I'm trying to evaluate the above data in a boxplot similar to this: https://www.r-graph-gallery.com/89-box-and-scatter-plot-with-ggplot2.html
I want the x axis to reflect my "Year" variable and each boxplot to evaluate the 8 methods as a distribution. Eventually I'd like to pinpoint the "Selected" variable in relation to that distribution but currently I just want this thing to render!
I figure out how to code my y variable and I get various errors no matter what I try. I think the PY needs to be as.factor but I've tried some code that way and I just get other errors.
anyway here is my code (Send Help):
# Libraries
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(ggplot2)
library(readxl) # For reading in Excel files
library(lubridate) # For handling dates
library(dplyr) # for mutate and pipe functions
# Path to current and prior data folders
DataPath_Current <- "C:/R Projects/Box Plot Test"
Ult_sum <- read_excel(path = paste0(DataPath_Current, "/estimate.XLSX"),
sheet = "Sheet1",
range = "A2:J12",
guess_max = 100)
# just want to see what my table looks like
Ult_sum
# create a dataset - the below is code I commented out
# data <- data.frame(
# name=c(Ult_sum[,1]),
# value=c(Ult_sum[1:11,2:8])
#)
value <- Ult_sum[2,]
# Plot
Ult_sum %>%
ggplot( aes(x= Year, y= value, fill=Year)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
I do not see how your code matches the screenshot of your dataset. However, just a general hint: ggplot likes data in long format. I suggest you reshape your data using tidyr::reshape_long oder data.table::melt. This way you get 3 columns: year, method, value, of which the first two should be a factor. The resulting dataset can then be neatly used in aes() as aes(x=year, y=value, fill=method).
Edit: Added an example. Does this do what you want?
library(data.table)
library(magrittr)
library(ggplot2)
DT <- data.table(year = factor(rep(2010:2014, 10)),
method1 = rnorm(50),
method2 = rnorm(50),
method3 = rnorm(50))
DT_long <- DT %>% melt(id.vars = "year")
ggplot(DT_long, aes(x = year, y = value, fill = variable)) +
geom_boxplot()
Related
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)
This is my R-script, I've been trying to include a legend onto the line plot but it isn't working? Any guidance? I also can't seem to get the geom_point() working either (I've taken the code for it out below).
library(ggsignif)
library(readxl)
library(svglite)
library(tidyverse)
library(ggplot2)
library(tidyr)
library(dplyr)
url <-'https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-020-2850-3/MediaObjects/41586_2020_2850_MOESM10_ESM.xlsx'
temp <-tempfile()
download.file(url, temp, mode='wb')
myData <- read_excel(path=temp, sheet = "ExFig.5f")
names(myData) <- NULL
view(myData)
Time_post_inj <- (myData[1])
Time_post_inj <- Time_post_inj[-c(1),]
dose_450_ug <- (myData[2])
dose_450_ug <- dose_450_ug[-c(1),]
dose_150_ug <- (myData[4])
dose_150_ug <- dose_150_ug[-c(1),]
dose_100_ug <- (myData[6])
dose_100_ug <- dose_100_ug[-c(1),]
dose_50_ug <- (myData[8])
dose_50_ug <- dose_50_ug[-c(1),]
colnames(Time_post_inj) <-c("Time_Post_Injection")
colnames(dose_450_ug) <-c("dose_450_µg")
colnames(dose_150_ug) <-c("dose_150_µg")
colnames(dose_100_ug) <-c("dose_100_µg")
colnames(dose_50_ug) <-c("dose_50_µg")
Newdata <-data.frame(Time_post_inj, dose_450_ug, dose_150_ug, dose_100_ug, dose_50_ug)
Newdata$Time_Post_Injection <-as.numeric(Newdata$Time_Post_Injection)
Newdata$dose_450_µg <-as.numeric(Newdata$dose_450_µg)
Newdata$dose_150_µg <-as.numeric(Newdata$dose_150_µg)
