Analysis and figures

library(tidyverse)
library(targets)
library(scales)
library(patchwork)
library(here)

# Generated via random.org
set.seed(8316)

# Load data
# Need to use this withr thing because tar_read() and tar_load() need to see the
# _targets folder in the current directory, but this .Rmd file is in a subfolder
withr::with_dir(here::here(), {
  source(tar_read(plot_funs))
  
  tar_load(survey_results)
  tar_load(sim_excel_final)
  tar_load(sim_final)
  tar_load(giving_aggregate)
  tar_load(giving_per_capita)
})

General demographics of recipients

Religion

religion_lookup <- tribble(
  ~Q5.10, ~religion,
  1, "Catholic",
  2, "Protestant",
  3, "Christian Orthodox",
  4, "Jewish",
  5, "Muslim",
  6, "Sikh",
  7, "Hindu",
  8, "Buddhist",
  9, "Atheist",
  10, "Other"
)

n_respondents <- nrow(survey_results)

religions <- survey_results %>% 
  count(Q5.10) %>% 
  left_join(religion_lookup, by = "Q5.10") %>% 
  arrange(n) %>% 
  mutate(religion = fct_inorder(religion)) %>% 
  mutate(percent = n / n_respondents)

ggplot(religions, aes(x = n, y = religion)) +
  geom_col(fill = clrs_okabe_ito$reddish_purple) +
  scale_x_continuous(sec.axis = sec_axis(~. / n_respondents, labels = percent)) +
  labs(x = "Number of respondents", y = NULL) +
  theme_ngo()

religion_others <- survey_results %>% 
  filter(!is.na(Q5.10_TEXT)) %>% 
  mutate(Q5.10_TEXT = str_to_lower(Q5.10_TEXT)) %>% 
  count(Q5.10_TEXT) %>% 
  arrange(desc(n))
religion_others

## # A tibble: 91 x 2
##    Q5.10_TEXT               n
##    <chr>                <int>
##  1 christian               34
##  2 agnostic                21
##  3 none                    18
##  4 spiritual                9
##  5 baptist                  8
##  6 born again christian     4
##  7 pagan                    4
##  8 wiccan                   4
##  9 lds                      3
## 10 methodist                3
## # … with 81 more rows

General trends in private philanthropy

Based on data from Giving USA, philanthropy in the United states continues to increase, both in aggregate and per capita.

Total giving

ggplot(giving_aggregate, aes(x = Year, y = `Total donations`)) +
  geom_line(size = 1, color = clrs_okabe_ito$orange) +
  scale_x_continuous(breaks = seq(1980, 2015, 5)) +
  scale_y_continuous(labels = dollar, breaks = seq(100, 450, 50)) + 
  coord_cartesian(ylim = c(100, 450)) +
  labs(x = NULL, y = "Billions of dollars") + 
  theme_ngo()

Average per capita giving

ggplot(giving_per_capita, aes(x = Year, y = `Average giving`)) +
  geom_line(size = 1, color = clrs_okabe_ito$sky_blue) +
  scale_x_continuous(breaks = seq(2000, 2014, 2)) +
  scale_y_continuous(labels = dollar, breaks = seq(1750, 2750, 250)) + 
  coord_cartesian(ylim = c(1750, 2750)) +
  labs(x = NULL, y = "Average annual donation") + 
  theme_ngo()

Simulations

Income across issue area, funding, and relationship

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

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

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

plot_income_relationship_extreme <- sim_excel_final %>% 
  mutate(persona_income = fct_recode(persona_income,
                                     "$50,000/year" = "Lower income",
                                     "$100,000/year" = "Higher income"),
         org_relationship = fct_recode(org_relationship,
                                       "Under crackdown" = "Crackdown")) %>% 
  group_by(org_relationship, persona_income) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_income))) +
  geom_pointrange(size = 0.75, fatten = 1.5, linetype = "21", pch = 2,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = clrs_ngo_pairs[[1]], 
                     guide = guide_legend(reverse = TRUE, nrow = 1,
                                          override.aes = list(size = 0.25, 
                                                              linetype = 0))) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 50))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

