x = "Difference in amount donated", y = NULL, tag = "A") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level2_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 2") %>%
  mutate(condition = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ "Issue",
    title %in% c("Government", "Private") ~ "Funding"
  )) %>% 
  mutate(facet_title = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ paste(title, "issues"),
    title %in% c("Government", "Private") ~ paste(title, "funding")
  )) %>% 
  mutate(title = case_when(
    title %in% c("Humanitarian assistance", "Private") ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_amount_b <- ggplot(filter(level2_amount, condition == "Issue"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("red", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = NULL, y = NULL, tag = "B") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diff_amount_c <- ggplot(filter(level2_amount, condition == "Funding"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("orange", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = "Difference in amount donated", y = NULL, tag = "C") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level3_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 3") %>%
  separate(title, c("issue", "funding"), sep = " \\| ") %>%
  mutate(issue = paste(issue, "issues"),
         funding = paste(funding, "funding")) %>% 
  mutate(facet_title = paste0(issue, "\n", funding)) %>% 
  mutate(title = case_when(
    funding == "Private funding" ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_amount_d <- ggplot(level3_amount, aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("green", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = "Difference in amount donated", y = NULL, tag = "D",
       caption = "Point shows posterior median; thick black lines show 80% credible interval;\nthin black lines show 95% credible interval") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diffs_amounts_all <- plot_diff_amount_a / 
  (plot_diff_amount_b / plot_diff_amount_c) / 
  plot_diff_amount_d

plot_diffs_amounts_all

tbl_amounts_tidy <- all_models_amount %>% 
  unnest(tidy) %>% 
  filter(term %in% c("mu", "mu_diff", "pct_change")) %>% 
  group_by(title) %>% 
  mutate(group_id = 1:n()) %>% 
  mutate(term = case_when(
    term == "mu" ~ paste0(term, "_", group_id),
    TRUE ~ term
  )) %>% 
  select(title, term, estimate) %>% 
  spread(term, estimate) %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  ungroup()

tbl_amounts_probs <- all_models_amount %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "mu_diff") %>% 
  group_by(title) %>% 
  summarize(p.greater0 = mean(.value > 0),
            p.less0 = mean(.value < 0),
            p.diff.not0 = ifelse(median(.value) > 0, p.greater0, p.less0)) %>% 
  ungroup()

# Save combined table for later use in manuscript
tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  saveRDS(here("data", "derived_data", "results_models_amount.rds"))

tbl_amount_1 <- tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown − No crackdown",
                        Issue = "*Humanitarian assistance − Human rights*",
                        Funding = "*Private − Government funding*")) %>% 
  mutate_at(vars(mu_1, mu_2, mu_diff, p.diff.not0), funs(as.character(round(., 2)))) %>% 
  mutate(pct_change = percent_format(accuracy = 0.1)(pct_change)) %>%
  select(`Frame` = title, `Amount~Treatment~` = mu_2, `Amount~Control~` = mu_1,
         `$\\Delta$` = mu_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
Mean values and differences in means for amount donated in “crackdown” (treatment) and “no crackdown” (control) conditions; values represent posterior medians {#tbl:amount-diffs}
H1b AmountTreatment AmountControl \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Crackdown − No crackdown 16.34 12.93 3.39 26.3% 0.97
Private − Government funding 15.13 13.71 1.42 10.4% 0.79
Humanitarian assistance − Human rights 14.06 14.85 -0.82 -5.5% 0.67
H2b and H3b AmountCrackdown AmountNo crackdown \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Human rights issues 17.4 14.86 2.54 17.2% 0.83
Humanitarian assistance issues 15.91 11.68 4.3 36.9% 0.95
Government funding 13.83 12.24 1.61 13.1% 0.74
Private funding 18.95 14.23 4.62 32.4% 0.97
H2b and H3b (nested) AmountCrackdown AmountNo crackdown \(\Delta\) \(\%\Delta\) \(p(\Delta \neq 0)\)
Human rights issues, Government funding 10.56 15.15 -4.46 -29.5% 0.91
Human rights issues, Private funding 23.76 14.5 9.19 63.8% 0.99
Humanitarian assistance issues, Government funding 21.42 11.89 9.35 77.9% 0.99
Humanitarian assistance issues, Private funding 15.69 15.72 -0.05 -0.3% 0.51

