Final_Project_Notes.Rmd

---
title: 'Final Project Visualizations'
date: "`r Sys.Date()`"
output:
  html_document:
    toc: yes 
    toc_float: yes
  toc: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, 
                      fig.fullwidth = TRUE, fig.align = "center")
```

## Libraries & Data

```{r echo = TRUE}
library(tidyverse)
library(glue)
library(here)
library(visdat)
library(skimr)
library(plotly)
library(reactable)

load(here::here("data" , "tidy_data", "Hopkins_Final.RData"))

```





## Correlation Graphs


### Five Eyes

```{r}

breaks_d <- seq(1955, 2020, by = 10)


lm_graph_5ey <- five_eyes_dat %>% 
  mutate(across(where(is.numeric), round, 2))%>%
  ggplot( aes(x = year, 
              y = life_expectancy, 
              color = country, 
              text = paste("Per Capita GDP: ", gdp_per_cap)) ) + 
  geom_point() +
  geom_smooth(method = "lm", 
              se = TRUE, 
              show.legend = FALSE) +
  labs(title = "Five Eyes Nations",
       y ="Life Expectancy", 
       x = "Year + Per Capita GDP + Nation",
       color = "Nation",
       text = "Per Capita",
       subtitle = "life_expectancy ~ per_cap_gdp + country + year") +
  scale_x_continuous(breaks = breaks_d) +  
  theme_minimal()
#lm_graph_5ey

lm_graph_5ey <- plotly::ggplotly(lm_graph_5ey)



lm_graph_5ey 



```


### Five Other

```{r}

lm_graph_5ot <- five_other_dat %>%
  mutate(across(where(is.numeric), round, 2))%>%
  ggplot( aes(x = year, 
              y = life_expectancy,  
              color = country, 
              text = paste("Per Capita GDP: ", gdp_per_cap)) ) + 
  geom_point() +
  geom_smooth(method = "lm", 
              se = TRUE, 
              show.legend = FALSE) +
  labs(title = "Five Other Nations",
       y ="Life Expectancy", 
        x = "Year + Per Capita GDP + Nation",
       color = "Nation",
       text = "Per Capita",
       subtitle = "life_expectancy ~ per_cap_gdp + country + year") +
  scale_x_continuous(breaks = breaks_d)  +  
  theme_minimal()
  
plotly::ggplotly(lm_graph_5ot)
```





## Linear Models


### Five Eyes

```{r}

fm_5graphs <- as.formula("life_expectancy ~ gdp_per_cap + country + year")

my_mod_5ey <- lm(formula = fm_5graphs , data = five_eyes_dat)




clean_my_mod_5ey <- broom::tidy(my_mod_5ey, 
                                conf.int = TRUE,  
                                conf.level = 0.95) %>%
  mutate(across(where(is.numeric), round, 4))



clean_my_mod_5ey %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 


```





### Five Other

```{r}
my_mod_5ot <- lm(formula = fm_5graphs , data = five_other_dat)
clean_my_mod_5ot  <- broom::tidy(my_mod_5ot , 
                                 conf.int = TRUE,  
                                 conf.level = 0.95) %>%
  mutate(across(where(is.numeric), round, 4))

clean_my_mod_5ot %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 
```


## Choropleth Overviews


```{r}
gapminder_map <- gapminder %>%
  filter(year == 1960 | year == 2016) %>% tibble()




gapminder_map$country <- recode(gapminder_map$country,
                                `Macedonia, FYR`  = "North Macedonia",
                                `West Bank and Gaza` = "Palestine",
                                `Swaziland` =  "Eswatini") 

gapminder_map <- gapminder_map %>% 
  select(year, country, life_expectancy)


gapminder_map_1960 <-  gapminder_map %>% 
  filter(year == 1960) %>% 
  complete(country = world_map2_ISO$country, 
           fill = (list(water = NA )) ) %>%
  left_join(world_map2_ISO, by = "country") %>%
  replace_na(list(year = 1960))

gapminder_map_2016 <-  gapminder_map %>% 
  filter(year == 2016) %>% 
  complete(country = world_map2_ISO$country, 
           fill = (list(water = NA )) ) %>%
  left_join(world_map2_ISO, by = "country") %>%
  replace_na(list(year = 2016))



```


### Global Life Expectancy 2016

