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1 Required data

Required are:

  • cleaned and prepared GLODAPv2.2020 file
GLODAP <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_MLR_fitting_ready.csv",
                 sep = ""))

2 Predictor correlation

The correlation between:

  • pairs of seven potential predictor variables and
  • C* and seven potential predictor variables

were investigated based on:

  • property-property plots and
  • calculated correlation coeffcients.

2.1 Correlation plots

For an overview, a random subset of data from all eras was plotted separately for both basins, with color indicating neutral density slabs (high density = dark-purple color).

for (i_basin in unique(GLODAP$basin)) {
# i_basin <- unique(GLODAP$basin)[1]

print(
GLODAP %>% 
  filter(basin == i_basin) %>% 
  sample_frac(0.05) %>% 
  ggpairs(columns = c(params_local$MLR_target,
                      params_local$MLR_predictors),
          upper = "blank",
          ggplot2::aes(col = gamma_slab, fill = gamma_slab, alpha = 0.01)) +
      scale_fill_viridis_d(direction = -1) +
      scale_color_viridis_d(direction = -1) +
      labs(title = paste("Basin:", i_basin ,"| era: all | subsample size: 5 % of",
                         nrow(GLODAP %>% filter(basin == i_basin))))
)
}

Version Author Date
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
585b07f jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
6c0bec6 jens-daniel-mueller 2021-03-05
af70b94 jens-daniel-mueller 2021-03-04
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
9d0b2d0 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
7bcb4eb jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
7a9a4cb jens-daniel-mueller 2020-12-15
61b263c jens-daniel-mueller 2020-12-15
4d612dd jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
5d96d3c jens-daniel-mueller 2020-12-11

Version Author Date
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
585b07f jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
6c0bec6 jens-daniel-mueller 2021-03-05
af70b94 jens-daniel-mueller 2021-03-04
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
9d0b2d0 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
7bcb4eb jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
7a9a4cb jens-daniel-mueller 2020-12-15
61b263c jens-daniel-mueller 2020-12-15
4d612dd jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
5d96d3c jens-daniel-mueller 2020-12-11

Individual correlation plots for each basin, era and neutral density (gamma) slab are available at:

/nfs/kryo/work/jenmueller/emlr_cant/observations/v_XXX/figures/Observations_correlation/

if (params_local$plot_all_figures == "y") {

for (i_basin in unique(GLODAP$basin)) {
  for (i_era in unique(GLODAP$era)) {
    # i_basin <- unique(GLODAP$basin)[1]
    # i_era   <- unique(GLODAP$era)[1]

    GLODAP_basin_era <- GLODAP %>%
      filter(basin == i_basin,
             era == i_era)
    
    for (i_gamma_slab in unique(GLODAP_basin_era$gamma_slab)) {
      # i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[5]

      GLODAP_highlight <- GLODAP_basin_era %>%
        mutate(gamma_highlight = if_else(gamma_slab == i_gamma_slab,
                                         "in", "out")) %>%
        arrange(desc(gamma_highlight))
      
      p <- GLODAP_highlight %>%
        ggpairs(
          columns = c(params_local$MLR_target,
                      params_local$MLR_predictors),
          ggplot2::aes(
            col = gamma_highlight,
            fill = gamma_highlight,
            alpha = 0.01
          )
        ) +
        scale_fill_manual(values = c("red", "grey")) +
        scale_color_manual(values = c("red", "grey")) +
        labs(
          title = paste(
            i_era,
            "|",
            i_basin,
            "| Gamma slab",
            i_gamma_slab,
            "| # obs total",
            nrow(GLODAP_basin_era),
            "| # obs slab",
            nrow(GLODAP_highlight %>%
                   filter(gamma_highlight == "in"))
          )
        )
      
      png(
        filename = paste(
          path_version_figures,
          "Observations_correlation/",
          paste(
            "Predictor_correlation",
            i_era,
            i_basin,
            i_gamma_slab,
            ".png",
            sep = "_"
          ),
          sep = ""),
        width = 12,
        height = 12,
        units = "in",
        res = 300
      )
      
      print(p)
      
      dev.off()
      
    }
  }
}

}

2.2 Correlation assesment

2.2.1 Calculation of correlation coeffcients

Correlation coefficients were calculated individually within each slabs, era and basin.

