Load and clean data

library(tidyverse)
library(here)
library(csvy)

# Calculate the quantile (and median) of an ordered factor
# Borrowed from @jrnold: https://gist.github.com/jrnold/6759254
#
# In the case of ties, the first in order will win. For instance, 
# bloop <- factor(c("A", "A", "B", "B"), ordered = TRUE)
# median(bloop)  # "A"
quantile.ordered <- function(x, probs = seq(0, 1, 0.25)) {
  tab <- table(x)
  cdf <- cumsum(tab / sum(tab))
  idx <- sapply(probs, function(p) min(which(cdf >= p)))
  levels(x)[idx] 
}

quantile.factor <- quantile.ordered
median.ordered <- function(x) quantile(x, 0.5)
median.factor <- median.ordered


# Load data
# results_file <- file.path(here(), "data", "test_data.csv")
results_file <- here("private", 
                     "NGO Crackdowns and Philanthropy, March 2018 pilot_March 23, 2018_20.01.csv")

# Possible answers
favorability <- c("Very unfavorable", "Unfavorable", "Neutral", 
                  "Favorable", "Very favorable")

likelihood <- c("Extremely unlikely", "Somewhat unlikely", 
                "Neither likely nor unlikely", 
                "Somewhat likely", "Extremely likely")

frequency_charity <- c("Once a week", "Once a month", "Once every three months", 
                       "Once every six months", "Once a year", "Once every few years", 
                       "Never")

frequency_public_affairs <- c("Most of the time", "Some of the time", 
                              "Only now and then", "Hardly at all")

levels_ideology <- c("Strong liberal", "Liberal", "Independent, leaning liberal", 
                     "Independent", "Independent, leaning conservative", 
                     "Conservative", "Very conservative")

levels_education <- c("Less than high school", "High school graduate", 
                      "Some college", "2 year degree", "4 year degree", 
                      "Graduate or professional degree", "Doctorate")

frequency_religion <- c("More than once a week", "Once a week", "Once or twice a month", 
                        "A few times a year", "Seldom", "Never", "Don't know")

levels_gender <- c("Female", "Male", "Transgender", "Other", "Prefer not to say")

levels_income <- c("Less than $10,000", "$10,000 – $19,999", "$20,000 – $29,999", 
                   "$30,000 – $39,999", "$40,000 – $49,999", "$50,000 – $59,999", 
                   "$60,000 – $69,999", "$70,000 – $79,999", "$80,000 – $89,999", 
                   "$90,000 – $99,999", "$100,000 – $149,999", "More than $150,000", 
                   "Prefer not to say")

levels_age <- c("Under 18", "18 – 24", "25 – 34", "35 – 44", "45 – 54", 
                "55 – 64", "65 – 74", "75 – 84", "85 or older")

attention1_answer <- c("Green", "Yellow")
attention2_answer <- "Blue"
levels_attention <- c("Correct", "Incorrect")

# Qualtrics stores question information in the first 3 rows. All we care about
# are the column names in the first row.
results_meta <- read_csv(results_file, n_max = 2)

# The first 10 rows need to be skipped because the consent question has a bunch
# of line breaks and it messes with CSV line counting. The actual responses
# start on row 11. This doesn't entirely make sense, since read_csv can read the
# line break-filled row in just fine. Skipping said lines makes it choke. <shrug>
results_raw <- read_csv(results_file, skip = 16,
                        col_names = colnames(results_meta))

# Confirmation codes to exclude. These are people who failed the attention
# checks or who took the survey outside of MTurk.
#
# The code for determining these is in a script that is excluded from the main
# repository because of privacy issues (it deals directly with MTurk worker
# IDs), called "private/approve_mturkers.R". It outputs this text list of codes.
codes_to_exclude <- read_csv(here("data", "codes_to_exclude.csv"))