Newdata$dose_100_µg <-as.numeric(Newdata$dose_100_µg)
Newdata$dose_50_µg <-as.numeric(Newdata$dose_50_µg)
str(Newdata)
ggplot(data=Newdata, aes(x=Time_Post_Injection, y=hCD4_occupancy, group = 1)) + geom_line(aes(y=dose_450_µg)) + geom_line(aes(y=dose_150_µg)) + geom_line(aes(y=dose_100_µg)) + geom_line(aes(y=dose_50_µg))
Newdata
tidyr::pivot_longer(Time_Post_Injection, names_to = "DOSE", values_to = "VALUE") %>%
ggplot2::ggplot(aes(Time_Post_Injection, VALUE, group = DOSE, color = DOSE)) + ggplot2::geom_line()
The following is a full reprex, meaning that if you copy and paste, it will reproduce the plot exactly as below. You can see I have simplified your parsing considerably too; this starts with the url and produces the plot with a lot less data wrangling:
library(ggplot2) # Only load packages you really need
# This format is a handy way of keeping a long string on a single page
url <- paste0("https://static-content.springer.com/esm/art%3A10.",
"1038%2Fs41586-020-2850-3/MediaObjects/41586_2020",
"_2850_MOESM10_ESM.xlsx")
temp <- tempfile()
download.file(url, temp, mode = 'wb')
# Instead of loading an entire library to use one function, we can
# access read_excel by doing readxl::read_excel
myData <- readxl::read_excel(temp, sheet = "ExFig.5f")
# This single line subsets the data frame to chop out the first row
# and the empty columns. It also converts all columns to numeric
NewData <- as.data.frame(lapply(myData[-1, -c(3, 5, 7)], as.numeric))
names(NewData) <-c("Time_Post_Injection", "dose_450_ug",
"dose_150_ug", "dose_100_ug", "dose_50_ug")
# This switches your data to long format, which helps ggplot to work
# We put all the values in one column and have the dosages as labels
# in another column instead of having multiple columns. This allows us
# to map Color to the dosages.
NewData <- cbind(NewData[1], stack(NewData[-1]))
# Now we just tell ggplot to map colours to ind
ggplot(NewData, aes(x = Time_Post_Injection, y = values, color = ind)) +
geom_line() +
geom_point() +
scale_color_discrete(name = "Dose") +
labs(x = "Time Pist Injection") +
theme_bw()
Created on 2020-11-11 by the reprex package (v0.3.0)
Hi the main problem is that you did not get your data into a easy to handle format
library(dplyr)
library(tidyr)
library(ggplot2)
Newdata %>%
# get data in easy to handle format
tidyr::pivot_longer(-Time_Post_Injection, names_to = "DOSE", values_to = "VALUE") %>%
# plot and use the new DOSE column as group and color so you do not need one geom per line! (you can change geom_line() to geom_point also())
ggplot2::ggplot(aes(Time_Post_Injection, VALUE, group = DOSE, color = DOSE)) +
ggplot2::geom_line()
I am trying to fill the area under a time series line based on a factor value of 0 and 1. The area should only be filled if the value is equal to 1.
I have managed to colour code the time series line based on the factor value with the following code:
install.packages("scales")
library("scales")
library("ggplot2")
ggplot(plot.timeseries) +
geom_line(aes(x = Date, y = Price, color = Index, group = 1)) +
scale_x_date(labels = date_format("%Y"), breaks = date_breaks("years")) +
scale_colour_manual(values = c("red3", "green3"))
This provides the following graph:
I have also tried this:
ggplot(plot.timeseries, aes(x=Date, y = Price, fill=Index)) +
geom_area(alpha=0.6) +
theme_classic() +
scale_fill_manual(values=c("#999999", "#32CD32"))
which comes out as a complete mess:
Ideally the final result should look like plot1 where the parts of the line in green are filled.
