Setup packages and working environment
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.4.4 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ 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/ruoleiw0921/homework5
library (dplyr)library (readr)library (reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
<- here ("data-raw" , "CM_Data_Explorer.xlsx" )
[1] "/Users/ruoleiw0921/homework5/data-raw/CM_Data_Explorer.xlsx"
<- partial (.f = read_excel,path = path_to_sheet,sheet = "2.3 EV" ,col_names = FALSE <- 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`
# 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 |> # transpose the data t () |> # turn it back into a tibble as_tibble () |> # make them meaningful rename (scenario = V1, year = V2) |> # fill scenario down fill (scenario) |> #insert "Current" at top 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`.
# 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
<- read_key_minerals_sheet (range = "A7" ) |> pull ()
New names:
• `` -> `...1`
[1] "Constrained nickel supply"
<- read_key_minerals_sheet (range = "A8:W20" )
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`
# A tibble: 13 × 23
...1 ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9
<chr> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Copper 3.81e+2 NA 617. 1389. 1736. 2233. 2418. 2313.
2 Cobalt 6.35e+1 NA 61.2 38.0 29.1 35.4 40.9 47.4
3 Graphite 5.57e+2 NA 936. 1590. 1782. 1691. 1492. 1077.
4 Lithium 6.96e+1 NA 123. 219. 307. 407. 450. 413.
5 Mangane… 7.45e+1 NA 53.9 156. 336. 533. 712. 858.
6 Nickel 3.13e+2 NA 554. 513. 589. 630. 692. 688.
7 Silicon 8.70e+0 NA 40.5 139. 224. 352. 374. 373.
8 Neodymi… 3.96e+0 NA 7.25 12.2 15.1 18.8 21.8 22.9
9 Dyspros… 4.13e-1 NA 0.741 1.21 1.48 1.84 2.13 2.23
10 Praseod… 5.94e-1 NA 1.09 1.83 2.27 2.83 3.27 3.43
11 Terbium 8.06e-2 NA 0.148 0.252 0.314 0.391 0.452 0.474
12 Total EV 1.47e+3 NA 2394. 4059. 5022. 5906. 6207. 5799.
13 <NA> NA NA NA NA NA NA NA NA
# ℹ 14 more variables: ...10 <lgl>, ...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>
<- names (division_info)
[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_header_processed |> add_column (division_info_col_names = division_info_col_names)
# A tibble: 23 × 3
scenario year division_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
<- division_info |> rename (mineral = ` ...1 ` ) |> pivot_longer (cols = - mineral,names_to = "division_info_col_names" ) |> add_column (division_name)
# A tibble: 286 × 4
mineral division_info_col_names value division_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 276 more rows
<- division_info_long |> left_join (sheet_headers_and_col_names, by = join_by (division_info_col_names)) |> filter (! is.na (mineral)) |> filter (! is.na (year)) |> mutate (unit = case_when (== "Share of EV market in total demand" ~ "Percent" ,.default = "kiloton" year = as.integer (year)|> select (division_name, mineral, scenario, unit, year, value)
# A tibble: 228 × 6
division_name mineral 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 scenario kilo… 2025 617.