for (i_basin in unique(GLODAP$basin)) {
  for (i_era in unique(GLODAP$era)) {
    # i_basin <- unique(GLODAP$basin)[1]
    # i_era   <- unique(GLODAP$era)[1]
    
    GLODAP_basin_era <- GLODAP %>%
      filter(basin == i_basin,
             era == i_era) %>%
      select(basin,
             era,
             gamma_slab,
             params_local$MLR_target,
             params_local$MLR_predictors)
    
    for (i_gamma_slab in unique(GLODAP_basin_era$gamma_slab)) {
      # i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[5]
      print(i_gamma_slab)
      
      GLODAP_basin_era_slab <- GLODAP_basin_era %>%
        filter(gamma_slab == i_gamma_slab)
      
      # calculate correlation table
      cor_target_predictor_temp <- GLODAP_basin_era_slab %>%
        select(-c(basin, era, gamma_slab)) %>%
        correlate() %>%
        focus(params_local$MLR_target) %>%
        mutate(basin = i_basin,
               era = i_era,
               gamma_slab = i_gamma_slab)
      
      if (exists("cor_target_predictor")) {
        cor_target_predictor <-
          bind_rows(cor_target_predictor, cor_target_predictor_temp)
      }
      
      if (!exists("cor_target_predictor")) {
        cor_target_predictor <- cor_target_predictor_temp
      }
      
      
      cor_predictors_temp <- GLODAP_basin_era_slab %>%
        select(-c(basin, era, gamma_slab)) %>%
        correlate() %>%
        shave %>%
        stretch() %>%
        filter(!is.na(r),
               x != params_local$MLR_target,
               y != params_local$MLR_target) %>%
        mutate(pair = paste(x, y, sep = " + ")) %>%
        select(-c(x, y)) %>%
        mutate(basin = i_basin,
               era = i_era,
               gamma_slab = i_gamma_slab)
      
      if (exists("cor_predictors")) {
        cor_predictors <- bind_rows(cor_predictors, cor_predictors_temp)
      }
      
      if (!exists("cor_predictors")) {
        cor_predictors <- cor_predictors_temp
      }
      
      
      
    }
  }
}

rm(cor_predictors_temp, cor_target_predictor_temp,
   i_gamma_slab, i_era, i_basin,
   GLODAP_basin_era, GLODAP_basin_era_slab)

2.2.2 Predictor pairs

Below, the range of correlations coefficients for each predictor pair is plotted per basin (facet) and density slab (color). Note that the range indicates the min and max values of in total 3 calculated coefficients (one per era).

# calculate min, max, mean across all eras
cor_predictors_stats <- cor_predictors %>% 
  group_by(pair, basin, gamma_slab) %>% 
  summarise(mean_r = mean(r),
            min_r = min(r),
            max_r = max(r)) %>% 
  ungroup()

# plot figure
cor_predictors_stats %>% 
  mutate(pair = reorder(pair, mean_r)) %>%
  ggplot() +
  geom_vline(xintercept = c(-0.9, 0.9), col = "red") +
  geom_vline(xintercept = 0) +
  geom_linerange(
    aes(y = pair, xmin = min_r, xmax = max_r, col = gamma_slab),
    position = position_dodge(width = 0.6)) +
  facet_wrap(~basin) +
  scale_color_viridis_d(direction = -1) +
  labs(x = "correlation coefficient", y = "") +
  theme(legend.position = "top")

