---
title: "Latter Day Saint Membership worldwide"
subtitle: "In 2022"
date: "August 17, 2023"
categories: [church]
image: "files/newplot.png"
format:
html:
code-fold: show
code-tools: true
---
```{r, echo=FALSE, message=FALSE, warning = FALSE}
library(tidyverse)
library(plotly)
library(MetBrewer)
library(sf)
library(scales)
library(countrycode)
country <- read_csv("files/membership_by_country.csv")
member_population <- read_csv("files/membership_and_population.csv")
member_population_clean <- member_population |>
select(Country, Area, Members, Population, `2022`, `%LDS`) |>
mutate(ISO_A3 = countrycode(Country, origin = "country.name", destination = "iso3c")) |>
mutate(pct_lds = Members / Population) |>
mutate(label = paste0(Country, "<br>",
label_percent(accuracy = 0.01)(pct_lds),
"<br>",
label_comma()(Population)))
world_map <- read_sf("files/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp") |>
filter(ADM0_A3 != "ATA")
map_with_members <- world_map |>
left_join(member_population_clean, by = "ISO_A3")
east_europe <- member_population_clean |>
filter(Area == "Europe East")
central_europe <- member_population_clean |>
filter(Area == "Europe Central")
unassigned <- member_population_clean |>
filter(Area == "Unassigned**")
europe <- rbind(east_europe, central_europe, unassigned)
north_europe <- member_population_clean |>
filter(Area == "Europe North")
clean_north_europe <- north_europe |>
filter(!row_number() %in% c(1))
sa_south <- member_population_clean |>
filter(Area == "SA South")
sa_north <- member_population_clean |>
filter(Area =="SA Northwest")
brazil <- member_population_clean |>
filter(Area == "Brazil")
south_america <- rbind(sa_south, sa_north, brazil)
north_america_sans_mexico <- member_population_clean |>
filter(Area == "North America*")
mexico <- member_population_clean |>
filter(Area == "Mexico")
central_america <- member_population_clean |>
filter(Area == "Central America")
north_america <- rbind(mexico, north_america_sans_mexico, central_america)
asia <- member_population_clean |>
filter(Area == "Asia")
asia_north <- member_population_clean |>
filter(Area == "Asia North") |>
mutate(country_nice = case_match(
Country,
"Federated States of Micronesia" ~ "FSM; Micronesia",
"Northern Mariana Islands" ~ "NMI; Mariana Isl",
.default = Country
))
middle_east_na <- member_population_clean |>
filter(Area == "ME/AN") |>
mutate(country_short = case_match(
Country,
"Saudi Arabia(estimate)" ~ "Saudi Arabia (est.)",
"Syria(estimate)" ~ "Syria (est.)",
"United Arab Emirates" ~ "UAE",
.default = Country
))
south_africa <- member_population_clean |>
filter(Area == "Africa South")
west_africa <- member_population_clean |>
filter(Area == "Africa West")
central_africa <- member_population_clean |>
filter(Area == "Africa Central") |>
mutate(country_small = case_match(
Country,
"Democratic Republic of the Congo" ~ "Dem. Rep. Congo",
"Republic of the Congo" ~ "Congo",
"Central African Republic" ~ "CAFR",
.default = Country
))
pacific <- member_population_clean |>
filter(Area == "Pacific")
philippines <- member_population_clean |>
filter(Area == "Philippines")
oceania <- rbind(pacific, philippines)
caribbean <- member_population_clean |>
filter(Area == "Caribbean") |>
mutate(country_shorter = case_match(
Country,
"Trinidad and Tobago" ~ "T&T",
"Saint Vincent and the Grenadines" ~ "SV&G",
"Turks and Caicos Islands" ~ "TCA",
"Antigua and Barbuda" ~ "AN&B",
"British Virgin Islands" ~ "BVI",
"Saint Kitts and Nevis" ~ "St. K&N",
"United States Virgin Islands" ~ "U.S VI",
.default = Country))
```
##
# Plots
## Europe
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
member_percent <- europe |>
mutate(Country = fct_reorder(Country, pct_lds)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds))+
geom_point(size = 4)+
scale_y_continuous(labels = label_percent()) +
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position = "none")+
labs(title = "Population and Membership: Central and East Europe",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(member_percent, tooltip = c("text", "words"))
```
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
north_europe_percent <- clean_north_europe |>
mutate(Country = fct_reorder(Country, Population)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5))+
labs(title = "Population and Membership: Northern Europe",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(north_europe_percent, tooltip = c("text", "words"))
```
```{r, echo=FALSE}
#| fig-width: 8
#| fig-height: 7
europe_plot <- europe |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Europe",
subtitle = "Plot 1",
y = NULL)
europe_plot
```
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
north_europe_plot <- clean_north_europe |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Europe",
subtitle = "Plot 2",
y = NULL)
north_europe_plot
```
## South America
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
south_america_plotly <- south_america |>
mutate(Country = fct_reorder(Country, Population)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: Central and East Europe",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(south_america_plotly, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
sa_plot <- south_america |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: South America",
y = NULL)
sa_plot
```
## North America
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
percent_na <- north_america |>
mutate(Country = fct_reorder(Country, Population)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: North America",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(percent_na, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
na_plot <- north_america |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: North America",
y = NULL)
na_plot
```
## Asia
```{r, echo=FALSE}
#| fig-width: 5
#| fig-height: 5
asia_members <- asia_north |>
mutate(country_nice = fct_reorder(country_nice, Population)) |>
ggplot(aes(x = country_nice,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: North Asia",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(asia_members, tooltip = c("text", "words"))
