In maddoxamei/Data-Munging-Project-2: Does not matter.

Birth Rate

Distribution of birth rates by continent

# Cut this distribution plot
ggplot2::ggplot(tidyr::drop_na(data.melt.avg), 
       ggplot2::aes(x = babies_per_woman_avg, y = forcats::fct_rev(as.factor(continent)), fill = ..x..)) +
  ggridges::geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) + 
  ggplot2::scale_fill_gradient2(low = "blue", high = "red") +
  ggplot2::xlab("Babies per woman") +
  ggplot2::ylab("") +
  ggplot2::ggtitle("Distribution of babies per woman") +
  ggplot2::theme_bw() +
  ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))

Scatter plots of birth rates by country

# Good - update color scheme
g <- ggplot2::ggplot(tidyr::drop_na(data.melt.avg), 
            ggplot2::aes(x = babies_per_woman_avg, y = gini_avg, col = continent,  size = income_avg)) + 
  ggplot2::geom_point(alpha = 0.7) + ggplot2::xlab("Babies per woman") + 
  ggplot2::ylab("Gini coefficient") +
  ggplot2::ggtitle("Gini coefficient vs. babies per woman") + 
  ggplot2::theme_bw() + 
  ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5)) 

plotly::ggplotly(g)

Quality of Life

data <- data.melt.yearly %>% dplyr::filter(continent %in% c("Africa", "Asia", "Europe", "North America", "South America"))

fig.education <- data %>%
  plotly::group_by(continent) %>%
  plotly::plot_ly(x = ~year, color = ~continent, 
                  colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                  legendgroup = ~continent, 
                  hovertext = paste("Education Discrepancy: ", data$education_discrepancy_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                  hoverinfo = "text") %>%
  plotly::add_lines(y = ~education_discrepancy_avg, showlegend = FALSE) %>%
  plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Education Discrepancy"))

fig.suicide <- data %>%
  plotly::group_by(continent) %>%
  plotly::plot_ly(x = ~year, color = ~continent, 
                  colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                  legendgroup = ~continent, 
                  hovertext = paste("Suicide: ", data$suicide_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                  hoverinfo = "text") %>%
  plotly::add_lines(y = ~suicide_avg) %>%
  plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Suicide"))

fig.babies <- data %>%
  plotly::group_by(continent) %>%
  plotly::plot_ly(x = ~year, color = ~continent, 
                  colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                  legendgroup = ~continent, 
                  hovertext = paste("Babies: ", data$babies_per_woman_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                  hoverinfo = "text") %>%
  plotly::add_lines(y = ~babies_per_woman_avg, showlegend = FALSE) %>%
  plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Babies Per Woman"))

fig.gini <- data %>%
  plotly::group_by(continent) %>%
  plotly::plot_ly(x = ~year, color = ~continent, 
                  colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                  legendgroup = ~continent, 
                  hovertext = paste("Gini Coefficient: ", data$gini_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                  hoverinfo = "text") %>%
  plotly::add_lines(y = ~gini_avg, showlegend = FALSE) %>%
  plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Gini Coefficient")) 

fig <- plotly::subplot(fig.education, fig.suicide, fig.babies, fig.gini, nrows=1, titleY = TRUE, margin = c(0.085,0.01,0.1,0.1)) %>% plotly::layout(title = "Quality of Life over Time")

fig

-- cut everything below --

data <- data.melt.yearly %>% dplyr::filter(continent %in% c("Africa", "Asia", "Europe", "North America", "South America"))

fig.suicide <- plotly::plot_ly(type = "scatter", 
                       x = ~ data$year, 
                       y = ~ data$suicide_avg, 
                       color = ~data$continent, 
                       colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                       mode = "lines", 
                       hovertext = paste("Suicide: ", data$suicide_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text"
                       ) %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Suicide"))

fig.babies <- plotly::plot_ly(type = "scatter", 
                       x = ~ data$year, 
                       y = ~ data$babies_per_woman_avg, 
                       color = ~data$continent, 
                       colors = c('#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#A65628'),
                       mode = "lines", 
                       hovertext = paste("Babies per Woman: ", data$babies_per_woman_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text"
                       ) %>% plotly::add_lines(showLegend = FALSE) %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Babies Per Woman"))

fig <- plotly::subplot(fig.suicide, fig.babies, nrows = 2) %>% plotly::layout(title = "Quality of Life over Time")
fig

data <- data.melt.yearly %>% dplyr::filter(continent %in% c("Africa", "Asia", "Europe", "North America", "South America"))

fig.suicide <- plotly::plot_ly(type = "scatter", 
                       x = ~ data$year, 
                       y = ~ data$suicide_avg, 
                       hovertext = paste("Suicide: ", data$suicide_avg, 
                                    "<br>Year: ", data$year, 
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text", 
                       mode = "lines", 
                       transforms = list(
                         list(
                           type = "groupby", 
                           groups = data$continent, 
                           styles = list(
                             list(target = "Africa", value = list(line =list(color = '#E41A1C'))), 
                             list(target = "Asia", value = list(line =list(color = '#377EB8'))),
                             list(target = "Europe", value = list(line =list(color = '#4DAF4A'))),
                             list(target = "North America", value = list(line =list(color = '#984EA3'))),
                             list(target = "South America", value = list(line =list(color = '#A65628')))
                             )
                           )
                       )
)
# fig.suicide <- fig.suicide %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Suicide"))


