library(tidyverse)
library(rstanarm)
library(broom)
library(ggstance)
library(patchwork)
library(pander)
library(scales)
library(janitor)
library(huxtable)
library(here)

source(here("lib", "graphics.R"))
source(here("lib", "pander_options.R"))
source(here("lib", "modeling.R"))

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


# Helpful functions
safe_mean <- function(x, ...) suppressWarnings(mean(as.numeric(x), ...))
safe_sd <- function(x, ...) suppressWarnings(sd(as.numeric(x), ...))

my_as_numeric <- function(x) {
  is_num <- suppressWarnings(!any(is.na(as.numeric(x))))
  
  if (is_num) {
    as.numeric(x)
  } else {
    x
  }
}

level_summary <- function(x, variable, df = results) {
  variable <- as.character(variable)
  if (is.factor(df[[variable]])) {
    x_levels <- levels(df[[variable]])
  } else {
    x_levels <- unique(df[[variable]])
  }
  
  factor_summary <- data_frame(x) %>% 
    count(x) %>%
    mutate(pct = n / sum(n),
           fancy = paste0(x, " (", n, "; ", percent(pct), ")"),
           x = factor(x, levels = x_levels, ordered = TRUE)) 
  
  inline_summary <- factor_summary %>% arrange(x) %>% 
    pull(fancy) %>% paste(collapse = " | ")
  
  fig_bar <- ggplot(factor_summary, aes(x = x, y = n)) +
    geom_bar(stat = "identity", fill = "grey50") +
    theme_spark()
  
  file_name <- gsub("\\.| ", "_", tolower(variable))
  md_img <- paste0("![](output/figures/summary-table/", file_name, ")")

  ggsave(fig_bar, filename = here("output", "figures", "summary-table",
                                  paste0(file_name, ".pdf")), 
         width = 1, height = 0.2, device = cairo_pdf)
  ggsave(fig_bar, filename = here("output", "figures", "summary-table",
                                  paste0(file_name, ".png")), 
         width = 1, height = 0.2, type = "cairo", bg = "transparent", dpi = 300)

  output <- data_frame(spark = md_img, summary = inline_summary)
  return(output)
}

numeric_summary <- function(x, variable) {
  inline_summary <- paste0("Median: ", round(median(x, na.rm = TRUE), 2),
                           " | Mean: ", round(mean(x, na.rm = TRUE), 2), 
                           " | Std. Dev.: ", round(sd(x, na.rm = TRUE), 2))
  
  fig_hist <- ggplot(as.data.frame(x), aes(x = x)) +
    geom_histogram(fill = "grey50", binwidth = 10) +
    theme_spark()
  
  file_name <- gsub("\\.| ", "_", tolower(variable))
  md_img <- paste0("![](output/figures/summary-table/", file_name, ")")

  ggsave(fig_hist, filename = here("output", "figures", "summary-table",
                                   paste0(file_name, ".pdf")), 
         width = 1, height = 0.2, device = cairo_pdf)
  ggsave(fig_hist, filename = here("output", "figures", "summary-table",
                                   paste0(file_name, ".png")), 
         width = 1, height = 0.2, type = "cairo", bg = "transparent", dpi = 300)
  
  output <- data_frame(spark = md_img, summary = inline_summary)
  return(output)
}

my_summary <- function(x, variable, df) {
  x <- my_as_numeric(x)
  
  if (is.numeric(x)) {
    numeric_summary(x, variable)
  } else {
    level_summary(x, variable, df)
  }
}

Overview of data

Balance of experimental conditions

Crackdown Issue Funding n
No crackdown Human rights Government 68
No crackdown Human rights Private 67
No crackdown Humanitarian assistance Government 69
No crackdown Humanitarian assistance Private 66
Crackdown Human rights Government 66
Crackdown Human rights Private 69
Crackdown Humanitarian assistance Government 70
Crackdown Humanitarian assistance Private 71
Total - - 546

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$variable)) %>% 
  gather(variable, value) %>% 
  group_by(variable) %>% 
  nest() %>% 
  mutate(N = data %>% map_int(~ nrow(.)),
         summary = map2(.x = data, .y = variable, ~ my_summary(.x$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("output", "tables", "tbl-descriptive-stats.md")) %>%
  cat()
Descriptive statistics {#tbl:descriptive-stats}
Variable Details
Likelihood of donation Extremely unlikely (49; 9.0%) | Somewhat unlikely (114; 20.9%) | Neither likely nor unlikely (141; 25.8%) | Somewhat likely (195; 35.7%) | Extremely likely (47; 8.6%)
Likelihood of donation (binary) Not likely (304; 55.7%) | Likely (242; 44.3%)
Amount hypothetically donated ($) Median: 10 | Mean: 22.15 | Std. Dev.: 25.56
Gender Female (297; 54.4%) | Male (246; 45.1%) | Other (1; 0.2%) | Prefer not to say (2; 0.4%)
Age Under 18 (1; 0.2%) | 18 – 24 (47; 8.6%) | 25 – 34 (214; 39.2%) | 35 – 44 (131; 24.0%) | 45 – 54 (92; 16.8%) | 55 – 64 (44; 8.1%) | 65 – 74 (16; 2.9%) | 75 – 84 (1; 0.2%)
Income Less than $10,000 (35; 6.4%) | $10,000 – $19,999 (43; 7.9%) | $20,000 – $29,999 (51; 9.3%) | $30,000 – $39,999 (81; 14.8%) | $40,000 – $49,999 (60; 11.0%) | $50,000 – $59,999 (56; 10.3%) | $60,000 – $69,999 (44; 8.1%) | $70,000 – $79,999 (39; 7.1%) | $80,000 – $89,999 (25; 4.6%) | $90,000 – $99,999 (30; 5.5%) | $100,000 – $149,999 (47; 8.6%) | More than $150,000 (22; 4.0%) | Prefer not to say (13; 2.4%)
Education Less than high school (2; 0.4%) | High school graduate (48; 8.8%) | Some college (130; 23.8%) | 2 year degree (70; 12.8%) | 4 year degree (221; 40.5%) | Graduate or professional degree (66; 12.1%) | Doctorate (9; 1.6%)
Frequency of attending religious services More than once a week (19; 3.5%) | Once a week (76; 13.9%) | Once or twice a month (50; 9.2%) | A few times a year (72; 13.2%) | Seldom (99; 18.1%) | Never (226; 41.4%) | Don’t know (4; 0.7%)
Political views Strong liberal (80; 14.7%) | Liberal (151; 27.7%) | Independent, leaning liberal (86; 15.8%) | Independent (85; 15.6%) | Independent, leaning conservative (60; 11.0%) | Conservative (62; 11.4%) | Very conservative (22; 4.0%)
Frequency of following public affairs Most of the time (222; 40.7%) | Some of the time (217; 39.7%) | Only now and then (87; 15.9%) | Hardly at all (20; 3.7%)
Frequency of charitable donations Once a week (38; 7.0%) | Once a month (107; 19.6%) | Once every three months (107; 19.6%) | Once every six months (106; 19.4%) | Once a year (92; 16.8%) | Once every few years (57; 10.4%) | Never (39; 7.1%)
Volunteered in past 12 months No (296; 54.21%) | Yes (250; 45.79%)
Prior favorability towards humanitarian NGOs Very unfavorable (2; 0.4%) | Unfavorable (6; 1.1%) | Neutral (43; 7.9%) | Favorable (240; 44.0%) | Very favorable (255; 46.7%)
Prior favorability towards humanitarian NGOs (binary) Not favorable (51; 9.3%) | Favorable (495; 90.7%)
Prior favorability towards human rights NGOs Very unfavorable (5; 0.9%) | Unfavorable (12; 2.2%) | Neutral (64; 11.7%) | Favorable (233; 42.7%) | Very favorable (232; 42.5%)
Prior favorability towards human rights NGOs (binary) Not favorable (81; 14.8%) | Favorable (465; 85.2%)
Prior favorability towards development NGOs Very unfavorable (5; 0.9%) | Unfavorable (8; 1.5%) | Neutral (48; 8.8%) | Favorable (241; 44.1%) | Very favorable (244; 44.7%)
Prior favorability towards development NGOs (binary) Not favorable (61; 11.2%) | Favorable (485; 88.8%)
Attention check 2 Correct (544; 99.6%) | Incorrect (2; 0.4%)

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 38.8% 18.9 22.1 67
Total 43.0% 20.6 23.5 135
Humanitarian assistance Government 43.5% 17.8 21.3 69
Private 40.9% 21.8 26.7 66
Total 42.2% 19.8 24.1 135
Total 42.6% 20.2 23.8 270
Crackdown Human rights Government 28.8% 19.1 25.7 66
Private 58.0% 27.3 26.1 69
Total 43.7% 23.3 26.1 135
Humanitarian assistance Government 50.0% 30.5 32.8 70
Private 46.5% 19.1 21.4 71
Total 48.2% 24.8 28.1 141
Total 46.0% 24 27.1 276
Total 44.3% 22.1 25.6 546


