README.md
In jkeast/ResourceRefocus:
Resource Refocus R Data Visualization Package for 8760 Building End-Use Level Data 
Guide to Using Functions
Set Up
Run the following code to install the package
# install.packages("devtools")
# devtools::install_github("jkeast/ResourceRefocus")
library(ResourceRefocus)
To ensure the fonts are consistent with the Resource Refocus style guide, make sure the "Muli" and "Roboto" fonts are installed to your computer and install and library the extrafont package. Then, for each font family run ttf_import("[Path to font]") and finally
extrafont::loadfonts(device = "pdf")
Getting Data Ready for Plotting
I wrote a couple of functions to transform model outputs into a workable format for plotting:
simple_cap() capitalizes the first letter of each word (for labeling purposes)clean_enduse() modifies the names of enduse columns to be visually appealing (for labeling purposes)to_remove() determines if there is an enduse equalling sum of others and removes itconvert() converts energy from original units to kWhclean_data() utilizes the above functions and some existing R functions to make the data completely ready for plotting
clean_data() is automatically called by all plotting functions, but can also be used on its own if ever helpful. To utilize it, call clean_data() with the path to a csv of your data. E.g:
library(ResourceRefocus)
data <- clean_data(baseline_csv)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 6
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh sum_kWh
#> <dbl> <fct> <chr> <chr> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 1.99 63.5
#> 2 1 Jan Cooling Electricity 0.178 5.69
#> 3 1 Jan Domestic Hot Water NaturalGas 1.99 63.5
#> 4 1 Jan Exterior Lights Electricity 0.189 6.05
#> 5 1 Jan Fans Electricity 0.0177 0.568
#> 6 1 Jan Heating Electricity 0.851 27.2
In addition to the path to a csv, clean_data() has a number of other arguments:
by_month: designates whether to summarize data by month (the default) or omit month (NULL)by_enduse: designates whether to summarize data by enduse (the default) or omit enduse (NULL)by_hour: designates whether to summarize data by hour (the default) or omit hour (NULL)by_fuel: designates whether to summarize data by fuel (the default) or omit fuel (NULL)
Each of these arguments shape how granular the summaries are. The default for the function is to include all these variables, so you need to indicate which you want the function to NOT consider. For example, if you want to just focus on energy usage by month -- ignoring end-use, hour of the day, and fuel -- you can set by_enduse, by_hour, and by_fuel to NULL.
data <- clean_data(baseline_csv, by_enduse = NULL, by_hour = NULL, by_fuel = NULL)
head(data)
#> # A tibble: 6 x 3
#> month kWh sum_kWh
#> <fct> <dbl> <dbl>
#> 1 Jan 2.84 19611.
#> 2 Feb 2.75 16657.
#> 3 Mar 2.74 18351.
#> 4 Apr 2.67 17285.
#> 5 May 2.74 18370.
#> 6 Jun 2.70 17520.
You can also use these arguments in conjunction, say to focus on end-use and hour of the day:
data <- clean_data(baseline_csv, by_fuel = NULL, by_month = NULL)
head(data)
#> # A tibble: 6 x 4
#> # Groups: Hour [1]
#> Hour enduse kWh sum_kWh
#> <dbl> <chr> <dbl> <dbl>
#> 1 1 Appliances 1.88 688.
#> 2 1 Cooling 1.09 399.
#> 3 1 Domestic Hot Water 1.88 688.
#> 4 1 Exterior Lights 0.140 51.3
#> 5 1 Fans 0.0358 13.1
#> 6 1 Heating 0.224 82.2
clean_data() also passes a conversion_factor argument to the convert() function. This is what you should use if the source data is in some units other than Joules --- just supply clean_data() with the correct factor to convert from the original units to kWh. For example, say our original units are kBtu. We would want to divide the energy by 3.412 to convert to kWh:
data <- clean_data(baseline_csv, conversion_factor = 3.412)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#> `clean_data()` converting energy with conversion factor of 3.412, dividing original energy values by this factor. Override with conversion_factor if incorrect.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 6
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh sum_kWh
#> <dbl> <fct> <chr> <chr> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 2094375. 67020004.
