Commit a7503921 authored by Turner's avatar Turner
Browse files

shift analysis and plots

parent 3b8d0018

R/plots.R

+108 −1
Original line number Diff line number Diff line
@@ -394,7 +394,6 @@ plot_main_driver_of_trend <- function(CF_trends, 
                                      flow_CF_models_scored,

                                      EHA){





  flow_CF_models_scored |>

    filter(flow_type == "c") |>

    filter(pearson > 0.2, abs(bias) < 0.1) |>
@@ -760,7 +759,115 @@ return(final_plot) 


  }



plot_shift_sparks <- function(trend_shift_analysis,

                              flow_CF_models,

                              EHA = EHA){



  browser()



  st_read(EHA, quiet = TRUE) |> as_tibble() |>

    mutate(COMPLXID = str_replace(EHA_PtID, "\\_..*", "")) |>

    select(COMPLXID, State, PtName) |> unique() -> dam_names



  flow_CF_models |>

    filter(flow_type == "c") |>

    mutate(residual = CF_pred - CF_pred) |>

    select(COMPLXID, year, residual) ->

    model_residuals_all



  trend_shift_analysis |>

    mutate(shift_more_likely = if_else(p_shift * 2.15 < p_trend, T, F)) |>

    arrange(shift_yr) |>

    filter(shift_more_likely == TRUE) |> #count(shift_yr) |> arrange(shift_yr)

    select(COMPLXID, shift_yr) |>

    left_join(dam_names, by = join_by(COMPLXID)) |> #count(State) |> arrange(-n)

    filter(!PtName %in% c("Dworshak Fish", "Oswego Falls West")) |>

    pmap(function(COMPLXID, shift_yr, State, PtName){



      model_residuals_all |>

        filter(COMPLXID == !!COMPLXID) ->

        model_residuals_dam



      flow_CF_models |>

        filter(flow_type == "c") |>

        filter(COMPLXID == !!COMPLXID) |>

        select(year, CF, CF_pred) ->

        model_results_dam



      filter(model_results_dam, year <= shift_yr)[["CF"]] |>

        mean(na.rm = T) -> pre_mean



      filter(model_results_dam, year > shift_yr)[["CF"]] |>

        mean(na.rm = T) -> post_mean



      model_results_dam |>

        mutate(pre_mean = if_else(year <= shift_yr, pre_mean, NA),

               post_mean = if_else(year > shift_yr, post_mean, NA)) |>

        ggplot(aes(year, CF)) +

        geom_line(aes(y = pre_mean), col = "black") +

        geom_line(aes(y = post_mean), col = "black") +

        geom_line() +

        geom_line(aes(y = CF_pred), col = "#007833") +

        theme_classic() +

        scale_y_continuous(breaks = c(0, 1)) +

        theme(axis.line.x = element_blank(),

              axis.ticks.x = element_blank(),

              axis.text.x = element_blank(),

              plot.title = element_text(size = 10, hjust = 0.5),

              plot.title.position = "panel",

          axis.text.y = element_text(size = 10),

          axis.title.x = element_blank(),

          axis.title.y = element_text(size = 8, angle = 0, vjust = 0.5)) +

        geom_vline(xintercept = shift_yr + 0.5, linetype = 2) +

        expand_limits(y = c(0, 1)) +

        labs(title =  paste0("\n", PtName, " | ", State),

             y = "CF") ->

        CF_plots



      model_residuals_dam |>

        ggplot(aes(year, residual)) +

        #geom_point(pch = 4) +

        geom_area(fill = "#007833", col = "black", alpha = 0.5) +

        #geom_line(aes(y = pre_mean), col = "red") +

        #geom_line(aes(y = post_mean), col = "red") +

        scale_x_continuous(breaks = c(1980, 2000)) +

        #geom_hline(yintercept = 0) +

        theme_classic() +

        geom_vline(xintercept = shift_yr + 0.5, linetype = 2) +

        theme(axis.line.y = element_blank(),

              axis.ticks.y = element_blank(),

              axis.text.y = element_blank(),

              axis.text.x = element_text(size = 10)) + labs(y = NULL, x = NULL) ->

        residuals



      CF_plots / plot_spacer() /residuals +

        plot_layout(heights = c(0.7, -0.2, 0.4))





