Issue with splitting data in R - r

I am trying to run a code that would enable me to make a comparison between the current versus the past (last 6 months) socioeconomic situation and the voting preference in the last Spanish General Elections, which were in 2019.
If I run this code, I get the correct graphical output for all the parties that participated in the aforementioned elections:
#Analysis of Question 3 (current versus past situation of Spanish consumers, their perception of their own situation) in addition to Question 31a (facet of the respondents' voting preference)
library(ggplot2)
library(dplyr)
set.seed(123)
CIS_data_6 <- data.frame(
CIS$RECUERDO,
CIS$P3
)
dput(CIS_data_6)
CIS_data_6$CIS.P3 <- sub("\\(NO LEER\\) ", "", CIS_data_6$CIS.P3)
labs <- c("Mejor", "Igual", "N.C.", "N.S.", "Peor")
CIS_data_6$CIS.P3 <- factor(CIS_data_6$CIS.P3, labels = labs)
Recuerdo <- CIS_data_6$CIS.RECUERDO
Recuerdolabs <- c("N.P.", "PSOE", "PP", "VOX", "Unidas Podemos", "Ciudadanos")
cut_points <- c(0, 2, 1, 18, c(21, 6, 67), 4)
i <-c(Recuerdo, !is.na(cut_points))
Recuerdo_fac <- Recuerdolabs[i]
Recuerdo_fac[is.na(Recuerdo)] <- Recuerdolabs[length(Recuerdolabs)]
Recuerdo_fac <- factor(Recuerdo_fac, levels = Recuerdolabs)
CIS_data_6$CIS.RECUERDO <- Recuerdo
ggplot(CIS_data_6, aes(Recuerdo)) +
geom_bar() +
xlab("Current VS Past Individual Situation Based on Voting Preference") +
ylab("Count") +
coord_flip() +
facet_wrap(~ CIS.P3) +
theme_bw()
However, this is graphically a mess, since there is all kind of minor parties which are of no relevance to my analysis.
The ones that I do want to outline are the big ones (plus abstention): "N.P." (Abstention), "PSOE", "PP", "VOX", "Unidas Podemos" and "Ciudadanos", with their corresponding encoding in the survey, which I denoted as cut_points: c(0, 2, 1, 18, c(21, 6, 67), 4).
Does anyone know how can I modify my code so that I can get to the desired output, i.e., to have a faceted graph (5 graphs, to be precise) saying "Mejor" ('Better'), "Peor" ('Worse'), "Igual" ('Same'), "N.S." ('Don't Know'), "N.C." ('Don't Answer') on the x-axis in each graph, and the political parties being located on the y-axis?
Many, many thanks in advance!
P.S. I am relatively new to this forum, so please, in case I have not expressed myself correctly or if there is any information lacking in my question, just let me know so, and I will be happy to provide it.
EDIT
dput(CIS_data_6)
is, as follows:
structure(list(CIS.RECUERDO = structure(c(2L, 2L, 28L, 1L, 2L,
1L, 15L, 15L, 2L, 26L, NA, 28L, 28L, 26L, 2L, NA, 13L, 2L, 27L,
2L, 15L, 1L, 27L, NA, 2L, 1L, NA, 1L, 2L, 15L, 25L, 26L, 26L,
26L, 2L, 1L, 15L, 15L, 24L, 1L, 15L, 28L, 2L, 2L, 24L, 3L, NA,
1L, NA, 27L, 1L, 27L, 26L, 2L, 2L, 28L, NA, 15L, 24L, 23L, 27L,
23L, 28L, 1L, 23L, 3L, 1L, 21L, 26L, 27L, 2L, 2L, 3L, 2L, 1L,
2L, 2L, 28L, 28L, 28L, 1L, 15L, 15L, 1L, 1L, 26L, 23L, 2L, 2L,
15L, 15L, 2L, 28L, 28L, 2L, 1L, 1L, 25L, 28L, 28L, 26L, 23L,
1L, 28L, 27L, 1L, 28L, 15L, 2L, 3L, 27L, 1L, 1L, 1L, 15L, 1L,
2L, 1L, 1L, 28L, 28L, 15L, 13L, 1L, 1L, 2L, 3L, 3L, 15L, 3L,
1L, 1L, 28L, 1L, 23L, 26L, 1L, 2L, 1L, 2L, 1L, 27L, 27L, NA,
28L, 26L, 21L, 3L, 28L, 2L, 26L, 2L, 26L, 1L, 26L, 2L, 15L, 3L,
2L, 1L, 15L, 1L, 2L, 2L, 28L, 2L, 28L, 15L, 1L, 2L, 2L, 15L,
NA, 28L, 1L, 1L, 28L, 28L, 15L, 28L, 1L, 1L, 26L, 28L, 2L, 2L,
26L, 28L, 1L, 28L, 3L, 1L, 13L, 26L, 15L, 28L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 28L, 26L, 1L, 28L, 28L, 28L, 27L, 23L, 13L, 2L,
27L, 2L, 26L, 2L, 15L, 28L, 1L, 2L, 2L, 24L, 2L, 26L, 2L, 27L,
1L, 28L, 15L, 15L, 13L, 21L, 28L, 13L, NA, 27L, 1L, 23L, 2L,
28L, 1L, 28L, 2L, 15L, 1L, 1L, 28L, 1L, 27L, 24L, 1L, 26L, 26L,
13L, 1L, 26L, 26L, 15L, 28L, 2L, 1L, 15L, 24L, 1L, 28L, 28L,
1L, 3L, 1L, 2L, 15L, 23L, 25L, 28L, 26L, 1L, 27L, 1L, 28L, 1L,
13L, 15L, 23L, 1L, 1L, 28L, 1L, 25L, 2L, 1L, 2L, 26L, 2L, NA,
26L, 25L, 25L, 2L, 2L, 28L, 13L, 15L, 23L, 26L, 2L, 1L, 24L,
2L, 1L, 1L, NA, 28L, 2L, 15L, 2L, 1L, 2L, 1L, 2L, 24L, 28L, 1L,
15L, 2L, 28L, 25L, 1L, 2L, 1L, 27L, 15L, 28L, 2L, 28L, 23L, 22L,
1L, NA, 3L, 2L, 28L, NA, 21L, 1L, 26L, 26L, 2L, 1L, 27L, 1L,
2L, 1L, 26L, 1L, 26L, 24L, 1L, 13L, 26L, 28L, 26L, 26L, 2L, 24L,
27L, 3L, 1L, 2L, 28L, 28L, 27L, 26L, 27L, 23L, 26L, 15L, 2L,
2L, 15L, 27L, 1L, 2L, 1L, 26L, 28L, 3L, 2L, 2L, 1L, 26L, 2L,
2L, 3L, 2L, 2L, 26L, 1L, 2L, 2L, 2L, 3L, 23L, 1L, 3L, 26L, 23L,
15L, 1L, 28L, 2L, 15L, 23L, 26L, 1L, 13L, 15L, 2L, 26L, 2L, 26L,
15L, 2L, 25L, 1L, 26L, 3L, 26L, 2L, 2L, 2L, 3L, 15L, 15L, 28L,
26L, 2L, 3L, 15L, 22L, 2L, 13L, 1L, 1L, 13L, 1L, 2L, 28L, 13L,
26L, 2L, 27L, 2L, 2L, 27L, 3L, 1L, 26L, 28L, 27L, 2L, 1L, 1L,
23L, 26L, 28L, 1L, 2L, 24L, 1L, 28L, 27L, 24L, 1L, 2L, 25L, 1L,
