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 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")
I have a very large igraph but when I plot, I want that just specific labels appear. Those labels are saved in different dfs (activator and receptor).
I tried with:
nodes <- data.frame(symbol = LETTERS[1:7])
edges <- data.frame(from=c("B", "C", "D", "D", "D", "E", "A", "G"),
to = c("A", "B", "A", "E", "B", "A", "F", "F"))
activator <- c('A')
receptor <- c('E', 'F', 'G')
g <- graph_from_data_frame(edges, directed=TRUE, vertices=nodes)
print(g, e=TRUE, v=TRUE)
plot(g, vertex.label1 = ifelse(V(g)$name %in% c(activator, receptor), V(g)$name, NA))
But it is not working. Any comments?
I wonder if there is a way to arrange multiple of the nice transition plots of the Gmisc package on one page (e.g. two next to each other or two-by-two)? I tried various common approaches (e.g. par(mfrow = c(2,2)) and grid.arrange()) but was not successful thus far. I would appreciate any help. Thanks!
library(Gmisc)
data.1 <- data.frame(source = c("A", "A", "A", "B", "B", "C", "C"),
target = c("A", "B", "C", "B", "C", "C", "C"))
data.2 <- data.frame(source = c("D", "D", "E", "E", "E", "E", "F"),
target = c("D", "E", "D", "E", "F", "F", "F"))
transitions.1 <- getRefClass("Transition")$new(table(data.1$source, data.1$target), label = c("Before", "After"))
transitions.2 <- getRefClass("Transition")$new(table(data.2$source, data.2$target), label = c("Before", "After"))
# wish to render transition 1 and transition 2 next to each other
transitions.1$render()
transitions.2$render()
This was actually a bug prior to the 1.9 version (uploading to CRAN when writing this, available now from GitHub). What you need to do is use the grid::viewport system:
library(grid)
grid.newpage()
pushViewport(viewport(name = "basevp", layout = grid.layout(nrow=1, ncol=2)))
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 1))
transitions.1$render(new_page = FALSE)
popViewport()
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 2))
transitions.2$render(new_page = FALSE)
I'm working on a project where I need to repeatedly subset a data.frame based on different combinations of attributes. Right now I'm subsetting the data.frame using the merge function as I don't know what the attributes input will be at run time, and this works. However, I'm wondering if there is a faster way to create the subsets.
require(data.table)
df <- structure(list(att1 = c("e", "a", "c", "a", "d", "e", "a", "d", "b", "a", "c", "a", "b", "e", "e", "c", "d", "d", "a", "e", "b"),
att2 = c("b", "d", "c", "a", "e", "c", "e", "d", "e", "b", "e", "e", "c", "e", "a", "a", "e", "c", "b", "b", "d"),
att3 = c("c", "b", "e", "b", "d", "d", "d", "c", "c", "d", "e", "a", "d", "c", "e", "a", "d", "e", "d", "a", "e"),
att4 = c("c", "a", "b", "a", "e", "c", "a", "a", "b", "a", "a", "e", "c", "d", "b", "e", "b", "d", "d", "b", "e")),
.Names = c("att1", "att2", "att3", "att4"), class = "data.frame", row.names = c(NA, -21L))
#create combinations of attributes
#attributes to search through
cnames <- colnames(df)
att_combos <- data.table()
for(i in 2:length(cnames)){
combos <- combn(cnames, i)
for(x in 1:ncol(combos)){
df_sub <- unique(df[,combos[1:nrow(combos), x]])
att_combos <- rbind(att_combos, df_sub, fill = T)
}
}
rm(df_sub, i, x, combos, cnames)
for(i in 1:nrow(att_combos)){
att_sub <- att_combos[i, ]
att_sub <- att_sub[, is.na(att_sub)==F, with = F]
#need to subset data.frame here - very slow on large data.frames
#anyway to speed this up?
df_subset_for_analysis <- merge(df, att_sub)
}
I would use data.table keys on the columns you want to subset on, and then generate a data.table (at runtime) with the combinations you are interested in, and then merge the two.
Here is an example with a single combination of attributes (simple_combinations) and one with multiple combinations of attributes (multiple_combinations):
require(data.table)
df <- structure(list(att1 = c("e", "a", "c", "a", "d", "e", "a", "d", "b", "a", "c", "a", "b", "e", "e", "c", "d", "d", "a", "e", "b"),
att2 = c("b", "d", "c", "a", "e", "c", "e", "d", "e", "b", "e", "e", "c", "e", "a", "a", "e", "c", "b", "b", "d"),
att3 = c("c", "b", "e", "b", "d", "d", "d", "c", "c", "d", "e", "a", "d", "c", "e", "a", "d", "e", "d", "a", "e"),
att4 = c("c", "a", "b", "a", "e", "c", "a", "a", "b", "a", "a", "e", "c", "d", "b", "e", "b", "d", "d", "b", "e")),
.Names = c("att1", "att2", "att3", "att4"), class = "data.frame", row.names = c(NA, -21L))
# Convert to data.table
dt <- data.table(df)
# Set key on the columns used for "subsetting"
setkey(dt, att1, att2, att3, att4)
# Simple subset on a single set of attributes
simple_combinations <- data.table(att1 = "d", att2 = "e", att3 = "d", att4 = "e")
setkey(simple_combinations, att1, att2, att3, att4)
# Merge to generate simple output subset (simple_combinations of att present in dt)
simple_subset <- merge(dt, simple_combinations)
# Complex (multiple) sets of attributes
multiple_combinations <- data.table(expand.grid(att1=c("d"), att2=c("c", "d", "e"),
att3 = c("d"), att4 = c("b", "e")))
setkey(multiple_combinations, att1, att2, att3, att4)
# Merge to generate output subset (multiple_combinations of att present in dt)
multiple_subset <- merge(dt, multiple_combinations)
The output is in simple_subset and multiple_subset.