Original computing environment

## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXXFLAGS=-O3 -Wno-unused-variable -Wno-unused-function -Wno-unused-local-typedefs
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FLIBS = -L`gfortran -print-file-name=libgfortran.dylib | xargs dirname`
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
## ─ Session info ──────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.5.2 (2018-12-20)
##  os       macOS Mojave 10.14.3        
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Denver              
##  date     2019-03-13                  
## 
## ─ Packages ──────────────────────────────────────────────────────────────
##  package                * version      date       lib
##  arrayhelpers             1.0-20160527 2016-05-28 [1]
##  assertthat               0.2.0        2017-04-11 [1]
##  backports                1.1.3        2018-12-14 [1]
##  base64enc                0.1-3        2015-07-28 [1]
##  bindr                    0.1.1        2018-03-13 [1]
##  bindrcpp               * 0.2.2        2018-03-29 [1]
##  boot                     1.3-20       2017-08-06 [1]
##  broom                  * 0.5.1        2018-12-05 [1]
##  callr                    3.1.1        2018-12-21 [1]
##  cellranger               1.1.0        2016-07-27 [1]
##  cli                      1.0.1        2018-09-25 [1]
##  coda                     0.19-2       2018-10-08 [1]
##  codetools                0.2-15       2016-10-05 [1]
##  colorspace               1.4-0        2019-01-13 [1]
##  crackdownsphilanthropy * 0.0.0.9000   2019-03-13 [1]
##  crayon                   1.3.4        2017-09-16 [1]
##  curl                     3.3          2019-01-10 [1]
##  dagitty                  0.2-2        2016-08-26 [1]
##  desc                     1.2.0        2018-05-01 [1]
##  devtools                 2.0.1        2018-10-26 [1]
##  digest                   0.6.18       2018-10-10 [1]
##  dplyr                  * 0.7.8        2018-11-10 [1]
##  evaluate                 0.13         2019-02-12 [1]
##  farver                   1.1.0        2018-11-20 [1]
##  forcats                * 0.3.0        2018-02-19 [1]
##  fs                       1.2.6        2018-08-23 [1]
##  generics                 0.0.2        2018-11-29 [1]
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## 
## [1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library
---
title: "Results"
author: "Andrew Heiss and Suparna Chaudhry"
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 load-libraries-data, warning=FALSE, message=FALSE}
library(tidyverse)
library(crackdownsphilanthropy)
library(rstan)
library(tidybayes)
library(broom)
library(ggstance)
library(ggdag)
library(ggraph)
library(patchwork)
library(pander)
library(scales)
library(janitor)
library(here)

source(here("analysis", "options.R"))

# Load data
results <- readRDS(here("data", "derived_data", "results_clean.rds"))
# qwraps2::lazyload_cache_dir("02_analysis_cache/html")
```


# Causal pathway

Our theory and hypotheses are laid out in the causal pathway below. Our outcomes (Y: % likely to donate and amount donated) "listen to" or respond to C (crackdown), which is our main treatment or exposure. Funding (F) and issue (I) serve as heuristics for donation and influence a government's decision to crack down on NGOs, hence the dual arrows to C and Y. 

```{r causal-dag, fig.width=10/3, fig.height=2}
theory_dag <- dagify(Y ~ I + C + F,
                     C ~ I + F,
                     outcome = "Y",
                     exposure = "C") %>% 
  tidy_dagitty(layout = "sugiyama")

plot_dag <- ggplot(theory_dag, aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point(size = 6) +
  geom_dag_edges(start_cap = circle(4, "mm"),
                 end_cap = circle(4, "mm")) +
  geom_dag_text(size = pts(6), family = "Roboto Condensed", fontface = "bold") +
  scale_dag() +
  theme_ngos() +
  theme(panel.grid = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank())

plot_dag %T>% 
  print() %T>%
  ggsave(., filename = here("analysis", "output", "figures", "causal-path.pdf"),
         width = 10/3, height = 2, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("analysis", "output", "figures", "causal-path.png"),
         width = 10/3, height = 2, units = "in", type = "cairo", dpi = 300)
```


# Overview of data

## Balance of experimental conditions

```{r check-conditions, results="asis"}
results %>% count(crackdown, issue, funding) %>% 
  rename(Crackdown = crackdown, Issue = issue, Funding = funding) %>% 
  janitor::adorn_totals(., where = "row") %T>% 
  pandoc.table() %>% 
  pandoc.table.return(caption = "Balance of experimental conditions {#tbl:experimental-conditions}") %>% 
  cat(file = here("analysis", "output", "tables", "tbl-experimental-conditions.md"))
```

## Descriptive statistics table

```{r tbl-descriptive-statistics, results="asis", warning=FALSE}
vars_to_summarize <- tribble(
  ~variable, ~clean_name,
  "donate_likely", "Likelihood of donation",
  "donate_likely_bin", "Likelihood of donation (binary)",
  "amount_donate", "Amount hypothetically donated ($)",
  "gender", "Gender",
  "age", "Age",
  "income", "Income",
  "education", "Education",
  "religiosity", "Frequency of attending religious services",
  "ideology", "Political views",
  "political_knowledge", "Frequency of following public affairs",
  "give_charity", "Frequency of charitable donations",
  "volunteer", "Volunteered in past 12 months",
  "favor_humanitarian", "Prior favorability towards humanitarian NGOs",
  "favor_humanitarian_bin", "Prior favorability towards humanitarian NGOs (binary)",
  "favor_human_rights", "Prior favorability towards human rights NGOs",
  "favor_human_rights_bin", "Prior favorability towards human rights NGOs (binary)",
  "favor_development", "Prior favorability towards development NGOs",
  "favor_development_bin", "Prior favorability towards development NGOs (binary)",
  "check2", "Attention check 2"
)

results_summary_stats <- results %>% 
  select(one_of(vars_to_summarize$variable)) %>% 
  gather(variable, value) %>% 
  group_by(variable) %>% 
  nest() %>% 
  mutate(N = data %>% map_int(~ nrow(.)),
         summary = map2(.x = data, .y = variable, ~ md_summary_row(.x$value, .y, results))) %>% 
  left_join(vars_to_summarize, by = "variable") %>% 
  mutate(variable = factor(variable, levels = vars_to_summarize$variable, ordered = TRUE)) %>% 
  arrange(variable) %>% 
  select(-data, -variable) %>% 
  unnest(summary) %>% 
  select(Variable = clean_name, N, ` ` = spark, Details = summary)

results_summary_stats %>% 
  select(-N) %>% 
  pandoc.table.return(caption = "Descriptive statistics {#tbl:descriptive-stats}",
                      split.cell = 80, split.table = Inf) %T>% 
  cat(file = here("analysis", "output", "tables", "tbl-descriptive-stats.md")) %>%
  cat()
```

## Average likelihood and amount donated across conditions