```{r}
map_2016 <- gapminder_map_2016 %>%
  filter(code_3 != "ATA") %>%
  ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = life_expectancy) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "",
       title = "Life Expectancy (2016)") +
  theme_void() 

plotly::ggplotly(map_2016)


```




###  Global Life Expectancy 1960

```{r}
map_1960 <- gapminder_map_1960 %>%
  filter(code_3 != "ATA") %>%
  ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = life_expectancy) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "",
       title = "Life Expectancy (1960)") +
  theme_void() 


plotly::ggplotly(map_1960 )
```











## ANOVA Tests for Life Expectancy


### Five Eyes 2000s

```{r}

five_eyes_2000s <- five_eyes_dat %>%
  filter(decade == "2000s")

anova_one  <- aov(life_expectancy ~ country,  five_eyes_2000s )

results_one  <- TukeyHSD(anova_one) %>% 
  broom::tidy() %>% dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))

results_one$decade <- "2000s"


```



### Five Eyes 1960s

```{r}

five_eyes_1960s <- five_eyes_dat %>%
  filter(decade == "1960s")

anova_two <- aov(life_expectancy ~ country,  five_eyes_1960s  )

results_two <- TukeyHSD(anova_two ) %>% 
  broom::tidy() %>% dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))

results_two$decade <- "1960s"


```




```{r}
results_five_eyes <- results_one %>% 
  bind_rows(results_two) %>% 
  select(decade, everything())


results_five_eyes  %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 
```


```{r}

results_five_eyes %>% 
ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  facet_wrap(~decade) +
  labs(title = "Life Expectancy Means: 1960s & 2000s",
       subtitle = "ANOVA Results for Five Eyes Nations. 95% CI.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))


gg <- results_five_eyes  %>% 
ggplot(aes(estimate, contrast , 
           color = contrast,
           text = paste(contrast, "<br /> Diff:", estimate,"<br />", 
                        "CI:", conf.low, conf.high))) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  facet_wrap(~decade) +
  labs(title = "Life Expectancy Means: 1960s & 2000s",
       subtitle = "ANOVA Results for Five Eyes Nations. 95% CI.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))

plotly::ggplotly(gg, tooltip = c("text" ))




```







### Five Others 2000s


```{r}
five_ot_2000s <- five_other_dat %>%
  filter(decade == "2000s")

anova_three <- aov(life_expectancy ~ country,  five_ot_2000s  )

results_three <- TukeyHSD(anova_three ) %>% 
  broom::tidy() %>% dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_three$decade <-  "2000s"


```



### Five Others 1960s


```{r}

five_ot_1960s <- five_other_dat %>%
  filter(decade == "1960s")

anova_four <- aov(life_expectancy ~ country,  five_ot_1960s )

results_four <- TukeyHSD(anova_four ) %>% 
  broom::tidy() %>% dplyr::arrange(desc(abs(estimate))) %>%
  mutate(across(where(is.numeric), round, 4))


results_four$decade <-  "1960s"


```






```{r}

results_five_ot <- results_three %>% 
  bind_rows(results_four) %>% 
  select(decade, everything()) 


results_five_ot %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 



```



```{r}

results_five_ot %>% 
ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  facet_wrap(~decade) +
  labs(title = "Life Expectancy Means: 1970s & 2000s",
       subtitle = "ANOVA Results for Five Other Nations. 95% CI.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))


```



## Enery Usage Per Capita


### General Trends


```{r}

energy_big_ten <- energy_tidy %>% 
  filter(country %in% big_ten)

energy_big_ten <- energy_big_ten %>% 
  left_join(big_ten_dat, by =c("country", "year", "decade") )

energy_big_ten <- energy_big_ten %>% 
  select(country, year, energy, life_expectancy) %>%
  filter(year != 2015) %>% 
  filter(year >= 1990)
  


energy_big_ten %>% 
  plot_ly(x = ~year, 
          y = ~energy, 
          text = ~paste("Nation:", country,
                        "<br />Energy:", energy,
                        "<br />Year:", year),
          mode = "lines+markers",
          color = ~country, colors = "Set3",
          showlegend = TRUE ) %>%
  layout(
    xaxis = list(title = "Year"),
    yaxis = list(title = "Energy Usage Per Capita"),
    title = "Big Ten: Energy Usage Per Capita (Gapminder Data)" )



```


### Life Expectancy vs. Energy Usage