# Clean everything
results_check_attention <- results_raw %>%
  # Only select necessary columns
  select(id = ResponseId, confirmation_code = mTurkCode, duration = `Duration (in seconds)`,
         start_date = StartDate, end_date = EndDate,
         crackdown, issue, funding, starts_with("Q")) %>%
  rename(favor_humanitarian = Q2.1_1, favor_human_rights = Q2.1_2, 
         favor_development = Q2.1_3, donate_likely = Q2.3, 
         amount_donate = Q2.4_1, amount_keep = Q2.4_2, amount_why = Q2.5,
         give_charity = Q3.2, volunteer = Q3.3, political_knowledge = Q3.4,
         ideology = Q3.5, education = Q3.6, religiosity = Q3.7,
         gender = Q3.9, gender_other = Q3.9_4_TEXT, income = Q3.10, age = Q3.11) %>%
  # Clean up experimental condition columns
  mutate(issue = recode(issue, `human rights for refugees` = "Human rights",
                        `humanitarian assistance for refugees` = "Humanitarian assistance"),
         funding = recode(funding, `government donors` = "Government",
                          `individual, private donors` = "Private")) %>%
  mutate(crackdown = ifelse(str_detect(crackdown, "harshly restrict"), 
                            "Crackdown", "No crackdown")) %>% 
  mutate(crackdown = factor(crackdown, levels = c("No crackdown", "Crackdown"), 
                            ordered = TRUE),
         issue = factor(issue, levels = c("Human rights", "Humanitarian assistance"), 
                        ordered = TRUE),
         funding = factor(funding, levels = c("Government", "Private"), 
                          ordered = TRUE)) %>% 
  # Attention checks
  mutate(attention1_correct = str_split(Q1.3, ",") %>% 
           map_lgl(~ all(attention1_answer %in% .) & length(.) == length(attention1_answer)),
         attention2_correct = Q3.8 == attention2_answer) %>% 
  # This person contacted me separately to say that they accidentally did the first
  # attention check wrong, but that they did pay attention: 6518634
  mutate(attention1_correct = ifelse(confirmation_code == 6518634,
                                     TRUE, attention1_correct)) 

