Following this question, Multiple values in one tile with geom_tile and matrix plot, I'd like the tile attributes in agreement_num to be symmetrical, i.e. for the reverse pair combination, I'd like to plot the same agreement_num. I'm creating a new question since this entails finding the reverse combination of pairs, assigning the corresponding agreement_num and then inserting new rows for each combination and agreement_num. E.g. from Kazakhstan to Kyrgyzstan there are six rows and six unique agreement_num. The expected output would be to have these rows in the dataframe:
from to weight agreement_num
Kyrgyzstan Kazakhstan 1.337996e+08 51
Kyrgyzstan Kazakhstan 1.337996e+08 176
Kyrgyzstan Kazakhstan 1.337996e+08 58
Kyrgyzstan Kazakhstan 1.337996e+08 224
Kyrgyzstan Kazakhstan 1.337996e+08 133
Kyrgyzstan Kazakhstan 1.337996e+08 135
to be able to plot the same tile attribute for the reverse country combination in the matrix plot.
I've managed to get quite far to create the data below, but cannot figure out the last steps of inserting new rows and assigning the agreement_num for each row:
# Create empty vectors to store values
oneway <- logical(nrow(plot_data))
additional_value <- list()
# Iterate over each row of the data frame
for (i in 1:nrow(plot_data)) {
# Find indices of reverse pairs
reverse_indices <- which(plot_data$from == plot_data$to[i] & plot_data$to == plot_data$from[i] & seq_len(nrow(plot_data)) != i)
# Assign the oneway value
oneway[i] <- length(reverse_indices) > 0
# Assign additional value based on reverse combination
if (oneway[i]) {
# Add the additional values from the "agreement_num" column of the reverse pairs
additional_value[[i]] <- unique(plot_data$agreement_num[reverse_indices])
} else {
# Assign NA if oneway is FALSE
additional_value[[i]] <- NA
}
}
# Add the vectors as new columns to the data frame
plot_data$oneway <- oneway
plot_data$additional_value <- additional_value
Reproducible data:
plot_data <- structure(list(from = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L), levels = c("Afghanistan",
"Kazakhstan", "Kyrgyzstan", "Tajikistan", "Turkmenistan", "Uzbekistan"
), class = "factor"), to = structure(c(1L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 2L, 6L), levels = c("Afghanistan",
"Kazakhstan", "Kyrgyzstan", "Tajikistan", "Turkmenistan", "Uzbekistan"
), class = "factor"), weight = c(1291072130433.34, 480160896152.234,
480160896152.234, 480160896152.234, 480160896152.234, 480160896152.234,
480160896152.234, 3474907531417.02, 3474907531417.02, 3474907531417.02,
867103764128.709, 867103764128.709, 7791981051421.92, 7791981051421.92,
133799551.098735, 1102379004.66647), agreement_num = c(NA, 51L,
176L, 58L, 224L, 133L, 135L, 58L, 51L, 224L, 51L, 224L, 51L,
224L, NA, NA), com.x = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L), com.y = c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA,
-16L))
Perhaps there is an easier way to do this, would be happy to get suggestions.
Example of expected output:
plot_data
from to weight agreement_num
Kazakhstan Afghanistan 1.291072e+12 NA
Kazakhstan Kyrgyzstan 4.801609e+1 51
Kazakhstan Kyrgyzstan 4.801609e+1 176
Kazakhstan Kyrgyzstan 4.801609e+1 58
Kazakhstan Kyrgyzstan 4.801609e+1 224
Kazakhstan Kyrgyzstan 4.801609e+1 133
Kazakhstan Kyrgyzstan 4.801609e+1 135
[other rows]
Kyrgyzstan Kazakhstan 1.337996e+08 NA
desired output
from to weight agreement_num
Kazakhstan Afghanistan 1.291072e+12 NA
Kazakhstan Kyrgyzstan 4.801609e+1 51
Kazakhstan Kyrgyzstan 4.801609e+1 176
Kazakhstan Kyrgyzstan 4.801609e+1 58