```
::: {.column-margin}
The shortened country names in the plots to the side are as such:
Federated States of Micronesia
- FSM
Northern Mariana Islands
- NMI
:::
```{r, echo=FALSE}
asia_members_2 <- asia |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: Asia",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(asia_members_2, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
asia_north_plot <- asia_north |>
mutate(country_nice = fct_reorder(country_nice, Members)) |>
ggplot(aes(x = Members, y = country_nice, color = country_nice))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Asia",
subtitle = "Plot 1",
y = NULL)
asia_north_plot
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
asia_plot <- asia |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Asia",
subtitle = "Plot 2",
y = NULL)
asia_plot
```
## Middle East and North Africa
```{r, echo=FALSE}
middle_east_north_africa <- middle_east_na |>
mutate(country_short = fct_reorder(country_short, Members)) |>
ggplot(aes(x = country_short,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: Middle East and North Africa",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(middle_east_north_africa, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
name_plot <- middle_east_na |>
mutate(country_short = fct_reorder(country_short, Members)) |>
ggplot(aes(x = Members, y = country_short, color = country_short))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Middle East and North Africa",
y = NULL)
name_plot
```
::: {.column-margin}
The shortened country name in the plot to the side is the United Arab Emirates (UAE)
:::
## Africa
```{r, echo=FALSE}
africa_west <- west_africa |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: West Africa",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(africa_west, tooltip = c("text", "words"))
```
```{r, echo=FALSE}
africa_south <- south_africa |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: Southern Africa",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(africa_south, tooltip = c("text", "words"))
```
```{r, echo=FALSE}
africa_central <- central_africa |>
mutate(country_small = fct_reorder(country_small, Members)) |>
ggplot(aes(x = country_small,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position="none")+
labs(title = "Population and Membership: Central Africa",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(africa_central, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
west_africa_plot <- west_africa |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: West Africa",
y = NULL)
west_africa_plot
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
central_africa_plot <- central_africa |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Central Africa",
y = NULL)
central_africa_plot
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
south_africa_plot <- south_africa |>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: South Africa",
y = NULL)
south_africa_plot
```
## Oceania
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
oceania_percent <- oceania |>
mutate(Country = fct_reorder(Country, Population)) |>
ggplot(aes(x = Country,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position = "none")+
labs(title = "Population and Membership: Oceania",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(oceania_percent, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
oceania_plot <- oceania|>
mutate(Country = fct_reorder(Country, Members)) |>
ggplot(aes(x = Members, y = Country, color = Country))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Oceania",
y = NULL)
oceania_plot
```
## Caribbean
```{r, echo=FALSE}
#| fig-width: 6
#| fig-height: 6
caribbean_percent <- caribbean |>
mutate(country_shorter = fct_reorder(country_shorter, Population)) |>
ggplot(aes(x = country_shorter,
y = pct_lds,
text = Population,
words = Members,
color = pct_lds,))+
geom_point(size = 4)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5), legend.position = "none")+
labs(title = "Population and Membership: Caribbean",
x = NULL,
y = "LDS Percentage of total population",
color = "Percent LDS")
ggplotly(caribbean_percent, tooltip = c("text", "words"))
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
#| fig-width: 8
#| fig-height: 7
caribbean_plot <- caribbean |>
mutate(country_shorter = fct_reorder(country_shorter, Members)) |>
ggplot(aes(x = Members, y = country_shorter, color = country_shorter))+
geom_point(size = 4)+
theme_bw()+
theme(legend.position="none")+
scale_x_continuous(labels =
scales::label_number())+
scale_x_continuous(labels = scales::label_comma())+
labs(title = "Membership by Country: Caribbean",
y = NULL)
caribbean_plot
```
::: {.column-margin}
The shortened country names in the plots to the side are as such:
Trinidad and Tobago
-T&T
Saint Vincent and
the Grenadines
-SV&G
Turks and Caicos Islands
-TCA
Antigua and Barbuda
-AN&B
British Virgin Islands
-BVI
Saint Kitts and Nevis
-St. K&N
US Virgin Islands
-U.S VI
:::
# Maps
```{r fig.width=9, fig.height=5, echo = FALSE, message = FALSE, warning = FALSE}
#| column: page
asdf <- ggplot() +
# Plot breaks bc it has a color and a line for every country, which can't be plotted. Using style fixes it and makes it work.
# https://github.com/plotly/plotly.R/issues/1641#issuecomment-550477069
geom_sf(data = map_with_members,
aes(fill = pct_lds, text = label, color = ADMIN),
linewidth = 0.05) +
coord_sf(crs = "+proj=robin") +
scale_fill_viridis_c(
labels = label_percent(),
na.value = "grey90",
option = "plasma") +
theme_void() +
theme(legend.position = "bottom")+
labs(fill = "Percent LDS")
interactive_map <- ggplotly(asdf, tooltip = "text")
interactive_map %>%
style(
hoveron = "fills",
# override the color mapping
line.color = toRGB("black"),
# don't apply these style rules to the first trace, which is the background graticule/grid
traces = seq.int(2, length(interactive_map$x$data))
) %>%
hide_legend()
```
*Data Source*: https://en.wikipedia.org/wiki/Membership_statistics_of_the_Church_of_Jesus_Christ_of_Latter-day_Saints