results <- results_check_attention %>% 
  filter(!(confirmation_code %in% codes_to_exclude$confirmation_code)) %>%
  # Factorize variables
  mutate(donate_likely = factor(donate_likely, levels = likelihood, ordered = TRUE)) %>% 
  mutate_at(vars(favor_humanitarian, favor_human_rights, favor_development),
            funs(factor(., levels = favorability, ordered = TRUE))) %>% 
  mutate(give_charity = factor(give_charity, 
                               levels = frequency_charity, ordered = TRUE),
         volunteer = factor(volunteer, levels = c("No", "Yes"), ordered = TRUE),
         political_knowledge = factor(political_knowledge, 
                                      levels = frequency_public_affairs, ordered = TRUE),
         ideology = factor(ideology, levels = levels_ideology, ordered = TRUE),
         education = factor(education, levels = levels_education, ordered = TRUE),
         religiosity = factor(religiosity, levels = frequency_religion, ordered = TRUE),
         gender = recode(gender, `Other:` = "Other"),
         gender = factor(gender, levels = levels_gender, ordered = TRUE),
         income = factor(income, levels = levels_income, ordered = TRUE),
         age = factor(age, levels = levels_age, ordered = TRUE),
         check1 = factor(attention1_correct, levels = c(TRUE, FALSE),
                         labels = levels_attention, ordered = TRUE),
         check2 = factor(attention2_correct, levels = c(TRUE, FALSE),
                         labels = levels_attention, ordered = TRUE)) %>% 
  # Dichotomize variables
  mutate(donate_likely_bin = fct_recode(donate_likely,
                                        `Not likely` = "Extremely unlikely",
                                        `Not likely` = "Somewhat unlikely",
                                        `Not likely` = "Neither likely nor unlikely",
                                        Likely = "Somewhat likely",
                                        Likely = "Extremely likely")) %>% 
  mutate_at(vars(favor_humanitarian, favor_human_rights, favor_development),
            funs(bin = fct_recode(., 
                                  `Not favorable` = "Very unfavorable",
                                  `Not favorable` = "Unfavorable",
                                  `Not favorable` = "Neutral",
                                  Favorable = "Favorable",
                                  Favorable = "Very favorable"))) %>% 
  mutate(give_charity_3 = fct_recode(give_charity,
                                     `At least once a month` = "Once a week",
                                     `At least once a month` = "Once a month",
                                     `Once a month-once a year` = "Once every three months",
                                     `Once a month-once a year` = "Once every six months",
                                     `Once a month-once a year` = "Once a year",
                                     Rarely = "Once every few years",
                                     Rarely = "Never") %>% fct_rev(),
         give_charity_2 = fct_recode(give_charity,
                                     `At least once a year` = "Once a week",
                                     `At least once a year` = "Once a month",
                                     `At least once a year` = "Once every three months",
                                     `At least once a year` = "Once every six months",
                                     `At least once a year` = "Once a year",
                                     Rarely = "Once every few years",
                                     Rarely = "Never")) %>% 
  mutate(political_knowledge_bin = fct_recode(political_knowledge,
                                              `Often` = "Most of the time",
                                              `Often` = "Some of the time",
                                              `Not often` = "Only now and then",
                                              `Not often` = "Hardly at all") %>% fct_rev()) %>% 
  mutate(ideology_3 = fct_recode(ideology,
                                 `Liberal` = "Strong liberal",
                                 `Liberal` = "Liberal",
                                 `Liberal` = "Independent, leaning liberal",
                                 `Independent` = "Independent",
                                 `Conservative` = "Independent, leaning conservative",
                                 `Conservative` = "Conservative",
                                 `Conservative` = "Very conservative") %>% fct_rev()) %>% 
  mutate(ideology_bin = fct_recode(ideology_3,
                                   `Not liberal` = "Independent",
                                   `Not liberal` = "Conservative")) %>% 
  mutate(education_bin = fct_recode(education,
                                    `No BA` = "Less than high school",
                                    `No BA` = "High school graduate",
                                    `No BA` = "Some college",
                                    `No BA` = "2 year degree",
                                    `BA and above` = "4 year degree",
                                    `BA and above` = "Graduate or professional degree",
                                    `BA and above` = "Doctorate")) %>% 
  mutate(religiosity_bin = fct_recode(religiosity,
                                      `At least once a month` = "More than once a week",
                                      `At least once a month` = "Once a week",
                                      `At least once a month` = "Once or twice a month",
                                      `Rarely` = "A few times a year",
                                      `Rarely` = "Seldom",
                                      `Rarely` = "Never",
                                      NULL = "Don't know") %>% fct_rev()) %>% 
  mutate(income_clean = fct_recode(income, NULL = "Prefer not to say"),
         income_bin = factor(income_clean >= median(.$income), levels = c(FALSE, TRUE),
                             labels = paste(c("Less than", "At least"), median(.$income)))) %>% 
  mutate(age_bin = factor(age >= median(.$age), levels = c(FALSE, TRUE),
                          labels = paste(c("Less than", "At least"), median(.$age))),
         gender_bin = fct_collapse(gender,
                                   Female = "Female",
                                   `Not Female` = c("Male", "Transgender", 
                                                    "Other", "Prefer not to say")))

# Save final clean data
saveRDS(results, here("data", "results_clean.rds"))
write_csvy(results, file = here("data", "results_clean.csv"),
           metadata = here("data", "results_clean.yaml"), na = "NA")

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)                  
##  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)                  
##  colorspace   1.3-2      2016-12-14 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)                  
##  csvy       * 0.2.1      2018-04-30 Github (leeper/csvy@01a2f9d)    
##  data.table   1.10.4-3   2017-10-27 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)                  
##  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)                  
##  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                           
##  gtable       0.2.0      2016-02-26 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)                  
##  httr         1.3.1      2017-08-20 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)                  
##  lattice      0.20-35    2017-03-25 CRAN (R 3.5.1)                  
##  lazyeval     0.2.1      2017-10-29 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)                  
##  memoise      1.1.0      2017-04-21 CRAN (R 3.5.0)                  
##  methods    * 3.5.1      2018-07-05 local                           
##  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)                  
##  parallel     3.5.1      2018-07-05 local                           
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##  pkgconfig    2.0.1      2017-03-21 CRAN (R 3.5.0)                  
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##  rstudioapi   0.7        2017-09-07 CRAN (R 3.5.0)                  
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##  scales       0.5.0.9000 2018-07-16 Github (hadley/scales@419236a)  
##  stats      * 3.5.1      2018-07-05 local                           
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##  tools        3.5.1      2018-07-05 local                           
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##  withr        2.1.2      2018-07-16 Github (jimhester/withr@fe56f20)
##  xml2         1.2.0      2018-01-24 CRAN (R 3.5.0)                  
##  yaml         2.1.19     2018-05-01 CRAN (R 3.5.0)
---
title: "Clean data"
author: "Andrew Heiss and Suparna Chaudhry"
date: "Last run: `r format(Sys.time(), '%B %e, %Y')`"
output: 
  html_document:
    code_folding: show
editor_options: 
  chunk_output_type: console
---

