I'm trying to get two distributions in one graph with the googlevis function gvisAreaChart but it is not working as I want:
library(googleVis)
df <- data.frame(a=rnorm(n = 400,mean = .1,sd = .01),b=rnorm(n = 400,mean = .13,sd = .01))
dfplot <- rbind(data.frame(x=density(df$a)$x,y=density(df$a)$y,var=rep("a",512)),data.frame(x=density(df$b)$x,y=density(df$b)$y,var=rep("b",512)))
plot(gvisAreaChart(dfplot,xvar = 'x',yvar='y'))
I would like to give the two distributions different colors and also when they overlap. I know this is possible in ggplot but I'm looking for a solution with googleVis because it works better with Shiny.
Thanks.
EDIT: Found very cumbersome workaround:
dfplot <- data.table(x=seq(0,by = .001))
df <- data.table(a=rnorm(n = 400,mean = .1,sd = .01),b=rnorm(n = 400,mean = .13,sd = .01))
dfa <- data.table(x=round(density(df$a)$x,3),y1=density(df$a)$y,var=rep("a",512))
dfb <- data.table(x=round(density(df$b)$x,3),y2=density(df$b)$y,var=rep("b",512))
dfa[duplicated(x),test:=TRUE];dfa<-dfa[is.na(test)]
dfb[duplicated(x),test:=TRUE];dfb<-dfb[is.na(test)]
setkey(dfplot,x);setkey(dfa,x);setkey(dfb,x)
merge <- merge(dfplot,dfa,all.x=T,all.y=F,allow.cartesian=T)
merge <- merge(merge,dfb,all.x=T,all.y=F,allow.cartesian=T)
merge <- merge[,max:=pmax(y1,y2,na.rm=T)]
merge <- merge[!is.na(max)]
merge<-subset(merge,select = c('x','y1','y2'))
plot(gvisAreaChart(merge,xvar = 'x',yvar=c('y1','y2')))
unfortunately I am not that familiar with googleVis, but hopefully you can accept the solution with rCharts
rm(list = ls())
library(shiny)
library(rCharts)
df <- data.frame(a=rnorm(n = 400,mean = .1,sd = .01),b=rnorm(n = 400,mean = .13,sd = .01))
dfplot <- rbind(data.frame(x=density(df$a)$x,y=density(df$a)$y,var=rep("a",512)),data.frame(x=density(df$b)$x,y=density(df$b)$y,var=rep("b",512)))
hPlot(x = "x", y = "y", group = "var", data = dfplot, type = "area")
Related
Below is the code to develop sparkline in R. Wanted to check if we can change the color . I mean the negative values to be black and positive values to be red
library(shiny)
library(DT)
library(data.table)
library(sparkline)
## Create demo data sets
my_mtcars <- data.table(mtcars, keep.rownames = TRUE)
names(my_mtcars)[1] <- 'car_id'
set.seed(0)
data_for_sparklines <- data.table(car_id = rep(my_mtcars$car_id, 5),
category = 1:5,
value = c(runif(80),-runif(80)))
sparkline_html <- data_for_sparklines[, .(sparkbar = spk_chr(value, type = 'bar',barColor = "black")), by = 'car_id']
my_mtcars <- merge(my_mtcars, sparkline_html, by = 'car_id')
spk_add_deps(datatable(my_mtcars, escape = FALSE))
I want to make a 2 box plots with y being weight and x being the before and after. so two different boxplot will be displayed at the same time.
`rats_before = data.frame(
rat_num = paste0(rep("rat number",200),1:200),
weight = rweibull(200,shape= 10,scale = 20))
rats_after = data.frame(
rat_num = paste0(rep("rat number",200),1:200),
weight = rweibull(200,shape= 9,scale = 21))
rats = merge(rats_before,rats_after, by = c("rat_num"))`
i know the next part is not even close but it will give you a idea of what im trying to do.
rat_boxplot = qplot(y = weight, x = (rats_after, rats_before), geom = "boxplot", data = rats)
Or, if you want to do this in base R -
rats_before = data.frame(
rat_num = paste0(rep("rat number",200),1:200),
weight = rweibull(200,shape= 10,scale = 20))
rats_after = data.frame(
rat_num = paste0(rep("rat number",200),1:200),
weight = rweibull(200,shape= 9,scale = 21))
rats <- rbind(rats_before, rats_after)
rats$type <- c(rep("before", nrow(rats_before)), rep("after", nrow(rats_after)))
rats$type <- factor(rats$type)
rats$type <- relevel(rats$type, ref = 2)
boxplot(weight ~ type, data = rats)
You can add a column to each df ans userbind which will bind the rows of the two df instead of merge you can use. Then you simply have to use the aes of a ggplot.
rats_before$condition = "before"
rats_after$condition = "after"
rats = rbind(rats_before,rats_after)
ggplot(rats)+geom_boxplot(aes(condition,weight))
Hope I understood your question.