Version Author Date
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
585b07f jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
6c0bec6 jens-daniel-mueller 2021-03-05
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
9d0b2d0 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
5d96d3c jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
0ff728b jens-daniel-mueller 2020-12-01
91435ae jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30
bc61ce3 Jens Müller 2020-11-30 
# print table
kable(cor_predictors_stats) %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
pair basin gamma_slab mean_r min_r max_r
aou + nitrate Atlantic (-Inf,26] 0.9023751 0.8823703 0.9188740
aou + nitrate Atlantic (26,28.2] 0.8497682 0.8460103 0.8559783
aou + nitrate Atlantic (28.2, Inf] 0.6543331 0.2987857 0.8382487
aou + nitrate Indo-Pacific (-Inf,26] 0.9279977 0.8900168 0.9727810
aou + nitrate Indo-Pacific (26,28.1] 0.8698723 0.8270094 0.8918242
aou + nitrate Indo-Pacific (28.1, Inf] 0.7726827 0.7020998 0.8661750
aou + phosphate Atlantic (-Inf,26] 0.5422936 0.2598012 0.7434863
aou + phosphate Atlantic (26,28.2] 0.8226072 0.8185323 0.8257789
aou + phosphate Atlantic (28.2, Inf] 0.6402840 0.2585115 0.8400544
aou + phosphate Indo-Pacific (-Inf,26] 0.9421463 0.9043755 0.9734355
aou + phosphate Indo-Pacific (26,28.1] 0.9131789 0.8887854 0.9291946
aou + phosphate Indo-Pacific (28.1, Inf] 0.7634814 0.6804230 0.8401635
aou + phosphate_star Atlantic (-Inf,26] -0.1733598 -0.4766688 0.3362197
aou + phosphate_star Atlantic (26,28.2] 0.4116665 0.3496995 0.4604067
aou + phosphate_star Atlantic (28.2, Inf] 0.1881227 -0.4929523 0.5332518
aou + phosphate_star Indo-Pacific (-Inf,26] 0.6907002 0.5252778 0.8117232
aou + phosphate_star Indo-Pacific (26,28.1] 0.3973642 0.3099802 0.4946232
aou + phosphate_star Indo-Pacific (28.1, Inf] -0.6877056 -0.7235766 -0.6447285
aou + silicate Atlantic (-Inf,26] 0.2575781 -0.0090087 0.5754309
aou + silicate Atlantic (26,28.2] 0.5046701 0.4213469 0.6080963
aou + silicate Atlantic (28.2, Inf] 0.7231502 0.4655243 0.8667673
aou + silicate Indo-Pacific (-Inf,26] 0.7612281 0.6806252 0.8539364
aou + silicate Indo-Pacific (26,28.1] 0.6506044 0.6013762 0.7472330
aou + silicate Indo-Pacific (28.1, Inf] 0.6632363 0.6264779 0.7284222
nitrate + phosphate Atlantic (-Inf,26] 0.6939950 0.5117436 0.8173702
nitrate + phosphate Atlantic (26,28.2] 0.9924594 0.9914240 0.9939900
nitrate + phosphate Atlantic (28.2, Inf] 0.9825189 0.9655311 0.9931844
nitrate + phosphate Indo-Pacific (-Inf,26] 0.9692465 0.9543975 0.9774665
nitrate + phosphate Indo-Pacific (26,28.1] 0.9848393 0.9773303 0.9903013
nitrate + phosphate Indo-Pacific (28.1, Inf] 0.9302885 0.8663697 0.9714872
nitrate + phosphate_star Atlantic (-Inf,26] 0.0541938 -0.1506424 0.3908953
nitrate + phosphate_star Atlantic (26,28.2] 0.8108042 0.7643849 0.8441759
nitrate + phosphate_star Atlantic (28.2, Inf] 0.8161710 0.6579978 0.9018502
nitrate + phosphate_star Indo-Pacific (-Inf,26] 0.8680068 0.8048000 0.9153999
nitrate + phosphate_star Indo-Pacific (26,28.1] 0.7772318 0.7452512 0.8216409
nitrate + phosphate_star Indo-Pacific (28.1, Inf] -0.1396453 -0.2058096 -0.0882964