# Load and clean data

```{r clean-data, warning=FALSE, message=FALSE}
library(tidyverse)
library(here)
library(csvy)

# Calculate the quantile (and median) of an ordered factor
# Borrowed from @jrnold: https://gist.github.com/jrnold/6759254
#
# In the case of ties, the first in order will win. For instance, 
# bloop <- factor(c("A", "A", "B", "B"), ordered = TRUE)
# median(bloop)  # "A"
quantile.ordered <- function(x, probs = seq(0, 1, 0.25)) {
  tab <- table(x)
  cdf <- cumsum(tab / sum(tab))
  idx <- sapply(probs, function(p) min(which(cdf >= p)))
  levels(x)[idx] 
}

quantile.factor <- quantile.ordered
median.ordered <- function(x) quantile(x, 0.5)
median.factor <- median.ordered


# Load data
# results_file <- file.path(here(), "data", "test_data.csv")
results_file <- here("private", 
                     "NGO Crackdowns and Philanthropy, March 2018 pilot_March 23, 2018_20.01.csv")

# Possible answers
favorability <- c("Very unfavorable", "Unfavorable", "Neutral", 
                  "Favorable", "Very favorable")

likelihood <- c("Extremely unlikely", "Somewhat unlikely", 
                "Neither likely nor unlikely", 
                "Somewhat likely", "Extremely likely")

frequency_charity <- c("Once a week", "Once a month", "Once every three months", 
                       "Once every six months", "Once a year", "Once every few years", 
                       "Never")

frequency_public_affairs <- c("Most of the time", "Some of the time", 
                              "Only now and then", "Hardly at all")

levels_ideology <- c("Strong liberal", "Liberal", "Independent, leaning liberal", 
                     "Independent", "Independent, leaning conservative", 
                     "Conservative", "Very conservative")

levels_education <- c("Less than high school", "High school graduate", 
                      "Some college", "2 year degree", "4 year degree", 
                      "Graduate or professional degree", "Doctorate")

frequency_religion <- c("More than once a week", "Once a week", "Once or twice a month", 
                        "A few times a year", "Seldom", "Never", "Don't know")

levels_gender <- c("Female", "Male", "Transgender", "Other", "Prefer not to say")

levels_income <- c("Less than $10,000", "$10,000 – $19,999", "$20,000 – $29,999", 
                   "$30,000 – $39,999", "$40,000 – $49,999", "$50,000 – $59,999", 
                   "$60,000 – $69,999", "$70,000 – $79,999", "$80,000 – $89,999", 
                   "$90,000 – $99,999", "$100,000 – $149,999", "More than $150,000", 
                   "Prefer not to say")

levels_age <- c("Under 18", "18 – 24", "25 – 34", "35 – 44", "45 – 54", 
                "55 – 64", "65 – 74", "75 – 84", "85 or older")

attention1_answer <- c("Green", "Yellow")
attention2_answer <- "Blue"
levels_attention <- c("Correct", "Incorrect")

# Qualtrics stores question information in the first 3 rows. All we care about
# are the column names in the first row.
results_meta <- read_csv(results_file, n_max = 2)

# The first 10 rows need to be skipped because the consent question has a bunch
# of line breaks and it messes with CSV line counting. The actual responses
# start on row 11. This doesn't entirely make sense, since read_csv can read the
# line break-filled row in just fine. Skipping said lines makes it choke. <shrug>
results_raw <- read_csv(results_file, skip = 16,
                        col_names = colnames(results_meta))

# Confirmation codes to exclude. These are people who failed the attention
# checks or who took the survey outside of MTurk.
#
# The code for determining these is in a script that is excluded from the main
# repository because of privacy issues (it deals directly with MTurk worker
# IDs), called "private/approve_mturkers.R". It outputs this text list of codes.
codes_to_exclude <- read_csv(here("data", "codes_to_exclude.csv"))

# Clean everything
results_check_attention <- results_raw %>%
  # Only select necessary columns
  select(id = ResponseId, confirmation_code = mTurkCode, duration = `Duration (in seconds)`,
         start_date = StartDate, end_date = EndDate,
         crackdown, issue, funding, starts_with("Q")) %>%
  rename(favor_humanitarian = Q2.1_1, favor_human_rights = Q2.1_2, 
         favor_development = Q2.1_3, donate_likely = Q2.3, 
         amount_donate = Q2.4_1, amount_keep = Q2.4_2, amount_why = Q2.5,
         give_charity = Q3.2, volunteer = Q3.3, political_knowledge = Q3.4,
         ideology = Q3.5, education = Q3.6, religiosity = Q3.7,