Visualize important variables

Prior favorability towards NGOs

favorability_cols <- data_frame(value = c("Very unfavorable", "Unfavorable", "Neutral",
                                          "Favorable", "Very favorable"),
                                color = ngo_pal("ordered")(5))

ngo_favorability_raw <- results %>%
  select(favor_humanitarian, favor_human_rights, favor_development) %>% 
  gather(variable, value) %>% 
  count(variable, value) %>% 
  left_join(vars_to_summarize, by = "variable") %>% 
  left_join(favorability_cols, by = "value") %>%
  mutate(clean_name = str_replace(clean_name, "towards", "towards\n")) %>% 
  mutate(value = factor(value, levels = levels(results$favor_humanitarian), ordered = TRUE),
         clean_name = fct_inorder(clean_name, ordered = TRUE)) %>%
  arrange(value) %>% 
  mutate(color = fct_inorder(color, ordered = TRUE))

ngo_favorability_high <- ngo_favorability_raw %>% 
  filter(!str_detect(value, "nfav")) %>% 
  mutate(n = ifelse(value == "Neutral", n / 2, n),
         n_plot = n,
         value = fct_rev(value), color = fct_rev(color))

ngo_favorability_low <- ngo_favorability_raw %>% 
  filter(str_detect(value, "nfav") | value == "Neutral") %>% 
  mutate(n = ifelse(value == "Neutral", n / 2, n),
         n_plot = -n)

ggplot(mapping = aes(x = n_plot, y = clean_name, fill = color)) +
  geom_barh(data = ngo_favorability_high, stat = "identity") +
  geom_barh(data = ngo_favorability_low, stat = "identity") +
  geom_vline(xintercept = 0, color = "black") +
  scale_x_continuous(labels = abs) +
  scale_fill_identity(labels = levels(ngo_favorability_raw$value),
                      breaks = levels(ngo_favorability_raw$color),
                      guide = "legend", name = NULL) +
  labs(x = NULL, y = NULL) +
  theme_ngos() +
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank())

Interpreting interactions in models

Ordinarily, people use ANOVA to analyze 3-way, 2 × 2 × 2 factorial designs, like this. A more flexible (yet identical!) way to do this is to use a regular regression model with interaction terms for each of the conditions, like so (here we use three, since we can use simper models to get averages for the larger umbrella groups, like just crackdowns and crackdown + issue):

  • Model 1:

    \[ y = \beta_0 + \beta_1 \text{Crackdown} \]

  • Model 2:

    \[ \begin{aligned} y =& \beta_0 + \beta_1 \text{Crackdown} + \beta_2 \text{Issue } + \\ & \beta_3 \text{Crackdown} \times \text{Issue} \end{aligned} \]

  • Model 3:

    \[ \begin{aligned} y =& \beta_0 + \beta_1 \text{Crackdown} + \beta_2 \text{Issue} + \beta_3 \text{Funding } + \\ & \beta_4 \text{Crackdown} \times \text{Issue } + \\ & \beta_5 \text{Crackdown} \times \text{Funding } + \\ & \beta_6 \text{Issue} \times \text{Funding } + \\ & \beta_7 \text{Crackdown} \times \text{Issue} \times \text{Funding} \end{aligned} \]

Adding different combinations of the coefficients provides the average values for each combination of factors, which corresponds to the average likelihood and amount donated table above.

Crackdown condition Issue condition Funding condition Model Coefficients to add together
No crackdown Human rights Government 3 Intercept
Private 3 Intercept + Funding
Total 2 Intercept
Humanitarian assistance Government 3 Intercept + Issue
Private 3 Intercept + Issue + Funding + (Issue × Funding)
Total 2 Intercept + Issue
Total 1 Intercept
Crackdown Human rights Government 3 Intercept + Crackdown
Private 3 Intercept + Crackdown + Funding + (Crackdown × Funding)
Total 2 Intercept + Crackdown
Humanitarian assistance Government 3 Intercept + Crackdown + Issue + (Crackdown × Issue)
Private 3 Intercept + Crackdown + Issue + Funding + (Crackdown × Issue) + (Crackdown × Funding) + (Issue × Funding) + (Crackdown × Issue × Funding)
Total 2 Intercept + Crackdown + Issue + (Crackdown × Issue)
Total 1 Intercept + Crackdown


Amount donated

Models

(1) (2) (3)
Intercept 20.207  20.641  22.033 
(1.597) (2.239) (3.049)
Crackdown (yes) 3.797  2.636  -2.692 
(2.254) (3.055) (4.337)
Issue (humanitarian)       -0.993  -4.030 
      (3.094) (4.312)
Funding (private)             -2.867 
            (4.444)
Crackdown × Issue       2.418  15.090 
      (4.366) (5.969)
Crackdown × Funding             10.831 
            (6.274)
Issue × Funding             6.595 
            (6.013)
Crackdown × Issue × Funding             -25.544 
            (8.628)
Observations 546      546      546     
Posterior sample size 4000.000  4000.000  4000.000 
Sigma 25.517  25.569  25.386 
.

Predicted medians

The Stan people really really don’t like posterior_linepred(), since it throws away a lot of the uncertainty that comes from actually getting a posterior distribution (like, they didn’t even want to invent it, and then when they did they wanted to hide it, and it has a big warning in the documentation). But this approach here mirrors using predict() to combine all the coefficients and intercept terms in a regular frequentist linear model and it’s probably appropriate for this situation because all the coefficients are actually linked together behind the scenes and create group mean estimates. If I use the full force of posterior_predict(), I add more uncertainty with each added coefficient (so in a 2×2×2 version, the 8th condition is actually the sum of all 8 coefficients, which means it gets 8 levels of uncertainity, which is probably excessive). But we do it both ways too, just to be on the safe side.

Differences in medians

# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_ci <- chains_fitted_ci %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_ci <- bind_cols(newdata_conditions_ci, chains_nested_ci) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_conditions_ci <- all_chains_ci %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_pairs_detail_ci <- pairs_conditions_ci %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs) #%>% 
  #mutate(pair = "Hey")

pairs_plot_data_ci <- pairs_conditions_ci %>% 
  select(issue, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_ci <- ggplot() + 
  geom_density(data = pairs_plot_data_ci, aes(x = value),
               fill = ngo_cols("blue"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_pairs_detail_ci,
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_ci, aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + pair)
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_cif <- chains_fitted_cif %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_cif <- bind_cols(newdata_conditions_cif, chains_nested_cif) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue, funding) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_conditions_cif <- all_chains_cif %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_pairs_detail_cif <- pairs_conditions_cif %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs)

pairs_plot_data_cif <- pairs_conditions_cif %>% 
  select(issue, funding, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue, -funding) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_cif <- ggplot() + 
  geom_density(data = pairs_plot_data_cif, aes(x = value),
               fill = ngo_cols("red"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_pairs_detail_cif, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_cif, aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + funding + pair)

Compare only individual conditions:

Median difference between likelihood to donate across single conditions {#tbl:amount-single-diffs}
Frame median %∆median p(∆ > 0) p(∆ < 0) p(∆ ≠ 0)
Crackdown - No crackdown 3.8 0.188 0.958 0.042 0.958
Humanitarian assistance - Human rights 0.387 0.018 0.559 0.441 0.559
Private - Government -0.709 -0.031 0.368 0.632 0.632


Compare nested conditions (what we’re really most concerned about):

Median difference between amounts donated in “crackdown” (treatment) and “no crackdown” (control) conditions {#tbl:amount-diffs}
Issue Funding median %∆median p(∆ > 0) p(∆ < 0) p(∆ ≠ 0)
3.8 0.188 0.958 0.042 0.958
Human rights 2.64 0.128 0.802 0.198 0.802
Humanitarian assistance 5 0.255 0.945 0.055 0.945
Human rights Government -2.69 -0.123 0.261 0.739 0.739
Human rights Private 8.2 0.434 0.975 0.025 0.975
Humanitarian assistance Government 12.2 0.677 0.998 0.002 0.998
Humanitarian assistance Private -2.33 -0.108 0.291 0.709 0.709


Likelihood of donation

Models

(1) (2) (3)
Intercept -0.299  -0.283  -0.170 
(0.120) (0.171) (0.234)
Crackdown (yes) 0.140  0.020  -0.681 
(0.166) (0.239) (0.340)
Issue (humanitarian)       -0.034  -0.061 
      (0.236) (0.312)
Funding (private)             -0.241 
            (0.337)
Crackdown × Issue       0.227  0.898 
      (0.335) (0.452)
Crackdown × Funding             1.399 
            (0.464)
Issue × Funding             0.086 
            (0.462)
Crackdown × Issue × Funding             -1.343 
            (0.641)
Observations 546      546      546     
Posterior sample size 4000.000  4000.000  4000.000 
Sigma 1.000  1.000  1.000 
.

Predicted medians

Differences in medians

# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_likely_ci <- chains_fitted_likely_ci %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_likely_ci <- bind_cols(newdata_conditions_ci, 
                                  chains_nested_likely_ci) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_likely_conditions_ci <- all_chains_likely_ci %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_likely_pairs_detail_ci <- pairs_likely_conditions_ci %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs)

pairs_likely_plot_data_ci <- pairs_likely_conditions_ci %>% 
  select(issue, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_likely_ci <- ggplot() + 
  geom_density(data = pairs_likely_plot_data_ci, aes(x = value),
               fill = ngo_cols("blue"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_likely_pairs_detail_ci, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_ci, 
             aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_continuous(labels = percent) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + pair)
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_likely_cif <- chains_fitted_likely_cif %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_likely_cif <- bind_cols(newdata_conditions_cif, 
                                   chains_nested_likely_cif) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue, funding) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_likely_conditions_cif <- all_chains_likely_cif %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_likely_pairs_detail_cif <- pairs_likely_conditions_cif %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs)

pairs_likely_plot_data_cif <- pairs_likely_conditions_cif %>% 
  select(issue, funding, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue, -funding) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_likely_cif <- ggplot() +
  geom_density(data = pairs_likely_plot_data_cif, aes(x = value),
               fill = ngo_cols("red"), colour = NA, alpha = 0.4) +
  geom_segment(data = diffs_coef_likely_pairs_detail_cif,
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_cif,
             aes(xintercept = `median`), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_continuous(labels = percent) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + funding + pair)

Compare only individual conditions:

Median difference between likelihood to donate across single conditions {#tbl:likelihood-single-diffs}
Frame median %∆median p(∆ > 0) p(∆ < 0) p(∆ ≠ 0)
Crackdown - No crackdown 0.034 0.08 0.794 0.206 0.794
Humanitarian assistance - Human rights 0.021 0.049 0.694 0.306 0.694
Private - Government 0.038 0.089 0.8 0.2 0.8