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

plot_income

Education across issue area, funding, and relationship

plot_education_issue <- sim_final %>% 
  group_by(org_issue, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrange(size = 0.75, fatten = 1.5,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = clrs_ngo_pairs[[2]], guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_funding <- sim_final %>% 
  group_by(org_funding, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrange(size = 0.75, fatten = 1.5,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = clrs_ngo_pairs[[2]], guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_education_relationship <- sim_final %>% 
  group_by(org_relationship, persona_education) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)),
             x = avg_share, color = fct_rev(persona_education))) +
  geom_pointrange(size = 0.75, fatten = 1.5,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = clrs_ngo_pairs[[2]], 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25,
                                                              linetype = 0)),
                     labels = label_wrap(15)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

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

Religiosity across issue area, funding, and relationship

plot_religion_issue <- sim_final %>% 
  group_by(org_issue, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Issue area") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrange(size = 0.75, fatten = 1.5, pch = 17,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = clrs_ngo_pairs[[4]], guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_funding <- sim_final %>% 
  group_by(org_funding, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Funding sources") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrange(size = 0.75, fatten = 1.5, pch = 17,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.06)) +
  scale_color_manual(values = clrs_ngo_pairs[[4]], guide = FALSE) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(facet)) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank())

plot_religion_relationship <- sim_final %>% 
  group_by(org_relationship, persona_religion) %>% 
  summarize(avg_share = mean(share)) %>% 
  mutate(facet = "Relationship with host government") %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = fct_rev(persona_religion))) +
  geom_pointrange(size = 0.75, fatten = 1.5, pch = 17,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  coord_cartesian(xlim = c(0, 0.1)) +
  scale_color_manual(values = clrs_ngo_pairs[[4]], 
                     guide = guide_legend(reverse = TRUE, ncol = 1,
                                          override.aes = list(size = 0.25,
                                                              linetype = 0)),
                     labels = label_wrap(20)) +
  labs(x = "Average donation share", y = NULL, color = NULL) +
  facet_wrap(vars(str_wrap(facet, 20))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

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

Education and religion across issue area, funding, and relationship

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

plot_education_religion

Social trust across issue area

plot_issue_social <- sim_final %>% 
  group_by(org_issue, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_issue, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrange(size = 0.75, fatten = 1.5, 
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = clrs_ngo_pairs[[3]], 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25,
                                                              linetype = 0))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

plot_issue_social

Social trust across relationship

plot_relationship_social <- sim_final %>% 
  group_by(org_relationship, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_relationship, 10)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrange(size = 0.75, fatten = 1.5,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = clrs_ngo_pairs[[3]], 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25,
                                                              linetype = 0))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

plot_relationship_social

Social trust across funding

plot_funding_social <- sim_final %>% 
  group_by(org_funding, persona_trust, persona_ideology, persona_experience) %>% 
  summarize(avg_share = mean(share)) %>% 
  ggplot(aes(y = fct_rev(str_wrap_factor(org_funding, 15)), 
             x = avg_share, color = persona_trust)) +
  geom_pointrange(size = 0.75, fatten = 1.5,
                  aes(xmin = 0, xmax = ..x..), position = position_dodge(width = 0.5)) + 
  scale_x_continuous(labels = percent_format(accuracy = 1), expand = expansion(add = c(0, 0.002))) +
  scale_color_manual(values = clrs_ngo_pairs[[3]], 
                     guide = guide_legend(reverse = TRUE,
                                          override.aes = list(size = 0.25,
                                                              linetype = 0))) +
  labs(x = "Average share of donations", y = NULL, color = NULL) +
  facet_grid(rows = vars(persona_ideology), cols = vars(str_wrap(persona_experience, 100))) +
  theme_ngo() +
  theme(panel.grid.major.y = element_blank(),
        legend.key.width = unit(0.5, "lines"))

plot_funding_social

Original computing environment

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

devtools::session_info()