         gender = Q3.9, gender_other = Q3.9_4_TEXT, income = Q3.10, age = Q3.11) %>%
  # Clean up experimental condition columns
  mutate(issue = recode(issue, `human rights for refugees` = "Human rights",
                        `humanitarian assistance for refugees` = "Humanitarian assistance"),
         funding = recode(funding, `government donors` = "Government",
                          `individual, private donors` = "Private")) %>%
  mutate(crackdown = ifelse(str_detect(crackdown, "harshly restrict"), 
                            "Crackdown", "No crackdown")) %>% 
  mutate(crackdown = factor(crackdown, levels = c("No crackdown", "Crackdown"), 
                            ordered = TRUE),
         issue = factor(issue, levels = c("Human rights", "Humanitarian assistance"), 
                        ordered = TRUE),
         funding = factor(funding, levels = c("Government", "Private"), 
                          ordered = TRUE)) %>% 
  # Attention checks
  mutate(attention1_correct = str_split(Q1.3, ",") %>% 
           map_lgl(~ all(attention1_answer %in% .) & length(.) == length(attention1_answer)),
         attention2_correct = Q3.8 == attention2_answer) %>% 
  # This person contacted me separately to say that they accidentally did the first
  # attention check wrong, but that they did pay attention: 6518634
  mutate(attention1_correct = ifelse(confirmation_code == 6518634,
                                     TRUE, attention1_correct)) 

results <- results_check_attention %>% 
  filter(!(confirmation_code %in% codes_to_exclude$confirmation_code)) %>%
  # Factorize variables
  mutate(donate_likely = factor(donate_likely, levels = likelihood, ordered = TRUE)) %>% 
  mutate_at(vars(favor_humanitarian, favor_human_rights, favor_development),
            funs(factor(., levels = favorability, ordered = TRUE))) %>% 
  mutate(give_charity = factor(give_charity, 
                               levels = frequency_charity, ordered = TRUE),
         volunteer = factor(volunteer, levels = c("No", "Yes"), ordered = TRUE),
         political_knowledge = factor(political_knowledge, 
                                      levels = frequency_public_affairs, ordered = TRUE),
         ideology = factor(ideology, levels = levels_ideology, ordered = TRUE),
         education = factor(education, levels = levels_education, ordered = TRUE),
         religiosity = factor(religiosity, levels = frequency_religion, ordered = TRUE),
         gender = recode(gender, `Other:` = "Other"),
         gender = factor(gender, levels = levels_gender, ordered = TRUE),
         income = factor(income, levels = levels_income, ordered = TRUE),
         age = factor(age, levels = levels_age, ordered = TRUE),
         check1 = factor(attention1_correct, levels = c(TRUE, FALSE),
                         labels = levels_attention, ordered = TRUE),
         check2 = factor(attention2_correct, levels = c(TRUE, FALSE),
                         labels = levels_attention, ordered = TRUE)) %>% 
  # Dichotomize variables
  mutate(donate_likely_bin = fct_recode(donate_likely,
                                        `Not likely` = "Extremely unlikely",
                                        `Not likely` = "Somewhat unlikely",
                                        `Not likely` = "Neither likely nor unlikely",
                                        Likely = "Somewhat likely",
                                        Likely = "Extremely likely")) %>% 
  mutate_at(vars(favor_humanitarian, favor_human_rights, favor_development),
            funs(bin = fct_recode(., 
                                  `Not favorable` = "Very unfavorable",
                                  `Not favorable` = "Unfavorable",
                                  `Not favorable` = "Neutral",
                                  Favorable = "Favorable",
                                  Favorable = "Very favorable"))) %>% 
  mutate(give_charity_3 = fct_recode(give_charity,
                                     `At least once a month` = "Once a week",
                                     `At least once a month` = "Once a month",
                                     `Once a month-once a year` = "Once every three months",
                                     `Once a month-once a year` = "Once every six months",
                                     `Once a month-once a year` = "Once a year",
                                     Rarely = "Once every few years",
                                     Rarely = "Never") %>% fct_rev(),