Compare nested conditions (what we’re really most concerned about):

Median difference between likelihood to donate in “crackdown” (treatment) and “crackdown” (control) conditions {#tbl:likelihood-diffs}
Issue Funding median %∆median p(∆ > 0) p(∆ < 0) p(∆ ≠ 0)
0.034 0.08 0.794 0.206 0.794
Human rights 0.005 0.011 0.536 0.464 0.536
Humanitarian assistance 0.063 0.15 0.857 0.143 0.857
Human rights Government -0.158 -0.344 0.025 0.975 0.975
Human rights Private 0.176 0.443 0.982 0.018 0.982
Humanitarian assistance Government 0.052 0.117 0.743 0.257 0.743
Humanitarian assistance Private 0.064 0.158 0.771 0.229 0.771

Summary of hypotheses

hypotheses <- tribble(
  ~dv, ~hypothesis, ~summary,
  "Likelihood", "H1a: Donors more likely\nto give to NGOs\nfacing crackdown", diffs_coef_likely_pairs_detail_c,
  "Likelihood", "H2a: Donors more likely\nto give to humanitarian\nNGOs facing crackdown", diffs_coef_likely_pairs_detail_ci,
  "Likelihood", "H3a: Donors more likely\nto give to privately funded\nNGOs facing crackdown", diffs_coef_likely_pairs_detail_cif,
  "Amount", "H1b: Donors give more \nto NGOs facing crackdown", diffs_coef_pairs_detail_c,
  "Amount", "H2b: Donors give more \nto humanitarian NGOs\nfacing crackdown", diffs_coef_pairs_detail_ci,
  "Amount", "H3b: Donors give more \nto privately funded NGOs\nfacing crackdown", diffs_coef_pairs_detail_cif
)

plot_hypotheses <- hypotheses %>% 
  unnest(summary) %>% 
  select(-diffs_coefs) %>% 
  mutate(condition = paste0(issue, " + ", funding),
         condition = recode(condition, 
                            `Human rights + NA` = "Human rights",
                            `Humanitarian assistance + NA` = "Humanitarian assistance",
                            `NA + NA` = "Crackdown only")) %>% 
  mutate(hypothesis_custom = case_when(
    .$condition == "Human rights + Government" ~ paste(hypothesis, "\n(government funding condition)"),
    .$condition == "Humanitarian assistance + Government" ~ paste(hypothesis, "\n(government funding condition)"),
    .$condition == "Human rights + Private" ~ paste(hypothesis, "\n(private funding condition)"),
    .$condition == "Humanitarian assistance + Private" ~ paste(hypothesis, "\n(private funding condition)"),
    TRUE ~ hypothesis
  )) %>% 
  mutate(issue = fct_rev(fct_explicit_na(issue, na_level = "Crackdown only")),
         hypothesis_facet = ifelse(str_detect(hypothesis, "H3"), 2, 1),
         p_lab = case_when(
           .$dv == "Likelihood" ~ sprintf("∆ = %.2f; p(∆ ≠ 0) = %.2f", .$median, .$p.diff.not0),
           .$dv == "Amount" ~ sprintf("∆ = $%.2f; p(∆ ≠ 0) = %.2f", .$median, .$p.diff.not0)
         ))

plot_hypotheses_likely <- filter(plot_hypotheses, dv == "Likelihood")
plot_hypotheses_amount <- filter(plot_hypotheses, dv == "Amount")

plot_summary_likely <- ggplot(plot_hypotheses_likely, 
                              aes(x = median, y = fct_rev(hypothesis_custom))) +
  geom_pointrangeh(aes(xmin = q5, xmax = q95, color = issue),
                   position = position_dodgev(height = 0.5)) +
  geom_vline(xintercept = 0, size = 0.25, linetype = "dashed") +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Human rights"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Humanitarian assistance"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = -0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Crackdown only"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.2, show.legend = FALSE) +
  labs(x = expression(paste("Effect of crackdown: (Crackdown - No crackdown)"["median"])),
       y = NULL, subtitle = "Likelihood of donation") +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = ngo_cols("dark grey", "blue", "red", name = FALSE), name = NULL) +
  coord_cartesian(clip = "off") +
  theme_ngos() + 
  theme(strip.text = element_blank(),
        panel.grid.minor = element_blank(),
        plot.subtitle = element_text(size = rel(1.2), face = "plain", #hjust = 0.5,
                                    family = "Encode Sans Condensed Medium")) +
  facet_wrap(~ hypothesis_facet, nrow = 1, scales = "free")

plot_summary_amount <- ggplot(plot_hypotheses_amount, 
                              aes(x = median, y = fct_rev(hypothesis_custom))) +
  geom_pointrangeh(aes(xmin = q5, xmax = q95, color = issue),
                   position = position_dodgev(height = 0.5)) +
  geom_vline(xintercept = 0, size = 0.25, linetype = "dashed") +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Human rights"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Humanitarian assistance"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = -0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Crackdown only"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.2, show.legend = FALSE) +
  labs(x = expression(paste("Effect of crackdown: (Crackdown - No crackdown)"["median"])),
       y = NULL, subtitle = "Amount donated",
       caption = "90% credible intervals shown around each point") +
  scale_x_continuous(labels = dollar_format()) +
  scale_color_manual(values = ngo_cols("dark grey", "blue", "red", name = FALSE), guide = FALSE) +
  coord_cartesian(clip = "off") +
  theme_ngos() + 
  theme(strip.text = element_blank(),
        panel.grid.minor = element_blank(),
        plot.subtitle = element_text(size = rel(1.2), face = "plain", #hjust = 0.5,
                                    family = "Encode Sans Condensed Medium")) +
  facet_wrap(~ hypothesis_facet, nrow = 1, scales = "free")

plot_summary_diffs <- plot_summary_likely + plot_summary_amount +
  plot_layout(ncol = 1)

