I am using future_map to create several plots where I iterate through a list of variables and output/save a png file per variable to a folder. So there is no output that needs to be shown in the console or the "plot" pane.
The plotting part of the function:
ggplot(aes(sample = value,
color = key)) +
stat_qq(alpha = 0.8, size = 0.5) +
theme_light() +
theme(legend.position = "none") +
stat_qq_line() +
facet_wrap(~key,
ncol = 4) +
ggtitle(.var) +
ggsave(filename = here::here(paste0(.path,
.var,
".png")),
units = "cm",
width = 25,
height = 10)}
How I map the function:
plan(multiprocess(workers = 10))
future_map(names_list,
~check_dists(df_lips_imputed, .x, "doc/distributions/testing2/"),
verbose = FALSE)
However, after all files are created, I can see they are in the folder, this is slowly printed (takes a while, ~1k iterations):
[[1]]
[[2]]
[[3]]
...
Does anyone know how to suppress this output?
Many thanks!
If you install the development version of furrr with
devtools::install_github("DavisVaughan/furrr")
You can then use future_walk, which is acts like walk does versus map. With walk the function acts by side effects and so the return value is simply the input.
I was having the same issue. I'm not sure if this will change the time that it takes to print out the list elements at the end, but if you save your future_map call as a throwaway variable, it will save the output in that variable instead of printing out and clogging up your console or log file:
x <- future_map(names_list,
~check_dists(df_lips_imputed, .x, "doc/distributions/testing2/"),
verbose = FALSE)
Related
I have 11 plots and used a looping function to plot them see my code below. However, I can't get them to fit in just 1 page or less. The plots are actually too big. I am using R software and writing my work in RMarkdown. I have spent almost an entire week trying to resolve this.
group_by(Firm_category) %>%
doo(
~ggboxplot(
data =., x = "Means.type", y = "means",
fill ="grey", palette = "npg", legend = "none",
ggtheme = theme_pubr()
),
result = "plots"
)
graph3
# Add statistical tests to each corresponding plot
Firm_category <- graph3$Firm_category
xx <- for(i in 1:length(Firm_category)){
graph3.i <- graph3$plots[[i]] +
labs(title = Firm_category[i]) +
stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
print(graph3.i)
}
#output3.long data sample below as comments
#Firm_category billmonth Means.type means
#Agric 1 Before 38.4444
#Agric 1 After 51.9
Complete data is on my github: https://github.com/Fridahnyakundi/Descriptives-in-R/blob/master/Output3.csv
This code prints all the graphs but in like 4 pages. I want to group them into a grid. I have tried to add all these codes below just before my last curly bracket and none is working, please help me out.
library(cowplot)
print(plot_grid(plotlist = graph3.i[1:11], nrow = 4, ncol = 3))
library(ggpubr)
print(ggarrange(graph3.i[1:11], nrow = 4, ncol = 3))
I tried the gridExtra command as well (they all seem to do the same thing). I am the one with a mistake and I guess it has to do with my list. I read a lot of similar work here, some suggested
dev.new()
dev.off()
I still didn't get what they do. But adding either of them caused my code to stop.
I tried defining my 'for' loop function say call it 'XX', then later call it to make a list of graph but it returned NULL output.
I have tried defining an empty list (as I read in some answers here) then counting them to make a list that can be printed but I got so many errors.
I have done this for almost 3 days and will appreciate your help in resolving this.
Thanks!
I tried to complete your code ... and this works (but I don't have your 'stat.test' object). Basically, I added a graph3.i <- list() and replaced graph3.i in the loop ..