#> 2 1 Jan Cooling Electricity 187742. 6007750.
#> 3 1 Jan Domestic Hot Water NaturalGas 2094375. 67020004.
#> 4 1 Jan Exterior Lights Electricity 199399. 6380760.
#> 5 1 Jan Fans Electricity 18724. 599180.
#> 6 1 Jan Heating Electricity 898006. 28736193.
As you can see, the function also sends a message reminding you which conversion factor you used.
clean_data() also has an emissions_conversions argument for the path to csv with hourly tonne CO2-e/MWh and tonne CO2-e/therm conversion factors (see inst/extdataGHG_index_E3_2030.csv for example). If utilized, clean_data() will also return emissions summaries.
data <- clean_data(baseline_csv, emissions_conversions = CO2_conversions)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `tonne CO2-e/MWh` = col_double(),
#> `tonne CO2-e/therm` = col_double()
#> )
#> Joining, by = "Date/Time"
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `clean_data()` converting energy with conversion factor of 105480400, dividing original energy values by this factor. Override with conversion_factor if incorrect.
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 8
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh mean_CO2e sum_kWh sum_CO2e
#> <dbl> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 1.99 0.316 63.5 10.1
#> 2 1 Jan Cooling Electricity 0.178 0.0294 5.69 0.940
#> 3 1 Jan Domestic Hot Water NaturalGas 1.99 0.316 63.5 10.1
#> 4 1 Jan Exterior Lights Electricity 0.189 0.0298 6.05 0.954
#> 5 1 Jan Fans Electricity 0.0177 0.00290 0.568 0.0928
#> 6 1 Jan Heating Electricity 0.851 0.139 27.2 4.44
Using the plotting functions
Plot one model
plot_model() plots the consumption or emissions generated by one model. It takes the following arguments:
model: character string of path to csv containing model datatitle: character string of desired plot title. Default is NULL
and passes by_month and conversion_factor (and emissions_conversions if applicable) to clean_data(), which it calls automatically.
plot_model(baseline_csv, title = "Placeholder Title")
plot_model(baseline_csv, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_model(baseline_csv, by_month = NULL, title = "Placeholder Title")
plot_model(baseline_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_emissions() is identical to plot_model() except that it only plots emissions – no need to specify result.
plot_emissions(baseline_csv, title = "Placeholder Title", emissions_conversions = CO2_conversions)
plot_emissions(baseline_csv, title = "Placeholder Title", emissions_conversions = CO2_conversions, by_month = NULL)
Compare different models
plot_comps() shows the comparison of a baseline model to proposed. It takes the following arguments:
baseline: character string of path to csv containing baseline dataproposed: character string of path to csv containing proposed datatitle: character string of desired plot title. Default is NULLbw: boolean designating whether to plot in color (FALSE, default), or black and white (TRUE)
and passes by_month and conversion_factor (and emissions_conversions if applicable) to clean_data(), which it calls automatically.