```{r tbl-results-conditions, results="asis"}
conditions_summary <- bind_rows(group_by(results, crackdown, issue, funding) %>% nest(),
                                group_by(results, crackdown, issue) %>% nest(),
                                group_by(results, crackdown) %>% nest(),
                                results %>% nest()) %>% 
  arrange(crackdown, issue, funding) %>% 
  mutate(summary = data %>%
           map(~ summarize(., pct_likely = table(donate_likely_bin)[["Likely"]] /
                             length(donate_likely_bin),
                           mean_donation = mean(amount_donate, na.rm = TRUE),
                           sd_donation = sd(amount_donate, na.rm = TRUE),
                           N = nrow(.)))) %>% 
  unnest(summary) %>% select(-data)

conditions_summary_clean <- conditions_summary %>% 
  mutate(funding = ifelse(is.na(funding) & !is.na(issue) , "*Total*", as.character(funding)),
         issue = ifelse(is.na(issue) & !is.na(crackdown), "*Total*", as.character(issue)),
         crackdown = ifelse(is.na(crackdown), "*Total*", as.character(crackdown))) %>% 
  group_by(crackdown) %>% 
  mutate(issue = replace(issue, duplicated(issue), NA)) %>% 
  ungroup() %>% 
  mutate(crackdown = replace(crackdown, duplicated(crackdown), NA)) %>% 
  mutate(pct_likely = percent(pct_likely)) %>% 
  rename(`Crackdown condition` = crackdown, `Issue condition` = issue,
         `Funding condition` = funding, `% likely to donate` = pct_likely,
         `Amount donated (mean)` = mean_donation, `Amount donated (sd)` = sd_donation)

conditions_summary_clean %T>% 
  pandoc.table() %>% 
  pandoc.table.return(caption = "Average likelihood and amount donated across experimental conditions {#tbl:avg-results}") %>% 
  cat(file = here("analysis", "output", "tables", "tbl-avg-results.md"))
```

\

# Visualize important variables

## Likelihood of donation

```{r fig-likelihood-bars, fig.width=9, fig.height=4}
donate_summary <- results %>% 
  count(donate_likely) %>% 
  mutate(perc = n / sum(n)) %>% 
  mutate(highlight = ifelse(donate_likely %in% c("Extremely likely", "Somewhat likely"), TRUE, FALSE))

plot_donate_summary <- ggplot(donate_summary, aes(x = n, y = donate_likely, 
                                                  fill = highlight)) +
  geom_barh(stat = "identity") +
  scale_x_continuous(sec.axis = sec_axis(~ . / sum(donate_summary$n),
                                         labels = percent_format(accuracy = 1))) +
  scale_fill_manual(values = ngo_cols("green", "blue", name = FALSE), guide = FALSE) +
  labs(x = NULL, y = NULL) +
  theme_ngos() +
  theme(panel.grid.major.y = element_blank())

plot_donate_summary %T>% 
  print() %T>%
  ggsave(., filename = here("analysis", "output", "figures", "donate_summary.pdf"),
         width = 9, height = 4, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("analysis", "output", "figures", "donate_summary.png"),
         width = 9, height = 4, units = "in", type = "cairo", dpi = 300)
```

## Amount donated

```{r fig-amount-bars, fig.width=9, fig.height=2.75}
plot_amount_summary <- ggplot(results, aes(x = amount_donate)) +
  geom_histogram(bins = 20, fill = ngo_cols("blue")) +
  scale_x_continuous(labels = dollar) +
  labs(x = NULL, y = NULL) +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank())

plot_amount_summary %T>% 
  print() %T>%
  ggsave(., filename = here("analysis", "output", "figures", "amount_summary.pdf"),
         width = 9, height = 2.75, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("analysis", "output", "figures", "amount_summary.png"),
         width = 9, height = 2.75, units = "in", type = "cairo", dpi = 300)
```

\