13L, 1L, 26L, 13L, NA, 3L, 28L, 15L, 1L, 15L, 15L, 2L, 28L, 2L,
2L, 2L, 26L, 3L, 25L, 3L, 2L, 1L, 28L, 25L, 26L, 2L, 28L, 27L,
1L, 2L, 24L, 27L, 23L, 2L, 13L, 2L, 27L, 13L, 13L, 2L, 1L, 1L,
25L, 13L, 28L, 22L, 2L, 23L, 28L, 27L, NA, 28L, 2L, 2L, 13L,
2L, 2L, 28L, 2L, 22L, 26L, 2L, NA, 28L, 2L, 2L, 28L, 13L, 2L,
27L, 28L, 3L, 2L, 2L, 24L, 15L, 2L, 23L, 2L, 1L, NA, 1L, 15L,
2L, 28L, 2L, 2L, 2L, 26L, 1L, 1L, 26L, 26L, 15L, 2L, 26L, 2L,
28L, 3L, 27L, 13L, 24L, 13L, 2L, 1L, 2L, 2L, 1L, 25L, 23L, 2L,
26L, 24L, 15L, 15L, 2L, 28L, 26L, 2L, 26L, 2L, 26L, 27L, 28L,
15L, 3L, 2L, 13L, 21L, 15L, 2L, 2L, 1L, NA, 26L, 1L, 2L, NA,
26L, 28L, 1L, 28L, 3L, 2L, 13L, 28L, 15L, 25L, 2L, 13L, 2L, 1L,
13L, 2L, 2L, 2L, 24L, 26L, NA, 2L, 28L, 28L, 1L, 15L, NA, 22L,
26L, 28L, 26L, 28L, 26L, 1L, 2L, 2L, 13L, 2L, 2L, 1L, 2L, 28L,
28L, 3L, 27L, 1L, 1L, NA, 26L, 28L, 2L, 2L, 2L, 2L, 15L, 26L,
3L, 28L, 25L, 28L, 15L, 1L, 26L, 2L, 28L, 26L, 1L, 15L, 26L,
1L, 8L, NA, 2L, 2L, 13L, 28L, 27L, 1L, 23L, 2L, 28L, 24L, 2L,
2L, 28L, 2L, 2L, 28L, 1L, NA, NA, 26L, 3L, 3L, 24L, 28L, 28L,
28L, 27L, 2L, 2L, 2L, 1L, 24L, 15L, 27L, 1L, 1L, 2L, 26L, 28L,
28L, 26L, 1L, 15L, 15L, NA, 2L, 15L, 27L, 28L, 2L, 26L, 24L,
28L, 26L, 23L, 26L, 1L, 26L, 23L, 26L, 2L, 27L, 13L, 28L, 27L,
24L, 26L, 26L, 27L, 26L, 26L, 15L, 2L, 13L, 2L, 3L, 15L, 1L,
1L, 1L, 2L, 2L, 26L, 26L, 2L, 2L, NA, 24L, 15L, 26L, 24L, 27L,
28L, 2L, 2L, 2L, 24L, 27L, 2L, 2L, 28L, 26L, 26L, 26L, 23L, 1L,
2L, 28L, 24L, NA, 13L, 2L, 26L, 3L, 25L, 1L, NA, 3L, 26L, 1L,
25L, 2L, 28L, 26L, 2L, 13L, 2L, 1L, 15L, 26L, 15L, 2L, 2L, 1L,
2L, 15L, 2L, 3L, 23L, 2L, 1L, 28L, 1L, 23L, 28L, 28L, 2L, 1L,
NA, 2L, 1L, 15L, 1L, 2L, 24L, 2L, 2L, 1L, NA, 25L, 3L, 2L, 26L,
13L, 2L, 28L, 1L, 13L, 1L, 27L, 15L, 2L, 25L, 2L, 1L, 1L, 28L,
28L, 26L, 21L, 27L, 26L, 1L, 1L, 3L, NA, 28L, 15L, 28L, 2L, 1L,
2L, 2L, 28L, 2L, 15L, 2L, 27L, 13L, 2L, 13L, 2L, 21L, 13L, 2L,
13L, 2L, 2L, 26L, 28L, 24L, 24L, 2L, 24L, 28L, 13L, 15L, 1L,
2L, 15L, NA, 2L, 27L, 15L, 15L, 1L, 2L, 2L, 2L, 28L, 26L, 1L,
27L, 1L, 28L, 26L, 2L, 1L, 1L, 23L, 24L, 15L, 2L, 1L, 28L, 25L,
28L, 27L, 28L, 1L, 15L, 21L, 27L, 1L, 2L, 1L, 28L, 28L, 24L,
1L, 2L, 2L, 23L, 28L, 23L, 28L, 2L, 2L, NA, 2L, 26L, 26L, 2L,
1L, 1L, 2L, 27L, 2L, 1L, 28L, 2L, 28L, 26L, 28L, NA, 13L, 2L,
2L, NA, 28L, 1L, 3L, 13L, 13L, 2L, 2L, 21L, 15L, 15L, 1L, 28L,
2L, 1L, 13L, 2L, 2L, 26L, 26L, 24L, 3L, 28L, 3L, 15L, 2L, 22L,
27L, 26L, 26L, 1L, 28L, 2L, 28L, 2L, 28L, 2L, 1L, 1L, 1L, 28L,
2L, 15L, 2L, 2L, 2L, NA, 27L, 1L, 2L, 22L, 1L, 27L, 2L, 13L,
1L, 2L, 2L, NA, 3L, 26L, 1L, 2L, 2L, 2L, 26L, 15L, 27L, 28L,
15L, 3L, 28L, 28L, 15L, 26L, NA, 2L, 28L, 28L, 26L, 26L, 2L,
28L, 13L, 28L, 25L, 2L, 27L, 2L, 1L, 13L, 26L, 28L, 26L, 2L,
15L, 26L, 1L, 26L, 26L, 1L, 13L, 1L, 13L, 1L, 26L, 15L, 26L,
1L, 2L, 2L, 2L, 28L, 3L, 28L, 2L, 26L, 13L, 2L, 23L, 2L, 3L,
15L, NA, 2L, NA, 26L, 6L, 2L, 3L, 3L, 2L, 3L, 28L, 27L, 21L,
4L, 6L, 1L, 22L, 28L, 27L, 26L, 4L, 28L, 27L, NA, 26L, 6L, 6L,
2L, 6L, 28L, 7L, 2L, 26L, 27L, NA, 2L, 26L, 2L, 26L, 28L, 4L,
2L, 27L, 6L, 3L, 24L, 27L, 2L, 2L, NA, 23L, 28L, 3L, 27L, 1L,
2L, 27L, 26L, 24L, NA, 27L, 2L, 4L, 4L, 2L, 2L, 24L, 27L, NA,
2L, 2L, NA, 26L, 4L, 6L, NA, 28L, 26L, 4L, 4L, 3L, 24L, 23L,
7L, 2L, 25L, 27L, 2L, 4L, 28L, NA, 2L, 6L, 2L, 2L, 2L, 13L, 6L,
26L, 2L, 2L, 24L, 2L, NA, 28L, 2L, 6L, 1L, 4L, 4L, 3L, 2L, 27L,
2L, NA, 2L, 2L, 7L, 2L, 7L, 28L, NA, 2L, 2L, 27L, 2L, 2L, NA,
3L, 2L, 26L, 27L, 2L, 6L, 1L, 2L, NA, 6L, 26L, 24L, 4L, 4L, 4L,
28L, 4L, 4L, 4L, 4L, 13L, 7L, 26L, 28L, 27L, 6L, NA, NA, 28L,
6L, 26L, 2L, 4L, 24L, 26L, 2L, 1L, 21L, 28L, 7L, 27L, 26L, 28L,
2L, 27L, 26L, 2L, 4L, 26L, 2L, 1L, 27L, 26L, 7L, 28L, 26L, 28L,
27L, 3L, NA, 27L, 28L, 4L, 2L, 4L, 26L, 2L, 7L, 26L, 1L, 2L,
1L, 2L, 6L, 26L, 2L, 2L, 7L, 4L, 26L, 2L, 27L, 2L, 27L, 26L,
27L, 6L, 2L, 26L, 13L, 6L, 2L, 23L, 23L, 4L, 26L, 28L, 3L, 28L,
22L, 26L, 1L, 6L, 26L, 27L, 6L, 27L, 2L, 6L, 21L, 4L, 6L, 28L,
28L, 4L, 4L, 2L, 28L, 2L, 21L, 2L, 2L, 7L, 23L, 27L, 4L, NA,
27L, 22L, 4L, 6L, 26L, 26L, 6L, 27L, 27L, 7L, 1L, 4L, 26L, 6L,
4L, 13L, NA, 24L, 2L, 2L, 2L, 2L, 4L, 6L, 28L, 3L, 3L, 6L, NA,
1L, 2L, 1L, 2L, 2L, 3L, 25L, 23L, 2L, 4L, 28L, 28L, 3L, 28L,
28L, 2L, 2L, 28L, 28L, 6L, 1L, 28L, 2L, 2L, 4L, 26L, 6L, 2L,
28L, 2L, 25L, 2L, 7L, 6L, 27L, 28L, NA, NA, 27L, 28L, 6L, 4L,
28L, 26L, 6L, 13L, 1L, 2L, 4L, 4L, 28L, 6L, 2L, 23L, 26L, 2L,
7L, 2L, 4L, NA, 2L, 27L, 7L, 26L, 2L, 13L, 1L, 6L, 27L, 23L,
26L, 22L, 6L, 2L, 27L, 2L, 28L, 2L, 2L, 27L, 28L, 24L, 27L, 6L,
27L, NA, 27L, 6L, 6L, 26L, 2L, 28L, 7L, NA, 26L, 26L, 7L, 6L,
24L, 2L, 6L, 23L, 26L, 27L, 26L, 28L, 28L, 26L, 2L, 23L, 6L,
6L, 27L, 25L, 21L, 6L, NA, 2L, 28L, 4L, 27L, 23L, 1L, 23L, 6L,
13L, 7L, 2L, 26L, 28L, 28L, 24L, 6L, 2L, 25L, 26L, 4L, 26L, 26L,