Tom
I am plotting multiple shapefiles using spplot. Here's a data to construct that
library(raster)
library(randomcoloR)
my.shp <- getData('GADM', country = 'BRA', level = 2)
my.shp$ID<- 1:nrow(my.shp)
My data consists of a variable X for 10 years as shown where each column is a year
df <- matrix(sample(100:5000, 55040, replace = T), nrow = 5504, ncol = 10)
df <- data.frame(ID = 1:nrow(my.shp), df)
my.dat <- merge(my.shp, df, by = "ID")
variable.names <- paste0("X",1:10)
spplot(my.dat, rev(variable.names), col = NA, at = seq(from = 100, to = 5000, by = 500),
col.regions = distinctColorPalette(length(seq(from = 100, to = 5000, by = 500))),
main = list(label = "TEST"))
My problem is this plot takes so much time (around an hour) to get plotted and was wondering if there is something inherently wrong in the code itself that it is taking too long to plot. My laptop has a 32 GB RAM.
Thanks
I haven't compared this plot to your spplot because I don't want to spend an hour waiting for it.
Instead I'm proposing to use library(mapdeck) to plot an interactive map, which takes a matter of seconds.
Two things to note
You need a Mapbox Access token
You need to convert the sp object to sf
library(raster)
my.shp <- getData('GADM', country = 'BRA', level = 2)
my.shp$ID <- 1:nrow(my.shp)
df <- matrix(sample(100:5000, 55040, replace = T), nrow = 5504, ncol = 10)
df <- data.frame(ID = 1:nrow(my.shp), df)
my.dat <- merge(my.shp, df, by = "ID")
library(sf)
sf <- sf::st_as_sf( my.dat )
library(mapdeck)
set_token( "YOUR_MAPBOX_TOKEN" )
mapdeck() %>%
add_sf(
data = sf
, fill_colour = "GID_2"
)
Are you willing/able to switch to sf instead of sp?
The sf plot function is considerably faster than spplot, although the layout differs a bit.
library(sf)
my.dat_sf <- st_as_sf(my.dat)
plot(my.dat_sf[rev(variable.names)], max.plot=10, breaks=c(seq(from = 100, to = 5000, by = 500),5000),
pal = distinctColorPalette(length(seq(from = 100, to = 5000, by = 500))),
main = "TEST", border=NA, key.pos=4)
Additionally, you could try to simplify the polygon with rmapshaper::ms_simplify() for Spatial*-objects or sf::st_simplify() for SimpleFeatures, which lets you reduce the object size by quite a bit, depending on the given dTolerance. Thus plotting, will also be faster with simplified polygons.
The original SpatialPolygon:
format(object.size(my.dat_sf), units="Kb")
"25599.2 Kb"
and a simplified SimpleFeature:
dat_sf_simple <- st_transform(my.dat_sf, crs = 3035)
dat_sf_simple <- st_simplify(dat_sf_simple, dTolerance = 1000, preserveTopology = T)
dat_sf_simple <- st_transform(dat_sf_simple, crs = 4326)
format(object.size(dat_sf_simple), units="Kb")
"7864.2 Kb"
Plot the simplified SimpleFeature, which takes about 1 minute on my machine with 8GB RAM.
plot(dat_sf_simple[rev(variable.names)], max.plot=10, breaks=c(seq(from = 100, to = 5000, by = 500),5000),
pal = distinctColorPalette(length(seq(from = 100, to = 5000, by = 500))),
main = "TEST", border=NA, key.pos=4)
You could also try out with ggplot2, but I am pretty sure the most performant solution will be the sf plot.
library(ggplot2)
library(dplyr)
library(tidyr)
dat_sf_simple_gg <- dat_sf_simple %>%
dplyr::select(rev(variable.names), geometry) %>%
gather(VAR, SID, -geometry)
ggplot() +
geom_sf(data = dat_sf_simple_gg, aes(fill=SID)) +
facet_wrap(~VAR, ncol = 2)
I want to skip a empty panel using lattice package in R.
set.seed(1)
df1 <- data.frame("treatment" = c(rep("A",16),rep("B",16),rep("C",16)),
"disease_type" = c(rep("1",8),rep("2",8)),
"days_after_application" = rep(c(rep("10-24",4),rep("24-48",4)),6),
"severity" = rnorm(48, mean = 80, sd = 5))
df1[(df1$disease_type == "2" & df1$days_after_application == "24-48"),"severity"] <- NA
library(lattice)
figure1 <- bwplot(treatment~severity|days_after_application+disease_type,
data = df1,layout = c(2,2),
strip = strip.custom(strip.names = TRUE))
jpeg("figure1.jpeg")
print(figure1)
dev.off()
Here is what I get
My question is how I can remove/skip empty panel in the top right WITHOUT changing layout?