```{r all-datasets-nested}
all_models <- tribble(
  ~title, ~df,
  "Crackdown", results,
  # Ha, cheat here by renaming the issue and funding columns to crackdown. 
  # All other tests are based on the crackdown column except these two, so
  # rather than build all sorts of convoluted arguments and functions, we just
  # rename these as issue and funding
  "Issue", mutate(results, crackdown = issue),
  "Funding", mutate(results, crackdown = funding),
  # Create all the nested conditions
  "Human rights | Crackdown", filter(results, issue == "Human rights"),
  "Humanitarian assistance | Crackdown", filter(results, issue != "Human rights"),
  "Government | Crackdown", filter(results, funding == "Government"),
  "Private | Crackdown", filter(results, funding == "Private"),
  "Human rights | Government | Crackdown", filter(results, issue == "Human rights", funding == "Government"),
  "Human rights | Private | Crackdown", filter(results, issue == "Human rights", funding == "Private"),
  "Humanitarian assistance | Government | Crackdown", filter(results, issue != "Human rights", funding == "Government"),
  "Humanitarian assistance | Private | Crackdown", filter(results, issue != "Human rights", funding == "Private")
) 
```

# Treatment effects: Likelihood of donation

This time around, we're not using interactionful regression models to calculate all these differences in groups. Instead we use real live Stan code to estimate the differences in group means and proportions! 

## Priors and models

We estimate the proportion of people responding that they'd be likely to donate to the organization with a binomial distribution, with a prior $\theta$ distribution of $\text{Beta}(5, 5)$. We build the following model in Stan:

$$
\begin{aligned}
n_{\text{group 1, group 2}} &\sim \text{Binomial}(n_{\text{total in group}}, \theta_{\text{group}}) &\text{[likelihood]}\\
\text{Difference} &= n_{\text{group 2}} - n_{\text{group 1}} &\text{[difference in proportions]} \\
n &: \text{Number likely to donate} \\
\\
\theta_{\text{group 1, group 2}} &\sim \text{Beta}(5, 5) &\text{[prior prob. of being likely to donate]}
\end{aligned}
$$

```{r priors-likely, fig.width=2.75, fig.height=1.5, fig.align="center"}
likely_theta <- ggplot(data = tibble(x = c(0, 1)), aes(x = x)) +
  geom_area(stat = "function", fun = dbeta, args = list(shape1 = 5, shape2 = 5), 
            fill = "grey80", color = "black") +
  labs(x = expression(Probability ~ of ~ being ~ likely ~ to ~ donate ~ (theta)), y = "Density") +
  annotate(geom = "label", x = 0.5, y = 1, label = "Beta(5, 5)", size = pts(9)) +
  theme_ngos(base_size = 9, density = TRUE)
likely_theta

likely_theta %T>% 
  ggsave(., filename = here("analysis", "output", "figures", "prior-likely.pdf"),
         width = 2.75, height = 1.5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("analysis", "output", "figures", "prior-likely.png"),
         width = 2.75, height = 1.5, units = "in", type = "cairo", dpi = 300)
```

## Differences

```{r run-likely-models, results="hide", cache=TRUE}
# This takes forever because of MCMC sampling, so this chunk is cached

# Sample from the compiled model
all_models_likely_run <- all_models %>% 
  # Simplify data for sending to Stan
  mutate(stan_data_counts = df %>% map(~ {
    df_counts <- .x %>% 
      count(crackdown, donate_likely_bin) %>% 
      group_by(crackdown) %>% 
      mutate(total = sum(n)) %>% 
      filter(donate_likely_bin == "Likely")
    
    return(list(
      n_total_1 = df_counts$total[1],
      n_total_2 = df_counts$total[2],
      n_likely_1 = df_counts$n[1],
      n_likely_2 = df_counts$n[2]
    ))
  })) %>% 
  # Run the actual model on the simplified data
  mutate(model = stan_data_counts %>% map(~ {
    sampling(donate_likely(), data = .x, control = list(max_treedepth = 15),
             chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)
  })) 

# Extract stuff
all_models_likely <- all_models_likely_run %>% 
  # Extract posterior chains
  mutate(posterior_chains_long = map(model, ~ {
      .x %>% gather_draws(theta_1, theta_2, theta_diff, pct_change)
  })) %>% 
  mutate(posterior_chains_wide = map(model, ~ {
      .x %>% spread_draws(theta_1, theta_2, theta_diff, pct_change)
  })) %>% 
  # Get HDI medians in tidy form
  mutate(tidy = posterior_chains_long %>% map(~ {
    .x %>% median_hdci() %>% to_broom_names()
  }))
```

```{r plot-likely-diffs, warning=FALSE, fig.width=10/3, fig.height=5.5}
tidied_diffs_likely <- all_models_likely %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "theta_diff") %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  mutate(title = str_remove(title, " \\| Crackdown"))

level1_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown −\nNo crackdown",
                        Issue = "Humanitarian\nassistance −\nHuman rights",
                        Funding = "Private −\nGovernment\nfunding")) %>% 
  mutate(title = fct_inorder(title))

plot_diff_likely_a <- ggplot(level1_likely, aes(x = .value, y = fct_rev(title), fill = title)) +
  geom_halfeyeh(.width = c(0.8, 0.95)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_manual(values = ngo_cols(c("blue", "red", "orange"), name = FALSE), guide = FALSE) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "A") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level2_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 2") %>%
  mutate(condition = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ "Issue",
    title %in% c("Government", "Private") ~ "Funding"
  )) %>% 
  mutate(facet_title = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ paste(title, "issues"),
    title %in% c("Government", "Private") ~ paste(title, "funding")
  )) %>% 
  mutate(title = case_when(
    title %in% c("Humanitarian assistance", "Private") ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_likely_b <- ggplot(filter(level2_likely, condition == "Issue"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("red", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = NULL, y = NULL, tag = "B") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diff_likely_c <- ggplot(filter(level2_likely, condition == "Funding"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("orange", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "C") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level3_likely <- tidied_diffs_likely %>% 
  filter(category == "Level 3") %>%
  separate(title, c("issue", "funding"), sep = " \\| ") %>%
  mutate(issue = paste(issue, "issues"),
         funding = paste(funding, "funding")) %>% 
  mutate(facet_title = paste0(issue, "\n", funding)) %>% 
  mutate(title = case_when(
    funding == "Private funding" ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_likely_d <- ggplot(level3_likely, aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("green", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  labs(x = "Difference in donation likelihood", y = NULL, tag = "D",
       caption = "Point shows posterior median; thick black lines show 80% credible interval;\nthin black lines show 95% credible interval") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diffs_likely_all <- plot_diff_likely_a / 
  (plot_diff_likely_b / plot_diff_likely_c) / 
  plot_diff_likely_d