23L, 1L, 28L, 4L, 2L, 27L, 7L, 28L, 4L, 27L, NA, 6L, 7L, 15L,
26L, 28L, 15L, 26L, 24L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 26L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 23L, 28L, 3L, 2L, 15L, 24L, 28L, 15L,
2L, 15L, 26L, 15L, 28L, 13L, 2L, NA, 28L, 2L, 25L, 27L, 28L,
2L, 2L, 28L, 3L, NA, 2L, 28L, 15L, 27L, 24L, 3L, 13L, 2L, 2L,
15L, 28L, 15L, 28L, 28L, 1L, 24L, 2L, 13L, 28L, 28L, 1L, 15L,
NA, 28L, 2L, 1L, 3L, 13L, 15L, 26L, 26L, 27L, 2L, 6L, 1L, 26L,
26L, 2L, 28L, 23L, 13L, 1L, 27L, 2L, 15L, 2L, 2L, NA, 1L, 22L,
2L, NA, 1L, 1L, 23L, 2L, 26L, 24L, 28L, 27L, 2L, 26L, NA, 2L,
27L, 2L, 2L, 2L, 2L, 13L, 25L, 2L, 2L, NA, 26L, 24L, 23L, 2L,
2L, 26L, 24L, 28L, 2L, 1L, 21L, 1L, 26L, 1L, 2L, 15L, 2L, 28L,
26L, 27L, 1L, 1L, 1L, 27L, 24L, 28L, 24L, 1L, 24L, 26L, 1L, 2L,
15L, 2L, 23L, 27L, 26L, 1L, 24L, 24L, 27L, 1L, 26L, 26L, 1L,
2L, 21L, 28L, 1L, 2L, 27L, 1L, NA, 3L, 1L, 27L, 2L, 15L, 15L,
2L, 13L, 26L, 1L, 1L, NA, 28L, 13L, 15L, 26L, 1L, 3L, 1L, 3L,
2L, NA, 26L, 15L, 26L, 2L, 13L, 2L, 27L, 1L, 2L, 3L, 25L, 1L,
25L, 26L, 15L, 15L, 1L, 15L, 15L, 15L, 26L, 2L, 2L, 27L, 3L,
2L, 23L, 24L, 15L, NA, 2L, 15L, 28L, 15L, 28L, 28L, 2L, 23L,
13L, 24L, 2L, 27L, 2L, 3L, 2L, 28L, NA, 3L, 1L, 15L, 2L, 28L,
1L, 1L, 3L, 2L, 2L, 27L, 13L, 26L, 26L, 15L, 1L, 28L, 1L, 2L,
26L, 2L, 25L, 26L, 2L, 1L, 2L, 13L, NA, 28L, 1L, 13L, 28L, 1L,
13L, 23L, 23L, 1L, 2L, NA, 28L, 3L, 28L, 13L, 28L, 26L, 15L,
3L, 28L, 9L, 26L, 28L, 23L, 2L, 1L, 13L, 2L, 28L, 1L, 15L, 1L,
28L, 1L, 25L, 2L, NA, 26L, 2L, 2L, 26L, 28L, 2L, 23L, 13L, 28L,
28L, 1L, 3L, 26L, 3L, 28L, 25L, 21L, 1L, 1L, 28L, 28L, 26L, 2L,
27L, 1L, 21L, NA, 26L, 28L, 15L, 2L, 2L, 13L, 2L, 28L, 2L, 3L,
26L, 3L, 23L, 27L, 3L, 23L, 2L, 2L, 26L, 2L, 24L, 1L, 1L, 1L,
2L, 25L, 26L, 24L, 1L, 1L, 25L, 21L, 13L, 27L, 2L, 24L, 24L,
2L, 2L, 2L, 26L, 13L, 27L, 26L, 28L, 2L, 2L, 2L, 26L, 28L, 25L,
21L, 1L, 1L, 26L, 2L, 1L, 1L, 2L, 27L, 24L, 28L, 26L, 1L, 26L,
24L, 2L, 28L, 26L, 1L, 28L, 28L, 26L, 26L, 2L, 24L, 24L, 21L,
2L, 3L, 25L, 28L, 2L, 1L, 2L, 27L, 24L, 28L, NA, 26L, 24L, 1L,
2L, 2L, 26L, 1L, 26L, 1L, 2L, 1L, 1L, 2L, 27L, 1L, 2L, 27L, 26L,
1L, 26L, 1L, 2L, 1L, 28L, 1L, 3L, 26L, 2L, 26L, 26L, NA, 1L,
28L, 26L, 24L, 1L, 26L, 1L, 1L, 1L, 1L, 24L, 26L, 2L, 28L, 1L,
28L, 3L, 26L, 2L, 27L, 24L, 2L, 25L, 1L, 2L, 23L, 2L, 28L, 26L,
23L, 2L, 2L, 2L, 2L, 28L, 2L, 24L, 1L, 26L, 26L, 2L, 2L, 23L,
24L, 27L, 21L, 2L, 2L, 27L, 24L, 23L, 24L, 2L, 27L, 26L, 28L,
13L, 27L, 26L, 2L, 2L, 24L, 27L, 23L, 1L, 24L, 2L, 13L, NA, 2L,
26L, 25L, 2L, 26L, 28L, 2L, 24L, 28L, 1L, 2L, 23L, 15L, 26L,
2L, 3L, 26L, 2L, 15L, 2L, 1L, 1L, 21L, 2L, 1L, 2L, 13L, 1L, 2L,
13L, 26L, 1L, 27L, 23L, 1L, 2L, 1L, 2L, 28L, 15L, 13L, 1L, 1L,
28L, 13L, 1L, 2L, 15L, 27L, 2L, 28L, 26L, 26L, 28L, 1L, 24L,
28L, 2L, 15L, 13L, 15L, 28L, 26L, 23L, 2L, 2L, 3L, 2L, 23L, 2L,
23L, 15L, 13L, 21L, 15L, 23L, 1L, 27L, 27L, 2L, 21L, 15L, 15L,
1L, 2L, 15L, 1L, 2L, 15L, 2L, 3L, 2L, 2L, 15L, 26L, 23L, 2L,
15L, 2L, 2L, 2L, 15L, 15L, 1L, 2L, 15L, 26L, 3L, 15L, 1L, 2L,
2L, 26L, 1L, 24L, 3L, 26L, 2L, 2L, 2L, 15L, 2L, 26L, 15L, 26L,
2L, 26L, 22L, NA, 1L, 27L, 2L, 2L, 26L, 28L, 13L, 3L, 1L, 1L,
1L, 15L, NA, 2L, 24L, 26L, 22L, 28L, 1L, 13L, 13L, NA, 3L, 8L,
21L, 1L, 1L, 15L, 15L, 1L, 26L, NA, 2L, 28L, 3L, 28L, NA, 23L,
26L, 15L, 15L, 2L, NA, 15L, 1L, 3L, 15L, 26L, 13L, 2L, 2L, 1L,
2L, 15L, 1L, 2L, 24L, 2L, 24L, 25L, 3L, 28L, 2L, 15L, 1L, NA,
28L, 3L, 1L, 28L, 2L, 26L, 2L, 2L, 1L, 22L, 15L, 3L, 24L, 26L,
2L, 24L, 1L, 24L, 3L, 13L, 28L, 1L, 2L, 13L, 1L, 2L, 28L, 24L,
2L, 1L, 2L, 1L, 1L, 15L, 26L, 2L, 2L, 3L, 28L, 2L, 22L, NA, 1L,
2L, 26L, 3L, 1L, 1L, 2L, 1L, 26L, 26L, 26L, 2L, 15L, 2L, 3L,
28L, 1L, 2L, 2L, 2L, NA, 26L, NA, 15L, 24L, NA, 1L, 27L, 2L,
2L, 27L, 27L, 2L, 23L, 2L, 28L, 13L, 2L, 28L, 2L, 2L, 1L, 28L,
27L, 2L, 27L, 15L, 13L, 26L, 15L, 15L, 1L, 26L, 1L, NA, 26L,
28L, 1L, 1L, 1L, 24L, NA, 28L, 1L, 1L, 2L, 15L, 25L, 2L, 1L,
15L, 27L, 3L, 1L, NA, NA, 26L, 1L, 13L, 26L, NA, NA, 2L, 23L,
27L, 24L, 26L, 28L, 25L, 3L, 26L, 1L, 1L, 28L, 1L, 2L, 24L, 2L,
1L, 2L, 2L, 15L, 1L, 1L, 15L, 28L, 15L, 15L, 26L, 22L, 28L, 2L,
28L, 1L, 24L, 28L, 28L, 15L, 15L, 3L, 15L, 24L, 26L, 28L, 1L,
24L, 27L, 3L, 1L, 3L, 2L, 13L, 1L, 28L, 13L, NA, 1L, 1L, 28L,
28L, 2L, 26L, 13L, 28L, 24L, 2L, 13L, 15L, NA, 1L, 13L, 1L, 1L,
2L, 2L, 1L, 27L, 2L, 2L, 26L, 2L, 15L, 1L, 26L, 24L, 1L, 2L,
13L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 28L, 28L, 1L, 1L, 15L, 13L,
26L, 1L, 28L, 1L, NA, 2L, 26L, 3L, 23L, 2L, 2L, NA, 1L, 28L,
2L, 3L, 2L, 1L, 2L, 1L, 2L, NA, 15L, 2L, NA, 23L, 3L, 15L, 2L,
28L, 23L, 13L, 2L, 2L, 1L, 3L, 1L, 2L, 28L, NA, 2L, 28L, 22L,
13L, 2L, 27L, 26L, 15L, 1L, 2L, 13L, 1L, 2L, 13L, 15L, 15L, 26L,
15L, 13L, 2L, 3L, 24L, 23L, 13L, 15L, 26L, 1L, 26L, 21L, 23L,