plot_summary_diffs

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=-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 3.5.1 (2018-07-02)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/Denver              
##  date     2018-07-19
## Packages -----------------------------------------------------------------
##  package      * version    date       source                              
##  assertthat     0.2.0      2017-04-11 CRAN (R 3.5.0)                      
##  backports      1.1.2      2017-12-13 CRAN (R 3.5.0)                      
##  base         * 3.5.1      2018-07-05 local                               
##  base64enc      0.1-3      2015-07-28 CRAN (R 3.5.0)                      
##  bayesplot      1.5.0      2018-03-30 CRAN (R 3.5.0)                      
##  bindr          0.1.1      2018-03-13 CRAN (R 3.5.0)                      
##  bindrcpp     * 0.2.2      2018-03-29 CRAN (R 3.5.0)                      
##  broom        * 0.4.5      2018-07-03 CRAN (R 3.5.0)                      
##  cellranger     1.1.0      2016-07-27 CRAN (R 3.5.0)                      
##  cli            1.0.0      2017-11-05 CRAN (R 3.5.0)                      
##  coda           0.19-1     2016-12-08 CRAN (R 3.5.0)                      
##  codetools      0.2-15     2016-10-05 CRAN (R 3.5.1)                      
##  colorspace     1.3-2      2016-12-14 CRAN (R 3.5.0)                      
##  colourpicker   1.0        2017-09-27 CRAN (R 3.5.0)                      
##  compiler       3.5.1      2018-07-05 local                               
##  crayon         1.3.4      2017-09-16 CRAN (R 3.5.0)                      
##  crosstalk      1.0.0      2016-12-21 CRAN (R 3.5.0)                      
##  datasets     * 3.5.1      2018-07-05 local                               
##  devtools       1.13.5     2018-02-18 CRAN (R 3.5.0)                      
##  digest         0.6.15     2018-01-28 CRAN (R 3.5.0)                      
##  dplyr        * 0.7.6      2018-06-29 CRAN (R 3.5.1)                      
##  DT             0.4        2018-01-30 CRAN (R 3.5.0)                      
##  dygraphs       1.1.1.4    2017-01-04 CRAN (R 3.5.0)                      
##  evaluate       0.10.1     2017-06-24 CRAN (R 3.5.0)                      
##  forcats      * 0.3.0      2018-02-19 CRAN (R 3.5.0)                      
##  foreign        0.8-70     2017-11-28 CRAN (R 3.5.1)                      
##  ggplot2      * 3.0.0      2018-07-03 CRAN (R 3.5.0)                      
##  ggridges       0.5.0      2018-04-05 CRAN (R 3.5.0)                      
##  ggstance     * 0.3        2016-11-16 CRAN (R 3.5.0)                      
##  glue           1.2.0.9000 2018-04-30 Github (tidyverse/glue@b538962)     
##  graphics     * 3.5.1      2018-07-05 local                               
##  grDevices    * 3.5.1      2018-07-05 local                               
##  grid           3.5.1      2018-07-05 local                               
##  gridExtra      2.3        2017-09-09 CRAN (R 3.5.0)                      
##  gtable         0.2.0      2016-02-26 CRAN (R 3.5.0)                      
##  gtools         3.5.0      2015-05-29 CRAN (R 3.5.0)                      
##  haven          1.1.2      2018-06-27 CRAN (R 3.5.0)                      
##  here         * 0.1        2017-05-28 CRAN (R 3.5.0)                      
##  hms            0.4.2      2018-03-10 CRAN (R 3.5.0)                      
##  htmltools      0.3.6      2017-04-28 CRAN (R 3.5.0)                      
##  htmlwidgets    1.2        2018-04-19 CRAN (R 3.5.0)                      
##  httpuv         1.4.3      2018-05-10 cran (@1.4.3)                       
##  httr           1.3.1      2017-08-20 CRAN (R 3.5.0)                      
##  huxtable     * 4.0.1      2018-07-03 CRAN (R 3.5.0)                      
##  igraph         1.2.1      2018-03-10 CRAN (R 3.5.0)                      
##  inline         0.3.14     2015-04-13 CRAN (R 3.5.0)                      
##  janitor      * 1.0.0      2018-03-22 CRAN (R 3.5.0)                      
##  jsonlite       1.5        2017-06-01 CRAN (R 3.5.0)                      
##  knitr          1.20       2018-02-20 CRAN (R 3.5.0)                      
##  labeling       0.3        2014-08-23 CRAN (R 3.5.0)                      
##  later          0.7.3      2018-06-08 cran (@0.7.3)                       
##  lattice        0.20-35    2017-03-25 CRAN (R 3.5.1)                      
##  lazyeval       0.2.1      2017-10-29 CRAN (R 3.5.0)                      
##  lme4           1.1-17     2018-04-03 CRAN (R 3.5.0)                      
##  loo            2.0.0      2018-04-11 CRAN (R 3.5.0)                      
##  lubridate      1.7.4      2018-04-11 CRAN (R 3.5.0)                      
##  magrittr       1.5        2014-11-22 CRAN (R 3.5.0)                      
##  markdown       0.8        2017-04-20 CRAN (R 3.5.0)                      
##  MASS           7.3-50     2018-04-30 CRAN (R 3.5.1)                      
##  Matrix         1.2-14     2018-04-13 CRAN (R 3.5.1)                      
##  matrixStats    0.53.1     2018-02-11 CRAN (R 3.5.0)                      
##  memoise        1.1.0      2017-04-21 CRAN (R 3.5.0)                      
##  methods      * 3.5.1      2018-07-05 local                               
##  mime           0.5        2016-07-07 CRAN (R 3.5.0)                      
##  miniUI         0.1.1      2016-01-15 CRAN (R 3.5.0)                      
##  minqa          1.2.4      2014-10-09 CRAN (R 3.5.0)                      
##  mnormt         1.5-5      2016-10-15 CRAN (R 3.5.0)                      
##  modelr         0.1.2      2018-05-11 CRAN (R 3.5.0)                      
##  munsell        0.5.0      2018-06-12 cran (@0.5.0)                       
##  nlme           3.1-137    2018-04-07 CRAN (R 3.5.1)                      
##  nloptr         1.0.4      2017-08-22 CRAN (R 3.5.0)                      
##  pander       * 0.6.1      2017-08-06 CRAN (R 3.5.0)                      
##  parallel       3.5.1      2018-07-05 local                               
##  patchwork    * 0.0.1      2018-07-16 Github (thomasp85/patchwork@7fb35b1)
##  pillar         1.2.3      2018-05-25 CRAN (R 3.5.0)                      
##  pkgconfig      2.0.1      2017-03-21 CRAN (R 3.5.0)                      
##  plyr           1.8.4      2016-06-08 CRAN (R 3.5.0)                      
##  promises       1.0.1      2018-04-13 CRAN (R 3.5.0)                      
##  psych          1.8.4      2018-05-06 cran (@1.8.4)                       
##  purrr        * 0.2.5      2018-05-29 cran (@0.2.5)                       
##  R6             2.2.2      2017-06-17 CRAN (R 3.5.0)                      
##  Rcpp         * 0.12.17    2018-05-18 cran (@0.12.17)                     
##  readr        * 1.1.1      2017-05-16 CRAN (R 3.5.0)                      
##  readxl         1.1.0      2018-04-20 CRAN (R 3.5.0)                      
##  reshape2       1.4.3      2017-12-11 CRAN (R 3.5.0)                      
##  rlang          0.2.1      2018-05-30 CRAN (R 3.5.0)                      
##  rmarkdown      1.10       2018-06-11 CRAN (R 3.5.0)                      
##  rprojroot      1.3-2      2018-01-03 CRAN (R 3.5.0)                      
##  rsconnect      0.8.8      2018-03-09 CRAN (R 3.5.0)                      
##  rstan          2.17.3     2018-01-20 CRAN (R 3.5.0)                      
##  rstanarm     * 2.17.4     2018-04-13 CRAN (R 3.5.0)                      
##  rstantools     1.5.0      2018-04-17 CRAN (R 3.5.0)                      
##  rstudioapi     0.7        2017-09-07 CRAN (R 3.5.0)                      
##  rvest          0.3.2      2016-06-17 CRAN (R 3.5.0)                      
##  scales       * 0.5.0.9000 2018-07-16 Github (hadley/scales@419236a)      
##  shiny          1.0.5      2017-08-23 CRAN (R 3.5.0)                      
##  shinyjs        1.0        2018-01-08 CRAN (R 3.5.0)                      
##  shinystan      2.5.0      2018-05-01 CRAN (R 3.5.0)                      
##  shinythemes    1.1.1      2016-10-12 CRAN (R 3.5.0)                      
##  splines        3.5.1      2018-07-05 local                               
##  StanHeaders    2.17.2     2018-01-20 CRAN (R 3.5.0)                      
##  stats        * 3.5.1      2018-07-05 local                               
##  stats4         3.5.1      2018-07-05 local                               
##  stringi        1.2.3      2018-06-12 CRAN (R 3.5.0)                      
##  stringr      * 1.3.1      2018-05-10 CRAN (R 3.5.0)                      
##  survival       2.42-3     2018-04-16 CRAN (R 3.5.1)                      
##  threejs        0.3.1      2017-08-13 CRAN (R 3.5.0)                      
##  tibble       * 1.4.2      2018-01-22 CRAN (R 3.5.0)                      
##  tidyr        * 0.8.1      2018-05-18 CRAN (R 3.5.0)                      
##  tidyselect     0.2.4      2018-02-26 CRAN (R 3.5.0)                      
##  tidyverse    * 1.2.1      2017-11-14 CRAN (R 3.5.0)                      
##  tools          3.5.1      2018-07-05 local                               
##  utils        * 3.5.1      2018-07-05 local                               
##  withr          2.1.2      2018-07-16 Github (jimhester/withr@fe56f20)    
##  xml2           1.2.0      2018-01-24 CRAN (R 3.5.0)                      
##  xtable         1.8-2      2016-02-05 CRAN (R 3.5.0)                      
##  xts            0.10-2     2018-03-14 CRAN (R 3.5.0)                      
##  yaml           2.1.19     2018-05-01 CRAN (R 3.5.0)                      
##  zoo            1.8-1      2018-01-08 CRAN (R 3.5.0)
---
title: "Results"
author: "Andrew Heiss and Suparna Chaudhry"
date: "Last run: `r format(Sys.time(), '%B %e, %Y')`"
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(rstanarm)
library(broom)
library(ggstance)
library(patchwork)
library(pander)
library(scales)
library(janitor)
library(huxtable)
library(here)

source(here("lib", "graphics.R"))
source(here("lib", "pander_options.R"))
source(here("lib", "modeling.R"))

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


# Helpful functions
safe_mean <- function(x, ...) suppressWarnings(mean(as.numeric(x), ...))
safe_sd <- function(x, ...) suppressWarnings(sd(as.numeric(x), ...))

my_as_numeric <- function(x) {
  is_num <- suppressWarnings(!any(is.na(as.numeric(x))))
  
  if (is_num) {
    as.numeric(x)
  } else {
    x
  }
}

level_summary <- function(x, variable, df = results) {
  variable <- as.character(variable)
  if (is.factor(df[[variable]])) {
    x_levels <- levels(df[[variable]])
  } else {
    x_levels <- unique(df[[variable]])
  }
  
  factor_summary <- data_frame(x) %>% 
    count(x) %>%
    mutate(pct = n / sum(n),
           fancy = paste0(x, " (", n, "; ", percent(pct), ")"),
           x = factor(x, levels = x_levels, ordered = TRUE)) 
  
  inline_summary <- factor_summary %>% arrange(x) %>% 
    pull(fancy) %>% paste(collapse = " | ")
  
  fig_bar <- ggplot(factor_summary, aes(x = x, y = n)) +
    geom_bar(stat = "identity", fill = "grey50") +
    theme_spark()
  
  file_name <- gsub("\\.| ", "_", tolower(variable))
  md_img <- paste0("![](output/figures/summary-table/", file_name, ")")

  ggsave(fig_bar, filename = here("output", "figures", "summary-table",
                                  paste0(file_name, ".pdf")), 
         width = 1, height = 0.2, device = cairo_pdf)
  ggsave(fig_bar, filename = here("output", "figures", "summary-table",
                                  paste0(file_name, ".png")), 
         width = 1, height = 0.2, type = "cairo", bg = "transparent", dpi = 300)

  output <- data_frame(spark = md_img, summary = inline_summary)
  return(output)
}

numeric_summary <- function(x, variable) {
  inline_summary <- paste0("Median: ", round(median(x, na.rm = TRUE), 2),
                           " | Mean: ", round(mean(x, na.rm = TRUE), 2), 
                           " | Std. Dev.: ", round(sd(x, na.rm = TRUE), 2))
  
  fig_hist <- ggplot(as.data.frame(x), aes(x = x)) +
    geom_histogram(fill = "grey50", binwidth = 10) +
    theme_spark()
  
  file_name <- gsub("\\.| ", "_", tolower(variable))
  md_img <- paste0("![](output/figures/summary-table/", file_name, ")")

  ggsave(fig_hist, filename = here("output", "figures", "summary-table",
                                   paste0(file_name, ".pdf")), 
         width = 1, height = 0.2, device = cairo_pdf)
  ggsave(fig_hist, filename = here("output", "figures", "summary-table",
                                   paste0(file_name, ".png")), 
         width = 1, height = 0.2, type = "cairo", bg = "transparent", dpi = 300)
  
  output <- data_frame(spark = md_img, summary = inline_summary)
  return(output)
}

my_summary <- function(x, variable, df) {
  x <- my_as_numeric(x)
  
  if (is.numeric(x)) {
    numeric_summary(x, variable)
  } else {
    level_summary(x, variable, df)
  }
}
```


# 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("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$variable)) %>% 
  gather(variable, value) %>% 
  group_by(variable) %>% 
  nest() %>% 
  mutate(N = data %>% map_int(~ nrow(.)),
         summary = map2(.x = data, .y = variable, ~ my_summary(.x$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("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("output", "tables", "tbl-avg-results.md"))
```

