I am trying to create a graph where the x axis (a factor) is reordered by descending order of the y axis (numerical values), but only for one of two levels of another factor.
Originally, I tried using the code below:
reorder(factor1, desc(value1))
However, this code only reorganizes the graph (in a descending order) by the sum of the two values under each factor2 (I presume); while I am only interested in reorganizing the data for one level (i.e. "A") under factor2.
Here is some sample data to illustrate better.
sampledata <- data.frame(factor1 = c("A", "A", "B", "B", "C", "C", "D", "D", "E", "E",
"F", "F", "G", "G", "H", "H", "I", "I", "J", "J"),
factor2 = c("A", "H", "A", "H", "A", "H", "A", "H", "A", "H",
"A", "H", "A", "H", "A", "H", "A", "H", "A", "H"),
value1 = c(1, 5, 6, 2, 6, 8, 10, 21, 30, 5,
3, 5, 4, 50, 4, 7, 15, 48, 20, 21))
Here is what I used previously:
sampledata %>%
ggplot(aes(x=reorder(factor1, desc(value1)), y=value1, group=factor2, color=factor2)) +
geom_point()
The reason why I would like to reorder by a specific level (say factor2=="A") is that I can view any deviance of the values for factor2=="H" away from "A" points.
I would appreciate using tidyverse or dplyr as means to solve this problem.
library(ggplto2)
library(dplyr)
sampledata %>%
mutate(value2 = +(factor2=="A")*value1) %>%
ggplot(aes(x=reorder(factor1, desc(value2 + value1/max(value1))), y=value1,
group=factor2, color=factor2)) +
geom_point() +
xlab("factor1")
Related
I would like to create a tree map based on the count of "names". However, I am not sure how to do so. Seeking you help on this matter.
names <- c("A", "B", "B", "C", "D", "A", "A", "A", "A", "G", "B", "F", "F", "H")
names <- names %>% as.factor()
ggplot(names, aes(area= names, fill= names) + geom_treemap()
Many thanks
names <- c("A", "B", "B", "C", "D", "A", "A", "A", "A", "G", "B", "F", "F", "H")
names <- data.frame(names)
names <- names %>%
count(names)
ggplot(names, aes(area= n, fill= names)) + geom_treemap()
I'm trying to recreate this figure below, where the "to" variable (i.e. target genes) is further grouped into outer (labelled) categories (i.e. receptors).
I have generated some example data, unfortunately I'm not sure what format is needed for the additional outer categories, but it's possibly not far off the link format.
library(circlize)
links <- data.frame(from = c("A", "B", "C", "B", "C"),
to = c("D", "E", "F", "D", "E"),
value = c(1, 1, 1, 1, 1))
categories <- data.frame(from = c("D", "E", "F", "D", "E"),
to = c("X", "X", "Y", "Y", "Y"),
value = c(1, 1, 1, 1, 1))
chordDiagram(links)
Any assistance greatly appreciated!
I am working on a projection model for sports where I need to understand in a certain team's most recent game:
Who is their next opponent? (solved)
When is the last time their next opponent played?
reprex that can be used below. Using row 1 as an example, I would need to understand that "a"'s next opponent "e"'s most recent game was game_id_ 3.
game_id_ <- c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6)
game_date_ <- c(rep("2021-01-29", 6), rep("2021-01-30", 6))
team_ <- c("a", "b", "c", "d", "e", "f", "b", "c", "d", "f", "e", "a")
opp_ <- c("b", "a", "d", "c", "f", "e", "c", "b", "f", "d", "a", "e")
df <- data.frame(game_id_, game_date_, team_, opp_)
#Next opponent
df <- df %>%
arrange(game_date_, game_id_, team_) %>%
group_by(team_) %>%
mutate(next_opp = lead(opp_, n = 1L))
If I can provide more details, please let me know.
We can use match to return the corresponding game_id_
library(dplyr)
df %>%
arrange(game_date_, game_id_, team_) %>%
group_by(team_) %>%
mutate(next_opp = lead(opp_, n = 1L)) %>%
ungroup %>%
mutate(last_time = game_id_[match(next_opp, opp_)])
I have a dataset with calculated quantiles for each department and country. It looks like this:
df <- structure(list(quantile = c("p5", "p25", "p50", "p75", "p95",
"p5", "p25", "p50", "p75", "p95", "p5", "p25", "p50", "p75",
"p95", "p5", "p25", "p50", "p75", "p95"), value = c(6, 12, 20,
33, 61, 6, 14, 23, 38, 63, 7, 12, 17, 26, 50, 7, 12, 18, 26,
51), country = c("A", "A", "A", "A", "A", "B", "B", "B", "B",
"B", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B"), dep = c("D",
"D", "D", "D", "D", "D", "D", "D", "D", "D", "I", "I", "I", "I",
"I", "I", "I", "I", "I", "I"), kpi = c("F", "F", "F", "F", "F",
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F",
"F", "F")), row.names = c(NA, -20L), class = c("tbl_df", "tbl",
"data.frame"))
Now, I would like to build a boxplot for each department comparing countries and using p5/p95 instead of min/max similar to this plot but without outliers (hence, Train_number would be countries):
The corresponding code to this plot is (from question ggplot2, geom_boxplot with custom quantiles and outliers):
ggplot(MyData, aes(factor(Stations), Arrival_Lateness,
fill = factor(Train_number))) +
stat_summary(fun.data = f, geom="boxplot",
position=position_dodge(1))+
stat_summary(aes(color=factor(Train_number)),fun.y = q, geom="point",
position=position_dodge(1))
I tried to derive a solution from the code above and the provided answers. Unfortunately I lack the knowledge how to provide the neccessary values from the variables quantile and value to ggplot(). Is there an argument in the stat_summary() function I missed and could use? Or just another simple solution?
Whatever data you have provided from that you can generate the following plot
library(ggplot2)
f <- function(x) {
r <- quantile(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
r
}
ggplot(df, aes(factor(dep), value)) +
stat_summary(fun.data = f, geom="boxplot",
position=position_dodge(1))+
facet_grid(.~country, scales="free")
I don't know whether it is correct or not.
I have a graph. One can see that the complect subgraph A<->B<->C and E<->D<->F (pattern) occurs twice in the graph. I found the motifs and took 1st and 7th motifs from the list of igraphs.
libraty(igraph)
el <- matrix( c("A", "B",
"A", "C",
"B", "A",
"B", "C",
"C", "A",
"C", "B",
"C", "E",
"E", "D",
"E", "F",
"D", "E",
"D", "F",
"F", "E",
"F", "D"),
nc = 2, byrow = TRUE)
graph <- graph_from_edgelist(el)
pattern <- graph.isocreate(size=3, number = 15, directed=TRUE)
iso <- subgraph_isomorphisms(pattern, graph)
motifs <- lapply(iso, function (x) { induced_subgraph(graph, x) })
V(graph)$id <- seq_len(vcount(graph))
V(graph)$color <- "white"
par(mfrow=c(1,2))
plot(graph, edge.curved=TRUE, main="Original graph")
m1 <- V(motifs[[1]])$id; m2 <- V(motifs[[7]])$id
V(graph)[m1]$color="red"; V(graph)[m2]$color="green"
plot(graph, edge.curved=TRUE, main="Highlight graph")
I have a solution by hand selection motifs[[1]], motifs[[7]].
Question.
How to find the vertex lists of the pattern subgraph (for example, complect subgraph) automatically?