```{r}
energy_big_ten %>% 
  plot_ly(x = ~energy, 
          y = ~life_expectancy, 
          text = ~paste("Nation:", country,
                        "<br />Energy:", energy,
                        "<br />Life Expectancy:", life_expectancy),
          size = 25) %>%
  add_markers(frame = ~year, ids = ~country, 
              color = ~country, colors = "Set3",
              showlegend = TRUE,
) %>%
  layout(
    xaxis = list(title = "Energy Usage Per Capita"),
    yaxis = list(title = "Life Expectancy"),
    title = "Life Expectancy vs. Energy Usage Per Capita (Gapminder Data)" )


```



## Energy Choropleths



### Energy 2014

```{r}

energy_2014_all  <- energy_tidy  %>%
  filter(year == 2014) %>% 
  complete(country = world_map2_ISO$country, 
           fill = (list(energy = NA )) ) %>%
  left_join(world_map2_ISO, by = "country") %>%
  replace_na(list(year = 2014))

energy_2014_map  <- energy_2014_all   %>%
  filter(code_3 != "ATA") %>%
  ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = energy) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "",
       title = "Global: Energy Usage Per captia for 2014") +
  theme_void()

plotly::ggplotly(energy_2014_map )

```


### Energy 1990

```{r}
energy_1990_all  <- energy_tidy  %>%
  filter(year == 1990) %>% 
  complete(country = world_map2_ISO$country, 
           fill = (list(energy = NA )) ) %>%
  left_join(world_map2_ISO, by = "country") %>%
  replace_na(list(year = 1990))

energy_1990_map  <- energy_1990_all   %>%
  filter(code_3 != "ATA") %>%
  ggplot(aes(x = long, 
             y = lat, 
             group = group, 
             label = country)) +
  geom_polygon(aes(fill = energy) )+
  scale_fill_viridis_c(option = "C") +
  labs(fill = "",
       title = "Global: Energy Usage Per captia for 1990") +
  theme_void()

plotly::ggplotly(energy_1990_map )

```






## Other Versions


### Five Eyes Anova 2000s
```{r}

anova_one_plot <- results_one %>%
  ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  labs(title = "Five Eyes in the 2000s: Life Expectancy",
       subtitle = "95% CI.  ANOVA Results.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))


anova_one_plot 

```





### Five Eyes Anova 1960s

```{r}
anova_two_plot <-  results_two %>%
  ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  labs(title = "Five Eyes in the 1960s: Life Expectancy",
       subtitle = "95% CI.  ANOVA Results.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))


anova_two_plot 


```





### Five Other Anova 2000s

```{r}

anova_three_plot <- results_three %>%
  ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  labs(title = "Five Others in the 2000s: Life Expectancy",
       subtitle = "95% CI.  ANOVA Results.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))


anova_three_plot 

```





### Five Other Anova 1960s

```{r}
    
anova_four_plot <- results_four %>%
  ggplot(aes(estimate, contrast , color = contrast)) +
  geom_point() +
  geom_errorbarh(aes(xmin =  conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, alpha = 0.3, linetype = 2) +
  labs(title = "Five Other in the 1960s: Life Expectancy",
       subtitle = "95% CI.  ANOVA Results.",
       y = "", x = "Mean Difference Estimate") +
  guides(color = "none") +
  theme(axis.text.y = element_text(size=7))

anova_four_plot 
```






### Five Eyes Anova 2000s dat

```{r}
results_one %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 


```


### Five Eyes Anova 1960s dat

```{r}
results_two %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 
```





### Five Other Anova 2000s dat
```{r}

results_three  %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 
```


### Five Other Anova 1960s dat

```{r}
results_four %>%
  reactable(., highlight = TRUE, 
            striped = TRUE,
            theme = reactableTheme(
              stripedColor = "#EDEDED",
              highlightColor = "#FFE4E1") ) 
```