\

# Visualize important variables

## Prior favorability towards NGOs

```{r fig-ngo-favorability, fig.width=7, fig.height=4}
favorability_cols <- data_frame(value = c("Very unfavorable", "Unfavorable", "Neutral",
                                          "Favorable", "Very favorable"),
                                color = ngo_pal("ordered")(5))

ngo_favorability_raw <- results %>%
  select(favor_humanitarian, favor_human_rights, favor_development) %>% 
  gather(variable, value) %>% 
  count(variable, value) %>% 
  left_join(vars_to_summarize, by = "variable") %>% 
  left_join(favorability_cols, by = "value") %>%
  mutate(clean_name = str_replace(clean_name, "towards", "towards\n")) %>% 
  mutate(value = factor(value, levels = levels(results$favor_humanitarian), ordered = TRUE),
         clean_name = fct_inorder(clean_name, ordered = TRUE)) %>%
  arrange(value) %>% 
  mutate(color = fct_inorder(color, ordered = TRUE))

ngo_favorability_high <- ngo_favorability_raw %>% 
  filter(!str_detect(value, "nfav")) %>% 
  mutate(n = ifelse(value == "Neutral", n / 2, n),
         n_plot = n,
         value = fct_rev(value), color = fct_rev(color))

ngo_favorability_low <- ngo_favorability_raw %>% 
  filter(str_detect(value, "nfav") | value == "Neutral") %>% 
  mutate(n = ifelse(value == "Neutral", n / 2, n),
         n_plot = -n)

ggplot(mapping = aes(x = n_plot, y = clean_name, fill = color)) +
  geom_barh(data = ngo_favorability_high, stat = "identity") +
  geom_barh(data = ngo_favorability_low, stat = "identity") +
  geom_vline(xintercept = 0, color = "black") +
  scale_x_continuous(labels = abs) +
  scale_fill_identity(labels = levels(ngo_favorability_raw$value),
                      breaks = levels(ngo_favorability_raw$color),
                      guide = "legend", name = NULL) +
  labs(x = NULL, y = NULL) +
  theme_ngos() +
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank())
```

## 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)) +
  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("output", "figures", "donate_summary.pdf"),
         width = 9, height = 4, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("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("output", "figures", "amount_summary.pdf"),
         width = 9, height = 2.75, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount_summary.png"),
         width = 9, height = 2.75, units = "in", type = "cairo", dpi = 300)
```

\

# Interpreting interactions in models

Ordinarily, people use ANOVA to analyze 3-way, 2 × 2 × 2 factorial designs, [like this](https://mypages.valdosta.edu/mwhatley/3600/2x2x2.htm). A more flexible ([yet identical!](https://www.theanalysisfactor.com/why-anova-and-linear-regression-are-the-same-analysis/)) way to do this is to use a regular regression model with interaction terms for each of the conditions, like so (here we use three, since we can use simper models to get averages for the larger umbrella groups, like just crackdowns and crackdown + issue):

- Model 1: 

    $$
    y = \beta_0 + \beta_1 \text{Crackdown}
    $$

- Model 2: 

    $$
    \begin{aligned}
    y =& \beta_0 + \beta_1 \text{Crackdown} + \beta_2 \text{Issue } + \\
    & \beta_3 \text{Crackdown} \times \text{Issue}
    \end{aligned}
    $$

- Model 3: 

    $$
    \begin{aligned}
    y =& \beta_0 + \beta_1 \text{Crackdown} + \beta_2 \text{Issue} + \beta_3 \text{Funding } + \\
    & \beta_4 \text{Crackdown} \times \text{Issue } + \\
    & \beta_5 \text{Crackdown} \times \text{Funding } + \\
    & \beta_6 \text{Issue} \times \text{Funding } + \\
    & \beta_7 \text{Crackdown} \times \text{Issue} \times \text{Funding}
    \end{aligned}
    $$

Adding different combinations of the coefficients provides the average values for each combination of factors, which corresponds to the average likelihood and amount donated table above. 

```{r interaction-interpretation, results="asis"}
model1 <- "1"
model2 <- "2"
model3 <- "3"

interpretation <- tribble(
  ~Model, ~`Coefficients to add together`,
  model3, "Intercept",
  model3, "Intercept + Funding",
  model2, "Intercept",
  model3, "Intercept + Issue",
  model3, "Intercept + Issue + Funding + (Issue × Funding)",
  model2, "Intercept + Issue",
  model1, "Intercept",
  model3, "Intercept + Crackdown",
  model3, "Intercept + Crackdown + Funding + (Crackdown × Funding)",
  model2, "Intercept + Crackdown",
  model3, "Intercept + Crackdown + Issue + (Crackdown × Issue)",
  model3, "Intercept + Crackdown + Issue + Funding + (Crackdown × Issue) + (Crackdown × Funding) + (Issue × Funding) + (Crackdown × Issue × Funding)",
  model2, "Intercept + Crackdown + Issue + (Crackdown × Issue)",
  model1, "Intercept + Crackdown"
)

conditions_summary_clean %>% 
  select(1:3) %>% 
  slice(-n()) %>% 
  bind_cols(interpretation) %T>% 
  pandoc.table() %>% 
  pandoc.table.return(caption = "Combinations of coefficients to add to determine means across experimental conditions {#tbl:coefficients-to-add}") %>% 
  cat(file = here("output", "tables", "tbl-coefficients-to-add.md"))
```

\

# Amount donated

## Models

```{r build-models-amount, warning=FALSE, message=FALSE, cache=TRUE}
# Basic interaction models
m_amount_c <- stan_glm(amount_donate ~ crackdown,
                       data = results, family = gaussian(),
                       prior = cauchy(location = 0, scale = 2.5),
                       prior_intercept = cauchy(location = 0, scale = 10),
                       chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)

m_amount_ci <- update(m_amount_c, . ~ . + issue + crackdown * issue)

m_amount_cif <- update(m_amount_c, . ~ . + 
                         issue + funding + crackdown * issue + crackdown * funding + 
                         issue * funding + crackdown * issue * funding)

# Models with just issue and just funding
m_amount_i <- update(m_amount_c, . ~ issue)
m_amount_f <- update(m_amount_c, . ~ funding)
```

```{r tbl-models-amount, warning=FALSE, results="asis"}
# Get subset of coefficients
coefs_to_include <- list(m_amount_c, m_amount_ci, m_amount_cif) %>% 
  map(~ tidy(.)) %>% bind_rows() %>% distinct(term) %>% pull(term)

huxreg(m_amount_c, m_amount_ci, m_amount_cif, 
       coefs = clean_coefs_named[clean_coefs_named %in% coefs_to_include],
       statistics = model_stats_bayes,
       stars = NULL) %T>% 
  print_hux() %>% 
  to_md(max_width = 100) %>% 
  cat(file = here("output", "tables", "tbl-models-amount.md"))
```

## Predicted medians

The Stan people really really don't like `posterior_linepred()`, since it throws away a lot of the uncertainty that comes from actually getting a posterior distribution (like, [they didn't even want to invent it, and then when they did they wanted to hide it](https://github.com/stan-dev/rstanarm/issues/85), and it has a big warning in the documentation). But this approach here mirrors using `predict()` to combine all the coefficients and intercept terms in a regular frequentist linear model and it's probably appropriate for this situation because all the coefficients are actually linked together behind the scenes and create group mean estimates. If I use the full force of `posterior_predict()`, I add more uncertainty with each added coefficient (so in a 2×2×2 version, the 8th condition is actually the sum of all 8 coefficients, which means it gets 8 levels of uncertainity, which is probably excessive). But we do it both ways too, just to be on the safe side.

```{r amount-median-c}
newdata_conditions_c <- results %>% expand(nesting(crackdown))

chains_fitted_c <- posterior_linpred(m_amount_c, 
                                     newdata = newdata_conditions_c)

# posterior_fit <- posterior_predict(m_amount_c, 
#                                    newdata = newdata_conditions_c,
#                                    draws = 50, seed = BAYES_SEED)

coef_summary_c <- newdata_conditions_c %>% 
  bind_cols(tidyMCMC(chains_fitted_c, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

# newdata <- newdata_conditions %>% 
#   bind_cols(tidyMCMC(posterior_fit, estimate.method = "median",
#                      conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_amount_c <- ggplot(coef_summary_c, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.25) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("green")) + 
  scale_y_continuous(labels = dollar) +
  labs(x = NULL, y = "Median amount donated") +
  # coord_cartesian(ylim = c(0, 100)) +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank())
```

```{r amount-median-i}
newdata_conditions_i <- results %>% expand(nesting(issue))

chains_fitted_i <- posterior_linpred(m_amount_i, 
                                     newdata = newdata_conditions_i)

coef_summary_i <- newdata_conditions_i %>% 
  bind_cols(tidyMCMC(chains_fitted_i, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))
```

```{r amount-median-f}
newdata_conditions_f <- results %>% expand(nesting(funding))

chains_fitted_f <- posterior_linpred(m_amount_f, 
                                     newdata = newdata_conditions_f)

coef_summary_f <- newdata_conditions_f %>% 
  bind_cols(tidyMCMC(chains_fitted_f, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))
```

