H5 ValeriaLee

library(tidyverse) 

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl) 
library(here) 

here() starts at /Users/avery/Desktop

path_to_sheet <- here("data-raw", "CM_Data_Explorer.xlsx")
path_to_sheet

[1] "/Users/avery/Desktop/data-raw/CM_Data_Explorer.xlsx"

read_key_minerals_sheet <- partial(
  .f = read_excel,
  path = path_to_sheet,
  sheet = "2.3 EV",
  col_names = FALSE
)


sheet_header <- read_key_minerals_sheet(range = "A4:W5")

New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`
• `` -> `...15`
• `` -> `...16`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`

sheet_header

# A tibble: 2 × 23
  ...1   ...2 ...3  ...4    ...5  ...6  ...7  ...8  ...9 ...10 ...11 ...12 ...13
  <lgl> <dbl> <lgl> <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
1 NA       NA NA    State…    NA    NA    NA    NA    NA NA    Anno…    NA    NA
2 NA     2022 NA    2025    2030  2035  2040  2045  2050 NA    2025   2030  2035
# ℹ 10 more variables: ...14 <dbl>, ...15 <dbl>, ...16 <dbl>, ...17 <lgl>,
#   ...18 <chr>, ...19 <dbl>, ...20 <dbl>, ...21 <dbl>, ...22 <dbl>,
#   ...23 <dbl>

sheet_header_processed <- sheet_header |> 
  t() |>
  as_tibble() |>
  rename(scenario = V1, year = V2) |>
  fill(scenario) |>
  replace_na(list(scenario = "Current Year"))

Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
`.name_repair` is omitted as of tibble 2.0.0.
ℹ Using compatibility `.name_repair`.

sheet_header_processed

# A tibble: 23 × 2
   scenario                 year 
   <chr>                    <chr>
 1 Current Year             <NA> 
 2 Current Year             2022 
 3 Current Year             <NA> 
 4 Stated policies scenario 2025 
 5 Stated policies scenario 2030 
 6 Stated policies scenario 2035 
 7 Stated policies scenario 2040 
 8 Stated policies scenario 2045 
 9 Stated policies scenario 2050 
10 Stated policies scenario <NA> 
# ℹ 13 more rows

mineral_name <- read_key_minerals_sheet(range = "A7") |> 
  pull()

New names:
• `` -> `...1`

mineral_name

[1] "Constrained nickel supply"

mineral_info <- read_key_minerals_sheet(range = "A8:W18")

New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`
• `` -> `...15`
• `` -> `...16`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`

mineral_info

# A tibble: 11 × 23
   ...1         ...2 ...3     ...4    ...5    ...6    ...7    ...8    ...9 ...10
   <chr>       <dbl> <lgl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <lgl>
 1 Copper    3.81e+2 NA    617.    1.39e+3 1.74e+3 2.23e+3 2.42e+3 2.31e+3 NA   
 2 Cobalt    6.35e+1 NA     61.2   3.80e+1 2.91e+1 3.54e+1 4.09e+1 4.74e+1 NA   
 3 Graphite  5.57e+2 NA    936.    1.59e+3 1.78e+3 1.69e+3 1.49e+3 1.08e+3 NA   
 4 Lithium   6.96e+1 NA    123.    2.19e+2 3.07e+2 4.07e+2 4.50e+2 4.13e+2 NA   
 5 Manganese 7.45e+1 NA     53.9   1.56e+2 3.36e+2 5.33e+2 7.12e+2 8.58e+2 NA   
 6 Nickel    3.13e+2 NA    554.    5.13e+2 5.89e+2 6.30e+2 6.92e+2 6.88e+2 NA   
 7 Silicon   8.70e+0 NA     40.5   1.39e+2 2.24e+2 3.52e+2 3.74e+2 3.73e+2 NA   
 8 Neodymium 3.96e+0 NA      7.25  1.22e+1 1.51e+1 1.88e+1 2.18e+1 2.29e+1 NA   
 9 Dysprosi… 4.13e-1 NA      0.741 1.21e+0 1.48e+0 1.84e+0 2.13e+0 2.23e+0 NA   
10 Praseody… 5.94e-1 NA      1.09  1.83e+0 2.27e+0 2.83e+0 3.27e+0 3.43e+0 NA   
11 Terbium   8.06e-2 NA      0.148 2.52e-1 3.14e-1 3.91e-1 4.52e-1 4.74e-1 NA   
# ℹ 13 more variables: ...11 <dbl>, ...12 <dbl>, ...13 <dbl>, ...14 <dbl>,
#   ...15 <dbl>, ...16 <dbl>, ...17 <lgl>, ...18 <dbl>, ...19 <dbl>,
#   ...20 <dbl>, ...21 <dbl>, ...22 <dbl>, ...23 <dbl>

mineral_info_col_names <- names(mineral_info)