plot_comps(baseline_csv, proposed_csv, title = "Placeholder Title")
plot_comps(baseline_csv, proposed_csv, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title")
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title", bw = TRUE)
Plot End-use Averages
plot_enduse_avgs() shows average hourly energy projections stratified by end-use. It takes the following arguments:
csv: character string of path to csv containing model outputstitle: character string of desired plot title. Default is NULLbw: boolean designating whether to plot in color (FALSE, default), or black and white (TRUE)
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_enduse_avgs(baseline_csv, title = "Placeholder Title")
plot_enduse_avgs(baseline_csv, by_month = NULL, title = "Placeholder Title")
plot_enduse_avgs(baseline_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_enduse_avgs(baseline_csv, title = "Placeholder Title", by_month = NULL, bw = TRUE)
Plot Dual-Fuel Averages
plot_dualfuel_avgs() shows average hourly energy projections from a dual-fuel model stratified by end-use. It takes the following arguments:
csv: character string of path to csv containing model outputstitle: character string of desired plot title. Default is NULL
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_dualfuel_avgs(dualfuel_csv, title = "Placeholder Title", by_month = NULL)
plot_dualfuel_avgs(dualfuel_csv, title = "Placeholder Title", result = "Emissions", by_month = NULL, emissions_conversions = CO2_conversions)
End Use Averages Barcharts
plot_stacked_enduses() creates barcharts to show average energy projections. If provided with two paths to csvs it will compare the two models. It can also stratify the data by month and/or visualize a dual-fuel model. It takes the following arguments:
baseline: character string of path to csv containing dataproposed: character string of path to csv containing data to compare, or NULL (default)title: character string of desired plot title. Default is NULLby_fuel: designates whether plotting dual-fuel (default) or not (NULL)
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_month = NULL, by_fuel = NULL)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_month = NULL, by_fuel = NULL, result = "Emissions", emissions_conversions = CO2_conversions)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_fuel = NULL)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(dualfuel_csv, title = "Placeholder Title", by_month = NULL)
#> Warning: Ignoring unknown parameters: identity
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(dualfuel_csv, title = "Placeholder Title")
#> Warning: Ignoring unknown parameters: identity
#> Warning: Ignoring unknown parameters: identity
jkeast/ResourceRefocus documentation built on Feb. 2, 2021, 3:35 a.m.
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Resource Refocus R Data Visualization Package for 8760 Building End-Use Level Data
Guide to Using Functions
Set Up
Run the following code to install the package
# install.packages("devtools")
# devtools::install_github("jkeast/ResourceRefocus")
library(ResourceRefocus)
To ensure the fonts are consistent with the Resource Refocus style guide, make sure the "Muli" and "Roboto" fonts are installed to your computer and install and library the extrafont package. Then, for each font family run ttf_import("[Path to font]") and finally
extrafont::loadfonts(device = "pdf")
Getting Data Ready for Plotting
I wrote a couple of functions to transform model outputs into a workable format for plotting:
simple_cap()capitalizes the first letter of each word (for labeling purposes)clean_enduse()modifies the names of enduse columns to be visually appealing (for labeling purposes)to_remove()determines if there is an enduse equalling sum of others and removes itconvert()converts energy from original units to kWhclean_data()utilizes the above functions and some existing R functions to make the data completely ready for plotting
clean_data() is automatically called by all plotting functions, but can also be used on its own if ever helpful. To utilize it, call clean_data() with the path to a csv of your data. E.g:
library(ResourceRefocus)
data <- clean_data(baseline_csv)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 6
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh sum_kWh
#> <dbl> <fct> <chr> <chr> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 1.99 63.5
#> 2 1 Jan Cooling Electricity 0.178 5.69
#> 3 1 Jan Domestic Hot Water NaturalGas 1.99 63.5
#> 4 1 Jan Exterior Lights Electricity 0.189 6.05
#> 5 1 Jan Fans Electricity 0.0177 0.568
#> 6 1 Jan Heating Electricity 0.851 27.2
In addition to the path to a csv, clean_data() has a number of other arguments:
by_month: designates whether to summarize data by month (the default) or omit month (NULL)by_enduse: designates whether to summarize data by enduse (the default) or omit enduse (NULL)by_hour: designates whether to summarize data by hour (the default) or omit hour (NULL)by_fuel: designates whether to summarize data by fuel (the default) or omit fuel (NULL)
Each of these arguments shape how granular the summaries are. The default for the function is to include all these variables, so you need to indicate which you want the function to NOT consider. For example, if you want to just focus on energy usage by month -- ignoring end-use, hour of the day, and fuel -- you can set by_enduse, by_hour, and by_fuel to NULL.
data <- clean_data(baseline_csv, by_enduse = NULL, by_hour = NULL, by_fuel = NULL)
head(data)
#> # A tibble: 6 x 3
#> month kWh sum_kWh
#> <fct> <dbl> <dbl>
#> 1 Jan 2.84 19611.
#> 2 Feb 2.75 16657.
#> 3 Mar 2.74 18351.
#> 4 Apr 2.67 17285.
#> 5 May 2.74 18370.
#> 6 Jun 2.70 17520.
You can also use these arguments in conjunction, say to focus on end-use and hour of the day:
data <- clean_data(baseline_csv, by_fuel = NULL, by_month = NULL)
head(data)
#> # A tibble: 6 x 4
#> # Groups: Hour [1]
#> Hour enduse kWh sum_kWh
#> <dbl> <chr> <dbl> <dbl>
#> 1 1 Appliances 1.88 688.
#> 2 1 Cooling 1.09 399.
#> 3 1 Domestic Hot Water 1.88 688.
#> 4 1 Exterior Lights 0.140 51.3
#> 5 1 Fans 0.0358 13.1
#> 6 1 Heating 0.224 82.2
clean_data() also passes a conversion_factor argument to the convert() function. This is what you should use if the source data is in some units other than Joules --- just supply clean_data() with the correct factor to convert from the original units to kWh. For example, say our original units are kBtu. We would want to divide the energy by 3.412 to convert to kWh:
data <- clean_data(baseline_csv, conversion_factor = 3.412)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#> `clean_data()` converting energy with conversion factor of 3.412, dividing original energy values by this factor. Override with conversion_factor if incorrect.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 6
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh sum_kWh
#> <dbl> <fct> <chr> <chr> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 2094375. 67020004.
#> 2 1 Jan Cooling Electricity 187742. 6007750.
#> 3 1 Jan Domestic Hot Water NaturalGas 2094375. 67020004.
#> 4 1 Jan Exterior Lights Electricity 199399. 6380760.
#> 5 1 Jan Fans Electricity 18724. 599180.
#> 6 1 Jan Heating Electricity 898006. 28736193.
As you can see, the function also sends a message reminding you which conversion factor you used.
clean_data() also has an emissions_conversions argument for the path to csv with hourly tonne CO2-e/MWh and tonne CO2-e/therm conversion factors (see inst/extdataGHG_index_E3_2030.csv for example). If utilized, clean_data() will also return emissions summaries.
data <- clean_data(baseline_csv, emissions_conversions = CO2_conversions)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `Misc_Loads:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `Electricity:Facility [J](Hourly)` = col_double(),
#> `Res_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `NonRes_lighting:InteriorLights:Electricity [J](Hourly)` = col_double(),
#> `ExteriorLights:Electricity [J](Hourly)` = col_double(),
#> `Cooling:Electricity [J](Hourly)` = col_double(),
#> `Heating:Electricity [J](Hourly)` = col_double(),
#> `Fans:Electricity [J](Hourly)` = col_double(),
#> `Appliances:InteriorEquipment:Electricity [J](Hourly)` = col_double(),
#> `DHW:NaturalGas [J](Hourly)` = col_double()
#> )
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> `Date/Time` = col_character(),
#> `tonne CO2-e/MWh` = col_double(),
#> `tonne CO2-e/therm` = col_double()
#> )
#> Joining, by = "Date/Time"
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `clean_data()` converting energy with conversion factor of 105480400, dividing original energy values by this factor. Override with conversion_factor if incorrect.
#> `clean_data()` converting energy from joules to kWh. Override with conversion_factor if input in other units.
#> `to_remove()` removing row summing electric end uses from data.
head(data)
#> # A tibble: 6 x 8
#> # Groups: Hour, month, enduse [6]
#> Hour month enduse fuel kWh mean_CO2e sum_kWh sum_CO2e
#> <dbl> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Jan Appliances Electricity 1.99 0.316 63.5 10.1
#> 2 1 Jan Cooling Electricity 0.178 0.0294 5.69 0.940
#> 3 1 Jan Domestic Hot Water NaturalGas 1.99 0.316 63.5 10.1
#> 4 1 Jan Exterior Lights Electricity 0.189 0.0298 6.05 0.954
#> 5 1 Jan Fans Electricity 0.0177 0.00290 0.568 0.0928
#> 6 1 Jan Heating Electricity 0.851 0.139 27.2 4.44
Using the plotting functions
Plot one model
plot_model() plots the consumption or emissions generated by one model. It takes the following arguments:
model: character string of path to csv containing model datatitle: character string of desired plot title. Default is NULL
and passes by_month and conversion_factor (and emissions_conversions if applicable) to clean_data(), which it calls automatically.
plot_model(baseline_csv, title = "Placeholder Title")
plot_model(baseline_csv, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_model(baseline_csv, by_month = NULL, title = "Placeholder Title")
plot_model(baseline_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_emissions() is identical to plot_model() except that it only plots emissions – no need to specify result.
plot_emissions(baseline_csv, title = "Placeholder Title", emissions_conversions = CO2_conversions)
plot_emissions(baseline_csv, title = "Placeholder Title", emissions_conversions = CO2_conversions, by_month = NULL)
Compare different models
plot_comps() shows the comparison of a baseline model to proposed. It takes the following arguments:
baseline: character string of path to csv containing baseline dataproposed: character string of path to csv containing proposed datatitle: character string of desired plot title. Default is NULLbw: boolean designating whether to plot in color (FALSE, default), or black and white (TRUE)
and passes by_month and conversion_factor (and emissions_conversions if applicable) to clean_data(), which it calls automatically.
plot_comps(baseline_csv, proposed_csv, title = "Placeholder Title")
plot_comps(baseline_csv, proposed_csv, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title")
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_comps(baseline_csv, proposed_csv, by_month = NULL, title = "Placeholder Title", bw = TRUE)
Plot End-use Averages
plot_enduse_avgs() shows average hourly energy projections stratified by end-use. It takes the following arguments:
csv: character string of path to csv containing model outputstitle: character string of desired plot title. Default is NULLbw: boolean designating whether to plot in color (FALSE, default), or black and white (TRUE)
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_enduse_avgs(baseline_csv, title = "Placeholder Title")
plot_enduse_avgs(baseline_csv, by_month = NULL, title = "Placeholder Title")
plot_enduse_avgs(baseline_csv, by_month = NULL, title = "Placeholder Title", result = "Emissions", emissions_conversions = CO2_conversions)
plot_enduse_avgs(baseline_csv, title = "Placeholder Title", by_month = NULL, bw = TRUE)
Plot Dual-Fuel Averages
plot_dualfuel_avgs() shows average hourly energy projections from a dual-fuel model stratified by end-use. It takes the following arguments:
csv: character string of path to csv containing model outputstitle: character string of desired plot title. Default is NULL
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_dualfuel_avgs(dualfuel_csv, title = "Placeholder Title", by_month = NULL)
plot_dualfuel_avgs(dualfuel_csv, title = "Placeholder Title", result = "Emissions", by_month = NULL, emissions_conversions = CO2_conversions)
End Use Averages Barcharts
plot_stacked_enduses() creates barcharts to show average energy projections. If provided with two paths to csvs it will compare the two models. It can also stratify the data by month and/or visualize a dual-fuel model. It takes the following arguments:
baseline: character string of path to csv containing dataproposed: character string of path to csv containing data to compare, or NULL (default)title: character string of desired plot title. Default is NULLby_fuel: designates whether plotting dual-fuel (default) or not (NULL)
and passes by_month and conversion_factor to clean_data(), which it calls automatically.
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_month = NULL, by_fuel = NULL)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_month = NULL, by_fuel = NULL, result = "Emissions", emissions_conversions = CO2_conversions)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(baseline_csv, proposed_csv, title = "Placeholder Title", by_fuel = NULL)
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(dualfuel_csv, title = "Placeholder Title", by_month = NULL)
#> Warning: Ignoring unknown parameters: identity
#> Warning: Ignoring unknown parameters: identity
plot_stacked_enduses(dualfuel_csv, title = "Placeholder Title")
#> Warning: Ignoring unknown parameters: identity
#> Warning: Ignoring unknown parameters: identity
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