3 Constrained nickel supply Copper Stated policies scenario kilo… 2030 1389.
4 Constrained nickel supply Copper Stated policies scenario kilo… 2035 1736.
5 Constrained nickel supply Copper Stated policies scenario kilo… 2040 2233.
6 Constrained nickel supply Copper Stated policies scenario kilo… 2045 2418.
7 Constrained nickel supply Copper Stated policies scenario kilo… 2050 2313.
8 Constrained nickel supply Copper Announced pledges scenar… kilo… 2025 732.
9 Constrained nickel supply Copper Announced pledges scenar… kilo… 2030 2113.
10 Constrained nickel supply Copper Announced pledges scenar… kilo… 2035 3587.
# ℹ 218 more rows
<- function (division_name_range, division_info_range) {<- read_key_minerals_sheet (range = division_name_range) |> pull ()<- read_key_minerals_sheet (range = division_info_range)<- names (division_info)<- division_info |> rename (mineral= ` ...1 ` ) |> pivot_longer (cols = - mineral,names_to = "division_info_col_names" ) |> add_column (division_name)<- division_info_long |> left_join (sheet_headers_and_col_names, by = join_by (division_info_col_names)) |> filter (! is.na (mineral)) |> filter (! is.na (year)) |> mutate (unit = case_when (== "Share of EV market in total demand" ~ "Percent" ,.default = "kiloton" year = as.integer (year)|> select (division_name, mineral, scenario, unit, year, value)
<- read_iea_division_table (division_name_range = "A7" ,division_info_range = "A8:W20"
New names:
New names:
• `` -> `...1`
<- read_iea_division_table (division_name_range = "A22" ,division_info_range = "A23:W35"
New names:
New names:
• `` -> `...1`
<- read_iea_division_table (division_name_range = "A37" ,division_info_range = "A38:W50"
New names:
New names:
• `` -> `...1`
<- read_iea_division_table (division_name_range = "A52" ,division_info_range = "A53:W65"
New names:
New names:
• `` -> `...1`
<- read_iea_division_table (division_name_range = "A67" ,division_info_range = "A68:W80"
New names:
New names:
• `` -> `...1`
<- read_iea_division_table (division_name_range = "A82" ,division_info_range = "A83:W95"
New names:
New names:
• `` -> `...1`
<- nickel_supply_table |> bind_rows (silicon_rich_anodes_table) |> bind_rows (solid_state_batteries_table) |> bind_rows (lower_battery_sizes_table) |> bind_rows (size_reduction_table) |> bind_rows (base_case_table)
# A tibble: 1,368 × 6
division_name mineral 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 scenario kilo… 2025 617.
3 Constrained nickel supply Copper Stated policies scenario kilo… 2030 1389.
4 Constrained nickel supply Copper Stated policies scenario kilo… 2035 1736.
5 Constrained nickel supply Copper Stated policies scenario kilo… 2040 2233.
6 Constrained nickel supply Copper Stated policies scenario kilo… 2045 2418.
7 Constrained nickel supply Copper Stated policies scenario kilo… 2050 2313.
8 Constrained nickel supply Copper Announced pledges scenar… kilo… 2025 732.
9 Constrained nickel supply Copper Announced pledges scenar… kilo… 2030 2113.
10 Constrained nickel supply Copper Announced pledges scenar… kilo… 2035 3587.
# ℹ 1,358 more rows
write_csv (final_iea_minerals_table,here ("data" , "iea_ev_market_for_critical_minerals.csv" ))
<- here ("data" , "iea_ev_market_for_critical_minerals.csv" ) |> read_csv ()
Rows: 1368 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): division_name, mineral, 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.
# A tibble: 1,368 × 6
division_name mineral scenario unit year value
<chr> <chr> <chr> <chr> <dbl> <dbl>
1 Constrained nickel supply Copper Current Year kilo… 2022 381.
2 Constrained nickel supply Copper Stated policies scenario kilo… 2025 617.
3 Constrained nickel supply Copper Stated policies scenario kilo… 2030 1389.
4 Constrained nickel supply Copper Stated policies scenario kilo… 2035 1736.
5 Constrained nickel supply Copper Stated policies scenario kilo… 2040 2233.
6 Constrained nickel supply Copper Stated policies scenario kilo… 2045 2418.
7 Constrained nickel supply Copper Stated policies scenario kilo… 2050 2313.
8 Constrained nickel supply Copper Announced pledges scenar… kilo… 2025 732.
9 Constrained nickel supply Copper Announced pledges scenar… kilo… 2030 2113.
10 Constrained nickel supply Copper Announced pledges scenar… kilo… 2035 3587.
# ℹ 1,358 more rows
Data Visualization 1
<- our_cleaned_data |> filter (mineral != "Total EV" )
# A tibble: 1,254 × 6
division_name mineral scenario unit year value
<chr> <chr> <chr> <chr> <dbl> <dbl>
1 Constrained nickel supply Copper Current Year kilo… 2022 381.
2 Constrained nickel supply Copper Stated policies scenario kilo… 2025 617.
3 Constrained nickel supply Copper Stated policies scenario kilo… 2030 1389.
4 Constrained nickel supply Copper Stated policies scenario kilo… 2035 1736.
5 Constrained nickel supply Copper Stated policies scenario kilo… 2040 2233.
6 Constrained nickel supply Copper Stated policies scenario kilo… 2045 2418.
7 Constrained nickel supply Copper Stated policies scenario kilo… 2050 2313.
8 Constrained nickel supply Copper Announced pledges scenar… kilo… 2025 732.
9 Constrained nickel supply Copper Announced pledges scenar… kilo… 2030 2113.
10 Constrained nickel supply Copper Announced pledges scenar… kilo… 2035 3587.
# ℹ 1,244 more rows
ggplot (mineral_data) + aes (x = mineral, y = value) + geom_boxplot (fill = "steelblue" ) + theme_minimal () + theme (axis.text = element_text (size = 5 ),axis.title = element_text (size = 11 )) + labs (title = "Projected Supply under Different Mineral" , x = "Mineral" , y = "Supply (kiloton)" )
Warning: Removed 132 rows containing non-finite values (`stat_boxplot()`).
The boxplot indicates that the demand for copper in EV markets is significantly higher than for cobalt, suggesting that policy and technological scenarios will likely impact copper demand more substantially due to its broader range of potential outcomes. Cobalt shows a consistently lower and tighter demand distribution, implying that changes in policies or technologies might result in less drastic fluctuations in its market demand.