plot_diffs_likely_all

plot_diffs_likely_all %T>% 
  ggsave(., filename = here("analysis", "output", "figures", "likely-diffs.pdf"),
         width = 10/3, height = 5.5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("analysis", "output", "figures", "likely-diffs.png"),
         width = 10/3, height = 5.5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-likely-diffs-level1, warning=FALSE, message=FALSE}
tbl_likely_tidy <- all_models_likely %>% 
  unnest(tidy) %>% 
  filter(term %in% c("theta_1", "theta_2", "theta_diff", "pct_change")) %>% 
  select(title, term, estimate) %>% 
  spread(term, estimate) %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) 

tbl_likely_probs <- all_models_likely %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "theta_diff") %>% 
  group_by(title) %>% 
  summarize(p.greater0 = mean(.value > 0),
            p.less0 = mean(.value < 0),
            p.diff.not0 = ifelse(median(.value) > 0, p.greater0, p.less0)) %>% 
  ungroup()

# Save combined table for later use in manuscript
tbl_likely_tidy %>% 
  left_join(tbl_likely_probs, by = "title") %>% 
  saveRDS(here("data", "derived_data", "results_models_likely.rds"))
  
tbl_likely_1 <- tbl_likely_tidy %>% 
  left_join(tbl_likely_probs, by = "title") %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown − No crackdown",
                        Issue = "*Humanitarian assistance − Human rights*",
                        Funding = "*Private − Government funding*")) %>% 
  mutate_at(vars(theta_1, theta_2, theta_diff, pct_change), 
            funs(percent_format(accuracy = 0.1)(.))) %>% 
  mutate(p.diff.not0 = as.character(round(p.diff.not0, 2))) %>%
  select(Frame = title, `% likely~Treatment~` = theta_2, `% likely~Control~` = theta_1,
         `$\\Delta$` = theta_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r tbl-likely-diffs-level2, warning=FALSE, message=FALSE}
tbl_likely_2 <- tbl_likely_tidy %>% 
  left_join(tbl_likely_probs, by = "title") %>% 
  filter(category == "Level 2") %>%
  mutate(title_clean = str_remove(title, " \\| Crackdown")) %>% 
  mutate(title_clean = case_when(
    title_clean %in% c("Human rights", "Humanitarian assistance") ~ paste(title_clean, "issues"),
    title_clean %in% c("Government", "Private") ~ paste(title_clean, "funding")
  )) %>% 
  mutate(title_clean = factor(title_clean, 
                              levels = c("Human rights issues", 
                                         "Humanitarian assistance issues", 
                                         "Government funding", "Private funding"))) %>% 
  arrange(title_clean) %>% 
  mutate_at(vars(theta_1, theta_2, theta_diff, pct_change), 
            funs(percent_format(accuracy = 0.1)(.))) %>% 
  mutate(p.diff.not0 = as.character(round(p.diff.not0, 2))) %>% 
  mutate(title_clean = as.character(title_clean)) %>% 
  select(`H~2a~ and H~3a~` = title_clean, 
         `% likely~Crackdown~` = theta_2, `% likely~No\\ crackdown~` = theta_1,
         `$\\Delta$` = theta_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r tbl-likely-diffs-level3, warning=FALSE, message=FALSE}
tbl_likely_3 <- tbl_likely_tidy %>% 
  left_join(tbl_likely_probs, by = "title") %>% 
  filter(category == "Level 3") %>%
  mutate(title_clean = str_remove(title, " \\| Crackdown")) %>% 
  separate(title_clean, c("Issue", "Funding"), sep = " \\| ") %>% 
  mutate(Issue = factor(Issue, levels = c("Human rights", "Humanitarian assistance")),
         Funding = factor(Funding, levels = c("Government", "Private"))) %>% 
  arrange(Issue, Funding) %>% 
  mutate_at(vars(theta_1, theta_2, theta_diff, pct_change), 
            funs(percent_format(accuracy = 0.1)(.))) %>% 
  mutate(p.diff.not0 = as.character(round(p.diff.not0, 2))) %>%
  mutate(Frame = paste0(Issue, " issues, ", Funding, " funding")) %>% 
  select(`H~2a~ and H~3a~ (nested)` = Frame,
         `% likely~Crackdown~` = theta_2, `% likely~No\\ crackdown~` = theta_1,
         `$\\Delta$` = theta_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r combine-likely-tables, results="asis"}
tbl_likely_2_header <- enframe(colnames(tbl_likely_2)) %>% 
  mutate(value = (value)) %>% 
  spread(name, value) %>% 
  set_names(colnames(tbl_likely_1))