## ─ Session info ─────────────────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.3 (2020-10-10)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2021-04-21                  
## 
## ─ Packages ─────────────────────────────────────────────────────────────────────────────
##  ! package     * version date       lib source        
##  P assertthat    0.2.1   2019-03-21 [?] CRAN (R 4.0.0)
##  P backports     1.2.1   2020-12-09 [?] CRAN (R 4.0.2)
##  P broom         0.7.5   2021-02-19 [?] CRAN (R 4.0.2)
##  P bslib         0.2.4   2021-01-25 [?] CRAN (R 4.0.2)
##  P cachem        1.0.4   2021-02-13 [?] CRAN (R 4.0.2)
##    callr         3.6.0   2021-03-28 [1] CRAN (R 4.0.2)
##  P cellranger    1.1.0   2016-07-27 [?] CRAN (R 4.0.0)
##    cli           2.4.0   2021-04-05 [1] CRAN (R 4.0.2)
##  P codetools     0.2-18  2020-11-04 [?] CRAN (R 4.0.2)
##  P colorspace    2.0-0   2020-11-11 [?] CRAN (R 4.0.2)
##  P crayon        1.4.1   2021-02-08 [?] CRAN (R 4.0.2)
##  P data.table    1.14.0  2021-02-21 [?] CRAN (R 4.0.2)
##  P DBI           1.1.1   2021-01-15 [?] CRAN (R 4.0.2)
##  P dbplyr        2.1.0   2021-02-03 [?] CRAN (R 4.0.2)
##  P desc          1.3.0   2021-03-05 [?] CRAN (R 4.0.2)
##    devtools      2.4.0   2021-04-07 [1] CRAN (R 4.0.2)
##  P digest        0.6.27  2020-10-24 [?] CRAN (R 4.0.2)
##  P dplyr       * 1.0.5   2021-03-05 [?] CRAN (R 4.0.2)
##  P ellipsis      0.3.1   2020-05-15 [?] CRAN (R 4.0.0)
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##  P fastmap       1.1.0   2021-01-25 [?] CRAN (R 4.0.2)
##  P forcats     * 0.5.1   2021-01-27 [?] CRAN (R 4.0.2)
##  P fs            1.5.0   2020-07-31 [?] CRAN (R 4.0.2)
##  P generics      0.1.0   2020-10-31 [?] CRAN (R 4.0.2)
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##  P haven         2.3.1   2020-06-01 [?] CRAN (R 4.0.2)
##  P here        * 1.0.1   2020-12-13 [?] CRAN (R 4.0.2)
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##  P htmltools     0.5.1.1 2021-01-22 [?] CRAN (R 4.0.2)
##  P httr          1.4.2   2020-07-20 [?] CRAN (R 4.0.2)
##  P igraph        1.2.6   2020-10-06 [?] CRAN (R 4.0.2)
##  P jquerylib     0.1.3   2020-12-17 [?] CRAN (R 4.0.2)
##  P jsonlite      1.7.2   2020-12-09 [?] CRAN (R 4.0.2)
##  P knitr         1.31    2021-01-27 [?] CRAN (R 4.0.2)
##  P labeling      0.4.2   2020-10-20 [?] CRAN (R 4.0.2)
##  P lifecycle     1.0.0   2021-02-15 [?] CRAN (R 4.0.2)
##  P lubridate     1.7.10  2021-02-26 [?] CRAN (R 4.0.2)
##  P magrittr      2.0.1   2020-11-17 [?] CRAN (R 4.0.2)
##  P memoise       2.0.0   2021-01-26 [?] CRAN (R 4.0.2)
##  P modelr        0.1.8   2020-05-19 [?] CRAN (R 4.0.2)
##  P munsell       0.5.0   2018-06-12 [?] CRAN (R 4.0.0)
##  P patchwork   * 1.1.1   2020-12-17 [?] CRAN (R 4.0.2)
##  P pillar        1.5.1   2021-03-05 [?] CRAN (R 4.0.2)
##  P pkgbuild      1.2.0   2020-12-15 [?] CRAN (R 4.0.2)
##  P pkgconfig     2.0.3   2019-09-22 [?] CRAN (R 4.0.0)
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##  P processx      3.5.1   2021-04-04 [?] CRAN (R 4.0.2)