I have tried following code. However, it doesn't work.
figure2 <- bwplot(treatment~severity|days_after_application+disease_type,
data = df1,layout = c(2,2),
strip = strip.custom(strip.names = TRUE),
skip = c(FALSE,FALSE,FALSE,TRUE))
jpeg("figure2.jpeg")
print(figure2)
dev.off()
Here is what I got
I also tried following codes. But it is not what I want since I do want 2 levels strips.
df1[(df1$disease_type == "2" & df1$days_after_application == "24-48"),] <- NA
bwplot(treatment~severity|interaction(days_after_application,disease_type),
data = df1,layout = c(2,2),
strip = strip.custom(strip.names = TRUE))
Thank you!
Get help from a Professor in Temple University.
Here is his solution:
figure4 <- bwplot(treatment~severity|days_after_application+disease_type,
data = df1,layout = c(2,2),
strip = strip.custom(strip.names = TRUE),
skip = c(FALSE,FALSE,FALSE,TRUE),
scales=list(alternating=FALSE), ## keep x-scale on bottom
between=list(x=1, y=1)) ## space between panels
pdf("figure4%03d.pdf",onefile = FALSE) ## force two pages in file.
print(figure4)
dev.off()
I want to do some box plots, but I have data with a different number of rows for each column.
My data looks like:
OT1 OT2 OT3 OT4 OT5 OT6
22,6130653 16,6666667 20,259481 9,7431602 0,2777778 16,0678643
21,1122919 32,2946176 11,396648 10,9458023 4,7128509 10,8938547
23,5119048 19,5360195 23,9327541 39,5634921 0,6715507 12,2591613
16,9880885 39,5365943 7,7568134 22,7453205 3,6410445 11,7610063
32,768937 25,2897351 9,6288027 4,1629535 3,7251656
40,7819933 15,6320021 5,9171598
23,7961828 14,3728125 2,1887585
I'd like to have a box plot for each column (OT1, OT2…), but with the first three and the last three grouped together.
I tried:
>mydata <- read.csv('L5.txt', header = T, sep = "\t")
>mydata_t <- t(mydata)
>boxplot(mydata_t, ylab = "OTU abundance (%)",las=2, at=c(1,2,3 5,6,7))
But it didn't work…
How can I do?
Thanks!
Combining both answers and extenting Henrik's answer, you can also group the OT's together in boxplot() as well:
dat <- read.table(text='OT1 OT2 OT3 OT4 OT5 OT6
22,6130653 16,6666667 20,259481 9,7431602 0,2777778 16,0678643
21,1122919 32,2946176 11,396648 10,9458023 4,7128509 10,8938547
23,5119048 19,5360195 23,9327541 39,5634921 0,6715507 12,2591613
16,9880885 39,5365943 7,7568134 22,7453205 3,6410445 11,7610063
32,768937 25,2897351 9,6288027 4,1629535 3,7251656
40,7819933 15,6320021 5,9171598
23,7961828 14,3728125 2,1887585',header=TRUE,fill=TRUE)
dat <- sapply(dat,function(x)as.numeric(gsub(',','.',x)))
dat.m <- melt(dat)
dat.m <- transform(dat.m,group=ifelse(grepl('1|2|3','4|5|6'),
'group1','group2'))
as.factor(dat.m$X2)
boxplot(dat.m$value~dat.m$X2,data=dat.m,
axes = FALSE,
at = 1:6 + c(0.2, 0, -0.2),
col = rainbow(6))
axis(side = 1, at = c(2, 5), labels = c("Group_1", "Group_2"))
axis(side = 2, at = seq(0, 40, by = 10))
legend("topright", legend = c("OT1", "OT2", "OT3", "OT4", "OT5", "OT6"), fill = rainbow(6))
abline(v = 3.5, col = "grey")
box()
Not easy to group boxplots using R basic plots, better to use ggplot2 here. Whatever the difficulty here is how to reformat your data and reshape them in the long format.
dat <- read.table(text='OT1 OT2 OT3 OT4 OT5 OT6
22,6130653 16,6666667 20,259481 9,7431602 0,2777778 16,0678643
21,1122919 32,2946176 11,396648 10,9458023 4,7128509 10,8938547
23,5119048 19,5360195 23,9327541 39,5634921 0,6715507 12,2591613
16,9880885 39,5365943 7,7568134 22,7453205 3,6410445 11,7610063
32,768937 25,2897351 9,6288027 4,1629535 3,7251656
40,7819933 15,6320021 5,9171598
23,7961828 14,3728125 2,1887585',header=TRUE,fill=TRUE)
dat = sapply(dat,function(x)as.numeric(gsub(',','.',x)))
dat.m <- melt(dat)
dat.m <- transform(dat.m,group=ifelse(grepl('1|2|3',Var2),
'group1','group2'))
ggplot(dat.m)+
geom_boxplot(aes(x=group,y=value,fill=Var2))
Or with boxplot, using #agstudy's 'dat':
df <- melt(dat)
boxplot(value ~ Var2, data = df, at = 1:6 + c(0.2, 0, -0.2))