1 Install R packages

# Enable repository from kwb-r
options(repos = c(
  kwbr = 'https://kwb-r.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))

# Download and install kwb.umberto in R
install.packages('kwb.umberto')

2 Load the R package kwb.umberto

library(kwb.umberto)

3 Import data

3.1 Directory with example .csv files

The example .csv file (in German format, i.e. decimals are indicated with , and ; is used as field separator) was exported from Umberto LCA+ (v.10.0.3.146) and attached to the R package kwb.umberto as shown below:

temp <- system.file("extdata/umberto-lca+_v10.1.0.3.146", 
                                 package = "kwb.umberto")

dir(temp, pattern = ".csv")
#> [1] "smartech2_model-v0.1.0_input-v0.3.1.csv"

3.2 Getting the data into R

Using the function kwb.umberto::import_rawdata() and specifying the parameter csv_dir = temp) imports the model results from one .csv file that is located in the folder /tmp/RtmpNFXLU0/Rinst22fb2a4949c9/kwb.umberto/extdata/umberto-lca+_v10.1.0.3.146.

rawdata <- kwb.umberto::import_rawdata(csv_dir = temp)
#> Importing csv file '/tmp/RtmpNFXLU0/Rinst22fb2a4949c9/kwb.umberto/extdata/umberto-lca+_v10.1.0.3.146/smartech2_model-v0.1.0_input-v0.3.1.csv'
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> Rows: 8456 Columns: 15
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (14): Project, Model, Net, Timestamp, Product, Product Name, Product Arr...
#> dbl  (1): Quantity
#> 
#> 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.

To access the structure of the imported data one can run the following command:

head(rawdata)
#> # A tibble: 6 × 15
#>   project               model net   timestamp product product_name product_arrow
#>   <chr>                 <chr> <chr> <chr>     <chr>   <chr>        <chr>        
#> 1 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> 2 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> 3 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> 4 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> 5 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> 6 smartech2_model-v0.1… 0_Re… Main… 12.09.20… VOL [A… VOL          A1 (P3 -> T0…
#> # ℹ 8 more variables: product_flow_amount <chr>, lci_method <chr>, phase <chr>,
#> #   process <chr>, material_type <chr>, material <chr>, quantity <dbl>,
#> #   unit <chr>

3.3 Data aggregation

Once the data is imported into R, it can be aggregated as shown in the subsequent subchapters.

3.3.1 Grouping

data_grouped <- kwb.umberto::group_data(rawdata)
head(data_grouped)
#> # A tibble: 6 × 5
#> # Groups:   lci_method, model, process [6]
#>   lci_method                                    model process unit  quantity_sum
#>   <chr>                                         <chr> <chr>   <chr>        <dbl>
#> 1 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T03: A… kg C…     3780859.
#> 2 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T06: C… kg C…      180612.
#> 3 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T07: C… kg C…      289429.
#> 4 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T14: P… kg C…      392609.
#> 5 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T15: S… kg C…         110.
#> 6 ReCiPe Midpoint (H) w/o LT - climate change … 0_Re… T21: f… kg C…     -217232.

3.3.2 Making pivot data

data_pivot <- kwb.umberto::pivot_data(data_grouped)
head(data_pivot)
#> # A tibble: 6 × 3
#> # Groups:   lci_method, process [6]
#>   lci_method                                          process `0_Reference_Agri`
#>   <chr>                                               <chr>                <dbl>
#> 1 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T03: A…           3780859.
#> 2 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T06: C…            180612.
#> 3 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T07: C…            289429.
#> 4 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T14: P…            392609.
#> 5 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T15: S…               110.
#> 6 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T21: f…           -217232.

data_pivot_list <- kwb.umberto::create_pivot_list(data_pivot)
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
#> Joining with `by = join_by(lci_method, process)`
head(data_pivot)
#> # A tibble: 6 × 3
#> # Groups:   lci_method, process [6]
#>   lci_method                                          process `0_Reference_Agri`
#>   <chr>                                               <chr>                <dbl>
#> 1 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T03: A…           3780859.
#> 2 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T06: C…            180612.
#> 3 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T07: C…            289429.
#> 4 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T14: P…            392609.
#> 5 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T15: S…               110.
#> 6 ReCiPe Midpoint (H) w/o LT - climate change w/o LT… T21: f…           -217232.

4 Data export

Finally the resulting data can be exported to an EXCEL spreatsheet. For each lci_method available in the imported dataset a sheet named lci_method_1 to lci_method_9 will be created, as there are 9 distinct lci_method available for this example data set:

ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT ,
ReCiPe Midpoint (H) w/o LT - freshwater ecotoxicity w/o LT, FETPinf w/o LT ,
ReCiPe Midpoint (H) w/o LT - freshwater eutrophication w/o LT, FEP w/o LT ,
ReCiPe Midpoint (H) w/o LT - human toxicity w/o LT, HTPinf w/o LT ,
ReCiPe Midpoint (H) w/o LT - marine ecotoxicity w/o LT, METPinf w/o LT ,
ReCiPe Midpoint (H) w/o LT - marine eutrophication w/o LT, MEP w/o LT ,
ReCiPe Midpoint (H) w/o LT - terrestrial acidification w/o LT, TAP100 w/o LT ,
cumulative energy demand - fossil, non-renewable energy resources, fossil ,
cumulative energy demand - nuclear, non-renewable energy resources, nuclear

export_path <- file.path(temp, "results.xlsx")
print(sprintf("Exporting aggregated results to %s", export_path))
#> [1] "Exporting aggregated results to /tmp/RtmpNFXLU0/Rinst22fb2a4949c9/kwb.umberto/extdata/umberto-lca+_v10.1.0.3.146/results.xlsx"
write_xlsx(data_pivot_list, 
           path = export_path)

5 Data visualisation

In addition a simple visualisation of the imported and grouped data can be performed by calling the function kwb.umberto::plot_results() as shown below:

rawdata <- kwb.umberto::import_rawdata(csv_dir = temp)
#> Importing csv file '/tmp/RtmpNFXLU0/Rinst22fb2a4949c9/kwb.umberto/extdata/umberto-lca+_v10.1.0.3.146/smartech2_model-v0.1.0_input-v0.3.1.csv'
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> Rows: 8456 Columns: 15
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (14): Project, Model, Net, Timestamp, Product, Product Name, Product Arr...
#> dbl  (1): Quantity
#> 
#> 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.
data_grouped <- kwb.umberto::group_data(rawdata)
kwb.umberto::plot_results(grouped_data = data_grouped)

Tutorial (Umberto LCA+, v10.0.3.146)