2L, 13L, 26L, 2L, 26L, 3L, 13L, 28L, 1L, 13L, 26L, 2L, 2L, 26L,
2L, 27L, 1L, 15L, 1L, 28L, 27L, 13L, 28L, 28L, 28L, 26L, NA,
21L, 3L, 27L, 15L, 1L, 28L, 28L, 23L, 3L, 23L, 3L, 24L, 9L, 2L,
9L, 23L, 27L, 26L, 24L, 26L, 2L, 28L, NA, 26L, NA, 2L, 28L, 2L,
24L, 28L, 9L, 27L, 24L, 6L, 13L, NA, 2L, 24L, 24L, 22L, 28L,
9L, 24L, NA, 24L, 24L, 2L, 24L, 23L, 28L, 28L, 26L, 26L, 2L,
8L, 1L, 26L, 22L, 2L, 23L, 1L, 27L, 22L, 2L, 26L, 15L, 2L, 24L,
2L, 2L, 2L, 15L, 26L, 26L, 8L, 9L, 2L, 8L, 2L, 26L, 2L, 8L, 15L,
25L, 8L, 15L, 15L, 2L, 1L, 8L, 9L, NA, 23L, 26L, 9L, 9L, 26L,
9L, 26L, 9L, 13L, 8L, 25L, 8L, 1L, 28L, 23L, 9L, 2L, 27L, 8L,
28L, 15L, 9L, 24L, 9L, 28L, 26L, 8L, 9L, 2L, 9L, 8L, 3L, 28L,
15L, 8L, 15L, NA, 2L, 27L, 2L, NA, 28L, 28L, 8L, 8L, 1L, 15L,
8L, 2L, 2L, 8L, 26L, NA, NA, 27L, 28L, 9L, 26L, 15L, 6L, 2L,
2L, 2L, NA, 26L, 26L, 8L, 15L, NA, 26L, 8L, 27L, 9L, 27L, 28L,
26L, 28L, 8L, 15L, 8L, 8L, 3L, 15L, NA, 9L, 8L, 9L, 15L, 9L,
28L, 28L, 9L, 9L, 26L, 28L, 28L, 2L, 2L, 26L, 2L, 1L, 1L, 2L,
1L, 2L, 23L, 25L, 3L, 2L, 15L, 28L, 26L, 1L, 28L, 26L, 23L, 2L,
1L, 25L, 26L, 24L), levels = c("PP ", "PSOE ", "Ciudadanos ",
"En Com├║ Podem ", "M├®s Comprom├¡s", "ERC ", "JxCat ",
"EAJ-PNV", "EH Bildu ", "CCa-NC ", "Na+ ", "PACMA ",
"VOX ", "CUP ", "Unidas Podemos ", "BNG ", "PRC ",
"Más País ", "En Común-Unidas Podemos ",
"Teruel Existe ", "Nulo ", "No tenía derecho a voto ",
"No tenía edad ", "Otros partidos ", "En blanco ", "No votó ",
"No recuerda ", "N.C. "), class = "factor"), CIS.P3 = structure(c(3L,
3L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 2L,
2L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
1L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 1L,
2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 1L,
3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L,
1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 3L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 2L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L,
1L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L,
2L, 2L, 4L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 3L,
2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
4L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 1L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 2L,
1L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L,
3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 1L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L,
3L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 2L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
1L, 1L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 5L, 1L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 3L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 1L, 3L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
3L, 2L, 1L, 2L, 5L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L,
2L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 1L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 2L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L,
2L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 2L,
2L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 1L,
3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 3L, 1L, 3L, 2L, 5L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L,
2L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L,
1L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 3L, 2L, 3L, 4L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L,
3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 2L, 1L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 4L,
2L, 1L, 2L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 1L,
2L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 2L,
1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 4L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L,
3L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 1L,
2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 1L,
4L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 3L, 3L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 1L,
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L,
3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
4L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L,
2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 2L,
2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 1L,
2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 4L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 2L,
2L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L), levels = c("Mejor", "(NO LEER) Igual", "Peor", "N.S.",
"N.C."), class = "factor")), class = "data.frame", row.names = c(NA,
-2787L))