    }) -> sparks_shift





  (sparks_shift[[1]] | sparks_shift[[5]] | sparks_shift[[9]] | sparks_shift[[13]]) /

    (sparks_shift[[2]] | sparks_shift[[6]] | sparks_shift[[10]] | sparks_shift[[14]]) /

    (sparks_shift[[3]] | sparks_shift[[7]] | sparks_shift[[11]] | sparks_shift[[15]]) /

    (sparks_shift[[4]] | sparks_shift[[8]] | sparks_shift[[12]] | sparks_shift[[16]]) ->

    final_plot



  ggsave(

    "./output/plots/shift_plots.pdf", final_plot,

    height = 9, width = 8

  )



  ggsave(

    "./output/plots/shift_plots.png", final_plot,

    height = 9, width = 8

  )



  return(final_plot)







}

R/trends.R

+219 −0
Original line number Diff line number Diff line
@@ -149,4 +149,223 @@ get_CF_trends <- function(annual_CFs){ 


}



get_modeled_trends <- function(flow_CF_models,

                               flow_CF_models_scored){



  flow_CF_models_scored |>

    filter(flow_type == "c") |>

    filter(pearson > 0.2, abs(bias) < 0.1) |>

    arrange(-pearson) |>

    pull(COMPLXID) -> viable_flow_models



  flow_CF_models |>

    filter(COMPLXID %in% viable_flow_models) |>

    select(COMPLXID, year, CF, flow_type, CF_pred) |>

    pivot_wider(names_from = "flow_type",

                values_from = "CF_pred") |>

    split(~COMPLXID) |>

    map_dfr(function(x){



      smwrStats::senSlope(CF ~ year, x) -> sen_CF

      smwrStats::senSlope(c ~ year, x) -> sen_flowC



      if(anyNA(x$n)){

        sen_flowN <- list(

          coefficients = c(NA_real_, NA_real_)

        )

      }else{

        sen_flowN <- smwrStats::senSlope(n ~ year, x)

      }





      bind_rows(

        tibble(

          int = sen_CF$coefficients[1],

          slope = sen_CF$coefficients[2],

          data = "CF"

        ),

        tibble(

          int = sen_flowC$coefficients[1],

          slope = sen_flowC$coefficients[2],

          data = "Assimilated flow"

        ),

        tibble(

          int = sen_flowN$coefficients[1],

          slope = sen_flowN$coefficients[2],

          data = "Naturalized flow"

        )

      ) |>

        mutate(COMPLXID = first(x$COMPLXID))





    }) -> all_slopes





  all_slopes |> select(-int) |>

    pivot_wider(names_from = "data", values_from = slope) |>

    mutate(explained_by_water = `Assimilated flow` / CF,

           explained_by_climate = `Naturalized flow` / CF) |>

    #select(COMPLXID, CF_trend = CF, explained_by_water, explained_by_climate) |>

    mutate(water_not_climate = if_else(

      explained_by_water > 0.5 & explained_by_climate < 0.3,

      TRUE, FALSE)) |>

    mutate(

      flow_attrib = case_when(

        explained_by_water >= 0.8 ~ ">80%  ",

        explained_by_water < 0.8 & explained_by_water >= 0.6 ~ "60-80%  ",

        explained_by_water < 0.6 & explained_by_water >= 0.4 ~ "40-60%  ",

        explained_by_water < 0.4 & explained_by_water >= 0.2 ~ "20-40%  ",

        explained_by_water < 0.2 ~ "<20%  "

      )) |>

    mutate(flow_attrib = factor(flow_attrib, levels = c(

      "<20%  ", "20-40%  ", "40-60%  ",

      "60-80%  ", ">80%  "

    ))) ->

    flow_cats



  return(

    flow_cats

  )







}





trend_vs_shift <- function(flow_CF_models, flow_cats){



  #tar_load(flow_cats)

  #tar_load(flow_CF_models)

  tar_load(CF_trends)