```{r amount-median-ci}
newdata_conditions_ci <- results %>% expand(nesting(crackdown, issue))

chains_fitted_ci <- posterior_linpred(m_amount_ci, 
                                      newdata = newdata_conditions_ci)

coef_summary_ci <- newdata_conditions_ci %>% 
  bind_cols(tidyMCMC(chains_fitted_ci, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_amount_ci <- ggplot(coef_summary_ci, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.25) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("blue")) + 
  scale_y_continuous(labels = dollar) +
  labs(x = NULL, y = "Median amount donated") +
  # coord_cartesian(ylim = c(0, 100)) +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        strip.text = element_text(margin = margin(5, 0, 2, 0, "pt"))) +
  facet_wrap(~ issue)
```

```{r amount-median-cif}
newdata_conditions_cif <- results %>% expand(nesting(crackdown, issue, funding))

chains_fitted_cif <- posterior_linpred(m_amount_cif, 
                                       newdata = newdata_conditions_cif)

coef_summary_cif <- newdata_conditions_cif %>% 
  bind_cols(tidyMCMC(chains_fitted_cif, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_amount_cif <- ggplot(coef_summary_cif, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.5) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("red")) +
  scale_y_continuous(labels = dollar) +
  labs(x = NULL, y = "Median amount donated") +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        strip.text = element_text(margin = margin(5, 0, 2, 0, "pt"))) +
  facet_wrap(~ issue + funding)
```

```{r plot-single-amount-medians, warning=FALSE, fig.width=7, fig.height=2.5}
coef_summary_singles <- bind_rows(coef_summary_c, coef_summary_i, coef_summary_f) %>% 
  gather(condition, value, crackdown, issue, funding) %>% 
  filter(!is.na(value)) %>% 
  mutate(value = fct_inorder(value, ordered = TRUE),
         condition = fct_inorder(str_to_title(condition), ordered = TRUE))

plot_amount_singles <- ggplot(coef_summary_singles, aes(y = estimate, x = value)) + 
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = condition)) +
  scale_y_continuous(labels = dollar) +
  scale_color_manual(values = ngo_cols("green", "blue", "red", name = FALSE), guide = FALSE) +
  labs(x = NULL, y = "Median amount donated") +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        strip.text = element_text(margin = margin(5, 0, 2, 0, "pt"))) +
  facet_wrap(~ condition, scales = "free_x")

plot_amount_singles %T>%
  print() %T>%
  ggsave(., filename = here("output", "figures", "amount_single_summary.pdf"),
         width = 7, height = 2.5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount_single_summary.png"),
         width = 7, height = 2.5, units = "in", type = "cairo", dpi = 300)
```

```{r plot-all-amount-medians, fig.width=8, fig.height=6}
plot_amount_all <- 
  (plot_amount_c / plot_amount_ci) | 
  plot_amount_cif

plot_amount_all +
  plot_annotation(title = "Median donation in control and crackdown groups,\nconditioned by other experimental groups",
                  subtitle = "90% credible intervals shown around each point",
                  tag_levels = "A", theme = theme_ngos())

(plot_amount_all + 
    plot_annotation(caption = "90% credible intervals shown around each point",
                    tag_levels = "A", theme = theme_ngos())) %T>% 
  ggsave(., filename = here("output", "figures", "amount-medians.pdf"),
         width = 8, height = 4.25, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount-medians.png"),
         width = 8, height = 4.25, units = "in", type = "cairo", dpi = 300)
```


## Differences in medians

```{r amount-diffs-c}
diffs_coef_pairs_c <- chains_fitted_c %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_c$crackdown)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_pairs_detail_c <- tidy_diffs(diffs_coef_pairs_c)

pairs_plot_data_c <- diffs_coef_pairs_c$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
  
plot_diffs_c <- ggplot() + 
  geom_density(data = pairs_plot_data_c, aes(x = value),
               fill = ngo_cols("green"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_pairs_detail_c, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_c, aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ pair)
```

```{r amount-diffs-i}
diffs_coef_pairs_i <- chains_fitted_i %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_i$issue)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_pairs_detail_i <- tidy_diffs(diffs_coef_pairs_i)

pairs_plot_data_i <- diffs_coef_pairs_i$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
```

```{r amount-diffs-f}
diffs_coef_pairs_f <- chains_fitted_f %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_f$funding)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_pairs_detail_f <- tidy_diffs(diffs_coef_pairs_f)

pairs_plot_data_f <- diffs_coef_pairs_f$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
```

```{r amount-diffs-ci}
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_ci <- chains_fitted_ci %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_ci <- bind_cols(newdata_conditions_ci, chains_nested_ci) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_conditions_ci <- all_chains_ci %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_pairs_detail_ci <- pairs_conditions_ci %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs) #%>% 
  #mutate(pair = "Hey")

pairs_plot_data_ci <- pairs_conditions_ci %>% 
  select(issue, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_ci <- ggplot() + 
  geom_density(data = pairs_plot_data_ci, aes(x = value),
               fill = ngo_cols("blue"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_pairs_detail_ci,
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_ci, aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + pair)
```

```{r amount-diffs-cif}
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_cif <- chains_fitted_cif %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_cif <- bind_cols(newdata_conditions_cif, chains_nested_cif) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue, funding) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_conditions_cif <- all_chains_cif %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_pairs_detail_cif <- pairs_conditions_cif %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs)

pairs_plot_data_cif <- pairs_conditions_cif %>% 
  select(issue, funding, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue, -funding) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_cif <- ggplot() + 
  geom_density(data = pairs_plot_data_cif, aes(x = value),
               fill = ngo_cols("red"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_pairs_detail_cif, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_cif, aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + funding + pair)
```

**Compare only individual conditions:**

```{r plot-single-amount-diffs, fig.width=7, fig.height=2.5}
diffs_plot_data_singles <- bind_rows(pairs_plot_data_c,
                                     pairs_plot_data_i,
                                     pairs_plot_data_f) %>% 
  mutate(pair = fct_inorder(pair, ordered = TRUE))

diffs_coef_pairs_detail_singles <- bind_rows(diffs_coef_pairs_detail_c,
                                             diffs_coef_pairs_detail_i,
                                             diffs_coef_pairs_detail_f) %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair),
         pair = fct_inorder(pair, ordered = TRUE))

plot_diffs_singles <- ggplot() +
  geom_density(data = diffs_plot_data_singles, 
               aes(x = value, fill = pair),
               colour = NA, alpha = 0.4) +
  geom_segment(data = diffs_coef_pairs_detail_singles, 
               aes(x = q5, xend = q95, y = 0, yend = 0, group = pair),
               size = 3) +
  geom_vline(data = diffs_coef_pairs_detail_singles, 
             aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  scale_x_continuous(labels = dollar) +
  scale_fill_manual(values = ngo_cols("green", "blue", "red", name = FALSE), guide = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ pair)

plot_diffs_singles %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "amount-singles-diffs.pdf"),
         width = 7, height = 2.5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount-singles-diffs.png"),
         width = 7, height = 2.5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-single-amount-diffs, results="asis"}
bind_rows(diffs_coef_pairs_detail_c,
          diffs_coef_pairs_detail_i,
          diffs_coef_pairs_detail_f) %>% 
  select(`Frame` = pair, `∆~median~` = median, 
         `%∆~median~` = median_pct_change,
         `p(∆ > 0)` = p.greater0, `p(∆ < 0)` = p.less0, `p(∆ ≠ 0)` = p.diff.not0) %>% 
  pandoc.table.return(missing = "—", 
                      caption = "Median difference between likelihood to donate across single conditions {#tbl:amount-single-diffs}") %T>%
  cat() %>% 
  cat(file = here("output", "tables", "tbl-amount-single-diffs.md"))
```

\

**Compare nested conditions (what we're really most concerned about):**

```{r plot-all-amount-diffs, fig.width=8, fig.height=6}
plot_diffs_all <- 
  (plot_diffs_c / plot_diffs_ci) | 
  plot_diffs_cif

plot_diffs_all +
  plot_annotation(title = "Differences in median amount donated in control and crackdown groups,\nconditioned by other experimental groups",
                  subtitle = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0",
                  tag_levels = "A", theme = theme_ngos())

(plot_diffs_all +
  plot_annotation(caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0",
                  tag_levels = "A", theme = theme_ngos())) %T>%
  ggsave(., filename = here("output", "figures", "amount-diffs.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("output", "figures", "amount-diffs.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-all-amount-diffs, results="asis"}
bind_rows(diffs_coef_pairs_detail_c,
          diffs_coef_pairs_detail_ci,
          diffs_coef_pairs_detail_cif) %>% 
  select(Issue = issue, Funding = funding, 
         `∆~median~` = median, 
         `%∆~median~` = median_pct_change,
         `p(∆ > 0)` = p.greater0, `p(∆ < 0)` = p.less0, `p(∆ ≠ 0)` = p.diff.not0) %>% 
  pandoc.table.return(missing = "—", 
                      caption = 'Median difference between amounts donated in "crackdown" (treatment) and "no crackdown" (control) conditions {#tbl:amount-diffs}') %T>%
  cat() %>% 
  cat(file = here("output", "tables", "tbl-amount-diffs.md"))
```

\

# Likelihood of donation

## Models

```{r build-models-likely, warning=FALSE, message=FALSE, cache=TRUE}
# Basic interaction models
# Weakly informative student t priors, since these coefficients are log-odds
# See https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations#prior-for-the-regression-coefficients-in-logistic-regression-non-sparse-case and https://arxiv.org/abs/1507.07170
m_likely_c <- stan_glm(donate_likely_bin ~ crackdown,
                       data = results, family = binomial(link = "logit"),
                       prior = student_t(3, 0, 2.5),
                       prior_intercept = student_t(3, 0, 10),
                       chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)

m_likely_ci <- update(m_likely_c, . ~ . + issue + crackdown * issue)

m_likely_cif <- update(m_likely_c, . ~ . + 
                         issue + funding + crackdown * issue + crackdown * funding + 
                         issue * funding + crackdown * issue * funding)

# Models with just issue and just funding
m_likely_i <- update(m_likely_c, . ~ issue)
m_likely_f <- update(m_likely_c, . ~ funding)
```

