Final

Source:Getty images/istockphoto

Introduction:

The dataset originates from the esteemed Pew Research Center and their data is featured in their comprehensive analysis of religious restrictions specifically in the Asian-Pacific, Middle East, and African regions. I had to take their data and compile it into an excel. It includes key variables including country, year, location, Government Restriction Index (GRI), and Social Hostilities Index (SHI).

The Government Restriction Index (GRI) was created through an examination of governmental regulations, policies, and laws that impose limitations on religious beliefs and practices. This includes assessing whether governments provide preferential treatment to specific religious groups, or enact bureaucratic hurdles hindering certain groups from accessing benefits. Based on these factors each country was given a score; the lower the better.

Conversely, the Social Hostilities Index (SHI) was crafted by tracking various events such as incidents of religion-related harassment, mob violence, terrorism or militant activities, and conflicts arising from religious conversions or the display of religious symbols and attire. I am curious to find out the relationship between GRI and SHI along with which countries in these regions have the lowest and highest values for each.

The data set presents an invaluable opportunity to delve into historical trends and discern the intricate relationship between GRI and SHI, enabling insightful cross-continental comparisons to uncover nuances in how religious restrictions impact social hostilities across different regions.

#Load libraries
install.packages("highcharter")

Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
(as 'lib' is unspecified)

install.packages("leaflet")

Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
(as 'lib' is unspecified)

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readr)
library(ggplot2)
library(highcharter)

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
Highcharts (www.highcharts.com) is a Highsoft software product which is
not free for commercial and Governmental use

library(dplyr)
library(purrr)
library(RColorBrewer)
library(leaflet)
DF <- read_csv("Final Dataset(Sheet1).csv")

Rows: 339 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): Country, Location, GRI, SRI
dbl (3): Year, Lat, Lon

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#filter for 2016

DF_filtered <- DF |> filter(Year == 2016)

#convert to numeric type
DF_filtered$GRI <- as.numeric(DF_filtered$GRI)
DF_filtered$SRI <- as.numeric(DF_filtered$SRI)

#Create scatterplot

DF_filtered_chart <- ggplot(DF_filtered, aes(x = GRI, y = SRI)) +
  xlab("Government Restriction Index ") +
  ylab("Social Hostilities Index") +
  ggtitle("GRI VS SRI In 2016") +
  theme_minimal(base_size = 14, base_family = "serif") +
  scale_x_continuous(breaks = 1:10) +  # Specify the breaks for the x-axis
  scale_y_continuous(breaks = 1:10)    # Specify the breaks for the y-axis

#create scatter plot
DF_filtered_chart +
 geom_point()

#line of regression
p4 <- DF_filtered_chart + geom_point() + geom_smooth(color = "red")
p4

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

#smooth
p5 <- DF_filtered_chart + geom_point() + geom_smooth(method='lm',formula=y~x)
p5

#load data for stat analysis
model <- lm(GRI ~ SRI, data = DF_filtered)
summary(model)

Call:
lm(formula = GRI ~ SRI, data = DF_filtered)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9313 -1.8475 -0.6447  1.7849  5.3680 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.96197    0.33615   8.811 2.32e-14 ***
SRI          0.36155    0.08747   4.133 7.08e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.331 on 108 degrees of freedom
Multiple R-squared:  0.1366,    Adjusted R-squared:  0.1286 
F-statistic: 17.09 on 1 and 108 DF,  p-value: 7.082e-05

y= 2.96197 + 0.36155 x GRI

P value= 7.082

R^2= 0.1366

The P-value suggests there is no correlation between the SHI and the GRI. R^2 suggests a relatively weak relationship between the two variables.