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##  P purrr       * 0.3.4   2020-04-17 [?] CRAN (R 4.0.0)
##  P R6            2.5.0   2020-10-28 [?] CRAN (R 4.0.2)
##  P Rcpp          1.0.6   2021-01-15 [?] CRAN (R 4.0.2)
##  P readr       * 1.4.0   2020-10-05 [?] CRAN (R 4.0.2)
##  P readxl        1.3.1   2019-03-13 [?] CRAN (R 4.0.0)
##    remotes       2.3.0   2021-04-01 [1] CRAN (R 4.0.2)
##    renv          0.13.0  2021-02-24 [1] CRAN (R 4.0.2)
##  P reprex        1.0.0   2021-01-27 [?] CRAN (R 4.0.2)
##  P rlang         0.4.10  2020-12-30 [?] CRAN (R 4.0.2)
##  P rmarkdown     2.7     2021-02-19 [?] CRAN (R 4.0.2)
##  P rprojroot     2.0.2   2020-11-15 [?] CRAN (R 4.0.2)
##  P rstudioapi    0.13    2020-11-12 [?] CRAN (R 4.0.2)
##  P rvest         1.0.0   2021-03-09 [?] CRAN (R 4.0.2)
##  P sass          0.3.1   2021-01-24 [?] CRAN (R 4.0.2)
##  P scales      * 1.1.1   2020-05-11 [?] CRAN (R 4.0.0)
##  P sessioninfo   1.1.1   2018-11-05 [?] CRAN (R 4.0.0)
##  P stringi       1.5.3   2020-09-09 [?] CRAN (R 4.0.2)
##  P stringr     * 1.4.0   2019-02-10 [?] CRAN (R 4.0.0)
##  P targets     * 0.2.0   2021-02-27 [?] CRAN (R 4.0.2)
##  P testthat      3.0.2   2021-02-14 [?] CRAN (R 4.0.2)
##  P tibble      * 3.1.0   2021-02-25 [?] CRAN (R 4.0.2)
##  P tidyr       * 1.1.3   2021-03-03 [?] CRAN (R 4.0.2)
##  P tidyselect    1.1.0   2020-05-11 [?] CRAN (R 4.0.0)
##  P tidyverse   * 1.3.0   2019-11-21 [?] CRAN (R 4.0.0)
##  P usethis       2.0.1   2021-02-10 [?] CRAN (R 4.0.2)
##  P utf8          1.1.4   2018-05-24 [?] CRAN (R 4.0.0)
##  P vctrs         0.3.7   2021-03-29 [?] CRAN (R 4.0.2)
##  P withr         2.4.1   2021-01-26 [?] CRAN (R 4.0.2)
##  P xfun          0.22    2021-03-11 [?] CRAN (R 4.0.2)
##  P xml2          1.3.2   2020-04-23 [?] CRAN (R 4.0.0)
##  P yaml          2.2.1   2020-02-01 [?] CRAN (R 4.0.0)
## 
## [1] /Users/andrew/Dropbox (Personal)/Research collaboration/Global policy special issue/renv/library/R-4.0/x86_64-apple-darwin17.0
## [2] /private/var/folders/4d/ynkkj1nj0yj0lt91mkw2mq100000gn/T/Rtmp0tLEGE/renv-system-library
## 
##  P ── Loaded and on-disk path mismatch.

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

## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
## 
## CXX14FLAGS=-O3 -march=native -mtune=native -fPIC

Copyright © 2020–2021 Suparna Chaudhry and Andrew Heiss and Marc Dotson. Licensed under the MIT License. Code available on GitHub.