The following code does the job:
library(tidyverse)
library(haven)
library(labelled)
CIS = read_spss("CISData.sav") %>%
mutate(
P3 = to_factor(P3),
RECUERDO = to_factor(RECUERDO)) %>%
mutate(P3 = sub("\\(NO LEER\\) ", "", P3))
include = c("PSOE", "PP", "VOX", "Unidas Podemos", "Ciudadanos")
CISb = CIS %>%
filter(RECUERDO %in% include) %>%
group_by(RECUERDO, P3) %>%
tally() %>%
mutate(pct = n/sum(n))
plot2 = ggplot(CISb, aes(x = pct, y = RECUERDO)) +
geom_bar(stat = "identity") +
facet_wrap(~P3, scales = "free_x") +
theme_bw() +
labs(x = "Percentage of Party's Voters in Each Response", y = "Voting Preference in 2019 General Elections",
title = "Current VS Past Individual Situation Based on Voting Preference")
plot2

Related

ggalluvial assign different color for each node

I was following this post, but I do not get how can I manage it with my data.
My plot looks like:
And I would like that the "strings" were the same color as the 2nd column, i.e. for ESR1 I would like the orange string, and for PIK3CA green.
Any idea about how can I manage with scale_fill_manual or any other argument?
Thanks!
My code:
colorfill <- c("white", "white", "darkgreen", "orange", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white")
ggplot(data = Allu,
aes(axis1 = Gene_mut, axis2 = Metastasis_Location, y = Freq)) +
geom_alluvium(aes(fill = Gene_mut),
curve_type = "quintic") +
geom_stratum(width = 1/4, fill = colorfill) +
geom_text(stat = "stratum", size = 3,
aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Metastasis_Location", "Gene_mut"),
expand = c(0.05, .05)) +
theme_void()
My data:
structure(list(Metastasis_Location = structure(c(1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L,
11L, 11L), .Label = c("adrenal", "bone", "breast", "liver", "lung",
"muscle", "node", "pancreatic", "peritoneum", "pleural", "skin"
), class = "factor"), T0_T2_THERAPY_COD = structure(c(2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A",
"F"), class = "factor"), T0_T2_PD_event = structure(c(2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("No Progression",
"Progression"), class = "factor"), Gene_mut = structure(c(4L,
5L, 1L, 3L, 4L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 6L, 3L, 6L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 6L, 2L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 5L, 6L, 3L, 6L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 3L, 4L, 4L, 5L, 6L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 3L, 4L, 3L, 4L, 5L, 6L, 3L, 3L, 4L, 5L, 6L, 6L, 6L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 3L, 4L, 3L, 4L, 5L,
6L, 3L, 4L, 5L, 6L, 3L, 4L, 5L, 6L, 1L, 6L, 3L, 3L, 4L, 4L, 5L
), .Label = c("AKT1", "ERBB2", "ESR1", "PIK3CA", "TP53", "WT"
), class = "factor"), LABO_ID = structure(c(45L, 8L, 13L, 11L,
11L, 26L, 7L, 15L, 23L, 26L, 35L, 39L, 7L, 19L, 26L, 32L, 33L,
35L, 39L, 15L, 19L, 35L, 1L, 37L, 34L, 43L, 47L, 3L, 10L, 18L,
20L, 28L, 31L, 36L, 42L, 9L, 10L, 14L, 18L, 20L, 28L, 31L, 36L,
44L, 45L, 8L, 10L, 18L, 28L, 42L, 2L, 7L, 39L, 7L, 39L, 3L, 4L,
42L, 5L, 42L, 6L, 21L, 1L, 10L, 22L, 28L, 46L, 9L, 10L, 14L,
28L, 46L, 10L, 28L, 48L, 25L, 23L, 32L, 33L, 40L, 43L, 24L, 3L,
18L, 24L, 28L, 31L, 36L, 42L, 18L, 27L, 28L, 31L, 36L, 45L, 18L,
24L, 27L, 28L, 42L, 16L, 16L, 18L, 18L, 18L, 29L, 23L, 39L, 39L,
40L, 1L, 12L, 47L, 3L, 18L, 20L, 28L, 31L, 36L, 38L, 42L, 5L,
18L, 20L, 27L, 28L, 31L, 36L, 38L, 41L, 45L, 8L, 18L, 27L, 28L,
42L, 48L, 6L, 17L, 30L, 31L, 31L, 18L, 18L, 18L, 29L, 39L, 39L,
40L, 43L, 31L, 31L, 48L, 30L, 13L, 34L, 18L, 36L, 18L, 36L, 18L
), .Label = c("ER-11", "ER-19", "ER-21", "ER-22", "ER-29", "ER-30",
"ER-31", "ER-32", "ER-33", "ER-38", "ER-40", "ER-43", "ER-49",
"ER-8", "ER-AZ-04", "ER-AZ-05", "ER-AZ-06", "ER-AZ-07", "ER-AZ-08",
"ER-AZ-10", "ER-AZ-11", "ER-AZ-11=ER-47", "ER-AZ-13", "ER-AZ-14",
"ER-AZ-15", "ER-AZ-16", "ER-AZ-17", "ER-AZ-18", "ER-AZ-20", "ER-AZ-20=ER-27",
"ER-AZ-21", "ER-AZ-23", "ER-AZ-23=ER-52", "ER-AZ-24", "ER-AZ-29",
"ER-AZ-31", "ER-AZ-33", "ER-AZ-35", "ER-AZ-37", "ER-AZ-38", "ER-AZ-39",
"ER-AZ-40", "ER-AZ-43", "ER-AZ-44", "ER-AZ-45", "ER-AZ-49", "ER-AZ-51",
"ER-AZ-53"), class = "factor"), Freq = c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -161L), groups = structure(list(
Metastasis_Location = structure(c(1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L), .Label = c("adrenal",
"bone", "breast", "liver", "lung", "muscle", "node", "pancreatic",
"peritoneum", "pleural", "skin"), class = "factor"), T0_T2_THERAPY_COD = structure(c(2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("A",
"F"), class = "factor"), T0_T2_PD_event = structure(c(2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L), .Label = c("No Progression",
"Progression"), class = "factor"), Gene_mut = structure(c(4L,
5L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 6L, 3L, 6L, 3L, 4L, 5L,
6L, 2L, 3L, 4L, 3L, 4L, 5L, 6L, 3L, 6L, 3L, 4L, 5L, 6L, 3L,
4L, 5L, 6L, 1L, 3L, 4L, 5L, 3L, 4L, 3L, 4L, 5L, 6L, 3L, 4L,
5L, 6L, 6L, 3L, 4L, 5L, 6L, 3L, 4L, 3L, 4L, 5L, 6L, 3L, 4L,
5L, 6L, 3L, 4L, 5L, 6L, 1L, 6L, 3L, 4L, 5L), .Label = c("AKT1",
"ERBB2", "ESR1", "PIK3CA", "TP53", "WT"), class = "factor"),
.rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8:12,
13:19, 20:22, 23L, 24L, 25:27, 28:35, 36:45, 46:50, 51L,
52L, 53L, 54:55, 56:58, 59L, 60L, 61L, 62L, 63L, 64:67,
68:72, 73:75, 76L, 77L, 78:79, 80L, 81L, 82L, 83:89,
90:95, 96:100, 101L, 102L, 103L, 104L, 105L, 106L, 107:108,
109L, 110L, 111:112, 113L, 114:121, 122:131, 132:137,
138:140, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L,
149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157:158,
159:160, 161L), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -72L), .drop = TRUE))

You're right to think of scale_fill_manual(). I think this is the more programmable alternative to passing a vector like colorfill to an aesthetic outside aes(). The following plot uses your data and color vector to control how the fill aesthetic is coded throughout the plot, and notice that fill is passed the same variable, Gene_mut, in both layers (alluvium and stratum):
ggplot(data = Allu,
aes(axis1 = Gene_mut, axis2 = Metastasis_Location, y = Freq)) +
geom_alluvium(aes(fill = Gene_mut),
curve_type = "quintic") +
geom_stratum(aes(fill = Gene_mut), width = 1/4) +
scale_fill_manual(values = colorfill) +
geom_text(stat = "stratum", size = 3,
aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Metastasis_Location", "Gene_mut"),
expand = c(0.05, .05)) +
theme_void()
Since Metastasis_Location takes different values than Gene_mut, fill treats those strata as having missing values, which by default are colored grey. You can change that behavior by passing a color string to the na.value parameter of scale_fill_manual().