         give_charity_2 = fct_recode(give_charity,
                                     `At least once a year` = "Once a week",
                                     `At least once a year` = "Once a month",
                                     `At least once a year` = "Once every three months",
                                     `At least once a year` = "Once every six months",
                                     `At least once a year` = "Once a year",
                                     Rarely = "Once every few years",
                                     Rarely = "Never")) %>% 
  mutate(political_knowledge_bin = fct_recode(political_knowledge,
                                              `Often` = "Most of the time",
                                              `Often` = "Some of the time",
                                              `Not often` = "Only now and then",
                                              `Not often` = "Hardly at all") %>% fct_rev()) %>% 
  mutate(ideology_3 = fct_recode(ideology,
                                 `Liberal` = "Strong liberal",
                                 `Liberal` = "Liberal",
                                 `Liberal` = "Independent, leaning liberal",
                                 `Independent` = "Independent",
                                 `Conservative` = "Independent, leaning conservative",
                                 `Conservative` = "Conservative",
                                 `Conservative` = "Very conservative") %>% fct_rev()) %>% 
  mutate(ideology_bin = fct_recode(ideology_3,
                                   `Not liberal` = "Independent",
                                   `Not liberal` = "Conservative")) %>% 
  mutate(education_bin = fct_recode(education,
                                    `No BA` = "Less than high school",
                                    `No BA` = "High school graduate",
                                    `No BA` = "Some college",
                                    `No BA` = "2 year degree",
                                    `BA and above` = "4 year degree",
                                    `BA and above` = "Graduate or professional degree",
                                    `BA and above` = "Doctorate")) %>% 
  mutate(religiosity_bin = fct_recode(religiosity,
                                      `At least once a month` = "More than once a week",
                                      `At least once a month` = "Once a week",
                                      `At least once a month` = "Once or twice a month",
                                      `Rarely` = "A few times a year",
                                      `Rarely` = "Seldom",
                                      `Rarely` = "Never",
                                      NULL = "Don't know") %>% fct_rev()) %>% 
  mutate(income_clean = fct_recode(income, NULL = "Prefer not to say"),
         income_bin = factor(income_clean >= median(.$income), levels = c(FALSE, TRUE),
                             labels = paste(c("Less than", "At least"), median(.$income)))) %>% 
  mutate(age_bin = factor(age >= median(.$age), levels = c(FALSE, TRUE),
                          labels = paste(c("Less than", "At least"), median(.$age))),
         gender_bin = fct_collapse(gender,
                                   Female = "Female",
                                   `Not Female` = c("Male", "Transgender", 
                                                    "Other", "Prefer not to say")))

# Save final clean data
saveRDS(results, here("data", "results_clean.rds"))
write_csvy(results, file = here("data", "results_clean.csv"),
           metadata = here("data", "results_clean.yaml"), na = "NA")
```

```{r completed-summary}
# Save information about completion rates
completed_skeleton <- results_check_attention %>% 
  left_join(codes_to_exclude, by = "confirmation_code") %>% 
  replace_na(list(reason = "Approved")) %>% 
  expand(reason, crackdown, issue, funding)

completed_summary <- results_check_attention %>% 
  left_join(codes_to_exclude, by = "confirmation_code") %>% 
  replace_na(list(reason = "Approved")) %>% 
  count(reason, crackdown, issue, funding) %>% 
  right_join(completed_skeleton, by = c("reason", "crackdown", "issue", "funding")) %>% 
  replace_na(list(n = 0))

saveRDS(completed_summary, here("data", "completion_summary.rds"))
write_csv(completed_summary, here("data", "completion_summary.csv"))
```


# 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>  