Is it what you wanted to do ?
library(magrittr)
library(dplyr)
library(rstatix)
library(ggplot2)
library(ggpubr)
data <- read.csv(url('http://raw.githubusercontent.com/Fridahnyakundi/Descriptives-in-R/master/Output3.csv'))
graph3 <- data %>% group_by(Firm_category) %>%
doo(
~ggboxplot(
data =., x = "Means.type", y = "means",
fill ="grey", palette = "npg", legend = "none",
ggtheme = theme_pubr()
),
result = "plots"
)
graph3
# Add statistical tests to each corresponding plot
graph3.i <- list()
Firm_category <- graph3$Firm_category
xx <- for(i in 1:length(Firm_category)){
graph3.i[[i]] <- graph3$plots[[i]] +
labs(title = Firm_category[i]) # +
# stat_pvalue_manual(stat.test[i, ], label = "p.adj.signif")
print(graph3.i)
}
library(cowplot)
print(plot_grid(plotlist = graph3.i[1:11], nrow = 4, ncol = 3))
I was using Seurat to analyse single cell RNA-seq data and I managed to draw a heatmap plot with DoHeatmap() after clustering and marker selection, but got a bunch of random characters appearing in the legend. They are random characters as they will change every time you run the code. I was worrying over it's something related to my own dataset, so I then tried the test Seurat object 'ifnb' but still got the same issue (see the red oval in the example plot).
example plot
I also tried importing the Seurat object in R in the terminal (via readRDS) and ran the plotting function, but got the same issue there, so it's not a Rstudio thing.
Here are the codes I ran:
'''
library(Seurat)
library(SeuratData)
library(patchwork)
InstallData("ifnb")
LoadData("ifnb")
ifnb.list <- SplitObject(ifnb, split.by = "stim")
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
features <- SelectIntegrationFeatures(object.list = ifnb.list)
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
immune.combined <- IntegrateData(anchorset = immune.anchors)
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
DefaultAssay(immune.combined) <- 'RNA'
immune_markers <- FindAllMarkers(immune.combined, latent.vars = "stim", test.use = "MAST", assay = 'RNA')
immune_markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10_immune
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA')
'''
Does anyone have any idea how to solve this issue other than reinstalling everything?
I have been having the same issue myself and while I have solved it by not needing the legend, I think you could use this approach and use a similar solution:
DoHeatmap(immune.combined, slot = 'data',features = top10_immune$gene, group.by = 'stim', assay = 'RNA') +
scale_color_manual(
values = my_colors,
limits = c('CTRL', 'STIM'))
Let me know if this works! It doesn't solve the source of the odd text values but it does the job! If you haven't already, I would recommend creating a forum question on the Seurat forums to see where these characters are coming from!
When I use seurat4.0, I met the same problem.
While I loaded 4.1, it disappeared
Here is a reproducible example
#load the packages
library(easypackages)
packages("tidyverse","readxl","sf","tmaptools","tmap","lubridate",
"lwgeom","Cairo","nngeo","purrr","scales", "ggthemes","janitor")
polls<-st_as_sf(read.csv(url("https://www.caerphilly.gov.uk/CaerphillyDocs/FOI/Datasets_polling_stations_csv.aspx")),
coords = c("Easting","Northing"),crs = 27700)%>%
mutate(date = sample(seq(as.Date('2020/01/01'), as.Date('2020/05/31'), by="day"), 147))
test_stack<-polls%>%st_join(polls%>%st_buffer(dist=1000),join=st_within)%>%
filter(Ballot.Box.Polling.Station.x!=Ballot.Box.Polling.Station.y)%>%
add_count(Ballot.Box.Polling.Station.x)%>%
rename(number_of_neighbours = n)%>%
mutate(interval_date = date.x-date.y)%>%
subset(select = -c(6:8,10,11,13:18))## removing this comment will summarise the data so that only number of neighbours is returned %>%
distinct(Ballot.Box.Polling.Station.x,number_of_neighbours,date.x)%>%
filter(number_of_neighbours >=2)
polls%>%mutate(id = as.numeric(row_number()))%>% mutate(thing = case_when(id %% 2 == 0 ~ "stuff",
id %% 2 !=0 ~ "type"))->polls
qtm(polls)
tmap_mode("view")
tm_shape(polls) + tm_markers(col = "thing")
tm_shape(polls) + tm_dots(col ="thing", size = 0.75)
what I'd like to do is change the colour and size of tm_markers, because in the thing I want to use it in it would be nice to make use of different colour markers easily.