Group columns and fit in one plot

I have the following data-frame:
structure(list(inst = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A8",
"b7", "X1"), class = "factor"), steps = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("5",
"10", "20"), class = "factor"), family = structure(c(6L,
1L, 4L, 5L, 7L, 2L, 3L, 6L, 7L, 5L, 4L, 1L, 2L, 3L, 3L, 6L, 4L,
1L, 2L, 7L, 5L, 6L, 5L, 4L, 1L, 7L, 2L, 3L, 6L, 5L, 4L, 7L, 1L,
2L, 3L, 6L, 1L, 3L, 5L, 4L, 2L, 7L, 6L, 4L, 1L, 5L, 7L, 2L, 3L,
6L, 4L, 1L, 5L, 7L, 2L, 3L, 6L, 3L, 5L, 4L, 7L, 1L, 2L), .Label = c("Bay",
"Grad", "none", "Upp", "base", "new", "tuna"
), class = "factor"), mean_error = c(5.930259, 6.0611, 6.241703,
6.270109, 6.277435, 6.587473, 6.865757, 5.936106, 6.084044, 6.140153,
6.142072, 6.146425, 6.364658, 6.621481, 6.759502, 7.02175, 7.16422,
7.19518, 7.36932, 7.395606, 7.44191, 5.113961, 5.123312, 5.289946,
5.292267, 5.455671, 5.768393, 5.840368, 5.140513, 5.346728, 5.371491,
5.463127, 5.475944, 5.602034, 5.995647, 5.784786, 6.00454, 6.121524,
6.22509, 6.24901, 6.37396, 6.41903, 4.0439, 4.223119, 4.260518,
4.31062, 4.500065, 4.822419, 5.107085, 4.221596, 4.371242, 4.505292,
4.524415, 4.681877, 4.703846, 5.14499, 4.944005, 5.007325, 5.0561975,
5.1926225, 5.3353825, 5.34204, 5.63557)), row.names = c(64L,
3L, 38L, 55L, 73L, 12L, 21L, 67L, 76L, 58L, 41L, 6L, 15L, 24L,
27L, 70L, 44L, 9L, 18L, 79L, 61L, 63L, 54L, 37L, 2L, 72L, 11L,
20L, 66L, 57L, 40L, 75L, 5L, 14L, 23L, 69L, 8L, 26L, 60L, 43L,
17L, 78L, 62L, 36L, 1L, 53L, 71L, 10L, 19L, 65L, 39L, 4L, 56L,
74L, 13L, 22L, 68L, 25L, 59L, 42L, 77L, 7L, 16L), class = "data.frame")
I am trying to create groups of three steps per inst in the x-axis and fit everything in one plot. The outcome should resemble this
So far I tried:
df_bri %>% select(steps, inst, family, mean_error) %>%
ggplot(aes(x = steps, y = mean_error, fill = mean_error)) +
geom_boxplot()
and I get this:
I don't know how to separate the groups into 3 steps per inst.

fill=steps is what you need:
ggplot(df, aes(x = inst, y = mean_error, fill = steps)) +
geom_boxplot()

How to create histograms for each unique combination of levels from two factors?

I cannot figure out how to use a loop to plot one histogram for each unique combination of levels from TWO factors.
Here is my data: https://www.dropbox.com/sh/exsjhu23fnpwf4r/AABvitLBN1nRMpXcyYMVIOIDa?dl=0
# perhaps need to have factors
df$freq <- as.factor(df$freq)
df$time <- as.factor(df$time)
I learned how to use a loop to plot histograms for ONE factor levels:
# space for plots
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21), nrow=3, ncol=7, byrow=T))
layout.show(a)
# loop
for (i in 1:length(unique(df$freq))) {
value <- subset(df, freq == unique (df$freq)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I tried variations of this loop for TWO factors but that unfortunately does not work:
for (i in 1:length(unique(df[c("freq", "time")]))) {
value <- subset(df, freq == unique (df$freq)[i] & time == unique(df$time)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I would also like to learn how to label each histogram based on the levels of TWO factors (not just one)...

It's more convenient to use by here.
For the titles we prefer characters to factors.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
Then open windows,
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7))
layout.show(a)
and plot.
by(df1, df1[c("freq", "time")], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
Result
Edit
To get the specific order we probably have to do some more stuff.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7, byrow=TRUE)) # with byrow
layout.show(a)
l <- split(df1, df1[c("freq", "time")])
m <- t(sapply(l, function(x) x[1, c(1, 3)])) # matrix of first rows of each subset
m[, 2] <- sub("m", "", m[, 2]) # use the values...
m <- apply(m, 1:2, as.numeric) # ... make numeric
Now we obtain the histograms within a lapply over the list ordered by m.
lapply(l[order(m[, 2], m[, 1])], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
New Result
Data
df1 <- structure(list(freq = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L), .Label = c("4",
"8", "12.5", "16", "20", "25", "31.5"), class = "factor"), thr = c(60L,
25L, 20L, 15L, 15L, 30L, 35L, 60L, 25L, 10L, 15L, 15L, 30L, 35L,
55L, 30L, 15L, 15L, 10L, 25L, 40L, 50L, 25L, 15L, 10L, 15L, 20L,
40L, 50L, 30L, 10L, 15L, 15L, 20L, 25L, 50L, 25L, 10L, 10L, 10L,
20L, 25L, 45L, 20L, 10L, 10L, 10L, 20L, 25L, 45L, 15L, 10L, 10L,
10L, 20L, 30L, 60L, 30L, 10L, 10L, 10L, 15L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 30L, 45L, 25L, 15L, 10L, 15L, 30L, 35L, 50L, 25L,
15L, 10L, 15L, 25L, 35L, 60L, 25L, 10L, 10L, 15L, 20L, 30L, 60L,
25L, 5L, 5L, 10L, 20L, 30L, 45L, 20L, 5L, 10L, 10L, 20L, 30L,
45L, 20L, 10L, 10L, 10L, 20L, 30L, 60L, 30L, 15L, 10L, 15L, 25L,
30L, 55L, 25L, 10L, 10L, 10L, 20L, 30L, 55L, 35L, 10L, 10L, 10L,
20L, 30L, 60L, 35L, 15L, 10L, 10L, 15L, 25L, 50L, 30L, 10L, 10L,
10L, 20L, 25L, 55L, 25L, 10L, 10L, 15L, 25L, 25L, 65L, 30L, 10L,
10L, 15L, 20L, 30L, 60L, 30L, 15L, 15L, 15L, 15L, 30L, 55L, 35L,
15L, 15L, 15L, 25L, 35L, 55L, 35L, 15L, 15L, 15L, 25L, 35L, 60L,
35L, 15L, 15L, 15L, 25L, 35L, 60L, 30L, 10L, 10L, 15L, 25L, 35L,
55L, 30L, 15L, 10L, 10L, 25L, 30L, 50L, 25L, 10L, 10L, 10L, 20L,
30L, 55L, 30L, 10L, 10L, 15L, 20L, 30L, 55L, 30L, 10L, 15L, 20L,
25L, 35L, 55L, 25L, 15L, 15L, 15L, 25L, 40L, 50L, 20L, 10L, 10L,
20L, 30L, 40L, 45L, 25L, 10L, 10L, 10L, 20L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 25L, 55L, 20L, 10L, 10L, 15L, 25L, 35L, 50L, 20L,
10L, 10L, 15L, 25L, 30L, 45L, 20L, 15L, 10L, 10L, 20L, 30L, 50L,
20L, 15L, 15L, 15L, 20L, 30L, 60L, 35L, 15L, 10L, 15L, 25L, 30L,
60L, 35L, 15L, 15L, 15L, 30L, 35L, 55L, 25L, 10L, 15L, 15L, 25L,
35L, 50L, 30L, 10L, 15L, 15L, 25L, 35L, 55L, 25L, 20L, 15L, 15L,
25L, 30L, 55L, 25L, 15L, 15L, 15L, 30L, 35L), time = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("3m", "6m", "9m"), class = "factor")), row.names = c(NA,
-322L), class = "data.frame")

ggplot troubleshoot: Error: Aesthetics must be either length 1 or the same as the data (24): x, y, fill