Kazakhstan Kyrgyzstan 4.801609e+1 224
Kazakhstan Kyrgyzstan 4.801609e+1 133
Kazakhstan Kyrgyzstan 4.801609e+1 135
[other rows]
Kyrgyzstan Kazakhstan 1.337996e+08 51
Kyrgyzstan Kazakhstan 1.337996e+08 176
Kyrgyzstan Kazakhstan 1.337996e+08 58
Kyrgyzstan Kazakhstan 1.337996e+08 224
Kyrgyzstan Kazakhstan 1.337996e+08 133
Kyrgyzstan Kazakhstan 1.337996e+08 135
not desired output
from to weight agreement_num
Kazakhstan Afghanistan 1.291072e+12 NA
Kazakhstan Kyrgyzstan 4.801609e+1 51
Kazakhstan Kyrgyzstan 4.801609e+1 176
Kazakhstan Kyrgyzstan 4.801609e+1 58
Kazakhstan Kyrgyzstan 4.801609e+1 224
Kazakhstan Kyrgyzstan 4.801609e+1 133
Kazakhstan Kyrgyzstan 4.801609e+1 135
Afghanistan Kazakhstan 1.291072e+12 NA #(don't want this row because Afghanistan Kazakhstan is not represented in plot_data)
[other rows]
Kyrgyzstan Kazakhstan 1.337996e+08 51
Kyrgyzstan Kazakhstan 1.337996e+08 176
Kyrgyzstan Kazakhstan 1.337996e+08 58
Kyrgyzstan Kazakhstan 1.337996e+08 224
Kyrgyzstan Kazakhstan 1.337996e+08 133
Kyrgyzstan Kazakhstan 1.337996e+08 135
Adding the code for plotting:
# Set agreement_num as factor
plot_data$agreement_num <- as.factor(plot_data$agreement_num)
# Allow multiple tiles
plot_data <- plot_data |>
mutate(
x = as.numeric(from),
y = as.numeric(to),
ymin = y - .5, ymax = y + .5
) |>
mutate(
n = n(),
xmin = x + scales::rescale(row_number(),
from = c(1, unique(n) + 1),
to = .5 * c(-1, 1)
),
xmax = x + scales::rescale(row_number() + 1,
from = c(1, unique(n) + 1),
to = .5 * c(-1, 1)
),
.by = c(from, to)
)
# Create color palette
par(mar=c(0,0,1,0))
coul <- brewer.pal(9, "Set3")
# Plot
ggplot(plot_data, aes(x = from, y = to, fill = agreement_num)) +
geom_rect(
aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)
) +
scale_fill_manual(values = coul) +
theme_bw() +
scale_x_discrete(drop = FALSE) +
scale_y_discrete(drop = FALSE) +
labs(title = "Community 2") +
theme(plot.title = element_text(size=17),
axis.text.x = element_text(
size = 12, angle = 270,
hjust = 0, vjust = 0
),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
aspect.ratio = 1
)
I think an NA in the agreement_num column suggests that's a pair we need to reverse and augment ...
library(dplyr)
plot_data |>
filter(is.na(agreement_num)) |>
select(from=to, to=from, weight) |>
inner_join(plot_data, by = c("from", "to")) |>
rename(from=to, to=from, weight=weight.x) |>
select(-weight.y) |>
bind_rows(plot_data)
# to from weight agreement_num com.x com.y
# 1 Kazakhstan Kyrgyzstan 1.337996e+08 51 2 2
# 2 Kazakhstan Kyrgyzstan 1.337996e+08 176 2 2
# 3 Kazakhstan Kyrgyzstan 1.337996e+08 58 2 2
# 4 Kazakhstan Kyrgyzstan 1.337996e+08 224 2 2
# 5 Kazakhstan Kyrgyzstan 1.337996e+08 133 2 2
# 6 Kazakhstan Kyrgyzstan 1.337996e+08 135 2 2
# 7 Afghanistan Kazakhstan 1.291072e+12 NA 2 2
# 8 Kyrgyzstan Kazakhstan 4.801609e+11 51 2 2
# 9 Kyrgyzstan Kazakhstan 4.801609e+11 176 2 2
# 10 Kyrgyzstan Kazakhstan 4.801609e+11 58 2 2
# 11 Kyrgyzstan Kazakhstan 4.801609e+11 224 2 2
# 12 Kyrgyzstan Kazakhstan 4.801609e+11 133 2 2
# 13 Kyrgyzstan Kazakhstan 4.801609e+11 135 2 2
# 14 Tajikistan Kazakhstan 3.474908e+12 58 2 2
# 15 Tajikistan Kazakhstan 3.474908e+12 51 2 2
# 16 Tajikistan Kazakhstan 3.474908e+12 224 2 2
# 17 Turkmenistan Kazakhstan 8.671038e+11 51 2 2
# 18 Turkmenistan Kazakhstan 8.671038e+11 224 2 2
# 19 Uzbekistan Kazakhstan 7.791981e+12 51 2 2
# 20 Uzbekistan Kazakhstan 7.791981e+12 224 2 2
# 21 Kazakhstan Kyrgyzstan 1.337996e+08 NA 2 2
# 22 Uzbekistan Kyrgyzstan 1.102379e+09 NA 2 2
(Note that the augmented rows are first.)