log10 y-axis labels do not appear on R ggaluvial ggplot ; cannot nudge strata labels to vertical center

Question

A compound question:

• I cannot get log10 y-axis labels to usefully display on the ggalluvial plot after many variations of scale_y_log10. I am having particular difficulty specifying and formatting breaks. scale_y_continuous produces the following:

Error in seq.default(min, max, by = by) : 'to' must be a finite number`

• Strata labels obscure the strata boundaries. The geom_label nudge_y parameter has no apparent effect. How should one center these labels in the strata? The default setting do not accomplish this.

Please see plot, code and data below.

Appreciate any advice.

Update: Adding reverse=FALSE and vjust="center" to geom_label(stat = "stratum", label.strata=TRUE,fill="white",vjust="center",reverse=FALSE) + appears to fix the issue with centering the strata labels:

originally posted ggalluvial plot:

library(tidyverse)
library(ggplot2)
library(ggalluvial)
library(scales)
#library(ggthemes) # for theme_economist

proAntiByActivity = ggplot(as.data.frame(a),
       aes(y = aggSpend,
           axis1 = cycle, axis3 = proAnti, axis2 = activityGroup)) +
  #geom_alluvium(aes(fill =cycle))+ #, width = 0, knot.pos = 0, reverse = FALSE) +
  geom_alluvium(aes(fill =cycle), width = 1/12, knot.pos = 1/6, reverse = FALSE, show.legend = TRUE) +
  guides(fill = FALSE) +
  geom_stratum(width = 1/5, reverse = FALSE) +
  #geom_text(stat = "stratum", label.strata = TRUE, reverse = FALSE) +
  geom_label(stat = "stratum", label.strata = TRUE,nudge_y = 0) +
  #scale_x_discrete(breaks = 1:3, labels = c("Election Cycle","Means",  "Pro/Anti")) +
  scale_x_continuous(breaks = 1:3, labels = c("Election Cycle","Means",  "Pro/Anti")) +
  #scale_y_continuous(name="total spent")+
  #scale_y_continuous(trans = "log10",name="total spent",limits=NULL) +
  #coord_trans(y="log10")+
  #scale_y_log10(name="log total spent",breaks = 1e+100*c(2e+03,2e+04,2e+05,2e+06,2e+07,2e+08,2e+09), labels = c(2e+03,2e+04,2e+05,2e+06,2e+07,2e+08,2e+09)) +
  scale_y_log10(name="log total spent",breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", scientific_format())) +
  #scale_y_log10(name="log total spent",breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(.x))) +
#scale_y_log10(name="log total spent",breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) +
  #theme_economist()+
  ggtitle("Money Flows for U.S. Elections By Activity 2010-2020")