```{r tbl-models-likely, warning=FALSE, results="asis"}
# Get subset of coefficients
coefs_to_include <- list(m_likely_c, m_likely_ci, m_likely_cif) %>% 
  map(~ tidy(.)) %>% bind_rows() %>% distinct(term) %>% pull(term)

huxreg(m_likely_c, m_likely_ci, m_likely_cif, 
       coefs = clean_coefs_named[clean_coefs_named %in% coefs_to_include],
       statistics = model_stats_bayes,
       stars = NULL) %T>% 
  print_hux() %>% 
  to_md(max_width = 100) %>% 
  cat(file = here("output", "tables", "tbl-models-likelihood.md"))
```

## Predicted medians

```{r likely-median-c}
newdata_conditions_c <- results %>% expand(nesting(crackdown))

# transform = TRUE does the same thing as plogis(as.matrix(m_likely_c))
chains_fitted_likely_c <- posterior_linpred(m_likely_c, 
                                            newdata = newdata_conditions_c, 
                                            transform = TRUE)

coef_summary_likely_c <- newdata_conditions_c %>% 
  bind_cols(tidyMCMC(chains_fitted_likely_c, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_likely_c <- ggplot(coef_summary_likely_c, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.25) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("green")) + 
  scale_y_continuous(labels = percent) +
  labs(x = NULL, y = "Probability of donating") +
  # coord_cartesian(ylim = c(0, 100)) +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank())
```

```{r likely-median-i}
newdata_conditions_i <- results %>% expand(nesting(issue))

chains_fitted_likely_i <- posterior_linpred(m_likely_i, 
                                            newdata = newdata_conditions_i, 
                                            transform = TRUE)

coef_summary_likely_i <- newdata_conditions_i %>% 
  bind_cols(tidyMCMC(chains_fitted_likely_i, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_likely_i <- ggplot(coef_summary_likely_i, aes(y = estimate, x = issue)) + 
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("green")) + 
  scale_y_continuous(labels = percent) +
  labs(x = NULL, y = "Probability of donating") +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank())
```

```{r likely-median-f}
newdata_conditions_f <- results %>% expand(nesting(funding))

chains_fitted_likely_f <- posterior_linpred(m_likely_f, 
                                            newdata = newdata_conditions_f, 
                                            transform = TRUE)

coef_summary_likely_f <- newdata_conditions_f %>% 
  bind_cols(tidyMCMC(chains_fitted_likely_f, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_likely_f <- ggplot(coef_summary_likely_f, aes(y = estimate, x = funding)) + 
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("green")) + 
  scale_y_continuous(labels = percent) +
  labs(x = NULL, y = "Probability of donating") +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank())
```

```{r likely-median-ci}
newdata_conditions_ci <- results %>% expand(nesting(crackdown, issue))

chains_fitted_likely_ci <- posterior_linpred(m_likely_ci, 
                                             newdata = newdata_conditions_ci,
                                             transform = TRUE)

coef_summary_likely_ci <- newdata_conditions_ci %>% 
  bind_cols(tidyMCMC(chains_fitted_likely_ci, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_likely_ci <- ggplot(coef_summary_likely_ci, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.25) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("blue")) + 
  scale_y_continuous(labels = percent) +
  labs(x = NULL, y = "Probability of donating") +
  # coord_cartesian(ylim = c(0, 100)) +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        strip.text = element_text(margin = margin(5, 0, 2, 0, "pt"))) +
  facet_wrap(~ issue)
```

```{r likely-median-cif}
newdata_conditions_cif <- results %>% expand(nesting(crackdown, issue, funding))

chains_fitted_likely_cif <- posterior_linpred(m_likely_cif, 
                                              newdata = newdata_conditions_cif,
                                              transform = TRUE)

coef_summary_likely_cif <- newdata_conditions_cif %>% 
  bind_cols(tidyMCMC(chains_fitted_likely_cif, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval"))

plot_likely_cif <- ggplot(coef_summary_likely_cif, aes(y = estimate, x = crackdown)) + 
  # geom_point(data = results, aes(y = amount_donate, x = crackdown),
  #            color = "grey50", size = 0.5, alpha = 0.5) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high), color = ngo_cols("red")) +
  scale_y_continuous(labels = percent) +
  labs(x = NULL, y = "Probability of donating") +
  theme_ngos() +
  theme(panel.grid.major.x = element_blank(),
        strip.text = element_text(margin = margin(5, 0, 2, 0, "pt"))) +
  facet_wrap(~ issue + funding)
```

```{r plot-all-likely-medians, fig.width=8, fig.height=6}
plot_likely_all <- 
  (plot_likely_c / plot_likely_ci) | 
  plot_likely_cif

plot_likely_all +
  plot_annotation(title = "Median likelihood of donation in control and crackdown groups,\nconditioned by other experimental groups",
                  subtitle = "90% credible intervals shown around each point",
                  tag_levels = "A", theme = theme_ngos())

(plot_likely_all +
  plot_annotation(caption = "90% credible intervals shown around each point",
                  tag_levels = "A", theme = theme_ngos())) %T>% 
  ggsave(., filename = here("output", "figures", "likelihood-medians.pdf"),
         width = 8, height = 4.25, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "likelihood-medians.png"),
         width = 8, height = 4.25, units = "in", type = "cairo", dpi = 300)
```


## Differences in medians

```{r likely-diffs-c}
diffs_coef_likely_pairs_c <- chains_fitted_likely_c %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_c$crackdown)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_likely_pairs_detail_c <- tidy_diffs(diffs_coef_likely_pairs_c)

pairs_likely_plot_data_c <- diffs_coef_likely_pairs_c$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
  
plot_diffs_likely_c <- ggplot() + 
  geom_density(data = pairs_likely_plot_data_c, aes(x = value),
               fill = ngo_cols("green"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_likely_pairs_detail_c, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_c, 
             aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_continuous(labels = percent) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ pair)
```

```{r likely-diffs-i}
diffs_coef_likely_pairs_i <- chains_fitted_likely_i %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_i$issue)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_likely_pairs_detail_i <- tidy_diffs(diffs_coef_likely_pairs_i)

pairs_likely_plot_data_i <- diffs_coef_likely_pairs_i$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
```

```{r likely-diffs-f}
diffs_coef_likely_pairs_f <- chains_fitted_likely_f %>% 
  as_data_frame() %>% 
  magrittr::set_colnames(levels(newdata_conditions_f$funding)) %>% 
  mcmc_pairwise_diffs()

diffs_coef_likely_pairs_detail_f <- tidy_diffs(diffs_coef_likely_pairs_f)

pairs_likely_plot_data_f <- diffs_coef_likely_pairs_f$mcmc_diffs %>%
  gather(pair, value) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))
```

```{r likely-diffs-ci}
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_likely_ci <- chains_fitted_likely_ci %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_likely_ci <- bind_cols(newdata_conditions_ci, 
                                  chains_nested_likely_ci) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_likely_conditions_ci <- all_chains_likely_ci %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_likely_pairs_detail_ci <- pairs_likely_conditions_ci %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs)

pairs_likely_plot_data_ci <- pairs_likely_conditions_ci %>% 
  select(issue, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_likely_ci <- ggplot() + 
  geom_density(data = pairs_likely_plot_data_ci, aes(x = value),
               fill = ngo_cols("blue"), colour = NA, alpha = 0.4) + 
  geom_segment(data = diffs_coef_likely_pairs_detail_ci, 
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_ci, 
             aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_continuous(labels = percent) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + pair)
```

```{r likely-diffs-cif}
# Nest each fitted chain into a single row that corresponds to each newdata_condition
chains_nested_likely_cif <- chains_fitted_likely_cif %>% 
  as_data_frame() %>% 
  mutate(id = 1:n()) %>% 
  gather(condition, value, -id) %>% 
  group_by(condition) %>% 
  nest()

# Add the fitted chains to the conditions, spread the main crackdown condition
# into two columns, then nest everything by issue
all_chains_likely_cif <- bind_cols(newdata_conditions_cif, 
                                   chains_nested_likely_cif) %>% 
  unnest(data) %>% 
  select(-condition) %>% 
  group_by(issue, funding) %>% 
  spread(crackdown, value) %>% 
  select(-id) %>% 
  nest()

# All the pairwise differences in a single data frame, grouped by issue
pairs_likely_conditions_cif <- all_chains_likely_cif %>% 
  mutate(diffs_coef_pairs = data %>% map(~ mcmc_pairwise_diffs(.)),
         diffs_coef_pairs_detail = diffs_coef_pairs %>% map(~ tidy_diffs(.)),
         diffs_coefs = diffs_coef_pairs %>% map(~ .$mcmc_diffs))

# Extract coefficient details and plot data
diffs_coef_likely_pairs_detail_cif <- pairs_likely_conditions_cif %>% 
  unnest(diffs_coef_pairs_detail) %>% 
  select(-data, -diffs_coef_pairs, -diffs_coefs)

pairs_likely_plot_data_cif <- pairs_likely_conditions_cif %>% 
  select(issue, funding, diffs_coefs) %>% 
  unnest(diffs_coefs) %>%
  gather(pair, value, -issue, -funding) %>%
  mutate(pair1 = pair) %>%
  separate(pair1, into = c("group1", "group2"), sep = " - ") %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair))

plot_diffs_likely_cif <- ggplot() +
  geom_density(data = pairs_likely_plot_data_cif, aes(x = value),
               fill = ngo_cols("red"), colour = NA, alpha = 0.4) +
  geom_segment(data = diffs_coef_likely_pairs_detail_cif,
               aes(x = q5, xend = q95, y = 0, yend = 0),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_cif,
             aes(xintercept = `median`), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  guides(fill = FALSE, color = FALSE) +
  scale_x_continuous(labels = percent) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ issue + funding + pair)
```

**Compare only individual conditions:**