nitrate + silicate Atlantic (-Inf,26] 0.4833689 0.3594654 0.6667275
nitrate + silicate Atlantic (26,28.2] 0.6723928 0.6054068 0.7547735
nitrate + silicate Atlantic (28.2, Inf] 0.9109951 0.7869083 0.9761971
nitrate + silicate Indo-Pacific (-Inf,26] 0.8861818 0.8635500 0.8992106
nitrate + silicate Indo-Pacific (26,28.1] 0.7614373 0.7440481 0.7932007
nitrate + silicate Indo-Pacific (28.1, Inf] 0.7854278 0.7442723 0.8147302
phosphate + phosphate_star Atlantic (-Inf,26] 0.6851429 0.4715637 0.8695592
phosphate + phosphate_star Atlantic (26,28.2] 0.8434871 0.8039118 0.8708875
phosphate + phosphate_star Atlantic (28.2, Inf] 0.8432769 0.7107125 0.9143822
phosphate + phosphate_star Indo-Pacific (-Inf,26] 0.8765585 0.8191963 0.9092677
phosphate + phosphate_star Indo-Pacific (26,28.1] 0.7265507 0.6908297 0.7867655
phosphate + phosphate_star Indo-Pacific (28.1, Inf] -0.0673758 -0.1306307 0.0077083
sal + aou Atlantic (-Inf,26] 0.1369277 -0.3310034 0.4592283
sal + aou Atlantic (26,28.2] -0.3518209 -0.3708266 -0.3376168
sal + aou Atlantic (28.2, Inf] -0.2722572 -0.5887224 0.3108904
sal + aou Indo-Pacific (-Inf,26] -0.1427131 -0.2119195 -0.0525508
sal + aou Indo-Pacific (26,28.1] -0.0104237 -0.1487352 0.0791408
sal + aou Indo-Pacific (28.1, Inf] 0.4381001 0.2748165 0.5285879
sal + nitrate Atlantic (-Inf,26] -0.0473435 -0.3308324 0.1247558
sal + nitrate Atlantic (26,28.2] -0.7366711 -0.7578511 -0.7256783
sal + nitrate Atlantic (28.2, Inf] -0.8520824 -0.9129323 -0.7478006
sal + nitrate Indo-Pacific (-Inf,26] -0.3628061 -0.3843728 -0.3316537
sal + nitrate Indo-Pacific (26,28.1] -0.1531361 -0.3037177 0.1348102
sal + nitrate Indo-Pacific (28.1, Inf] -0.0464716 -0.1597012 0.0640355
sal + phosphate Atlantic (-Inf,26] -0.5657649 -0.7613570 -0.4334744
sal + phosphate Atlantic (26,28.2] -0.7696367 -0.7902254 -0.7540245
sal + phosphate Atlantic (28.2, Inf] -0.8561618 -0.9146446 -0.7512426
sal + phosphate Indo-Pacific (-Inf,26] -0.2331966 -0.2688773 -0.2002269
sal + phosphate Indo-Pacific (26,28.1] -0.1483684 -0.2954408 0.0791816
sal + phosphate Indo-Pacific (28.1, Inf] -0.0608389 -0.2007789 0.0297573
sal + phosphate_star Atlantic (-Inf,26] -0.8881511 -0.9084437 -0.8595142
sal + phosphate_star Atlantic (26,28.2] -0.9093510 -0.9139892 -0.9037945
sal + phosphate_star Atlantic (28.2, Inf] -0.9559723 -0.9752144 -0.9223373
sal + phosphate_star Indo-Pacific (-Inf,26] -0.4825926 -0.5100133 -0.4363244
sal + phosphate_star Indo-Pacific (26,28.1] -0.2359722 -0.5698878 0.2191250
sal + phosphate_star Indo-Pacific (28.1, Inf] -0.7756090 -0.8264981 -0.7071927
sal + silicate Atlantic (-Inf,26] -0.7094091 -0.7405506 -0.6732885
sal + silicate Atlantic (26,28.2] -0.4615601 -0.4773367 -0.4394293
sal + silicate Atlantic (28.2, Inf] -0.7202054 -0.8846868 -0.4463975
sal + silicate Indo-Pacific (-Inf,26] -0.5099271 -0.5881732 -0.3952421
sal + silicate Indo-Pacific (26,28.1] 0.1317132 -0.0832250 0.4172679
sal + silicate Indo-Pacific (28.1, Inf] 0.0636811 -0.1011285 0.1893801
sal + temp Atlantic (-Inf,26] 0.9282488 0.8591490 0.9718026
sal + temp Atlantic (26,28.2] 0.7919537 0.7661651 0.8236916
sal + temp Atlantic (28.2, Inf] 0.4643470 0.2151571 0.9466866
sal + temp Indo-Pacific (-Inf,26] 0.7511059 0.7322291 0.7860495