fig.babies <- plotly::plot_ly(type = "scatter",
                       x = ~ data$year,
                       y = ~ data$babies_per_woman_avg,
                       hovertext = paste("Babies: ", data$babies_per_woman_avg,
                                    "<br>Year: ", data$year,
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text",
                       mode = "lines",
                       transforms = list(
                         list(
                           type = "groupby",
                           groups = data$continent,
                           styles = list(
                             list(target = "Africa", value = list(line =list(color = '#E41A1C'))),
                             list(target = "Asia", value = list(line =list(color = '#377EB8'))),
                             list(target = "Europe", value = list(line =list(color = '#4DAF4A'))),
                             list(target = "North America", value = list(line =list(color = '#984EA3'))),
                             list(target = "South America", value = list(line =list(color = '#A65628')))
                             )
                           )
                       )
)
# fig.babies<- fig.babies %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Babies per Woman"))

fig.education <- plotly::plot_ly(type = "scatter",
                       x = ~ data$year,
                       y = ~ data$education_discrepancy_avg,
                       hovertext = paste("Education Discrepancy: ", data$education_discrepancy_avg,
                                    "<br>Year: ", data$year,
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text",
                       mode = "lines",
                       transforms = list(
                         list(
                           type = "groupby",
                           groups = data$continent,
                           styles = list(
                             list(target = "Africa", value = list(line =list(color = '#E41A1C'))),
                             list(target = "Asia", value = list(line =list(color = '#377EB8'))),
                             list(target = "Europe", value = list(line =list(color = '#4DAF4A'))),
                             list(target = "North America", value = list(line =list(color = '#984EA3'))),
                             list(target = "South America", value = list(line =list(color = '#A65628')))
                             )
                           )
                       )
)
fig.education<- fig.education %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Education Discrepancy"))

fig.gini <- plotly::plot_ly(type = "scatter",
                       x = ~ data$year,
                       y = ~ data$gini_avg,
                       hovertext = paste("Gini Coefficient: ", data$gini_avg,
                                    "<br>Year: ", data$year,
                                    "<br>Continent: ", data$continent),
                       hoverinfo = "text",
                       mode = "lines",
                       transforms = list(
                         list(
                           type = "groupby",
                           groups = data$continent,
                           styles = list(
                             list(target = "Africa", value = list(line =list(color = '#E41A1C'))),
                             list(target = "Asia", value = list(line =list(color = '#377EB8'))),
                             list(target = "Europe", value = list(line =list(color = '#4DAF4A'))),
                             list(target = "North America", value = list(line =list(color = '#984EA3'))),
                             list(target = "South America", value = list(line =list(color = '#A65628')))
                             )
                           )
                       )
)
fig.gini<- fig.gini %>% plotly::layout(xaxis = list(title = "Year"), yaxis = list(title = "Gini Coefficient"))

fig <- plotly::subplot(fig.suicide, fig.babies, nrows = 2) %>% plotly::layout(title = "Quality of Life over Time")
fig

# Create a plotly line plot instead
data <- data.melt.yearly %>% dplyr::filter(continent %in% c("Africa", "Asia", "Europe", "North America", "South America"))

require(gridExtra)
plot1 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=suicide_avg)) + ggplot2::ylab("suicide rate") 
plot2 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=babies_per_woman_avg)) + ggplot2::ylab("babies per woman") 
plot3 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=education_discrepancy_avg)) + ggplot2::ylab("Education inequality") 
plot4 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=gini_avg)) + ggplot2::ylab("Gini coefficient") 

ggpubr::ggarrange(plot1, plot2, plot3, plot4, ncol=4, nrow=1, common.legend = TRUE, legend="bottom")

# plot <- ggpubr::annotate_figure(plot, top = ggpubr::text_grob("Variables of interest over time",
#                color = "black", size = 14))

data <- data.melt.yearly %>% dplyr::filter(continent %in% c("Africa", "Europe", "Asia"))

require(gridExtra)
plot1 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=suicide_avg)) + ggplot2::ylab("suicide rate") 
plot2 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=babies_per_woman_avg)) + ggplot2::ylab("babies per woman") 
plot3 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=education_discrepancy_avg)) + ggplot2::ylab("Education inequality") 
plot4 <- ggplot2::ggplot(tidyr::drop_na(data), ggplot2::aes(x=year, col = continent)) +
  ggplot2::geom_line(ggplot2::aes(y=gini_avg)) + ggplot2::ylab("Gini coefficient") 

ggpubr::ggarrange(plot1, plot2, plot3, plot4, ncol=4, nrow=1, common.legend = TRUE, legend="bottom")

# annotate_figure(plot_new, top = text_grob("Variables of interest over time",
#                color = "black", size = 14))