Linked to this, is understanding how the clustering of markers works when the map mode is "view" and an html is generated.
Any help on tm_marker behaviour and tm_marker clustering would be brilliant.
thanks == "MANY!"
In the end it turns out to be much simpler than using markers. I don't like "markers", aesthetically, but I do like "dots" and tm_dots lets you sort out the colours more easily (or in my head it's easier..) . And the thing is. Well. Clustering can be applied to dots, bubbles, and tm_symbols.
It's all in here:
https://cran.r-project.org/web/packages/tmap/tmap.pdf
(p 89/90)
ANYWAY
tm_shape(polls) + tm_dots(col ="thing", size = 0.75,clustering = T)
that's the answer (for me). I can cluster and then colour by a field.
I am using a for loop to create 100 ggplots in R to be graphed onto one sheet of paper. However, I keep getting a mapping must be created by aes() error and I am not sure how to fix it.
I have tried the get function with and without environments, unclear where to go next.
for(i in 1:99){
nam <- paste("p", i, sep = "")
otunam <- paste("OTU", i, sep = "")
otunam1 <- get(otunam, envir = as.environment(histotu), inherits = TRUE)
plot <- ggplot(histotu, aes(x=otunam)) + geom_histogram(histotu, stat = "bin", binwidth = 0.01) + geom_vline(xintercept=expD[1,i], color = "red") + xlab(otunam)
assign(nam, plot)
}
I would like to clear this error and be able to make 100 graphs using grid.arrange. I have this part to work, but not the for loop to create the objects that it calls.
I am trying to generate plots by looping, here is my code:
n <- unique(wide_data$Product.Code)[1:3]
for (i in n)
{
my.prod2 <- filter(tall_bind, Product.Code == i, Date > ymd("2012/04/01"))
dev.new()
mypath <- file.path("C:","R","SAVEHERE",paste("myplot_", i, ".jpg", sep = ""))
jpeg(file=mypath)
mytitle = paste("Plot for product", i)
p <- qplot(Date, Sold, data = my.prod2, geom = "line", main=mytitle, group = Model, colour = Model) + facet_grid(Model ~ .)
ggsave("myplot_", i, plot=p, device= "jpg" )
}
I get the following error for the above code:
Saving 6.67 x 6.67 in image
Error in ggsave("myplot_", i, plot = p, device = "jpg") : could
not find function "device"
Earlier when I used dev.off() at the end of the loop, I found that though the graphs were being generated they were totally blank.
Could someone please help me understand where is the mistake in my code?
You can leave out the dev.new() and jpg() commands, and also your arguments to ggsave() are incorrect. This should work:
n <- unique(wide_data$Product.Code)[1:3]
for (i in n) {
my.prod2 <- filter(tall_bind, Product.Code == i, Date > ymd("2012/04/01"))
mypath <- file.path("C:","R","SAVEHERE",paste("myplot_", i, ".jpg", sep = ""))
mytitle = paste("Plot for product", i)
p <- qplot(Date, Sold, data = my.prod2, geom = "line", main=mytitle, group = Model, colour = Model) + facet_grid(Model ~ .)
ggsave(filename = mypath, plot = p)
}
What you did was creating a new default graphics device, typically a plotting window, then a jpeg graphics device, i.e. a file. Then you tried to make ggplot2 to plot to directly to file using ggsave, i.e. using its own (jpg) device, and not using either of the two graphics devices you created.
The error, however, was because you gave ggsave the wrong arguments. But even with the right arguments, you would still have ended up with additional unused graphics windows and files through the dev.new() and jpeg() commands. I suggest some extra reading of the help (e.g. type ?ggsave at the r console).
Typically, when using ggplot2 you do not need to worry about dev.new, jpeg and the like. qplot or ggplot and ggsave should do all you need.