sal + temp Indo-Pacific (26,28.1] 0.1394241 -0.3257970 0.5179756
sal + temp Indo-Pacific (28.1, Inf] 0.6640090 0.5516618 0.7574075
silicate + phosphate Atlantic (-Inf,26] 0.7463328 0.7218927 0.7719039
silicate + phosphate Atlantic (26,28.2] 0.6997652 0.6370500 0.7751127
silicate + phosphate Atlantic (28.2, Inf] 0.9058355 0.7689431 0.9797417
silicate + phosphate Indo-Pacific (-Inf,26] 0.8054182 0.7494571 0.8389260
silicate + phosphate Indo-Pacific (26,28.1] 0.7347214 0.7007987 0.7829188
silicate + phosphate Indo-Pacific (28.1, Inf] 0.7655142 0.6999056 0.8049697
silicate + phosphate_star Atlantic (-Inf,26] 0.6900571 0.6533103 0.7219868
silicate + phosphate_star Atlantic (26,28.2] 0.7129083 0.6823614 0.7403741
silicate + phosphate_star Atlantic (28.2, Inf] 0.6936300 0.3652654 0.8594449
silicate + phosphate_star Indo-Pacific (-Inf,26] 0.7739023 0.7126891 0.8563502
silicate + phosphate_star Indo-Pacific (26,28.1] 0.6896547 0.6708858 0.7159326
silicate + phosphate_star Indo-Pacific (28.1, Inf] -0.1321441 -0.2062023 -0.0096971
temp + aou Atlantic (-Inf,26] 0.1097169 -0.3425090 0.4329165
temp + aou Atlantic (26,28.2] -0.1541990 -0.2251707 -0.0603008
temp + aou Atlantic (28.2, Inf] 0.5099566 0.4938223 0.5246358
temp + aou Indo-Pacific (-Inf,26] -0.3033151 -0.4239825 -0.1918473
temp + aou Indo-Pacific (26,28.1] -0.3393591 -0.4878334 -0.2078194
temp + aou Indo-Pacific (28.1, Inf] 0.8742121 0.8635041 0.8872273
temp + nitrate Atlantic (-Inf,26] -0.0354229 -0.2709976 0.1353095
temp + nitrate Atlantic (26,28.2] -0.5572719 -0.6178836 -0.4772867
temp + nitrate Atlantic (28.2, Inf] -0.1232512 -0.6051405 0.1284445
temp + nitrate Indo-Pacific (-Inf,26] -0.5317602 -0.6794288 -0.3861544
temp + nitrate Indo-Pacific (26,28.1] -0.7066781 -0.7885778 -0.6645480
temp + nitrate Indo-Pacific (28.1, Inf] 0.4439504 0.3483953 0.5869496
temp + phosphate Atlantic (-Inf,26] -0.5062464 -0.6413374 -0.4215209
temp + phosphate Atlantic (26,28.2] -0.5831156 -0.6362513 -0.5078295
temp + phosphate Atlantic (28.2, Inf] -0.1393499 -0.6278047 0.1197551
temp + phosphate Indo-Pacific (-Inf,26] -0.4439968 -0.5911527 -0.3145635
temp + phosphate Indo-Pacific (26,28.1] -0.6359600 -0.7418304 -0.5739118
temp + phosphate Indo-Pacific (28.1, Inf] 0.4307803 0.3053523 0.5540156
temp + phosphate_star Atlantic (-Inf,26] -0.8198183 -0.8713230 -0.7758658
temp + phosphate_star Atlantic (26,28.2] -0.8914803 -0.8987917 -0.8823009
temp + phosphate_star Atlantic (28.2, Inf] -0.4893200 -0.9602729 -0.2471928
temp + phosphate_star Indo-Pacific (-Inf,26] -0.7272788 -0.8389880 -0.5535823
temp + phosphate_star Indo-Pacific (26,28.1] -0.9510068 -0.9573038 -0.9474141
temp + phosphate_star Indo-Pacific (28.1, Inf] -0.8918001 -0.9174238 -0.8626825
temp + silicate Atlantic (-Inf,26] -0.6378258 -0.7393488 -0.5464948
temp + silicate Atlantic (26,28.2] -0.6058317 -0.6321507 -0.5843090
temp + silicate Atlantic (28.2, Inf] 0.0061799 -0.2917156 0.1611065
temp + silicate Indo-Pacific (-Inf,26] -0.5738389 -0.7373553 -0.3945553
temp + silicate Indo-Pacific (26,28.1] -0.7875580 -0.8205969 -0.7572852
temp + silicate Indo-Pacific (28.1, Inf] 0.3797466 0.3232790 0.4363778 
rm(cor_predictors, cor_predictors_stats)