  CF_trends |>

    filter(analysis == "flow period plus") |>

    filter(sens_slope < 0,

           p_value < 0.05) |>

    pull(COMPLXID) -> dams_with_signif_neg_trend



  flow_cats |>

    filter(COMPLXID %in% dams_with_signif_neg_trend,

           explained_by_water <= 0.2) |>

    pull(COMPLXID) -> dams_for_shift_analysis



  flow_CF_models |>

    filter(flow_type == "c") |>

    mutate(residual = CF - CF_pred) |>

    select(COMPLXID, year, residual) ->

    model_residuals_all



  model_residuals_all |>

    filter(COMPLXID %in% dams_for_shift_analysis) |>

    #filter(COMPLXID == "hc0250") -> dam

    split(~COMPLXID) |>

    future_map_dfr(function(dam){



      dam |>

        ggplot(aes(year, residual)) +

        geom_line() +

        geom_hline(yintercept = 0)



      1985:2016 |>

        map_dfr(function(yr){



          dam[dam$year <= yr,]$residual > 0 -> pre_shift

          dam[dam$year > yr,]$residual < 0 -> post_shift



          tibble(

            pre = sum(pre_shift, na.rm = T)/length(pre_shift[!is.na(pre_shift)]),

            post = sum(post_shift, na.rm = T)/length(post_shift[!is.na(post_shift)]),

            year = yr

          )

        }) |> mutate(x = pre + post) |>

        arrange(-x) ->

        shift_yrs_ranked



      shift_yr = shift_yrs_ranked[["year"]][1]



      # compute Sen's slope

      senSlope(residual ~ year, data = dam,

               intercept = "A1m") -> sen

      sen$coefficients[[2]] -> computed_slope

      sen$coefficients[[1]] -> computed_intercept



      # compute difference of means

      dam |>

        mutate(period = if_else(year <= shift_yr,

                                "pre", "post")) |>

        summarise(mean_res = mean(residual, na.rm = T),

                  .by = "period") ->

        means



      diff(means$mean_res) -> difference_of_means





      # set up blocked bootstrap (block size = 3)

      tibble(

        index = 1:38

      ) |>

        mutate(`1` = dam[["residual"]][index],

               `2` = dam[["residual"]][index + 1],

               `3` = dam[["residual"]][index + 2]) ->

        blocks



      sample_size <- 10000



      1:sample_size |>

        map_dfr(function(x){



          1:(nrow(blocks)) |>

            sample(replace = T,

                   size = ceiling(40 / 3)) ->

            block_indices



          blocks[block_indices,] |>

            pivot_longer(!index) |>

            select(value) -> synth_residuals



          synth_residuals[1:40,] |>

            mutate(year = 1980:2019) ->

            blocked_sample



          senSlope(value ~ year, data = blocked_sample,

                   intercept = "A1m") ->

            block_sen



          # compute difference of means

          blocked_sample |>

            mutate(period = if_else(year <= shift_yr,

                                    "pre", "post")) |>

            summarise(mean_res = mean(value, na.rm = T),

                      .by = "period") ->

            block_means



          diff(block_means$mean_res) -> block_difference_of_means





          return(

            tibble(

              sample = x,

              block_mean_diff = block_difference_of_means,

              block_sens_slope = block_sen[["coefficients"]][[2]]

            )

          )



      }) -> bootstrap_results







      tibble(

        COMPLXID = dam$COMPLXID[1],

        p_shift = sum(bootstrap_results$block_mean_diff < difference_of_means) / sample_size,

        p_trend = sum(bootstrap_results$block_sens_slope < computed_slope) / sample_size,

        shift_yr = shift_yr

      )





    }) -> trend_shift_analysis





  return(trend_shift_analysis)



  }

_targets.R

+19 −0
Original line number Diff line number Diff line
@@ -122,6 +122,18 @@ list( 
    evaluate_models(model_results = flow_CF_models),

    format = "parquet"

  ),

  tar_target(

    flow_cats,

    get_modeled_trends(flow_CF_models = flow_CF_models,

                       flow_CF_models_scored = flow_CF_models_scored),

    format = "parquet"