Sample data:
> dput(droplevels(data[1:50, ]))
structure(list(QtySold = c(3L, -1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 6L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 4L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, -1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L,
1L, 1L, 4L, 5L, 1L, 3L, 1L, 2L, 30L, 2L, 10L, 6L, 10L, 8L, 10L,
20L, 10L, 5L, 1L, 3L, 30L, 20L, 10L, 10L, 90L, 10L, 12L, 10L,
15L, 10L, 10L, 20L, 10L, 10L, 32L, 10L, 10L, 20L, 1L, 2L, 1L,
2L, 2L, 5L, 2L, 1L, 3L, 3L, 1L, 1L, 2L, 4L, 2L, 4L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 3L,
1L, 4L, 2L, 1L, 4L, 4L, 5L, 1L, 3L, 2L, 2L, 4L, 1L, 3L, 3L, 1L,
3L, 6L, 7L, 1L, 3L, 7L, 2L, 1L, 4L, 2L, 3L, 3L, 5L, 2L, 1L, 1L,
4L, 6L, 1L, 1L, 1L, 1L, 2L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
5L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 2L, 2L, 3L, 4L, 1L, 3L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 4L, 2L, 1L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 3L, 1L, -1L, 1L, 1L, 1L, 2L, 2L, 2L, 10L, 1L, 1L, -4L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
4L, -1L, 2L, 3L, 1L, 1L, 5L, 1L, 10L, 1L, 1L, 4L, 1L, 1L, 1L,
2L, 30L, 2L, 12L, 20L, 5L, 1L, 5L, 3L, 4L, 12L, 6L, 10L, 8L,
4L, 6L, 8L, 3L, 6L, 1L, -1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 3L,
8L, 6L, 10L, 6L, 15L, 10L, 6L, 8L, 10L, 6L, 20L, 10L, 10L, 10L,
10L, 1L, 10L, 10L, 5L, 10L, 20L, 4L, 12L, 10L, 10L, 5L, 10L,
6L, 1L, 1L, 2L, 4L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 3L, 4L, 13L, 4L, 3L, 1L, 12L, 1L, 10L, 1L, 6L, 1L, 1L, 3L,
10L, 2L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 8L, 12L, 6L,
12L, 4L, 2L, 3L, 24L, 10L, 8L, 6L, 1L, 3L, 1L, 1L, 1L, 6L, 1L,
1L, 1L, -1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 10L,
-1L), PRODUCT_SUB_LINE_DESCR = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Handpieces",
"PRIVATE LABEL", "SUNDRY"), class = "factor"), MAJOR_CATEGORY_KEY = structure(c(23L,
23L, 23L, 23L, 21L, 21L, 21L, 23L, 23L, 23L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 25L, 19L, 19L, 4L, 4L, 9L, 9L,
9L, 9L, 9L, 9L, 23L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 17L, 16L, 4L, 4L, 4L, 4L, 11L, 11L, 4L, 4L, 4L,
4L, 11L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 21L, 12L, 12L, 12L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 13L, 13L, 7L, 18L, 7L,
19L, 19L, 19L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 13L, 13L, 11L, 11L, 6L, 12L, 10L, 10L,
4L, 4L, 4L, 4L, 4L, 4L, 10L, 19L, 19L, 19L, 19L, 19L, 4L, 4L,
11L, 11L, 11L, 11L, 11L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 11L, 16L, 16L, 16L, 16L, 16L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 16L, 16L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 21L, 4L,
4L, 4L, 19L, 19L, 19L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 12L, 12L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 13L,
4L, 4L, 4L, 4L, 4L, 4L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 18L,
21L, 21L, 18L, 18L, 18L, 18L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 19L, 19L, 18L, 18L, 4L, 11L), .Label = c("AIR ",
"AML ", "ANS ", "ASE ", "ASP ", "B&D ", "BLE ", "C&P ", "CBL ",
"CEM ", "CMP ", "CRN ", "END ", "FNP ", "GYP ", "HND ", "IMP ",
"INS ", "OTH ", "P&P ", "PRE ", "RTC ", "SME ", "UCL ", "XRY "
), class = "factor")), row.names = c(NA, 500L), class = "data.frame")
And the relevant code
> newdf = subset(data, select = c(QtySold, PRODUCT_SUB_LINE_DESCR, MAJOR_CATEGORY_KEY))
> sample = newdf %>%
+ filter(QtySold < 0) %>%
+ group_by(PRODUCT_SUB_LINE_DESCR,MAJOR_CATEGORY_KEY) %>%
+ summarise(returns = sum(QtySold)) %>%
+ spread(PRODUCT_SUB_LINE_DESCR,returns, fill=0) %>%
+ mutate(total_returns = `PRIVATE LABEL` + SUNDRY + Handpieces,
+ PercentageReturn_PL = round(`PRIVATE LABEL`/ total_returns*100,1))
> sample %>%
+ group_by(PRODUCT_SUB_LINE_DESCR) %>%
+ summarise(count=n()) %>%
+ mutate(percent= paste0(round(count/sum(count)*100,1),'%'))
Error in grouped_df_impl(data, unname(vars), drop) :
Column `PRODUCT_SUB_LINE_DESCR` is unknown
My scenario:
List item
I have a data set of three products: Sundry, Handpieces and Private Label.
I filtered the data set based on (QtySold < 0) category and group the new one based on "PRODUCT_SUB_LINE_DESCR" and
"MAJOR_CATEGORY_KEY".
I want to see the percentage of returned items which belong to Private Label category. My code has a mutate command with a new column "PercentageReturn_PL".
Finally, when I am done with the math, I want to plot this whole thing with the MAJOR_CATEGORY_KEY on the Y-axis and the stacked barplot show the segments of percentage break down for three products within one MAJOR_CATEGORY_KEY.
How can I fix this error?
> sample %>%
+ group_by(PRODUCT_SUB_LINE_DESCR) %>%
+ summarise(count=n()) %>%
+ mutate(percent= paste0(round(count/sum(count)*100,1),'%'))
Error in grouped_df_impl(data, unname(vars), drop) :
Column `PRODUCT_SUB_LINE_DESCR` is unknown

The problem was that you spread out a column and then tried to use it. Also a lot of extra stuff going on in the graph.
Here's a way to get the percent of returns that are PRIVATE LABEL by major category:
sample_long =
newdf %>%
filter(QtySold < 0) %>%
group_by(MAJOR_CATEGORY_KEY) %>%
summarize(returns = sum(QtySold),
percent_returns_pl = sum(QtySold[PRODUCT_SUB_LINE_DESCR == "PRIVATE LABEL"]) / returns)
And then we can plot easily:
ggplot(
sample_long,
aes(x = MAJOR_CATEGORY_KEY, y = percent_returns_pl)
) +
geom_col() +
geom_text(aes(label = scales::percent(percent_returns_pl)), vjust = -0.5) +
scale_y_continuous(labels = scales::percent)
It's a boring plot with sample data, should be much more interesting with the full data.