proAntiByActivity

Plot data

a<-structure(list(cycle = c("2010", "2010", "2010", "2010", "2010", 
"2010", "2010", "2010", "2010", "2010", "2010", "2010", "2010", 
"2010", "2010", "2010", "2010", "2010", "2010", "2010", "2010", 
"2010", "2010", "2010", "2010", "2010", "2010", "2010", "2010", 
"2010", "2010", "2010", "2010", "2018", "2018", "2018", "2018", 
"2018", "2018", "2018", "2018", "2018", "2018", "2018", "2018", 
"2018", "2018", "2018", "2018", "2018", "2018", "2018", "2018", 
"2018", "2018", "2018", "2018", "2018", "2018", "2018", "2018", 
"2018", "2018", "2018", "2018", "2018", "2018", "2018"), proAnti = c("antiDem", 
"antiDem", "antiDem", "antiDem", "antiDem", "antiDem", "antiGOP", 
"antiGOP", "antiGOP", "antiGOP", "antiGOP", "antiGOP", "antiOther", 
"antiOther", "antiOther", "antiOther", "antiOther", "proDem", 
"proDem", "proDem", "proDem", "proDem", "proDem", "proGOP", "proGOP", 
"proGOP", "proGOP", "proGOP", "proGOP", "proOther", "proOther", 
"proOther", "proOther", "antiDem", "antiDem", "antiDem", "antiDem", 
"antiDem", "antiDem", "antiGOP", "antiGOP", "antiGOP", "antiGOP", 
"antiGOP", "antiGOP", "antiOther", "antiOther", "antiOther", 
"antiOther", "antiOther", "proDem", "proDem", "proDem", "proDem", 
"proDem", "proDem", "proGOP", "proGOP", "proGOP", "proGOP", "proGOP", 
"proGOP", "proOther", "proOther", "proOther", "proOther", "proOther", 
"proOther"), activityGroup = c("Advertizing", "AdvertizingV2", 
"Outreach", "OutreachV2", "Overhead", "Strategy", "Advertizing", 
"AdvertizingV2", "Outreach", "OutreachV2", "Overhead", "Strategy", 
"Advertizing", "AdvertizingV2", "Outreach", "Overhead", "Strategy", 
"Advertizing", "AdvertizingV2", "Outreach", "OutreachV2", "Overhead", 
"Strategy", "Advertizing", "AdvertizingV2", "Outreach", "OutreachV2", 
"Overhead", "Strategy", "Advertizing", "Outreach", "Overhead", 
"Strategy", "Advertizing", "AdvertizingV2", "Outreach", "OutreachV2", 
"Overhead", "Strategy", "Advertizing", "AdvertizingV2", "Outreach", 
"OutreachV2", "Overhead", "Strategy", "Advertizing", "AdvertizingV2", 
"Outreach", "Overhead", "Strategy", "Advertizing", "AdvertizingV2", 
"Outreach", "OutreachV2", "Overhead", "Strategy", "Advertizing", 
"AdvertizingV2", "Outreach", "OutreachV2", "Overhead", "Strategy", 
"Advertizing", "AdvertizingV2", "Outreach", "OutreachV2", "Overhead", 
"Strategy"), aggSpend = c(159948962.660721, 40399.6402031971, 
9158355.18213395, 283702.113259935, 187198.211058457, 4563675.63724594, 
152982928.325557, 216256.874555558, 7993712.18735522, 823.580861836789, 
106873.732663197, 1778558.91739064, 1438915.09226517, 146.251315353075, 
1028161.44634096, 25685.7644535242, 196881.375812725, 23407657.0206254, 
133629.176224183, 14368371.0220397, 26389.4225797726, 1539957.71484811, 
1538921.52699396, 21854214.1121801, 470029.494152242, 11779580.9624892, 
522106.091640242, 791874.639134986, 2898174.32990864, 8.14755986834644, 
11010.7374694267, 5596.20969242995, 3061.26236033165, 311475306.900278, 
4535009.26475469, 14866419.8988148, 46627.7825986496, 139697.610043845, 
5685839.23231515, 442793202.646583, 9185627.87610291, 25475752.6745331, 
17801.571227516, 854976.211853977, 4386843.04877027, 16497196.406344, 
164121.870068956, 831667.837824925, 3.90373318422706, 129470.611578985, 
118114661.837676, 5903094.13420614, 43261891.7968911, 369371.87531218, 
2141258.36101987, 4361747.5737002, 69866481.4457689, 4214523.71305267, 
25400537.1038862, 242901.276117105, 92174.6149800344, 14024469.650107, 
6396957.39009888, 200539.496487483, 736056.134792953, 833.390308236151, 
2446.50392841341, 285246.096898001)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -68L), groups = structure(list(
    cycle = c("2010", "2010", "2010", "2010", "2010", "2010", 
    "2018", "2018", "2018", "2018", "2018", "2018"), proAnti = c("antiDem", 
    "antiGOP", "antiOther", "proDem", "proGOP", "proOther", "antiDem", 
    "antiGOP", "antiOther", "proDem", "proGOP", "proOther"), 
    .rows = list(1:6, 7:12, 13:17, 18:23, 24:29, 30:33, 34:39, 
        40:45, 46:50, 51:56, 57:62, 63:68)), row.names = c(NA, 
-12L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

Answer 1

The following is an alternative to ggalluvial using ggforce. I find that the spacing ggforce places between strata helps the understanding of the plot by resolving categories. Assume a is the dput output you've posted.

library(tidyverse)
library(ggforce)

df <- gather_set_data(a, 1:3)

ggplot(df, aes(x = x, id = id, split = y, value = log10(aggSpend))) +
  geom_parallel_sets(aes(fill = as.factor(cycle)), alpha = 0.5,
                     axis.width = 0.5) +
  geom_parallel_sets_axes(axis.width = 0.5, fill = "white", colour = "black") +
  geom_parallel_sets_labels(angle = 0) +
  scale_x_discrete(limits = c("cycle", "activityGroup", "proAnti"),
                   labels = c("Election Cycle", "Means", "Pro/Anti"),
                   name = "") +
  scale_y_continuous(name = "Log10 Total Spending") +
  guides(fill = "none")

With regard to the log10 axes, my recommendation is to title the y-axis appropriately and do a log10 transform on the data itself rather than the axis. See my comment underneath your question too for my objections.