  ),

  tar_target(

    trend_shift_analysis,

    trend_vs_shift(flow_CF_models = flow_CF_models,

                   flow_cats = flow_cats),

    format = "parquet"

  ),

  tar_target(

    CF_trend_map,

    plot_CF_trend_map(CFs = dam_annual_gen_cap_CF_1970_2021,
@@ -138,5 +150,12 @@ list( 
      EHA = EHA

      ),

    format = "rds"

  ),

  tar_target(

    sparks,

    plot_shift_sparks(trend_shift_analysis = trend_shift_analysis,

                      flow_CF_models = flow_CF_models,

                      EHA = EHA),

    format = "rds"

  )

)

_targets/meta/meta

+49 −4
Original line number Diff line number Diff line 
name|type|data|command|depend|seed|path|time|size|bytes|format|repository|iteration|parent|children|seconds|warnings|error

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CF_trend_map|stem|5f618bcd63a533d7|2493b3236a52efa3|bbfab65bf9a437e4|1449749074||t19569.6882917867s|ce7b3571c1e806bd|15261829|rds|local|vector|||5.02|1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf. 1m22mRemoved 1 row containing missing values geom_sf. 1m22mRemoved 13 rows containing missing values geom_sf. 1m22mRemoved 2 rows containing missing values geom_sf. 1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf. 1m22mRemoved 1 row containing missing values geom_sf. 1m22mRemoved 13 rows containing missing values geom_sf. 1m22mRemoved 2 rows containing missing values geom_sf|

.Random.seed|object|10bd526aa739ae83|||||||||||||||

block_difference_of_means|object|ba70adbf3c57d4d0|||||||||||||||

block_indices|object|91975510356067ba|||||||||||||||

block_means|object|81c8e96422609950|||||||||||||||

block_sen|object|d30bc66328344031|||||||||||||||

blocked_sample|object|262f88e03af953dc|||||||||||||||

blocks|object|7ce12a568f33f164|||||||||||||||

bootstrap_results|object|cb648a33de7adbe2|||||||||||||||

CF_driver_map|stem|540399b3d9ebad4d|fad15d29eb37ced1|c66b43fe54cf3e1b|780051749||t19573.8177419598s|d4d5968cf31d3855|14442800|rds|local|vector|||9.56|1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf. 1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf|

CF_trend_map|stem|5f618bcd63a533d7|2493b3236a52efa3|bbfab65bf9a437e4|1449749074||t19573.6517468623s|ce7b3571c1e806bd|15261829|rds|local|vector|||6.39|1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf. 1m22mRemoved 1 row containing missing values geom_sf. 1m22mRemoved 13 rows containing missing values geom_sf. 1m22mRemoved 2 rows containing missing values geom_sf. 1m22mRemoved 54407 rows containing missing values geom_raster.. 1m22mRemoved 4 rows containing missing values geom_sf. 1m22mRemoved 1 row containing missing values geom_sf. 1m22mRemoved 13 rows containing missing values geom_sf. 1m22mRemoved 2 rows containing missing values geom_sf|

CF_trends|stem|7ebebd1fd9e8bc90|48a9aa0dfb6d812e|fa1022101365b4b4|1102116871||t19569.1338983586s|7be06173099dde2d|71676|parquet|local|vector|||27715.96|UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures tha|

COMID_corrections|stem|fe576eb2f1f316a7|a5c6dd01c8e10d3f|ef46db3751d8e999|1378202948|./data/COMID_corrections.csv|t19557.68855602s|dcdbb2976eb6c19f|2790|file|local|vector|||1.03||

COMPLXID|object|2a7ba4ec88d4d9e5|||||||||||||||

computed_intercept|object|ccce95a0c44193fd|||||||||||||||

computed_slope|object|1306e090ce5c0569|||||||||||||||

convert_EIA_to_COMPLX|function|9df2b8bcf6782f60|||||||||||||||

correct_COMID_matching|function|f9d752099e09e4c2|||||||||||||||

dam_annual_gen_cap_CF_1970_2021|stem|27e68e5a9e4b3ac1|1e1968883cec7872|3c79369af4a2c155|-937239282||t19557.5727854494s|bb12815b67d3fb5b|726227|parquet|local|vector|||0.38||

