index.qmd

---
title: "Trust in police"
author: "James Goldie"
format: html
---

```{r}
#| label: setup
library(tidyverse)
library(here)
```

```{r}
#| label: combine

open_and_lengthen <- function(path) {
  read_csv(path,
    col_types = cols(
      occupation = col_character(),
      year = col_integer(),
      .default = col_double())) |>
    pivot_longer(
      -c(occupation, year),
      names_to = c("measure", "country_code"), names_sep = "_",
      values_to = "pct")
}

# open each file and bind together. (lengthen each csv before we combine in
# order to avoid blocks of nas)
list.files(
  here("data"),
  pattern = glob2rx("ipsos-trust-professions-bycountry-*.csv"),
  full.names = TRUE) |>
  map_dfr(open_and_lengthen) ->
trust_wide
```

```{r}
#| label: trust-vs-distrust
trust_wide |>
  filter(country_code != "TOT") |>
  pivot_wider(
    id_cols = c("occupation", "year", "country_code"),
    names_from = "measure",
    values_from = "pct") ->
trust_distrust
```

```{r}
#| label: add-country-names

# map country codes to names (note some changes in coding year-to-year)
countries <- tribble(
  ~ country_code, ~ country_name,
  "ARG",          "Argentina",
  "AUS",          "Australia",
  "BEL",          "Belgium",
  "BRA",          "Brazil",
  "CAN",          "Canada",
  "CHL",          "Chile",
  "CHN",          "China",
  "COL",          "Colombia",
  "GER",          "Germany",
  "DEU",          "Germany",
  "DNK",          "Denmark",
  "SPA",          "Spain",
  "ESP",          "Spain",
  "FRA",          "France",
  "GBR",          "United Kingdom",
  "GB",           "United Kingdom",
  "HUN",          "Hungary",
  "IND",          "India",
  "ITA",          "Italy",
  "JAP",          "Japan",
  "KOR",          "South Korea",
  "MEX",          "Mexico",
  "MYS",          "Malaysia",
  "NDL",          "Netherlands",
  "NLD",          "Netherlands",
  "PER",          "Peru",
  "POL",          "Poland",
  "KSA",          "Saudi Arabia",
  "SWE",          "Sweden",
  "TUR",          "Turkey",
  "USA",          "United States",
  "RSA",          "South Africa",
  "RUS",          "Russia")

# join country names and write out to disk
trust_distrust |>
  left_join(countries, by = "country_code") |>
  mutate(neither = 100 - trust - distrust) |>
  select(starts_with("country"), year, occupation, trust, neither, distrust) |>
  write_csv(
    here("data", "ipsos-trust-professions-bycountry.csv"), na = "") ->
trust_distrust_countrynames

trust_distrust_countrynames |>
  filter(country_name == "Saudi Arabia", occupation == "The police")
```

# Police vs. other occupations

```{r}
trust_distrust_countrynames |>
  mutate(occ_ispolice = occupation == "The police") |>
  group_by(country_name, year, occ_ispolice) |>
  summarise(across(c(trust, neither, distrust), mean)) |>
  mutate(
    occupation = if_else(occ_ispolice, "The police", "Other occupations")) |>
  ungroup() |>
  select(starts_with("country"), year, occupation, trust, neither, distrust) |>
  write_csv(
    here("data", "ipsos-trust-police-vs-others-bycountry.csv"), na = "") ->
trust_distrust_police
```