mineral_info_col_names

 [1] "...1"  "...2"  "...3"  "...4"  "...5"  "...6"  "...7"  "...8"  "...9" 
[10] "...10" "...11" "...12" "...13" "...14" "...15" "...16" "...17" "...18"
[19] "...19" "...20" "...21" "...22" "...23"

sheet_headers_and_col_names <- sheet_header_processed |> 
  add_column(mineral_info_col_names = mineral_info_col_names)

sheet_headers_and_col_names

# A tibble: 23 × 3
   scenario                 year  mineral_info_col_names
   <chr>                    <chr> <chr>                 
 1 Current Year             <NA>  ...1                  
 2 Current Year             2022  ...2                  
 3 Current Year             <NA>  ...3                  
 4 Stated policies scenario 2025  ...4                  
 5 Stated policies scenario 2030  ...5                  
 6 Stated policies scenario 2035  ...6                  
 7 Stated policies scenario 2040  ...7                  
 8 Stated policies scenario 2045  ...8                  
 9 Stated policies scenario 2050  ...9                  
10 Stated policies scenario <NA>  ...10                 
# ℹ 13 more rows

mineral_info_long <- mineral_info |> 
  rename(indicator = `...1`) |> 
  pivot_longer(cols = -indicator,
               names_to = "mineral_info_col_names") |> 
  add_column(mineral_name)

mineral_info_long

# A tibble: 242 × 4
   indicator mineral_info_col_names value mineral_name             
   <chr>     <chr>                  <dbl> <chr>                    
 1 Copper    ...2                    381. Constrained nickel supply
 2 Copper    ...3                     NA  Constrained nickel supply
 3 Copper    ...4                    617. Constrained nickel supply
 4 Copper    ...5                   1389. Constrained nickel supply
 5 Copper    ...6                   1736. Constrained nickel supply
 6 Copper    ...7                   2233. Constrained nickel supply
 7 Copper    ...8                   2418. Constrained nickel supply
 8 Copper    ...9                   2313. Constrained nickel supply
 9 Copper    ...10                    NA  Constrained nickel supply
10 Copper    ...11                   732. Constrained nickel supply
# ℹ 232 more rows

combined_data <- mineral_info_long |> 
  left_join(sheet_headers_and_col_names, by = join_by(mineral_info_col_names)) |> 
  filter(!is.na(year)) |> 
  mutate(unit = case_when(
    indicator == "Share of clean technologies in total demand" ~ "Percent",
    .default = "kiloton"
    ),
    year = as.integer(year)
  ) |> 
  select(mineral_name, indicator, scenario, unit, year, value)

combined_data

# A tibble: 209 × 6
   mineral_name              indicator scenario                unit   year value
   <chr>                     <chr>     <chr>                   <chr> <int> <dbl>
 1 Constrained nickel supply Copper    Current Year            kilo…  2022  381.
 2 Constrained nickel supply Copper    Stated policies scenar… kilo…  2025  617.
 3 Constrained nickel supply Copper    Stated policies scenar… kilo…  2030 1389.
 4 Constrained nickel supply Copper    Stated policies scenar… kilo…  2035 1736.
 5 Constrained nickel supply Copper    Stated policies scenar… kilo…  2040 2233.
 6 Constrained nickel supply Copper    Stated policies scenar… kilo…  2045 2418.
 7 Constrained nickel supply Copper    Stated policies scenar… kilo…  2050 2313.
 8 Constrained nickel supply Copper    Announced pledges scen… kilo…  2025  732.
 9 Constrained nickel supply Copper    Announced pledges scen… kilo…  2030 2113.
10 Constrained nickel supply Copper    Announced pledges scen… kilo…  2035 3587.
# ℹ 199 more rows

read_iea_mineral_table <-
  function(mineral_name_range, mineral_info_range) {
    mineral_name <-
      read_key_minerals_sheet(range = mineral_name_range) |>
      pull()
    
    mineral_info <- read_key_minerals_sheet(range = mineral_info_range)
    
    mineral_info_col_names <- names(mineral_info)
    
    mineral_info_long <- mineral_info |>
      rename(indicator = `...1`) |>
      pivot_longer(cols = -indicator,
                   names_to = "mineral_info_col_names") |>
      add_column(mineral_name)
    
    combined_data <- mineral_info_long |>
      left_join(sheet_headers_and_col_names, by = join_by(mineral_info_col_names)) |>
      # filter out what were empty columns (where years are NA)
      filter(!is.na(year)) |>
      # case_when is supercharged if else
      mutate(
        unit = case_when(
          indicator == "Share of clean technologies in total demand" ~ "Percent",
          .default = "kiloton"
        ),
        # convert the year column from character to numeric
        year = as.integer(year)
      ) |>
      select(mineral_name, indicator, scenario, unit, year, value) |>
      rename(demand_scenario = mineral_name)
    
    combined_data
  }

Constrained_nickel_supply_table <- read_iea_mineral_table(
  mineral_name_range = "A7",
  mineral_info_range = "A8:W19"
)