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dams_with_signif_neg_trend|object|22106bf37dd0fa37|||||||||||||||

DayFlow|stem|134be264bbd024f6|b70c126b26c07662|ef46db3751d8e999|958592900|./data/DayFlow/|t19557.7065932559s|f4066a65ff755f48|0|file|local|vector|||0||

difference_in_means|object|c70ce11703db34a4|||||||||||||||

difference_of_means|object|e811d827b42efc0a|||||||||||||||

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EIA_860|stem|db95ac5d1da1bd03|fd2da67a7010fb85|ef46db3751d8e999|-1646985544|./data/EIA/Plant/|t19552.6038846689s|c8fc69c6c41e109f|12288|file|local|vector|||2.19||

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evaluate_models|function|eaab896ddee0b533|||||||||||||||

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get_CF_trends|function|25bce1c1396e1a50|||||||||||||||

get_EIA_annual_gen|function|1f87eeb76f308b73|||||||||||||||

get_modeled_trends|function|a5a5b17929dfb78b|||||||||||||||

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means|object|a0cdf307db2991e4|||||||||||||||

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plant_to_flow_H_mapping|stem|5a9138f5f91453c7|7e1e72eda130934a|e9aaecd87ceaab15|-1974510388||t19569.660212322s|bfbe60212c7499ab|20997|parquet|local|vector|||0.14||

plant_to_flow_H_mapping_uncorrected|stem|e51a5c4774dd6067|95618b304ba141cb|72c8ca9d8ddf2434|-1018155617||t19569.1339064333s|25c8b81bae3036c7|20513|parquet|local|vector|||0.65||

plot_CF_trend_map|function|0a4f9fa8f8157b08|||||||||||||||

plot_main_driver_of_trend|function|9bcbf099b1504bf8|||||||||||||||

plot_shift_sparks|function|4b0592b120bed608|||||||||||||||

post_mean|object|fdaef38f0a85a2a9|||||||||||||||

post_shift|object|8668732562449cca|||||||||||||||

pre_mean|object|2ec2830481192df5|||||||||||||||

pre_shift|object|d15a31c8ac8922e1|||||||||||||||

prep_model_data|function|f8811de1e501a14f|||||||||||||||

sample_size|object|43cee3b5090dadd7|||||||||||||||

sen|object|af095699bd811c36|||||||||||||||

shift_years|object|441bb115678cb48e|||||||||||||||

shift_yr|object|f52ca559867c407e|||||||||||||||

shift_yrs_ranked|object|b1fb9c1e510f5808|||||||||||||||

simulate_annual_CF|function|619f40378adf543a|||||||||||||||

sparks|object|0308ebdb77b2837b|||||||||||||||

sparks_12_shift|object|fb250aee2445fc86|||||||||||||||

sparks_12_trend|object|9356ebe86b6fc7fc|||||||||||||||

sparks_shift|object|d57e169493470ffb|||||||||||||||

sparks_trend|object|fa96750c42319ef3|||||||||||||||

synth_CFs|object|0236b3c9003d0c1a|||||||||||||||

synth_residuals|object|e936c09c9b199388|||||||||||||||

trend_dam|object|ec04fa798394cfde|||||||||||||||

trend_shift_analysis|stem|41acbdde1fa64d13|b1342e6d17ac2314|3cb0d3dfbf8ed694|1344368724||t19573.8325407345s|a87afb643b843259|2810|parquet|local|vector|||1278.39|UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures that proper, parallelsafe random numbers are produced via the LEcuyerCMRG method. To disable this check, use seedNULL, or set option future.rng.onMisuse to ignore.. UNRELIABLE VALUE Future none unexpectedly generated random numbers without specifying argument seed. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify seedTRUE. This ensures tha|

trend_vs_shift|function|65597cb2e6feb15d|||||||||||||||

x|object|3e8a3887fa6864fc|||||||||||||||

yr|object|4f52fe23f66097d1|||||||||||||||