```{r plot-single-likely-diffs, fig.width=7, fig.height=2.5}
diffs_likely_plot_data_singles <- bind_rows(pairs_likely_plot_data_c,
                                            pairs_likely_plot_data_i,
                                            pairs_likely_plot_data_f) %>% 
  mutate(pair = fct_inorder(pair, ordered = TRUE))

diffs_coef_likely_pairs_detail_singles <- bind_rows(diffs_coef_likely_pairs_detail_c,
                                                    diffs_coef_likely_pairs_detail_i,
                                                    diffs_coef_likely_pairs_detail_f) %>%
  mutate(pair = ifelse(nchar(pair) > 25,
                       str_replace(pair, "-", "-\n"),
                       pair),
         pair = fct_inorder(pair, ordered = TRUE))

plot_diffs_likely_singles <- ggplot() +
  geom_density(data = diffs_likely_plot_data_singles, 
               aes(x = value, fill = pair),
               colour = NA, alpha = 0.4) +
  geom_segment(data = diffs_coef_likely_pairs_detail_singles, 
               aes(x = q5, xend = q95, y = 0, yend = 0, group = pair),
               size = 3) +
  geom_vline(data = diffs_coef_likely_pairs_detail_singles, 
             aes(xintercept = median), size = 0.25) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  scale_x_continuous(labels = percent) +
  scale_fill_manual(values = ngo_cols("green", "blue", "red", name = FALSE), guide = FALSE) +
  labs(x = "Difference in medians") +
  theme_ngos(density = TRUE) +
  facet_wrap(~ pair)

plot_diffs_likely_singles %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "likelihood-singles-diffs.pdf"),
         width = 7, height = 2.5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "likelihood-singles-diffs.png"),
         width = 7, height = 2.5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-single-likely-diffs, results="asis"}
bind_rows(diffs_coef_likely_pairs_detail_c,
          diffs_coef_likely_pairs_detail_i,
          diffs_coef_likely_pairs_detail_f) %>% 
  select(`Frame` = pair, `∆~median~` = median, 
         `%∆~median~` = median_pct_change,
         `p(∆ > 0)` = p.greater0, `p(∆ < 0)` = p.less0, `p(∆ ≠ 0)` = p.diff.not0) %>% 
  pandoc.table.return(missing = "—", 
                      caption = "Median difference between likelihood to donate across single conditions {#tbl:likelihood-single-diffs}") %T>%
  cat() %>% 
  cat(file = here("output", "tables", "tbl-likelihood-single-diffs.md"))
```

\

**Compare nested conditions (what we're really most concerned about):**

```{r plot-all-likely-diffs, fig.width=8, fig.height=6}
plot_diffs_likely_all <- 
  (plot_diffs_likely_c / plot_diffs_likely_ci) | 
  plot_diffs_likely_cif

plot_diffs_likely_all +
  plot_annotation(title = "Differences in donation likelihood in control and crackdown groups,\nconditioned by other experimental groups",
                  subtitle = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0",
                  tag_levels = "A", theme = theme_ngos())

(plot_diffs_likely_all +
    plot_annotation(caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0",
                    tag_levels = "A", theme = theme_ngos())) %T>%
  ggsave(., filename = here("output", "figures", "likelihood-diffs.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("output", "figures", "likelihood-diffs.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)
```

```{r tbl-all-likely-diffs, results="asis"}
bind_rows(diffs_coef_likely_pairs_detail_c,
          diffs_coef_likely_pairs_detail_ci,
          diffs_coef_likely_pairs_detail_cif) %>% 
  select(Issue = issue, Funding = funding, 
         `∆~median~` = median, 
         `%∆~median~` = median_pct_change,
         `p(∆ > 0)` = p.greater0, `p(∆ < 0)` = p.less0, `p(∆ ≠ 0)` = p.diff.not0) %>% 
  pandoc.table.return(missing = "—", 
                      caption = 'Median difference between likelihood to donate in "crackdown" (treatment) and "crackdown" (control) conditions {#tbl:likelihood-diffs}') %T>%
  cat() %>% 
  cat(file = here("output", "tables", "tbl-likelihood-diffs.md"))
```


# Summary of hypotheses

```{r hypotheses-summary-figure, fig.width=8, fig.height=6.5}
hypotheses <- tribble(
  ~dv, ~hypothesis, ~summary,
  "Likelihood", "H1a: Donors more likely\nto give to NGOs\nfacing crackdown", diffs_coef_likely_pairs_detail_c,
  "Likelihood", "H2a: Donors more likely\nto give to humanitarian\nNGOs facing crackdown", diffs_coef_likely_pairs_detail_ci,
  "Likelihood", "H3a: Donors more likely\nto give to privately funded\nNGOs facing crackdown", diffs_coef_likely_pairs_detail_cif,
  "Amount", "H1b: Donors give more \nto NGOs facing crackdown", diffs_coef_pairs_detail_c,
  "Amount", "H2b: Donors give more \nto humanitarian NGOs\nfacing crackdown", diffs_coef_pairs_detail_ci,
  "Amount", "H3b: Donors give more \nto privately funded NGOs\nfacing crackdown", diffs_coef_pairs_detail_cif
)

plot_hypotheses <- hypotheses %>% 
  unnest(summary) %>% 
  select(-diffs_coefs) %>% 
  mutate(condition = paste0(issue, " + ", funding),
         condition = recode(condition, 
                            `Human rights + NA` = "Human rights",
                            `Humanitarian assistance + NA` = "Humanitarian assistance",
                            `NA + NA` = "Crackdown only")) %>% 
  mutate(hypothesis_custom = case_when(
    .$condition == "Human rights + Government" ~ paste(hypothesis, "\n(government funding condition)"),
    .$condition == "Humanitarian assistance + Government" ~ paste(hypothesis, "\n(government funding condition)"),
    .$condition == "Human rights + Private" ~ paste(hypothesis, "\n(private funding condition)"),
    .$condition == "Humanitarian assistance + Private" ~ paste(hypothesis, "\n(private funding condition)"),
    TRUE ~ hypothesis
  )) %>% 
  mutate(issue = fct_rev(fct_explicit_na(issue, na_level = "Crackdown only")),
         hypothesis_facet = ifelse(str_detect(hypothesis, "H3"), 2, 1),
         p_lab = case_when(
           .$dv == "Likelihood" ~ sprintf("∆ = %.2f; p(∆ ≠ 0) = %.2f", .$median, .$p.diff.not0),
           .$dv == "Amount" ~ sprintf("∆ = $%.2f; p(∆ ≠ 0) = %.2f", .$median, .$p.diff.not0)
         ))

plot_hypotheses_likely <- filter(plot_hypotheses, dv == "Likelihood")
plot_hypotheses_amount <- filter(plot_hypotheses, dv == "Amount")

plot_summary_likely <- ggplot(plot_hypotheses_likely, 
                              aes(x = median, y = fct_rev(hypothesis_custom))) +
  geom_pointrangeh(aes(xmin = q5, xmax = q95, color = issue),
                   position = position_dodgev(height = 0.5)) +
  geom_vline(xintercept = 0, size = 0.25, linetype = "dashed") +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Human rights"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Humanitarian assistance"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = -0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_likely, issue == "Crackdown only"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.2, show.legend = FALSE) +
  labs(x = expression(paste("Effect of crackdown: (Crackdown - No crackdown)"["median"])),
       y = NULL, subtitle = "Likelihood of donation") +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = ngo_cols("dark grey", "blue", "red", name = FALSE), name = NULL) +
  coord_cartesian(clip = "off") +
  theme_ngos() + 
  theme(strip.text = element_blank(),
        panel.grid.minor = element_blank(),
        plot.subtitle = element_text(size = rel(1.2), face = "plain", #hjust = 0.5,
                                    family = "Encode Sans Condensed Medium")) +
  facet_wrap(~ hypothesis_facet, nrow = 1, scales = "free")

plot_summary_amount <- ggplot(plot_hypotheses_amount, 
                              aes(x = median, y = fct_rev(hypothesis_custom))) +
  geom_pointrangeh(aes(xmin = q5, xmax = q95, color = issue),
                   position = position_dodgev(height = 0.5)) +
  geom_vline(xintercept = 0, size = 0.25, linetype = "dashed") +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Human rights"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Humanitarian assistance"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = -0.35, show.legend = FALSE) +
  geom_label(data = filter(plot_hypotheses_amount, issue == "Crackdown only"), 
             aes(label = p_lab, color = issue), size = 3, nudge_y = 0.2, show.legend = FALSE) +
  labs(x = expression(paste("Effect of crackdown: (Crackdown - No crackdown)"["median"])),
       y = NULL, subtitle = "Amount donated",
       caption = "90% credible intervals shown around each point") +
  scale_x_continuous(labels = dollar_format()) +
  scale_color_manual(values = ngo_cols("dark grey", "blue", "red", name = FALSE), guide = FALSE) +
  coord_cartesian(clip = "off") +
  theme_ngos() + 
  theme(strip.text = element_blank(),
        panel.grid.minor = element_blank(),
        plot.subtitle = element_text(size = rel(1.2), face = "plain", #hjust = 0.5,
                                    family = "Encode Sans Condensed Medium")) +
  facet_wrap(~ hypothesis_facet, nrow = 1, scales = "free")

plot_summary_diffs <- plot_summary_likely + plot_summary_amount +
  plot_layout(ncol = 1)

plot_summary_diffs

plot_summary_diffs %T>%
  ggsave(., filename = here("output", "figures", "summary-diffs.pdf"),
         width = 8, height = 6.5, units = "in", device = cairo_pdf) %>%
  ggsave(., filename = here("output", "figures", "summary-diffs.png"),
         width = 8, height = 6.5, 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}
writeLines(readLines(file.path(Sys.getenv("HOME"), ".R/Makevars")))

devtools::session_info()
```

</div> 