Data Visualization 2
<- our_cleaned_data |> filter (mineral== "Copper" )
# A tibble: 114 × 6
division_name mineral scenario unit year value
<chr> <chr> <chr> <chr> <dbl> <dbl>
1 Constrained nickel supply Copper Current Year kilo… 2022 381.
2 Constrained nickel supply Copper Stated policies scenario kilo… 2025 617.
3 Constrained nickel supply Copper Stated policies scenario kilo… 2030 1389.
4 Constrained nickel supply Copper Stated policies scenario kilo… 2035 1736.
5 Constrained nickel supply Copper Stated policies scenario kilo… 2040 2233.
6 Constrained nickel supply Copper Stated policies scenario kilo… 2045 2418.
7 Constrained nickel supply Copper Stated policies scenario kilo… 2050 2313.
8 Constrained nickel supply Copper Announced pledges scenar… kilo… 2025 732.
9 Constrained nickel supply Copper Announced pledges scenar… kilo… 2030 2113.
10 Constrained nickel supply Copper Announced pledges scenar… kilo… 2035 3587.
# ℹ 104 more rows
ggplot (copper_data, aes (x = year, y = value, color = scenario, group = scenario)) + geom_point () + theme_minimal () + labs (title = "Projected Copper Demand under Different Scenarios" ,x = "Year" ,y = "Demand (kiloton)" ,color = "Scenario" ) + theme (legend.position = "bottom" )
Warning: Removed 12 rows containing missing values (`geom_point()`).
The ‘Net Zero Emissions by 2050 scenario’ indicates a sharp rise in copper demand, hinting at a significant impact of stringent climate policies on copper utilization. The ‘Stated policies scenario’ shows a more modest growth, suggesting that less aggressive policies will lead to a slower increase in demand.
Data Visualization 3
<- our_cleaned_data |> filter (mineral== "Cobalt" )
# A tibble: 114 × 6
division_name mineral scenario unit year value
<chr> <chr> <chr> <chr> <dbl> <dbl>
1 Constrained nickel supply Cobalt Current Year kilo… 2022 63.5
2 Constrained nickel supply Cobalt Stated policies scenario kilo… 2025 61.2
3 Constrained nickel supply Cobalt Stated policies scenario kilo… 2030 38.0
4 Constrained nickel supply Cobalt Stated policies scenario kilo… 2035 29.1
5 Constrained nickel supply Cobalt Stated policies scenario kilo… 2040 35.4
6 Constrained nickel supply Cobalt Stated policies scenario kilo… 2045 40.9
7 Constrained nickel supply Cobalt Stated policies scenario kilo… 2050 47.4
8 Constrained nickel supply Cobalt Announced pledges scenar… kilo… 2025 71.2
9 Constrained nickel supply Cobalt Announced pledges scenar… kilo… 2030 58.4
10 Constrained nickel supply Cobalt Announced pledges scenar… kilo… 2035 61.8
# ℹ 104 more rows
ggplot (cobalt_data, aes (x = year, y = value, color = scenario, group = scenario)) + geom_point () + theme_minimal () + labs (title = "Projected Cobalt Demand under Different Scenarios" ,x = "Year" ,y = "Demand (kiloton)" ,color = "Scenario" ) + theme (legend.position = "bottom" )
Warning: Removed 12 rows containing missing values (`geom_point()`).
The graph suggests that achieving net zero emissions by 2050 could significantly increase the supply of cobalt, reflecting a need for robust policy support and technological advancements to secure sufficient supply.
Data Visualization 4
<- ggplot (copper_data, aes (x= year, y= scenario, fill= value)) + geom_tile () + scale_fill_gradient (low= "lightblue" , high= "indianred" ) + theme_minimal () + xlab ('Year' ) + ylab ('Scenario' ) + ggtitle ('Heatmap of Copper Demand by Scenario' )
Future demand is expected to grow regardless of the scenario, but the rate of growth varies: Even the “Stated policies scenario” and the “Announced pledges scenario” depict a noticeable increase in demand, suggesting that technological advancements and policy measures will likely result in a greater need for copper.
Data Visualization 5
<- ggplot (cobalt_data, aes (x= year, y= scenario, fill= value)) + geom_tile () + scale_fill_gradient (low= "lightblue" , high= "indianred" ) + theme_minimal () + xlab ('Year' ) + ylab ('Scenario' ) + ggtitle ('Heatmap of Cobalt Demand by Scenario' )
The “Net Zero Emissions by 2050” scenario shows the highest demand, suggesting that aggressive climate policies will greatly increase cobalt needs, likely due to its role in renewable technologies and batteries.