tbl_likely_3_header <- enframe(colnames(tbl_likely_3)) %>% 
  mutate(value = (value)) %>% 
  spread(name, value) %>% 
  set_names(colnames(tbl_likely_1))

bind_rows(tbl_likely_1, 
          tbl_likely_2_header, 
          set_names(tbl_likely_2, colnames(tbl_likely_1)),
          tbl_likely_3_header,
          set_names(tbl_likely_3, colnames(tbl_likely_1))) %>% 
  rename(`H~1a~` = Frame) %>% 
  pandoc.table.return(caption = 'Likelihood of donation and differences in proportions in "crackdown" (treatment) and "no crackdown" (control) conditions; values represent posterior medians {#tbl:likely-diffs}',
                      justify = "lccccc") %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-likely-diffs.md"))
```

\

# Treatment effects: Amount donated

## Priors and models

Following [John Kruschke's "Bayesian Estimation Supersedes the t-test (BEST)" procedure](http://www.indiana.edu/~kruschke/BEST/BEST.pdf), we estimate means for each group with a t-distribution. We use the following priors for the distribution parameters:

$$
\begin{aligned}
x_{\text{group 1, group 2}} &\sim \text{Student } t(\nu, \mu, \sigma) &\text{[likelihood]}\\
\text{Difference} &= x_{\text{group 2}} - x_{\text{group 1}} &\text{[difference in means]} \\
x &: \text{Mean amount donated} \\
\\
\nu &\sim \text{Exponential}(1 / 29) &\text{[prior normality]} \\
\mu_{\text{group 1, group 2}} &\sim \mathcal{N}(\bar{x}_{\text{group 1, group 2}}, 10) &\text{[prior donation mean per group]}\\
\sigma_{\text{group 1, group 2}} &\sim \text{Cauchy}(0, 1)&\text{[prior donation sd per group]}
\end{aligned}
$$

```{r priors-amounts, fig.width=8, fig.height=1.5}
amount_nu <- ggplot(data = tibble(x = c(0, 200)), aes(x = x)) +
  geom_area(stat = "function", fun = dexp, args = list(rate = 1/29), 
            fill = "grey80", color = "black") +
  labs(x = expression(Normality ~ parameter ~ (nu)), y = "Density") +
  annotate(geom = "label", x = 100, y = 0.009, 
           label = "Exponential(1/29)", size = pts(9)) +
  theme_ngos(base_size = 9, density = TRUE)

amount_mu <- ggplot(data = tibble(x = c(0, 100)), aes(x = x)) +
  geom_area(stat = "function", fun = dnorm, args = list(mean = 50, sd = 10),
            fill = "grey80", color = "black") +
  scale_x_continuous(breaks = c(seq(0, 100, 25)),
                     labels = c("−$50", "−$25", "Group average", "+$25", "+$50")) +
  annotate(geom = "label", x = 50, y = 0.01, label = "N(bar(x), 10)", 
           parse = TRUE, size = pts(9)) +
  labs(x = expression(Average ~ donated ~ (mu)), y = NULL) +
  theme_ngos(base_size = 9, density = TRUE)

amount_sigma <- ggplot(data = tibble(x = c(0, 10)), aes(x = x)) +
  geom_area(stat = "function", fun = dcauchy, args = list(location = 0, scale = 1),
            fill = "grey80", color = "black") +
  scale_x_continuous(labels = dollar) +
  annotate(geom = "label", x = 5, y = 0.08, label = "Cauchy(0, 1)", size = pts(9)) +
  labs(x = expression(SD ~ donated ~ (sigma)), y = NULL) +
  theme_ngos(base_size = 9, density = TRUE)

amount_priors <- amount_nu + amount_mu + amount_sigma
amount_priors

amount_priors %T>% 
  ggsave(., filename = here("analysis", "output", "figures", "prior-amount.pdf"),
         width = 8, height = 1.5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("analysis", "output", "figures", "prior-amount.png"),
         width = 8, height = 1.5, units = "in", type = "cairo", dpi = 300)
```


## Differences

```{r run-amount-models, results="hide", cache=TRUE}
# Sample from the compiled model
all_models_amount_run <- all_models %>% 
  # Simplify data for sending to Stan
  mutate(stan_data = df %>% map(~ {
    select(.x, amount = amount_donate, group = crackdown)
  })) %>% 
  # Run the actual model on the simplified data
  mutate(model = stan_data %>% map(~ {
    sampling(amount_donated_best(), data = compose_data(.x),
             chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)
  })) 

# Extract stuff
all_models_amount <- all_models_amount_run %>% 
  # Extract posterior chains
  mutate(posterior_chains_long = map2(model, stan_data, ~ {
    .x %>% 
      recover_types(.y) %>% 
      gather_draws(mu[group], sigma[group], mu_diff, pct_change, cohen_d, cles, nu, log10nu)
  })) %>% 
  mutate(posterior_chains_wide = map2(model, stan_data, ~ {
    .x %>% 
      recover_types(.y) %>% 
      spread_draws(mu[group], sigma[group], mu_diff, pct_change, cohen_d, cles, nu, log10nu)
  })) %>% 
  # Get HDI medians in tidy form
  mutate(tidy = posterior_chains_long %>% map(~ {
    .x %>% median_hdci() %>% to_broom_names()
  }))
```