#plot scatterplot
DF_filtered_chart+
geom_point(size = 3, alpha = 0.5, aes(color = Location)) +
 geom_smooth(method = lm, se =FALSE, color = "black", lty = 2, linewidth = 0.3)

`geom_smooth()` using formula = 'y ~ x'

#filter 
DF_filtered2 <- DF |> 
  filter(!is.na(Year) & !is.na(Location) & !is.na(GRI) & !is.na(SRI)) |> 
  filter(Year %in% c(2007, 2016))

#convert to numeric
DF_filtered2$GRI <- as.numeric(DF_filtered2$GRI)

Warning: NAs introduced by coercion

DF_filtered2$SRI <- as.numeric(DF_filtered2$SRI)

Warning: NAs introduced by coercion

#facet scatterplot
DF_filtered_chart2 <- ggplot(DF_filtered2, aes(x = GRI, y = SRI, col = Location)) +
  geom_point(alpha = 0.8) +
  guides(size = "none") +
  theme(plot.title = element_blank(), legend.title = element_blank()) +
  xlab("Government Restriction Index") +
  ylab("Social Hostilities Index") +
  geom_text(aes(x = 6, y = 9, label = Year), cex = 12, color = "grey") +
  facet_grid(. ~ Year) +
  theme(strip.background = element_blank(),
        strip.text.x = element_blank(),
        strip.text.y = element_blank(),
        legend.position = "top") +
  scale_x_continuous(breaks = 0:10, limits = c(0, 10)) +  # Specify the breaks and limits for the x-axis
  scale_y_continuous(breaks = 0:10, limits = c(0, 10))    # Specify the breaks and limits for the y-axis

#print
print(DF_filtered_chart2)

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).

#create scatterplot
hc <- highchart()

unique_locations <- na.omit(unique(DF_filtered$Location))  # Remove NA values from unique locations

# Define colors for each location
location_colors <- c("#00FF2E", "#057DFF", "yellow")  

# Add series for each location, excluding Series 4
for (i in seq_along(unique_locations)) {
  loc <- unique_locations[i]
  
  # Skip if loc is NA
  if (is.na(loc)) {
    next  # Skip to the next iteration
  }
  
  data <- DF_filtered[DF_filtered$Location == loc, ]
  
  hc <- hc |> 
    hc_add_series(
      data = data,
      type = "scatter",
      hcaes(x = GRI, y = SRI, text = paste("Location:", Location, "<br>Country:", Country, "<br>SRI:", SRI, "<br>GRI:", GRI)),
      name = loc,
      color = location_colors[i],  # Assign color based on location
      marker = list(symbol = "circle", radius = 4)
    )
}

hc <- hc |> 
  hc_tooltip(pointFormat = "<b>{point.text}</b>") |> 
  hc_xAxis(title = list(text = "Government Restriction Index")) |>   # Add x-axis label
  hc_yAxis(title = list(text = "Social Hostilities Index")) |>   # Add y-axis label
  hc_title(text = "Scatter Plot of GRI vs. SHI In 2016") |>   # Add chart title
  hc_add_theme(hc_theme_darkunica()) |>   # Add Dark Unica theme
  hc_chart(events = list(load = JS("function() {
    this.renderer.text('Source: Pew Research Center', this.plotLeft + 10, this.plotTop + this.plotHeight + 40)
      .attr({ zIndex: 6, fill: 'white' }).add();
  }")))
  
hc

Explore the intersection of societal responsibility and well-being with this scatterplot showcasing the Global Responsibility Index (GRI) and Social Health Index (SHI) for countries across the Asia-Pacific, Middle East-North Africa, and Sub-Saharan Africa regions in 2016. Each point represents a nation, offering insight into its unique position on the global stage. Hover over any point to reveal the country name along with its corresponding SHI and GRI values, providing a snapshot of its societal dynamics. This visualization offers a glimpse into the complex interplay of factors shaping our world, inviting exploration and fostering understanding of global trends and disparities.

#filter for high GRI values
Top10df <- DF_filtered |> 
  filter(GRI >= 6.7 & GRI <= 10) |> 
  top_n(10, GRI)

#clarify its a data frame
Top10df <- as.data.frame(Top10df)  

#fixing dataset
Top10df_reshaped <- Top10df |> 
  select(Country, GRI, SRI) |> 
  pivot_longer(cols = c(GRI, SRI), names_to = "Index_Type", values_to = "Value")

