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 files (in German format, i.e. decimals are indicated with , and ; is used as field separator) were exported from Umberto NXT (v7.1.0.13.503) and attached to the R package kwb.umberto as shown below:

zipfile <- system.file("extdata/umberto-nxt_v7.1.0.13.503/Beispiel_Auswertung.zip", 
                       package = "kwb.umberto")
temp <- file.path(tempdir(), "Beispiel_Auswertung")
unzip(zipfile, exdir = temp)

dir(temp, pattern = ".csv")
#> [1] "Ref_High_C+N.csv"      "Ref_High_C+high N.csv" "Ref_Low_C+N.csv"      
#> [4] "Ref_Low_C+high N.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/RtmpI5zszv/Beispiel_Auswertung.

rawdata <- kwb.umberto::import_rawdata(csv_dir = temp)
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_High_C+N.csv'
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_High_C+high N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_Low_C+N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_Low_C+high N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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)
#>                project           model      net           timestamp
#>                 <char>          <char>   <char>              <char>
#> 1: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#> 2: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#> 3: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#> 4: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#> 5: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#> 6: POWERSTEP_LargeWWTP 0b_Ref_High_C+N Main Net 11.10.2017 13:07:54
#>                                product
#>                                 <char>
#> 1: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#> 2: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#> 3: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#> 4: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#> 5: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#> 6: VOL [A1 (P3 -> T01)] (75.000,00 m3)
#>                                                           lci_method  phase
#>                                                               <char> <char>
#> 1: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#> 2: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#> 3: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#> 4: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#> 5: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#> 6: ReCiPe Midpoint (H) w/o LT - climate change w/o LT, GWP100 w/o LT  Other
#>                        process material_type
#>                         <char>        <char>
#> 1:       T03: Activated sludge           Bad
#> 2:              T06: CHP plant           Bad
#> 3:              T06: CHP plant           Bad
#> 4:             T07: Centrifuge           Bad
#> 5: T11: Electricity WWTP (RER)           Bad
#> 6: T11: Electricity WWTP (RER)           Bad
#>                                                               material
#>                                                                 <char>
#> 1:                               Dinitrogen monoxide [air/unspecified]
#> 2:                 Dinitrogen monoxide [air/urban air close to ground]
#> 3:                 Methane, non-fossil [air/urban air close to ground]
#> 4:                               Methane, non-fossil [air/unspecified]
#> 5: Carbon dioxide, fossil [air/lower stratosphere + upper troposphere]
#> 6:      Carbon dioxide, fossil [air/non-urban air or from high stacks]
#>        quantity      unit
#>           <num>    <char>
#> 1: 6.656436e+03 kg CO2-Eq
#> 2: 1.041035e+02 kg CO2-Eq
#> 3: 5.003880e+02 kg CO2-Eq
#> 4: 2.809735e+02 kg CO2-Eq
#> 5: 2.282481e-05 kg CO2-Eq
#> 6: 1.010088e+04 kg CO2-Eq

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 … 0a_R… T03: A… kg C…       25498.
#> 2 ReCiPe Midpoint (H) w/o LT - climate change … 0a_R… T06: C… kg C…         607.
#> 3 ReCiPe Midpoint (H) w/o LT - climate change … 0a_R… T07: C… kg C…         277.
#> 4 ReCiPe Midpoint (H) w/o LT - climate change … 0a_R… T11: E… kg C…       12520.
#> 5 ReCiPe Midpoint (H) w/o LT - climate change … 0a_R… T12: E… kg C…        2602.
#> 6 ReCiPe Midpoint (H) w/o LT - climate change … 0a_R… T13: F… kg C…        1548.

3.3.2 Making pivot data

data_pivot <- kwb.umberto::pivot_data(data_grouped)
head(data_pivot)
#> # A tibble: 6 × 6
#> # Groups:   lci_method, process [6]
#>   lci_method    process `0a_Ref_Low_C+N` `0b_Ref_High_C+N` `0c_Ref_Low_C+high N`
#>   <chr>         <chr>              <dbl>             <dbl>                 <dbl>
#> 1 ReCiPe Midpo… T03: A…           25498.             6656.                17548.
#> 2 ReCiPe Midpo… T06: C…             607.              604.                  615.
#> 3 ReCiPe Midpo… T07: C…             277.              281.                  284.
#> 4 ReCiPe Midpo… T11: E…           12520.            11694.                13973.
#> 5 ReCiPe Midpo… T12: E…            2602.             2556.                 2666.
#> 6 ReCiPe Midpo… T13: F…            1548.             2016.                 2264.
#> # ℹ 1 more variable: `0d_Ref_High_C+high N` <dbl>

data_pivot_list <- kwb.umberto::create_pivot_list(data_pivot)
#> Warning: `arrange_()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `arrange()` instead.
#> ℹ See vignette('programming') for more help
#> ℹ The deprecated feature was likely used in the kwb.umberto package.
#>   Please report the issue at <https://github.com/KWB-R/kwb.umberto/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> 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 × 6
#> # Groups:   lci_method, process [6]
#>   lci_method    process `0a_Ref_Low_C+N` `0b_Ref_High_C+N` `0c_Ref_Low_C+high N`
#>   <chr>         <chr>              <dbl>             <dbl>                 <dbl>
#> 1 ReCiPe Midpo… T03: A…           25498.             6656.                17548.
#> 2 ReCiPe Midpo… T06: C…             607.              604.                  615.
#> 3 ReCiPe Midpo… T07: C…             277.              281.                  284.
#> 4 ReCiPe Midpo… T11: E…           12520.            11694.                13973.
#> 5 ReCiPe Midpo… T12: E…            2602.             2556.                 2666.
#> 6 ReCiPe Midpo… T13: F…            1548.             2016.                 2264.
#> # ℹ 1 more variable: `0d_Ref_High_C+high N` <dbl>

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/RtmpI5zszv/Beispiel_Auswertung/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/RtmpI5zszv/Beispiel_Auswertung/Ref_High_C+N.csv'
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_High_C+high N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_Low_C+N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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.
#> Importing csv file '/tmp/RtmpI5zszv/Beispiel_Auswertung/Ref_Low_C+high N.csv'
#> 
#> ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
#> 
#> Rows: 19996 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (11): Project, Model, Net, Timestamp, Product, LCI Method, Phase, Proces...
#> 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 NXT, v7.1.0.13.503)