```{r plot-amount-diffs, warning=FALSE, fig.width=10/3, fig.height=5.5}
# This will generate a warning because unnest() puts the crackdown values into
# one column and we cheated by renaming issue and funding as crackdown, so it
# gets mad when combining the factors in crackdown with those in issue and
# funding. But because we only care about the diffs, we don't actually need the
# category labels here, so it's all good.
tidied_diffs_amount <- all_models_amount %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "mu_diff") %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  mutate(title = str_remove(title, " \\| Crackdown"))

level1_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown −\nNo crackdown",
                        Issue = "Humanitarian\nassistance −\nHuman rights",
                        Funding = "Private −\nGovernment\nfunding")) %>% 
  mutate(title = fct_inorder(title))

plot_diff_amount_a <- ggplot(level1_amount, aes(x = .value, y = fct_rev(title), fill = title)) +
  geom_vline(xintercept = 0) +
  geom_halfeyeh(.width = c(0.8, 0.95)) +
  scale_x_continuous(labels = dollar) +
  scale_fill_manual(values = ngo_cols(c("blue", "red", "orange"), name = FALSE), guide = FALSE) +
  labs(x = "Difference in amount donated", y = NULL, tag = "A") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level2_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 2") %>%
  mutate(condition = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ "Issue",
    title %in% c("Government", "Private") ~ "Funding"
  )) %>% 
  mutate(facet_title = case_when(
    title %in% c("Human rights", "Humanitarian assistance") ~ paste(title, "issues"),
    title %in% c("Government", "Private") ~ paste(title, "funding")
  )) %>% 
  mutate(title = case_when(
    title %in% c("Humanitarian assistance", "Private") ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_amount_b <- ggplot(filter(level2_amount, condition == "Issue"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("red", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = NULL, y = NULL, tag = "B") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diff_amount_c <- ggplot(filter(level2_amount, condition == "Funding"), 
                             aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("orange", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = "Difference in amount donated", y = NULL, tag = "C") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

level3_amount <- tidied_diffs_amount %>% 
  filter(category == "Level 3") %>%
  separate(title, c("issue", "funding"), sep = " \\| ") %>%
  mutate(issue = paste(issue, "issues"),
         funding = paste(funding, "funding")) %>% 
  mutate(facet_title = paste0(issue, "\n", funding)) %>% 
  mutate(title = case_when(
    funding == "Private funding" ~ "",
    TRUE ~ "Crackdown −\nNo crackdown")
  )

plot_diff_amount_d <- ggplot(level3_amount, aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95), fill = ngo_cols("green", name = FALSE)) +
  geom_vline(xintercept = 0) +
  scale_x_continuous(labels = dollar) +
  labs(x = "Difference in amount donated", y = NULL, tag = "D",
       caption = "Point shows posterior median; thick black lines show 80% credible interval;\nthin black lines show 95% credible interval") +
  facet_wrap(~ facet_title, scales = "free_y") +
  theme_ngos(base_size = 8) +
  theme(panel.grid.major.y = element_blank())

plot_diffs_amounts_all <- plot_diff_amount_a / 
  (plot_diff_amount_b / plot_diff_amount_c) / 
  plot_diff_amount_d

plot_diffs_amounts_all

plot_diffs_amounts_all %T>% 
  ggsave(., filename = here("analysis", "output", "figures", "amount-diffs.pdf"),
         width = 10/3, height = 5.5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("analysis", "output", "figures", "amount-diffs.png"),
         width = 10/3, height = 5.5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-amount-diffs-level1, warning=FALSE, message=FALSE}
tbl_amounts_tidy <- all_models_amount %>% 
  unnest(tidy) %>% 
  filter(term %in% c("mu", "mu_diff", "pct_change")) %>% 
  group_by(title) %>% 
  mutate(group_id = 1:n()) %>% 
  mutate(term = case_when(
    term == "mu" ~ paste0(term, "_", group_id),
    TRUE ~ term
  )) %>% 
  select(title, term, estimate) %>% 
  spread(term, estimate) %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  ungroup()

tbl_amounts_probs <- all_models_amount %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "mu_diff") %>% 
  group_by(title) %>% 
  summarize(p.greater0 = mean(.value > 0),
            p.less0 = mean(.value < 0),
            p.diff.not0 = ifelse(median(.value) > 0, p.greater0, p.less0)) %>% 
  ungroup()