#plot
hc <- highchart() |> 
  hc_xAxis(categories = unique(Top10df_reshaped$Country),
           title = list(text = "Country")) |> 
  hc_add_series(name = "GRI", 
                data = Top10df_reshaped |>  filter(Index_Type == "GRI") |> pull(Value)) |>  
  hc_add_series(name = "SRI", 
                data = Top10df_reshaped |>  filter(Index_Type == "SRI") |>  pull(Value)) |> 
  hc_title(text = "Top 10 Countries with the Highest Government Restriction Index (GRI) in 2016") |> 
  hc_yAxis(title = list(text = "Value"))

hc

#plot
hc <- hc |>  
  hc_chart(borderColor = '#EBBA95',
           borderRadius = 10,
           borderWidth = 2,
           backgroundColor = list(
             linearGradient = c(0, 0, 500, 500),
             stops = list(
               list(0, 'rgb(255, 255, 255)'),
               list(1, 'rgb(200, 200, 255)')
             ))) |> 
  hc_xAxis(title = list(text = "Country")) |> 
  hc_yAxis(title = list(text = "Value")) |> 
  hc_title(text = "Top 10 Countries with the Highest Government Restriction Index (GRI) in 2016")

hc

# Reshape the data to have separate columns for each country's GRI and SRI values
Top10df_reshaped <- Top10df |>
  select(Country, GRI, SRI,Location) |>
  pivot_longer(cols = c(GRI, SRI), names_to = "Index_Type", values_to = "Value")

# Create Highcharter plot with 3D columns
hc <- highchart() |> 
  hc_chart(type = "column",
           options3d = list(enabled = TRUE, beta = 15, alpha = 15)) |>
  hc_xAxis(categories = unique(Top10df_reshaped$Country),
           title = list(text = "Country", margin = 20)) |>  # Adjust margin for the x-axis title
  hc_add_series(name = "GRI", 
                data = Top10df_reshaped |> filter(Index_Type == "GRI") |> pull(Value)) |> 
  hc_add_series(name = "SRI", 
                data = Top10df_reshaped |> filter(Index_Type == "SRI") |> pull(Value)) |>
  hc_title(text = "GRI and SRI for Top 10 Countries") |>
  hc_yAxis(title = list(text = "Value"))
hc

color_ranges <- list(
  list(from = 0, to = 1.4, color = "#90EE90"),  # Light green for values less than or equal to 1.4
  list(from = 1.5, to = 3.5, color = "#FFFF00"),   # Yellow for values between 1.5 and 3.5
  list(from = 3.6, to = 7.1, color = "#FFA500"),   # Orange for values between 3.6 and 7.1
  list(from = 7.2, to = 10, color = "#8B0000")    # Dark red for values 7.2 or higher
)

gri_color <- "#A9A9A9"  # Light grey color for GRI series
sri_color <- "#454545"  # Blue color for SRI series

#plot
hc <- highchart() |>
  hc_chart(type = "column") |>
  hc_xAxis(categories = unique(Top10df_reshaped$Country),
           title = list(text = "Country", margin = 20)) |>
  hc_add_series(name = "GRI", 
                data = Top10df_reshaped |>
                  filter(Index_Type == "GRI") |>
                  pull(Value),
                color = gri_color) |> # Set color for GRI series
  hc_add_series(name = "SHI", 
                data = Top10df_reshaped |>
                  filter(Index_Type == "SRI") |>
                  pull(Value),
                color = sri_color) |> # Set color for SRI series
  hc_title(text = "Top 10 Countries with the Highest Government Restriction Index (GRI) in 2016") |>
  hc_yAxis(title = list(text = "Value"),  # Configure y-axis title
           plotBands = color_ranges) 

hc <- hc |> hc_chart(events = list(load = JS("function() {
    this.renderer.text('Source: Pew Research Center', this.plotLeft + 10, this.plotTop + this.plotHeight + 100)
      .attr({ zIndex: 6, fill: 'black' }).add();
  }")))

hc

#plot
hc <- highchart() |>
  hc_chart(type = "column",
           options3d = list(enabled = TRUE, beta = 15, alpha = 15)) |> 
  hc_xAxis(categories = unique(Top10df_reshaped$Country),
           title = list(text = "Country", margin = 20)) |> 
  hc_add_series(name = "GRI", 
                data = Top10df_reshaped |> 
                  filter(Index_Type == "GRI") |> 
                  pull(Value),
                color = gri_color) |>   # Set color for GRI series
  hc_add_series(name = "SHI", 
                data = Top10df_reshaped |> 
                  filter(Index_Type == "SRI") |> 
                  pull(Value),
                color = sri_color) |>  # Set color for SRI series
  hc_title(text = "Top 10 Countries with the Highest Government Restriction Index (GRI) in 2016") |> 
  hc_yAxis(title = list(text = "Value"),  # Configure y-axis title
         plotBands = color_ranges) 