reshape/remould data frame to create normalized bar chart and pie chart

I have the following data_frame structure which has been read from a csv file (appended). Basically, this summarises for each Operator (A M D L J) whether their score is Excellent, Good, Ok, Poor or Terrible. The other fields date and scorer ( I plan to use later but are not required at the moment).
What I am struggling with is how to reduce this data to a format that allows me to plot a bar chart (normalized by dividing total counts for each operator) and a bar chart. How do I reduce this data frame to something like the following which will allow me to greate geom_bar.
Operator Score Count
A Good 11
A Poor 5
A Ok 3
A Terrible 0
A Excellent 0
D Good 36
D Poor 50
D Ok 10
D Terrible 1
D Excellent 0
I know I can subset the initial data frame according to operator and then get the numbers from summary
dfA = subset(df, Operator=='A')
summary(dfA)
but I would like to automate this process (i.e automatically remould the data frame into the above structure from which I can use ggplot2 to visualise the results). However, I have no idea where to start with this problem
structure(list(Operator = structure(c(5L, 5L, 5L, 5L, 5L, 5L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
3L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 5L, 2L, 2L, 2L,
2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 5L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 5L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 2L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 1L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 2L, 4L, 4L, 4L, 4L,
3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 2L,
2L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 5L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "D", "J", "L", "M"), class = "factor"),
ROI_Score = structure(c(3L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 3L,
2L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L,
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 3L,
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L,
3L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 1L, 1L, 3L, 3L,
1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L,
1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L,
1L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 3L,
3L, 1L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 2L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 1L,
3L, 3L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
4L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 2L,
3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
1L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 1L,
2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 2L,
2L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L), .Label = c("Good",
"OK", "Poor", "Terrible"), class = "factor"), Date = structure(c(3L,
3L, 5L, 5L, 5L, 7L, 3L, 3L, 9L, 9L, 9L, 11L, 11L, 3L, 3L,
5L, 5L, 5L, 7L, 7L, 7L, 11L, 11L, 11L, 3L, 15L, 15L, 21L,
13L, 17L, 17L, 19L, 21L, 13L, 13L, 13L, 15L, 15L, 17L, 17L,
17L, 19L, 19L, 19L, 21L, 21L, 30L, 30L, 23L, 25L, 25L, 25L,
27L, 27L, 27L, 29L, 29L, 29L, 23L, 23L, 25L, 25L, 25L, 27L,
27L, 27L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 36L, 2L, 36L,
36L, 36L, 39L, 39L, 34L, 34L, 34L, 36L, 36L, 36L, 39L, 39L,
2L, 2L, 32L, 34L, 34L, 36L, 41L, 41L, 41L, 43L, 1L, 38L,
38L, 41L, 42L, 43L, 38L, 38L, 41L, 41L, 41L, 42L, 42L, 42L,
43L, 43L, 1L, 1L, 1L, 38L, 42L, 42L, 42L, 42L, 1L, 1L, 1L,
3L, 3L, 7L, 3L, 3L, 3L, 5L, 7L, 11L, 3L, 3L, 3L, 3L, 5L,
5L, 5L, 7L, 7L, 7L, 9L, 9L, 11L, 11L, 11L, 13L, 15L, 17L,
19L, 19L, 21L, 21L, 13L, 21L, 13L, 13L, 13L, 15L, 17L, 17L,
17L, 19L, 19L, 21L, 21L, 21L, 29L, 29L, 29L, 30L, 23L, 25L,
29L, 29L, 23L, 23L, 23L, 25L, 25L, 25L, 27L, 27L, 30L, 30L,
30L, 32L, 32L, 32L, 2L, 2L, 39L, 39L, 32L, 32L, 32L, 34L,
34L, 34L, 36L, 36L, 2L, 2L, 2L, 43L, 1L, 38L, 41L, 41L, 42L,
42L, 42L, 43L, 43L, 1L, 1L, 43L, 1L, 42L, 1L, 1L, 1L, 32L,
32L, 36L, 2L, 36L, 36L, 36L, 39L, 39L, 34L, 34L, 34L, 36L,
36L, 36L, 39L, 39L, 2L, 2L, 32L, 34L, 34L, 36L, 10L, 4L,
6L, 6L, 10L, 10L, 10L, 12L, 4L, 4L, 12L, 12L, 6L, 6L, 6L,
8L, 8L, 8L, 12L, 12L, 14L, 16L, 14L, 14L, 18L, 20L, 14L,
18L, 18L, 18L, 14L, 14L, 14L, 16L, 16L, 16L, 22L, 22L, 22L,
28L, 28L, 31L, 28L, 28L, 28L, 31L, 31L, 31L, 33L, 33L, 33L,
35L, 35L, 35L, 37L, 37L, 37L, 33L, 33L, 33L, 35L, 37L, 37L,
40L, 40L, 32L, 32L, 32L, 2L, 2L, 39L, 39L, 32L, 32L, 32L,
34L, 34L, 34L, 36L, 36L, 2L, 2L, 2L, 6L, 6L, 10L, 10L, 10L,
10L, 4L, 4L, 6L, 6L, 8L, 8L, 8L, 10L, 10L, 12L, 4L, 8L, 8L,
8L, 8L, 12L, 4L, 4L, 4L, 4L, 8L, 12L, 16L, 16L, 14L, 16L,
18L, 18L, 20L, 20L, 20L, 14L, 14L, 20L, 20L, 22L, 22L, 14L,
16L, 18L, 18L, 18L, 18L, 24L, 24L, 24L, 26L, 26L, 31L, 31L,
24L, 26L, 26L, 26L, 26L, 24L, 24L, 24L, 24L, 31L, 31L, 40L,
37L, 33L, 33L, 33L, 33L, 35L, 35L, 35L, 37L, 37L, 37L, 37L,
40L), .Label = c("01/02/2013", "01/03/2013", "04/02/2013",
"04/03/2013", "05/02/2013", "05/03/2013", "06/02/2013", "06/03/2013",
"07/02/2013", "07/03/2013", "08/02/2013", "08/03/2013", "11/02/2013",
"11/03/2013", "12/02/2013", "12/03/2013", "13/02/2013", "13/03/2013",
"14/02/2013", "14/03/2013", "15/02/2013", "15/03/2013", "18/02/2013",
"18/03/2013", "19/02/2013", "19/03/2013", "20/02/2013", "20/03/2013",
"21/02/2013", "22/02/2013", "22/03/2013", "25/02/2013", "25/03/2013",
"26/02/2013", "26/03/2013", "27/02/2013", "27/03/2013", "28/01/2013",
"28/02/2013", "28/03/2013", "29/01/2013", "30/01/2013", "31/01/2013"
), class = "factor"), Scorer = structure(c(2L, 2L, 3L, 3L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 2L, 2L,
3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 1L,
3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
3L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 1L,
1L, 1L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 1L, 3L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
2L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
1L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L,
1L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 3L, 3L, 1L, 1L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 1L,
3L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 3L, 2L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 3L, 3L,
1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 1L,
2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L), .Label = c("", "B", "G"), class = "factor")), .Names = c("Operator",
"ROI_Score", "Date", "Scorer"), row.names = c(NA, -412L), class = "data.frame")

Here's to prepare your data using data.table:
require(data.table)
dt <- data.table(df)
ops <- as.character(unique(dt$Operator))
scr <- as.character(unique(dt$ROI_Score))
oo <- setkey(dt[, .N, by="Operator,ROI_Score"], Operator,
ROI_Score)[CJ(ops, scr)][is.na(N), N:= 0L]
And here's how you can get a normalised bar-chart with this data:
oo[, N.norm := N/sum(N), by=Operator]
One way to plot this would be with x = Operator:
require(ggplot2)
ggplot(data = oo, aes(x = Operator, y = N.norm)) +
geom_bar(positon="stack", stat="identity", aes(fill = ROI_Score))

You can simply do something like this to prepare your data :
data.frame(table(Operator=df$Operator, Score=df$ROI_Score))
Which gives :
Operator Score Freq
1 A Good 11
2 D Good 36
3 J Good 54
4 L Good 44
5 M Good 28
6 A OK 3
7 D OK 10
8 J OK 9
9 L OK 4
10 M OK 7
11 A Poor 5
12 D Poor 50
13 J Poor 56
14 L Poor 67
15 M Poor 27
16 A Terrible 0
17 D Terrible 1
18 J Terrible 0
19 L Terrible 0
20 M Terrible 0

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