Two graphical representations in the same graph

Question

I have a dataset of energy production for 2 types Renewable and Non-Renewable for 3 consecutive years.

I want to show the growth of Renewable energy production and Non-renewable production in 2 lines in the same facet for each country.

Target: For each country, in the same facet I want to observe which energy production(renewable or non-renewable) has a considerable growth.

> dput(top10ProducersRenwNonRenw_Mod)
structure(list(country = c("DE", "DE", "FR", "FR", "FR", "DE", 
"UK", "UK", "UK", "TR", "IT", "TR", "IT", "DE", "IT", "ES", "DE", 
"TR", "ES", "ES", "DE", "NO", "NO", "NO", "UA", "UA", "PL", "PL", 
"PL", "UA", "FR", "ES", "FR", "ES", "IT", "FR", "TR", "IT", "TR", 
"IT", "ES", "TR", "SE", "SE", "UK", "SE", "SE", "SE", "SE", "UK", 
"UK", "PL", "PL", "PL", "UA", "UA", "UA", "NO", "NO", "NO"), 
    country_name = c("Germany", "Germany", "France", "France", 
    "France", "Germany", "United Kingdom", "United Kingdom", 
    "United Kingdom", "Turkey", "Italy", "Turkey", "Italy", "Germany", 
    "Italy", "Spain", "Germany", "Turkey", "Spain", "Spain", 
    "Germany", "Norway", "Norway", "Norway", "Ukraine", "Ukraine", 
    "Poland", "Poland", "Poland", "Ukraine", "France", "Spain", 
    "France", "Spain", "Italy", "France", "Turkey", "Italy", 
    "Turkey", "Italy", "Spain", "Turkey", "Sweden", "Sweden", 
    "United Kingdom", "Sweden", "Sweden", "Sweden", "Sweden", 
    "United Kingdom", "United Kingdom", "Poland", "Poland", "Poland", 
    "Ukraine", "Ukraine", "Ukraine", "Norway", "Norway", "Norway"
    ), type2 = c("Non-Renewable", "Non-Renewable", "Non-Renewable", 
    "Non-Renewable", "Non-Renewable", "Non-Renewable", "Non-Renewable", 
    "Non-Renewable", "Non-Renewable", "Non-Renewable", "Non-Renewable", 
    "Non-Renewable", "Non-Renewable", "Renewable", "Non-Renewable", 
    "Non-Renewable", "Renewable", "Non-Renewable", "Non-Renewable", 
    "Non-Renewable", "Renewable", "Renewable", "Renewable", "Renewable", 
    "Non-Renewable", "Non-Renewable", "Non-Renewable", "Non-Renewable", 
    "Non-Renewable", "Non-Renewable", "Renewable", "Renewable", 
    "Renewable", "Renewable", "Renewable", "Renewable", "Renewable", 
    "Renewable", "Renewable", "Renewable", "Renewable", "Renewable", 
    "Renewable", "Non-Renewable", "Renewable", "Non-Renewable", 
    "Renewable", "Renewable", "Non-Renewable", "Renewable", "Renewable", 
    "Renewable", "Renewable", "Renewable", "Renewable", "Renewable", 
    "Renewable", "Non-Renewable", "Non-Renewable", "Non-Renewable"
    ), year = c("2016", "2017", "2017", "2018", "2016", "2018", 
    "2016", "2017", "2018", "2017", "2017", "2018", "2016", "2018", 
    "2018", "2017", "2017", "2016", "2016", "2018", "2016", "2018", 
    "2016", "2017", "2018", "2016", "2018", "2017", "2016", "2017", 
    "2018", "2016", "2016", "2018", "2018", "2017", "2018", "2016", 
    "2016", "2017", "2017", "2017", "2017", "2018", "2018", "2017", 
    "2018", "2016", "2016", "2017", "2016", "2017", "2018", "2016", 
    "2018", "2016", "2017", "2017", "2016", "2018"), ggwt_hours = c(471984, 
    449906, 448690.614, 447109.694, 445175.494, 393234.585, 268314.268, 
    253183.388, 239935.658, 200721.177, 200305.315, 195856.672, 
    190771, 185226.211, 185046, 182503, 175063, 174847, 164401, 
    163355.038, 147622, 146878.825, 146557, 146285, 145228.6, 
    141425, 138957.387, 137129.829, 137009, 134382.2, 113658.177, 
    103353, 99885.054, 99725.566, 96820, 93907.184, 93425.906, 
    90756.826, 87090, 86786.294, 84664, 83536.342, 82540, 80671.141, 
    79955.967, 78011, 77615.947, 77505, 75145, 73113.964, 58909.047, 
    18187.708, 15541.473, 15468, 13843.9, 12097, 12082.6, 3514, 
    3509, 3457.28)), row.names = c(NA, -60L), groups = structure(list(
    country = c("DE", "DE", "DE", "ES", "ES", "ES", "FR", "FR", 
    "FR", "IT", "IT", "IT", "NO", "NO", "NO", "PL", "PL", "PL", 
    "SE", "SE", "SE", "TR", "TR", "TR", "UA", "UA", "UA", "UK", 
    "UK", "UK"), country_name = c("Germany", "Germany", "Germany", 
    "Spain", "Spain", "Spain", "France", "France", "France", 
    "Italy", "Italy", "Italy", "Norway", "Norway", "Norway", 
    "Poland", "Poland", "Poland", "Sweden", "Sweden", "Sweden", 
    "Turkey", "Turkey", "Turkey", "Ukraine", "Ukraine", "Ukraine", 
    "United Kingdom", "United Kingdom", "United Kingdom"), year = c("2016", 
    "2017", "2018", "2016", "2017", "2018", "2016", "2017", "2018", 
    "2016", "2017", "2018", "2016", "2017", "2018", "2016", "2017", 
    "2018", "2016", "2017", "2018", "2016", "2017", "2018", "2016", 
    "2017", "2018", "2016", "2017", "2018"), .rows = structure(list(
        c(1L, 21L), c(2L, 17L), c(6L, 14L), c(19L, 32L), c(16L, 
        41L), c(20L, 34L), c(5L, 33L), c(3L, 36L), c(4L, 31L), 
        c(13L, 38L), c(11L, 40L), c(15L, 35L), c(23L, 59L), c(24L, 
        58L), c(22L, 60L), c(29L, 54L), c(28L, 52L), c(27L, 53L
        ), 48:49, c(43L, 46L), c(44L, 47L), c(18L, 39L), c(10L, 
        42L), c(12L, 37L), c(26L, 56L), c(30L, 57L), c(25L, 55L
        ), c(7L, 51L), c(8L, 50L), c(9L, 45L)), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, 30L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

Answer 1

An approach with forcats

library(forcats)
library(dplyr)
library(ggplot2)
top10ProducersRenwNonRenw_Mod %>%
     mutate(year = as_factor(year)) %>%
     ggplot(aes(x=year , y=ggwt_hours,  color = country_name,
                                         group=country_name)) +
     geom_line()+
 
     facet_wrap(.~type2,scale='free')+
     xlab('Year')

-output

Answer 2

One approach can be next, where you can compare what happened with each country across the type of energy produced:

library(ggplot2)
#Code
ggplot(top10ProducersRenwNonRenw_Mod,aes(x=factor(year),y=ggwt_hours,color=country_name,
                                         group=country_name))+
  geom_line()+
  scale_y_continuous(labels = scales::comma)+
  facet_wrap(.~type2,scale='free')+
  xlab('Year')+
  theme_bw()

Output: