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Bar plots with variable time steps

Source: R/variable_timesteps.R
variable_timesteps.Rd

Utility functions for plotting stacked bars with variable widths for displaying time-series data with variable time steps (like REMIND data).

Usage

add_timesteps_columns(
  data,
  timesteps,
  periods = "period",
  gaps = 0,
  interval_shift = c(-0.5, 0.5),
  timesteps_period = "period",
  timesteps_interval = "year"
)

add_remind_timesteps_columns(data, periods = "period", gaps = 0)

ggplot_bar_vts(
  data,
  timesteps,
  mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")),
  gaps = 0.1,
  position_fill = FALSE,
  interval_shift = c(-0.5, 0.5),
  timesteps_period = "period",
  timesteps_interval = "year"
)

ggplot_bar_remind_vts(
  data,
  mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")),
  gaps = 0.1,
  position_fill = FALSE
)

Arguments

data

A data frame.

timesteps

A data frame like remind_timesteps with columns period, year, and weight, where weight determines which share of year belongs to period.

periods

The column holding the period information in data (either a string or an object). Defaults to 'period'.

gaps

Gaps between bars as a fraction of the smallest bar width. Defaults to 0.1 (e.g. 0.1 * 5 years = 0.5 years).

interval_shift

numeric of length 2. Shifts added to the interval fix point to obtain the beginning and end of time interval. If the interval for period 1 should be [0.5, 1.5], interval_shift should be set to c(-0.5, 0.5) (default). If the interval for period 1 should be [0, 1], interval_shift should be set to c(-1, 0).

timesteps_period

character string giving the column name of the period in the timesteps data frame. Defaults to 'period'.

timesteps_interval

character string giving the column name of the time interval in the timesteps data frame. Defaults to 'year'.

mapping

aes() mapping with aesthetics x, y, and optionally fill.

position_fill

If TRUE, stacks bars and standardises each stack to have constant height.

Value

add_timesteps_columns() and add_remind_timesteps_columns() return a data frame. ggplot_bar_vts() and ggplot_bar_remind_vts() return a ggplot()-like object.

Details

add_timesteps_columns() calculates the x-axis position and width of bars based on the information in timesteps and joins it to data. add_remind_timesteps_columns() uses the remind_timesteps data frame. ggplot_bar_vts() produces a bar plot with bars positioned according to timesteps. ggplot_bar_remind_vts() uses the remind_timesteps data frame.

Author

Michaja Pehl

Examples

require(tidyverse)

# some example data
(data <- quitte_example_data %>%
    filter(first(scenario) == scenario,
           last(region) == region,
           first(variable) == variable))
#> # A tibble: 19 × 7
#>    model  scenario              region variable    unit   period  value
#>    <fct>  <fct>                 <fct>  <fct>       <fct>   <int>  <dbl>
#>  1 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2005 2.93e4
#>  2 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2010 3.17e4
#>  3 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2015 3.82e4
#>  4 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2020 4.48e4
#>  5 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2025 5.21e4
#>  6 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2030 6.02e4
#>  7 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2035 6.85e4
#>  8 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2040 7.72e4
#>  9 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2045 8.61e4
#> 10 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2050 9.57e4
#> 11 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2055 1.06e5
#> 12 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2060 1.15e5
#> 13 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2070 1.33e5
#> 14 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2080 1.62e5
#> 15 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2090 1.88e5
#> 16 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2100 2.14e5
#> 17 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2110 2.39e5
#> 18 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2130 2.03e5
#> 19 REMIND r7552c_1p5C_Def-rem-5 World  Consumption billi…   2150 2.57e5

# adding individual timesteps
add_timesteps_columns(data, remind_timesteps)
#> # A tibble: 19 × 9
#>    model  scenario      region variable unit  period  value  xpos width
#>    <fct>  <fct>         <fct>  <fct>    <fct>  <dbl>  <dbl> <dbl> <dbl>
#>  1 REMIND r7552c_1p5C_… World  Consump… bill…   2005 2.93e4 2005    5  
#>  2 REMIND r7552c_1p5C_… World  Consump… bill…   2010 3.17e4 2010    5  
#>  3 REMIND r7552c_1p5C_… World  Consump… bill…   2015 3.82e4 2015    5  
#>  4 REMIND r7552c_1p5C_… World  Consump… bill…   2020 4.48e4 2020    5  
#>  5 REMIND r7552c_1p5C_… World  Consump… bill…   2025 5.21e4 2025    5  
#>  6 REMIND r7552c_1p5C_… World  Consump… bill…   2030 6.02e4 2030    5  
#>  7 REMIND r7552c_1p5C_… World  Consump… bill…   2035 6.85e4 2035    5  
#>  8 REMIND r7552c_1p5C_… World  Consump… bill…   2040 7.72e4 2040    5  
#>  9 REMIND r7552c_1p5C_… World  Consump… bill…   2045 8.61e4 2045    5  
#> 10 REMIND r7552c_1p5C_… World  Consump… bill…   2050 9.57e4 2050    5  
#> 11 REMIND r7552c_1p5C_… World  Consump… bill…   2055 1.06e5 2055    5  
#> 12 REMIND r7552c_1p5C_… World  Consump… bill…   2060 1.15e5 2061.   7.5
#> 13 REMIND r7552c_1p5C_… World  Consump… bill…   2070 1.33e5 2070   10  
#> 14 REMIND r7552c_1p5C_… World  Consump… bill…   2080 1.62e5 2080   10  
#> 15 REMIND r7552c_1p5C_… World  Consump… bill…   2090 1.88e5 2090   10  
#> 16 REMIND r7552c_1p5C_… World  Consump… bill…   2100 2.14e5 2100   10  
#> 17 REMIND r7552c_1p5C_… World  Consump… bill…   2110 2.39e5 2112.  15  
#> 18 REMIND r7552c_1p5C_… World  Consump… bill…   2130 2.03e5 2130   20  
#> 19 REMIND r7552c_1p5C_… World  Consump… bill…   2150 2.57e5 2154.  27  