# Save combined table for later use in manuscript
tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  saveRDS(here("data", "derived_data", "results_models_amount.rds"))

tbl_amount_1 <- tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  filter(category == "Level 1") %>%
  mutate(title = recode(title, 
                        Crackdown = "Crackdown − No crackdown",
                        Issue = "*Humanitarian assistance − Human rights*",
                        Funding = "*Private − Government funding*")) %>% 
  mutate_at(vars(mu_1, mu_2, mu_diff, p.diff.not0), funs(as.character(round(., 2)))) %>% 
  mutate(pct_change = percent_format(accuracy = 0.1)(pct_change)) %>%
  select(`Frame` = title, `Amount~Treatment~` = mu_2, `Amount~Control~` = mu_1,
         `$\\Delta$` = mu_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r tbl-amount-diffs-level2, warning=FALSE, message=FALSE}
tbl_amount_2 <- tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  filter(category == "Level 2") %>%
  mutate(title_clean = str_remove(title, " \\| Crackdown")) %>% 
  mutate(title_clean = case_when(
    title_clean %in% c("Human rights", "Humanitarian assistance") ~ paste(title_clean, "issues"),
    title_clean %in% c("Government", "Private") ~ paste(title_clean, "funding")
  )) %>% 
  mutate(title_clean = factor(title_clean, 
                              levels = c("Human rights issues", 
                                         "Humanitarian assistance issues", 
                                         "Government funding", "Private funding"))) %>% 
  arrange(title_clean) %>% 
  mutate_at(vars(mu_1, mu_2, mu_diff, p.diff.not0), funs(as.character(round(., 2)))) %>% 
  mutate(pct_change = percent_format(accuracy = 0.1)(pct_change)) %>%
  mutate(title_clean = as.character(title_clean)) %>% 
  select(`H~2b~ and H~3b~` = title_clean, `Amount~Crackdown~` = mu_2, `Amount~No\\ crackdown~` = mu_1,
         `$\\Delta$` = mu_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r tbl-amount-diffs-level3, warning=FALSE, message=FALSE}
tbl_amount_3 <- tbl_amounts_tidy %>% 
  left_join(tbl_amounts_probs, by = "title") %>% 
  filter(category == "Level 3") %>%
  mutate(title_clean = str_remove(title, " \\| Crackdown")) %>% 
  separate(title_clean, c("Issue", "Funding"), sep = " \\| ") %>% 
  mutate(Issue = factor(Issue, levels = c("Human rights", "Humanitarian assistance")),
         Funding = factor(Funding, levels = c("Government", "Private"))) %>% 
  arrange(Issue, Funding) %>% 
  mutate(Frame = paste0(Issue, " issues, ", Funding, " funding")) %>% 
  mutate_at(vars(mu_1, mu_2, mu_diff, p.diff.not0), funs(as.character(round(., 2)))) %>% 
  mutate(pct_change = percent_format(accuracy = 0.1)(pct_change)) %>%
  select(`H~2b~ and H~3b~ (nested)` = Frame,
         `Amount~Crackdown~` = mu_2, `Amount~No\\ crackdown~` = mu_1,
         `$\\Delta$` = mu_diff, `$\\%\\Delta$` = pct_change, 
         `$p(\\Delta \\neq 0)$` = p.diff.not0)
```

```{r combine-amount-tables, results="asis"}
tbl_amount_2_header <- enframe(colnames(tbl_amount_2)) %>% 
  mutate(value = (value)) %>% 
  spread(name, value) %>% 
  set_names(colnames(tbl_amount_1))

tbl_amount_3_header <- enframe(colnames(tbl_amount_3)) %>% 
  mutate(value = (value)) %>% 
  spread(name, value) %>% 
  set_names(colnames(tbl_amount_1))

bind_rows(tbl_amount_1, 
          tbl_amount_2_header, 
          set_names(tbl_amount_2, colnames(tbl_amount_1)),
          tbl_amount_3_header,
          set_names(tbl_amount_3, colnames(tbl_amount_1))) %>% 
  rename(`H~1b~` = Frame) %>% 
  pandoc.table.return(caption = 'Mean values and differences in means for amount donated in "crackdown" (treatment) and "no crackdown" (control) conditions; values represent posterior medians {#tbl:amount-diffs}',
                      justify = "lccccc") %T>%
  cat() %>%
  cat(file = here("analysis", "output", "tables", "tbl-amount-diffs.md"))
```

## Effect size

```{r amount-effect-size, warning=FALSE}
# Effect size
# (μ₁ - μ₂) / sqrt( (σ₁² + σ₂²) / 2)
tidied_eff_size_amount <- all_models_amount %>% 
  unnest(posterior_chains_long) %>% 
  filter(.variable == "cohen_d") %>% 
  mutate(category = case_when(
    str_count(title, "\\|") == 0 ~ "Level 1",
    str_count(title, "\\|") == 1 ~ "Level 2",
    str_count(title, "\\|") == 2 ~ "Level 3"
  )) %>% 
  mutate(title = str_remove(title, " \\| Crackdown")) %>% 
  mutate(title = fct_inorder(title))

effect_sizes <- tribble(
  ~size, ~x_end,
  "Small", 0.2,
  "Medium", 0.5,
  "Large", 0.8
) %>% 
  mutate(x_start = -x_end)

ggplot(tidied_eff_size_amount, aes(x = .value, y = fct_rev(title))) +
  geom_halfeyeh(.width = c(0.8, 0.95)) +
  geom_vline(xintercept = 0, size = 1) +
  geom_vline(xintercept = c(0.2, -0.2), linetype = "dotted", size = 0.5) +
  geom_vline(xintercept = c(0.5, -0.5), linetype = "dotted", size = 0.5) +
  geom_vline(xintercept = c(0.5, -0.5), linetype = "dotted", size = 0.5) +
  coord_cartesian(xlim = c(-0.6, 0.6)) +
  labs(x = "Cohen's d (effect size)", y = NULL) +
  theme_ngos()
```

\

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

devtools::session_info()
```

</div> 
