World Energy Consumption and Climate
Ben Steves
ben-index.Rmd
#comment out if load_all() hasn't run. if you clear environment pane this fails
library(lucabenpkgr)
#other dependencies
require(plotly)
#> Loading required package: plotly
#> Loading required package: ggplot2
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#> Attaching package: 'plotly'
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#> last_plot
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#> filter
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#> layout
require(tidyr)
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require(dplyr)
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#> Attaching package: 'dplyr'
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#> filter, lag
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#> intersect, setdiff, setequal, union
require(lubridate)
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#> date, intersect, setdiff, union
require(forcats)
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require(grid)
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require(htmltools)
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require(gridExtra)
#> Loading required package: gridExtra
#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
#> logical.return = TRUE, : there is no package called 'gridExtra'
#require(mosaic)
data(world_climate)US CO2 Consumption by Fuel Type
us_co2 <- world_climate %>%
filter(country_code == 'USA',
indicator_code %in% c("EN.ATM.CO2E.SF.KT",
"EN.ATM.CO2E.LF.KT",
"EN.ATM.CO2E.GF.KT")) %>%
mutate(indicator_code_labelled = fct_recode(indicator_code,
`Solid Fuel (Coal)` = "EN.ATM.CO2E.SF.KT",
`Liquid Fuel (Petroleum)` = "EN.ATM.CO2E.LF.KT",
`Gaseous Fuel (Natural Gas)` = "EN.ATM.CO2E.GF.KT"))
plot_ly(data=us_co2,
x=~year,
y=~measure,
color=~indicator_code_labelled,
type="scatter",
mode="lines") %>%
layout(title = "USA CO2 Consumption - Fuel Outputs",
yaxis = list(title = "CO2 Emissions (kt)"),
xaxis = list(title = "Year", tickfont = list(size=10)),
legend = list(x = 0.8, y = 0.1))Choropleth Maps
Will just be looking at North and South American countries - this dataset has 3 letter country codes, and the values filtered for below will be applied to a filter in the climate dataset.
data(alpha3codes)
alpha3codes <- alpha3codes %>%
# filter(region == "Americas") %>%
select(alpha_3)
alpha3list <- as.list(as.data.frame(t(alpha3codes)))
world_climate <- world_climate %>%
filter(country_code %in% alpha3list)
# Display a map for whatever year and indicator
show_maps <- function(df, id_code, map_title, years, color, b) {
temp_df <- df %>%
filter(indicator_code == id_code)
year <- df$year[1]
title <- paste(map_title, year, sep=', ')
plot_ly(type='choropleth', locations=temp_df$country_code,
z=temp_df$measure, text=temp_df$country_name, colorscale=color
, reversescale = b) %>%
layout(title = title, geo = list(
scope = "world",
showframe = FALSE,
projection = list(type = 'eckert4'),
showlakes = TRUE,
lakecolor = toRGB('white'),
showocean=TRUE
# oceancolor="LightBlue"
))
}CO2 Gas emissions
years <- list("1997", "2007", "2017")
years <- ymd(years, truncated = 2L)
df1997 <- world_climate %>% filter(year == years[[1]])
df2007 <- world_climate %>% filter(year == years[[2]])
df2017 <- world_climate %>% filter(year == years[[3]])
df_list <- list(df1997, df2007, df2017)
id_code1 = "EN.ATM.GHGT.KT.CE"
map_title1 = "Total greenhouse gas emissions (kt of CO2 equivalent)"
output1 <- tagList(lapply(df_list, show_maps, id_code=id_code1,
map_title=map_title1, years=years, color="Reds", b=F))
output1Tabular View - Renewable Energy Output
elec_perc <- world_climate %>%
filter(year %in% years,
indicator_code == id_code2) %>%
pivot_wider(names_from = year, values_from = measure) %>%
select(country_name, `1997-01-01`, `2007-01-01`, `2017-01-01`) %>%
filter(!is.na(`1997-01-01`)) %>%
rename(Country = "country_name",
`1997` = `1997-01-01`,
`2007` = `2007-01-01`,
`2017` = `2017-01-01`)
elec_perc_cond <- elec_perc %>%
mutate(highlight97 = ifelse(`1997` >= 25.0 , "#90EE90", "white"),
highlight07 = ifelse(`2007` >= 25.0 , "#90EE90", "white"),
highlight17 = ifelse(`2017` >= 25.0 , "#90EE90", "white"))
plot_ly(data=elec_perc,
type='table',
# columnorder = c(1,2,3,4),
columnwidth = c(10, 10, 10, 10),
header = list(
values = c(names(elec_perc)),
align = c('left', rep('center', ncol(elec_perc))),
line = list(width = 1, color = 'black'),
fill = list(color = 'rgb(45, 112, 230)'),
font = list(family = "Arial", size = 14, color = "white")
),
cells = list(values=
rbind( t(as.matrix(unname(elec_perc)))),
align = c('left', rep('center', ncol(elec_perc))),
line = list(color = "black", width = 1),
fill = list(color = list('rgb(176, 200, 232)',
elec_perc_cond$highlight97,
elec_perc_cond$highlight07,
elec_perc_cond$highlight17)),
font = list(family = "Arial", size = 12, color = c("black"))
)
)