hc <- hc |>  hc_chart(events = list(load = JS("function() {
    this.renderer.text('Source: Pew Research Center', this.plotLeft + 10, this.plotTop + this.plotHeight + 40)
      .attr({ zIndex: 6, fill: 'black' }).add();
  }")))

hc

This bar graph shows the values of GRI and SHI in the top 10 countries with the highest Government Restriction Index. It also shows the bar graph for SHI. The background colors represent how Pew Research Center defines low, mild, high, and very high restrictions in regards to SHI. This way we can visually compare the heights of the SHI bars to see how they rank according to Pew Research Center.

While exploring the Pew Research Center website they discussed how in 2016, religious issues pervaded the countries of China, Indonesia, Iran, Malaysia, Maldives, Turkmenistan, Uzbekistan, Algeria, Egypt, and Syria, each grappling with distinct challenges and tensions. China’s government imposed stringent regulations on religious practices, targeting unregistered groups and minorities like Christians, Muslims (particularly Uighurs), and Tibetan Buddhists. Indonesia faced interreligious tensions, primarily between Muslims and Christians, occasionally erupting into violence. Iran’s strict controls on religious expression continued, particularly affecting Baha’is, Christians, and Sunni Muslims. Malaysia and the Maldives struggled with restrictions on religious freedom and discrimination against religious minorities, while Turkmenistan and Uzbekistan enforced tight control over religious activities, facing criticism for persecution of Jehovah’s Witnesses and Christians. Algeria saw tensions between its Muslim majority and religious minorities, while Egypt grappled with sectarian violence against Coptic Christians and restrictions on church construction. Syria’s complex civil war exacerbated religious dynamics, leading to persecution, displacement, and destruction of religious sites among various communities. These issues underscored the diverse religious landscapes and challenges encountered across the region, from state-imposed restrictions to interfaith tensions and conflict-related strife.

#filter highest SHI values
top_10_highest_sri_countries <- DF_filtered |>
  arrange(desc(SRI)) |>  # Arrange the dataframe by SRI in descending order
  head(10)  # Get the top 100 rows <- DF_filtered %>%

#create Leaflet
leaflet(top_10_highest_sri_countries) |> 
  addTiles() |>   # Add default OpenStreetMap tiles
  addMarkers(~Lon, ~Lat, popup = ~Country)

#create palette
palette <- colorNumeric(palette = "RdYlBu", domain = top_10_highest_sri_countries$SRI)

#Leaflet
leaflet(top_10_highest_sri_countries) |> 
  addTiles() |>   # Add default OpenStreetMap tiles
  addCircleMarkers(~Lon, ~Lat, 
                   radius = 8,  # Increased radius for larger points
                   color = ~palette(SRI),
                   fillOpacity = 0.7,
                   popup = ~paste("SRI: ", SRI)) |> 
  addLegend("bottomright", pal = palette, values = ~SRI, title = "Value", opacity = 1) |> 
  addControl(
    position = "topleft",
    html = "<h3>Top 10 Countries With The Highest SHI values in 2016</h3>"
  ) |> 
  addControl(
    position = "bottomleft",
    html = "<p>Pew Research Center</p>"
  )

The final chart presents a map highlighting the top 10 countries with the highest Social Health Index (SHI) in 2016 within the specified regions: Asia-Pacific, Middle East-North Africa, and Sub-Saharan Africa. The map uses a color gradient to show how these countries rank among themselves, with red indicating lower rankings and dark blue representing the highest rankings. This visual representation offers insight into the health and well-being dynamics across these regions. It allows us to observe the variations in health outcomes and highlights the disparities and successes among nations. By focusing on the SHI rankings, the map encourages discussions about equity and the need for collective efforts to improve health outcomes globally.