2.2.3 C* vs individual predictors

Below, the range of correlations coefficients for C* with each predictor is plotted per basin (facet) and density slab (color). Note that the range indicates the min and max values of in total 3 calculated coefficients (one per era).

cor_target_predictor <- cor_target_predictor %>% 
  rename(predictor = term)

# calculate min, max, mean across all eras
cor_target_predictor_stats <- cor_target_predictor %>%
  select(-era) %>% 
  group_by(predictor, basin, gamma_slab) %>% 
  summarise_all(list(mean_r = mean, min_r = min, max_r = max)) %>% 
  ungroup()

# plot figure
cor_target_predictor_stats %>% 
  mutate(predictor = reorder(predictor, mean_r)) %>%
  ggplot() +
  geom_vline(xintercept = c(-0.9, 0.9), col = "red") +
  geom_vline(xintercept = 0) +
  geom_linerange(
    aes(y = predictor, xmin = min_r, xmax = max_r, col = gamma_slab),
    position = position_dodge(width = 0.6)) +
  facet_wrap(~basin) +
  scale_color_viridis_d(direction = -1) +
  labs(x = "correlation coefficient", y = "C* correlation with...") +
  theme(legend.position = "top")

Version Author Date
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
585b07f jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
6c0bec6 jens-daniel-mueller 2021-03-05
3c2ec33 jens-daniel-mueller 2021-03-05
af70b94 jens-daniel-mueller 2021-03-04
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
9d0b2d0 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
7bcb4eb jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
7a9a4cb jens-daniel-mueller 2020-12-15
61b263c jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
5d96d3c jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
090e4d5 jens-daniel-mueller 2020-12-02
0ff728b jens-daniel-mueller 2020-12-01
91435ae jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30
bc61ce3 Jens Müller 2020-11-30 
# print table
kable(cor_target_predictor_stats) %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
predictor basin gamma_slab mean_r min_r max_r
aou Atlantic (-Inf,26] 0.5954940 0.5403772 0.6259285
aou Atlantic (26,28.2] -0.6860104 -0.7091660 -0.6602374
aou Atlantic (28.2, Inf] -0.3578273 -0.6996866 0.3171406
aou Indo-Pacific (-Inf,26] -0.2093870 -0.4901407 0.0231798
aou Indo-Pacific (26,28.1] -0.1599882 -0.2193908 -0.0870938
aou Indo-Pacific (28.1, Inf] 0.3029332 0.1423855 0.5714053
nitrate Atlantic (-Inf,26] 0.3387740 0.3021479 0.4038121
nitrate Atlantic (26,28.2] -0.8658015 -0.8988189 -0.8227791
nitrate Atlantic (28.2, Inf] -0.8290772 -0.9587749 -0.5966614
nitrate Indo-Pacific (-Inf,26] -0.2364714 -0.5229643 -0.0065778
nitrate Indo-Pacific (26,28.1] -0.3732911 -0.4458130 -0.2499881
nitrate Indo-Pacific (28.1, Inf] 0.0945425 -0.0526306 0.3856189
phosphate Atlantic (-Inf,26] -0.0542341 -0.4118203 0.1927518
phosphate Atlantic (26,28.2] -0.8959814 -0.9211525 -0.8615260
phosphate Atlantic (28.2, Inf] -0.8683216 -0.9683209 -0.6881572
phosphate Indo-Pacific (-Inf,26] -0.2345097 -0.5303663 -0.0046817
phosphate Indo-Pacific (26,28.1] -0.3817305 -0.4428987 -0.2661418
phosphate Indo-Pacific (28.1, Inf] -0.0477819 -0.2523948 0.2859410
phosphate_star Atlantic (-Inf,26] -0.4716389 -0.7976096 -0.2398918
phosphate_star Atlantic (26,28.2] -0.7456574 -0.8020098 -0.6679922
phosphate_star Atlantic (28.2, Inf] -0.9058320 -0.9545451 -0.8324191
phosphate_star Indo-Pacific (-Inf,26] -0.1073048 -0.3944798 0.0764190
phosphate_star Indo-Pacific (26,28.1] -0.4517015 -0.5978074 -0.3226919
phosphate_star Indo-Pacific (28.1, Inf] -0.4908459 -0.6174865 -0.3838022
sal Atlantic (-Inf,26] 0.3229008 0.1394940 0.6429462
sal Atlantic (26,28.2] 0.7040261 0.6630134 0.7634276
sal Atlantic (28.2, Inf] 0.8332341 0.6700284 0.9224981
sal Indo-Pacific (-Inf,26] -0.0209090 -0.0847747 0.0646838
sal Indo-Pacific (26,28.1] 0.3205295 0.1217238 0.4785595
sal Indo-Pacific (28.1, Inf] 0.2686642 0.2486360 0.2959155
silicate Atlantic (-Inf,26] -0.1481448 -0.5178173 0.0956953
silicate Atlantic (26,28.2] -0.5830373 -0.6663606 -0.4932436
silicate Atlantic (28.2, Inf] -0.7224781 -0.9318405 -0.3368431
silicate Indo-Pacific (-Inf,26] -0.2413512 -0.5023137 -0.0276715
silicate Indo-Pacific (26,28.1] 0.1362193 0.0401413 0.2577697
silicate Indo-Pacific (28.1, Inf] -0.0063520 -0.1043232 0.1746101
temp Atlantic (-Inf,26] 0.1430978 -0.1455071 0.5281247
temp Atlantic (26,28.2] 0.4026251 0.2703236 0.4907233
temp Atlantic (28.2, Inf] 0.2448353 -0.0490847 0.7371290
temp Indo-Pacific (-Inf,26] -0.3252096 -0.3443171 -0.2969229
temp Indo-Pacific (26,28.1] 0.1988360 0.0525078 0.3755751
temp Indo-Pacific (28.1, Inf] 0.3411732 0.1560601 0.6003943 
rm(cor_target_predictor, cor_target_predictor_stats)