New names:
New names:
• `` -> `...1`

Wilder_use_of_silicon_table <- read_iea_mineral_table(
  mineral_name_range = "A22",
  mineral_info_range = "A23:W34"
)

New names:
New names:
• `` -> `...1`

faster_uptake_table <- read_iea_mineral_table(
  mineral_name_range = "A37",
  mineral_info_range = "A38:W49"
)

New names:
New names:
• `` -> `...1`

lower_battery_size_table <- read_iea_mineral_table(
  mineral_name_range = "A52",
  mineral_info_range = "A53:W64"
)

New names:
New names:
• `` -> `...1`

 limited_battery_size_table<- read_iea_mineral_table(
  mineral_name_range = "A67",
  mineral_info_range = "A68:W79"
)

New names:
New names:
• `` -> `...1`

steps_table <- read_iea_mineral_table(
  mineral_name_range = "A82",
  mineral_info_range = "A83:W94"
)

New names:
New names:
• `` -> `...1`

cleaned_iea_ev_table <- steps_table |> 
  bind_rows(Constrained_nickel_supply_table) |> 
  bind_rows(Wilder_use_of_silicon_table) |>
  bind_rows(faster_uptake_table) |>
  bind_rows(lower_battery_size_table) |> 
  bind_rows(limited_battery_size_table)

cleaned_iea_ev_table

# A tibble: 1,368 × 6
   demand_scenario   indicator scenario                   unit     year value
   <chr>             <chr>     <chr>                      <chr>   <int> <dbl>
 1 STEPS - Base case Copper    Current Year               kiloton  2022  373.
 2 STEPS - Base case Copper    Stated policies scenario   kiloton  2025  580.
 3 STEPS - Base case Copper    Stated policies scenario   kiloton  2030 1044.
 4 STEPS - Base case Copper    Stated policies scenario   kiloton  2035 1219.
 5 STEPS - Base case Copper    Stated policies scenario   kiloton  2040 1421.
 6 STEPS - Base case Copper    Stated policies scenario   kiloton  2045 1458.
 7 STEPS - Base case Copper    Stated policies scenario   kiloton  2050 1346.
 8 STEPS - Base case Copper    Announced pledges scenario kiloton  2025  689.
 9 STEPS - Base case Copper    Announced pledges scenario kiloton  2030 1592.
10 STEPS - Base case Copper    Announced pledges scenario kiloton  2035 2570.
# ℹ 1,358 more rows

write_csv(cleaned_iea_ev_table,here("data", "iea_ev.csv"))

cleaned_data <- here("data", "iea_ev.csv") |> 
  read_csv()

Rows: 1368 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): demand_scenario, indicator, scenario, unit
dbl (2): year, value

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Comparison <- ggplot(cleaned_data, aes(x = year, y = value, color = indicator)) +
  geom_line() +
  labs(title = "Comparison of Mineral Demand Over Time",
       x = "Year",
       y = "Demand (kiloton)",
       color = "Mineral") +
  theme_minimal()+
  scale_fill_brewer(palette = "Set3")

Comparison

Minerals <- ggplot(cleaned_data, aes(x = scenario, y = value, fill = indicator)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Total Demand for Minerals Across Scenarios",
       x = "Scenario",
       y = "Total Demand (kiloton)",
       fill = "Mineral") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_brewer(palette = "Set3")
Minerals

Warning: Removed 144 rows containing missing values (`geom_bar()`).

Distribution_Copper <- ggplot(cleaned_data, aes(x = scenario, y = value, fill = scenario)) +
  geom_boxplot() +
  labs(title = "Distribution of Copper Demand in Different Scenarios",
       x = "Scenario",
       y = "Copper Demand (kiloton)",
       fill = "Scenario") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_brewer(palette = "Set3")
Distribution_Copper

Warning: Removed 144 rows containing non-finite values (`stat_boxplot()`).

copper_data <- cleaned_data %>%
  filter(indicator == "Copper")

Copper <- ggplot(copper_data, aes(x = factor(year), y = value, fill = scenario)) +
  geom_bar(stat = "identity", position = position_dodge()) + 
  labs(title = "Copper Supply by Scenario",
       x = "Year",
       y = "Supply (kiloton)",
       fill = "Scenario") +
  theme_minimal() +
  scale_fill_brewer(palette = "Set3") 

Copper

Warning: Removed 12 rows containing missing values (`geom_bar()`).

state_policy_lithium <- cleaned_data %>%
  filter(scenario == "Stated policies scenario",
         indicator == "Lithium",
         year != 2025)

Lithium <- ggplot(state_policy_lithium, aes(x = year, y = value, fill = demand_scenario)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(
    title = "Demand of Lithium Based on Stated Policies Scenario",
    x = "",
    y = "Demand for Lithium"
  )  +
  facet_wrap(~ scenario) +
  scale_fill_brewer(palette = "Set3")

Lithium

Warning: Removed 5 rows containing missing values (`position_stack()`).