── 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) # for reading excel files# install.packages("here") # install the package if you haven't yetlibrary(here) # for using relative file paths
here() starts at /Users/hahazhou/Desktop/Critical Minerals
read_key_minerals_sheet <-partial(# provide the function, in this case readxl::read_excel().f = read_excel,# provide any arguments you want filled inpath = path_to_sheet,sheet ="2.3 EV",col_names =FALSE)# now all we have to do is provide the rangesheet_header <-read_key_minerals_sheet(range ="A4:W5")
# 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 |># transpose the datat() |># turn it back into a tibbleas_tibble() |># make them meaningfulrename(scenario = V1, year = V2) |># fill scenario downfill(scenario) |>#insert "Current" at topreplace_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
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 elsemutate(unit =case_when( indicator =="Share of clean technologies in total demand"~"Percent",.default ="kiloton" ),# convert the year column from character to numericyear =as.integer(year) ) |>select(mineral_name, indicator, scenario, unit, year, value)combined_data
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 elsemutate(unit =case_when( indicator =="Share of clean technologies in total demand"~"Percent",.default ="kiloton" ),# convert the year column from character to numericyear =as.integer(year) ) |>select(mineral_name, indicator, scenario, unit, year, value)combined_data
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 elsemutate(unit =case_when( indicator =="Share of clean technologies in total demand"~"Percent",.default ="kiloton" ),# convert the year column from character to numericyear =as.integer(year) ) |>select(mineral_name, indicator, scenario, unit, year, value) |>rename(demand_scenario = mineral_name) combined_data }
Warning: The following aesthetics were dropped during statistical transformation:
colour, size
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation:
colour, size
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation:
colour, size
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette YlGn is 9
Returning the palette you asked for with that many colors
ggplot(final_iea_minerals_table) +aes(x = indicator,fill = demand_scenario,group = demand_scenario,weight = value ) +geom_bar() +scale_fill_brewer(palette ="YlGn", direction =1) +labs(x ="Type of Critical Mineral",y ="Total Demand Value",title ="Mineral demand for electric vehicles (kt)",fill ="Demand Scenario" ) +theme_gray()