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] corrr_0.4.3      broom_0.7.2      kableExtra_1.3.1 knitr_1.30      
 [5] olsrr_0.5.3      GGally_2.0.0     lubridate_1.7.9  metR_0.9.0      
 [9] scico_1.2.0      patchwork_1.1.1  collapse_1.5.0   forcats_0.5.0   
[13] stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4      readr_1.4.0     
[17] tidyr_1.1.2      tibble_3.0.4     ggplot2_3.3.2    tidyverse_1.3.0 
[21] workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 webshot_0.5.2            RColorBrewer_1.1-2      
 [4] httr_1.4.2               rprojroot_2.0.2          tools_4.0.3             
 [7] backports_1.1.10         R6_2.5.0                 nortest_1.0-4           
[10] DBI_1.1.0                colorspace_1.4-1         withr_2.3.0             
[13] gridExtra_2.3            tidyselect_1.1.0         curl_4.3                
[16] compiler_4.0.3           git2r_0.27.1             cli_2.1.0               
[19] rvest_0.3.6              xml2_1.3.2               labeling_0.4.2          
[22] scales_1.1.1             checkmate_2.0.0          goftest_1.2-2           
[25] digest_0.6.27            foreign_0.8-80           rmarkdown_2.5           
[28] rio_0.5.16               pkgconfig_2.0.3          htmltools_0.5.0         
[31] highr_0.8                dbplyr_1.4.4             rlang_0.4.9             
[34] readxl_1.3.1             rstudioapi_0.13          farver_2.0.3            
[37] generics_0.0.2           jsonlite_1.7.1           zip_2.1.1               
[40] car_3.0-10               magrittr_1.5             Matrix_1.2-18           
[43] Rcpp_1.0.5               munsell_0.5.0            fansi_0.4.1             
[46] abind_1.4-5              lifecycle_0.2.0          stringi_1.5.3           
[49] whisker_0.4              yaml_2.2.1               carData_3.0-4           
[52] plyr_1.8.6               grid_4.0.3               blob_1.2.1              
[55] parallel_4.0.3           promises_1.1.1           crayon_1.3.4            
[58] lattice_0.20-41          haven_2.3.1              hms_0.5.3               
[61] pillar_1.4.7             reprex_0.3.0             glue_1.4.2              
[64] evaluate_0.14            RcppArmadillo_0.10.1.2.0 data.table_1.13.2       
[67] modelr_0.1.8             vctrs_0.3.5              httpuv_1.5.4            
[70] cellranger_1.1.0         gtable_0.3.0             reshape_0.8.8           
[73] assertthat_0.2.1         xfun_0.18                openxlsx_4.2.3          
[76] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[79] ellipsis_0.3.1           here_0.1