The time series data can be accessed here:
https://drive.google.com/file/d/1qWsuJk41_fJZktLCAZSgfGvoDLqTt-jk/view?usp=sharing
Any help would be greatly appreciated!
Okay, here is what I did to get the graph shown below if that is what you want.
# -------------------------------------------------------------------------
# load required packages #
library(scales)
library("ggplot2")
library(dplyr)
# -------------------------------------------------------------------------
# load the data to a df #
plot.timeseries <- get(load("TimeSeries_Data.RData"))
# -------------------------------------------------------------------------
# transform the data (my_fill_color will have green and NA values)
my_object <- plot.timeseries %>%
select(Price, Index, Date) %>%
mutate(Index_ord_factor = factor(Index, levels = unique(Index), ordered=TRUE),
my_fill_color = case_when(
Index_ord_factor > 0 ~ "green" # ordered factor enables the '>' operation
))
# -------------------------------------------------------------------------
# Plot your graph using the transformed data
ggplot(my_object, mapping = aes(x=Date, y=Price)) +
geom_line(aes(color = Index, group = 1))+
geom_col(fill =my_object$my_fill_color, width = 1)
# -------------------------------------------------------------------------
Let me know if you need elaboration to understand the script. Attached is the output in my end.
For those that are interested I also received this alternative solution from Erik Chacon.
You can view his tutorial here for a better understanding of the ggplot2 extension he designed, which is used in this solution.
# Installing and loading necessary packages
install.packages("remotes")
remotes::install_github("ErickChacon/mbsi")
library(mbsi)
library(ggplot2)
load("timeseries.RData")
#converting factor to numeric
plot.timeseries$Index <- as.numeric(levels(plot.timeseries$Index))[plot.timeseries$Index]
ggplot(plot.timeseries, aes(Date, Price)) +
geom_line() +
stat_events(aes(event = I(1 * (Index > 0)), fill = "Index"),
threshold = min(plot.timeseries$Price),
fill = "green", alpha = 0.3)
My data set features a factor(TypeOfCat) and a numeric (AgeOfCat).
I've made the below box plot. In addition to a box representing each type of cat, I've also tried to add a box representing the ungrouped data (ie the entire cohort of cats and their ages). What I've got is not quite what I'm after though, as sum() of course won't provide all the information needed to create such a plot. Any help would be much appreciated.
Data set and current code:
Df1 <- data.frame(TypeOfCat=c("A","B","B","C","C","A","B","C","A","B","A","C"),
AgeOfCat=c(14,2,5,8,4,5,2,6,3,6,12,7))
Df2 <- data.frame(TypeOfCat=c("AllCats"),
AgeOfCat=sum(Df1$AgeOfCat)))
Df1 <- rbind(Df1, Df2)
qplot(Df1$TypeOfCat,Df1$AgeOfCat, geom = "boxplot") + coord_flip()
No need for sum. Just take all the values individually for AllCats:
# Your original code:
library(ggplot2)
Df1 <- data.frame(TypeOfCat=c("A","B","B","C","C","A","B","C","A","B","A","C"),
AgeOfCat=c(14,2,5,8,4,5,2,6,3,6,12,7))
# this is the different part:
Df2 <- data.frame(TypeOfCat=c("AllCats"),
AgeOfCat=Df1$AgeOfCat)
Df1 <- rbind(Df1, Df2)
qplot(Df1$TypeOfCat,Df1$AgeOfCat, geom = "boxplot") + coord_flip()
You can see you have all the observations if you add geom_point to the boxplot:
ggplot(Df1, aes(TypeOfCat, AgeOfCat)) +
geom_boxplot() +
geom_point(color='red') +
coord_flip()
Like this?
library(ggplot2)
# first double your data frame, but change "TypeOfCat", since it contains all:
df <- rbind(Df1, transform(Df1, TypeOfCat = "AllCats"))
# then plot it:
ggplot(data = df, mapping = aes(x = TypeOfCat, y = AgeOfCat)) +
geom_boxplot() + coord_flip()
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)