project_plotly

df_current <- read.csv("C:/Francis_studies/STFX/third_sem/coding_and_health_analytics/project/final_plots/Final_Report_Documents/World Energy Consumption Datasets/electricity_production.csv")
df_current[is.na(df_current)] = 0

electricity_production_current <- df_current[,c('continent','year','biofuel_electricity','hydro_electricity',
                                'nuclear_electricity','solar_electricity',
                                'wind_electricity','other_renewable_electricity',
                                "fossil_electricity","renewables_consumption",
                                "fossil_fuel_consumption","renewables_electricity",
                                "coal_electricity","gas_electricity","oil_electricity",
                                "coal_consumption","gas_consumption",'oil_consumption')]


electricity_production_current<-electricity_production_current %>%
  select( continent,year,
          biofuel_electricity,
          hydro_electricity,
          nuclear_electricity,
          solar_electricity,
          wind_electricity,
          other_renewable_electricity,
          renewables_electricity,
          fossil_electricity,
          coal_electricity,
          gas_electricity,
          oil_electricity,
          renewables_consumption,
          fossil_fuel_consumption,
          coal_consumption,
          gas_consumption,
          oil_consumption)

# renewable electricity production

df2_current <- mutate(electricity_production_current,total_renewable = (biofuel_electricity + hydro_electricity +
                                                          nuclear_electricity + solar_electricity +
                                                          wind_electricity + other_renewable_electricity))
grouped <- df2_current %>%
  group_by(continent,year) %>%
  summarise(renewable_production= sum(total_renewable))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

oil_coal_gas <- mutate(electricity_production_current, total_fossil = oil_electricity+gas_electricity+coal_electricity)
grouped1 <- oil_coal_gas%>%
  group_by(continent,year)%>%
  summarise(production = sum(total_fossil))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

accumulate_by <- function(dat, var) {
  var <- lazyeval::f_eval(var, dat)
  lvls <- plotly:::getLevels(var)
  dats <- lapply(seq_along(lvls), function(x) {
    cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
  })
  dplyr::bind_rows(dats)
}

fig1 <- electricity_production_current %>%
  group_by(continent,year)%>%
  summarise(production = sum(renewables_electricity))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

fig1<-fig1 %>%
  filter(between(year,1980,2019), continent %in% c("Asia", "Europe","Africa","Americas","Oceania"))%>%
  accumulate_by(~year)

fig2 <- electricity_production_current %>%
  group_by(continent,year)%>%
  summarise(consumption = sum(renewables_consumption))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

fig2<-fig2 %>%
  filter(between(year,1980,2019), continent %in% c("Asia", "Europe","Africa","Americas","Oceania"))%>%
  accumulate_by(~year)

fig3 <- electricity_production_current %>%
  group_by(continent,year)%>%
  summarise(consumption = sum(fossil_fuel_consumption))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

fig3<-fig3 %>%
  filter(between(year,1980,2019), continent %in% c("Asia", "Europe","Africa","Americas","Oceania"))%>%
  accumulate_by(~year)

fig4 <- electricity_production_current %>%
  group_by(continent,year)%>%
  summarise(production = sum(fossil_electricity))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

fig4<-fig4 %>%
  filter(between(year,1980,2019), continent %in% c("Asia", "Europe","Africa","Americas","Oceania"))%>%
  accumulate_by(~year)

fig5 <- grouped1
# fig5 %>%filter(fossil_production_sum > 0)
fig5<-grouped1 %>%
  filter(between(year,1980,2019), continent %in% c("Asia", "Europe","Africa","Americas","Oceania"))%>%
  accumulate_by(~year)

pltly1 <-plot_ly() %>%
  add_trace(
    x = ~year,
    y = ~production,
    split = ~continent,
    frame = ~frame,
    type = 'scatter',
    mode = 'lines',
    data = fig4,
    opacity = 1.0
  ) %>%
  add_trace(
    x = ~year,
    y = ~production,
    split = ~continent,
    frame = ~frame,
    type = 'scatter',
    mode = 'lines',
    data = fig1,
    opacity = 0.5
  ) %>%
  animation_opts(
    frame = 100,
    transition = 0,
    redraw = FALSE
  ) %>%
  layout(title = "Electricity production from fossil and renewable sources")%>%
  animation_slider(
    hide = T
  ) %>%
  animation_button(
    x = 1, xanchor = "right", y = 0, yanchor = "bottom"
  )

pltly1

pltly2 <-plot_ly() %>%
  add_trace(
    x = ~year,
    y = ~consumption,
    split = ~continent,
    frame = ~frame,
    type = 'scatter',
    mode = 'lines',
    data = fig2,
    opacity = 1.0
  ) %>%
  add_trace(
    x = ~year,
    y = ~consumption,
    split = ~continent,
    frame = ~frame,
    type = 'scatter',
    mode = 'lines',
    data = fig3,
    opacity = 0.5
  ) %>%
  animation_opts(
    frame = 100,
    transition = 0,
    redraw = FALSE
  ) %>%
  layout(title = "Electricity consumption from fossil and renewable sources")%>%
  animation_slider(
    hide = T
  ) %>%
  animation_button(
    x = 1, xanchor = "right", y = 0, yanchor = "bottom"
  )

pltly2