# adding remind timesteps with gaps
add_remind_timesteps_columns(data, gaps = 0.1)
#> # A tibble: 19 × 9
#>    model  scenario      region variable unit  period  value  xpos width
#>    <fct>  <fct>         <fct>  <fct>    <fct>  <dbl>  <dbl> <dbl> <dbl>
#>  1 REMIND r7552c_1p5C_… World  Consump… bill…   2005 2.93e4 2005    4.5
#>  2 REMIND r7552c_1p5C_… World  Consump… bill…   2010 3.17e4 2010    4.5
#>  3 REMIND r7552c_1p5C_… World  Consump… bill…   2015 3.82e4 2015    4.5
#>  4 REMIND r7552c_1p5C_… World  Consump… bill…   2020 4.48e4 2020    4.5
#>  5 REMIND r7552c_1p5C_… World  Consump… bill…   2025 5.21e4 2025    4.5
#>  6 REMIND r7552c_1p5C_… World  Consump… bill…   2030 6.02e4 2030    4.5
#>  7 REMIND r7552c_1p5C_… World  Consump… bill…   2035 6.85e4 2035    4.5
#>  8 REMIND r7552c_1p5C_… World  Consump… bill…   2040 7.72e4 2040    4.5
#>  9 REMIND r7552c_1p5C_… World  Consump… bill…   2045 8.61e4 2045    4.5
#> 10 REMIND r7552c_1p5C_… World  Consump… bill…   2050 9.57e4 2050    4.5
#> 11 REMIND r7552c_1p5C_… World  Consump… bill…   2055 1.06e5 2055    4.5
#> 12 REMIND r7552c_1p5C_… World  Consump… bill…   2060 1.15e5 2061.   7  
#> 13 REMIND r7552c_1p5C_… World  Consump… bill…   2070 1.33e5 2070    9.5
#> 14 REMIND r7552c_1p5C_… World  Consump… bill…   2080 1.62e5 2080    9.5
#> 15 REMIND r7552c_1p5C_… World  Consump… bill…   2090 1.88e5 2090    9.5
#> 16 REMIND r7552c_1p5C_… World  Consump… bill…   2100 2.14e5 2100    9.5
#> 17 REMIND r7552c_1p5C_… World  Consump… bill…   2110 2.39e5 2112.  14.5
#> 18 REMIND r7552c_1p5C_… World  Consump… bill…   2130 2.03e5 2130   19.5
#> 19 REMIND r7552c_1p5C_… World  Consump… bill…   2150 2.57e5 2154.  26.5

# plotting individual timesteps without gaps
ggplot_bar_vts(data, remind_timesteps, gaps = 0)


# plotting remind timegaps, using further ggplot2 functions
ggplot_bar_remind_vts(
    data = quitte_example_data %>%
        filter(scenario %in% levels(quitte_example_data$scenario)[1:3],
               last(region) == region,
               grepl('PE\\|', variable),
               2100 >= period)) +
    scale_fill_manual(
        values = mip::plotstyle(grep('^PE\\|',
                                     levels(quitte_example_data$variable),
                                     value = TRUE))) +
    facet_wrap(~ scenario)
#> 


# another data set with a different time column
data2 <- data.frame(variable = c('Wind', 'Solar', 'Wind', 'Solar'),
    tau = c(1,1,2,2),
    value = 1:4)

# some timesteps dataframe with hourly data
timesteps <- data.frame(tau = c(rep(1,2),rep(2,4)),
                            hour = 1:6,
                            weight = 1)

# plotting with different timesteps than periods and years
ggplot_bar_vts(data2, timesteps,
              mapping = aes(tau, value, group = variable, fill = variable),
              timesteps_period = 'tau',
              timesteps_interval = 'hour',
              interval_shift = c(-1,0))