I have a df:
test<- data.frame (Metrics = c("PCT_PF_READS (%)" , "PCT_Q30_R1 (%)" , "PCT_Q30_R2 (%)"),
LowerLimit = c(80,80,80),
Percent = c(93.1,95.1,92.4)
)
> test
Metrics LowerLimit Percent
1 PCT_PF_READS (%) 80 93.1
2 PCT_Q30_R1 (%) 80 95.1
3 PCT_Q30_R2 (%) 80 92.4
I am trying to plot in ggplot2 but I want to specify the yaxis.
If I do:
ggplot(data=test3, aes(x= Metrics,y=Percent,)) +
geom_bar(stat="identity" )
If I try to set the yaxis to start at 75, I get a blank plot:
ggplot(data=test3, aes(x= Metrics,y=Percent,)) +
geom_bar(stat="identity" ) + scale_y_continuous(limits = c(75,100))
with the message
Warning message:
Removed 3 rows containing missing values (geom_bar)
But the values are in range????
Does this answer your question?
library(tidyverse)
test<- data.frame (Metrics = c("PCT_PF_READS (%)" , "PCT_Q30_R1 (%)" , "PCT_Q30_R2 (%)"),
LowerLimit = c(80,80,80),
Percent = c(93.1,95.1,92.4)
)
# Starting plot:
ggplot(data = test, aes(x = Metrics, y = Percent)) +
geom_bar(stat = "identity")
# If you cut off any of the bar using "limit" the bar is removed,
# E.g. this removes the middle bar (Percent = 95.1)
ggplot(data = test, aes(x = Metrics, y = Percent)) +
geom_bar(stat = "identity") +
scale_y_continuous(limits = c(0,95))
#> Warning: Removed 1 rows containing missing values (position_stack).
# A better solution is to use "coord_cartesian()"
ggplot(data = test, aes(x = Metrics, y = Percent)) +
geom_bar(stat = "identity") +
coord_cartesian(ylim = c(75, 100))
# Although it's generally advised to keep the whole axis,
# as 'chopping off' the bottom can be misleading
# Another alternative is to write the percentages on the plot:
ggplot(data = test, aes(x = Metrics, y = Percent)) +
geom_bar(stat = "identity") +
geom_text(aes(label = paste0(Percent, "%")),
nudge_y = 2)
Created on 2022-10-19 by the reprex package (v2.0.1)
Related
For my thesis I am using R-studio. I want to make a graph on ggplot2 with x= age(H2_lft) and y = IMT value (Mean_IMT_alg). I want to plot a graph with multiple variables(cardiovascular risk factors) to see the relationship between a certain variable/cardiovascular risk factor (e.g. smoking(H2_roken)/gender(H1_geslacht)/ethnicity(H1_EtnTotaal) and the IMT value on a certain age.
First, I plotted multiple lines (each line represented a variable) in a graph. But I think this is a little too messy. I actually want to have multiple 'pannels/graphs' with x= age and y = IMT value. And in every graph I want to have a different variable.
I hope my explanation is clear enough and someone can help me :)
My first code (multiple lines in same plot) is:
t <- ggplot(data = Dataset, aes(x = H2_lft, y = MeanIMT_alg)) +
geom_smooth(se = FALSE, aes(group = H1_EtnTotaal, colour = H1_EtnTotaal)) +
geom_smooth(se = FALSE, aes(group = H2_Roken, colour = H2_Roken)) +
geom_smooth(se = FALSE, aes(group = H1_geslacht, colour = H1_geslacht)) +
stat_smooth(method = lm, se=FALSE) +
theme_classic()
t + labs(x = "Age (years)", y = "Mean IMT (mm)", title ="IMT", caption = "Figure 2: mean IMT", color = "cardiovascular risk factors", fil = "cardiovascular risk factors")
To accomplish multiple panels i used 'facet_wrap'. The problem however is that when using 'groups' in facet_Wrap, R makes groups that proceed on each other. But i want the groups to be unrelated of eachother. For example: I want one graph with a line for Marroccan ethnicity, one line with current smoking and one line with Male participants. I do not want a graph with: morroccan women that currently smoke or: Dutch men that never smoked. So, I want the graph with all the lines but split into several graphs.
The code that I used to accomplish this is:
t <- ggplot(data = Dataset, aes(x = H2_lft, y = MeanIMT_alg)) +
geom_smooth(se = FALSE, aes(group = H1_EtnTotaal, colour = H1_EtnTotaal)) +
geom_smooth(se = FALSE, aes(group = H2_Roken, colour = H2_Roken)) +
geom_smooth(se = FALSE, aes(group = H1_geslacht, colour = H1_geslacht)) +
stat_smooth(method = lm, se=FALSE)+
facet_wrap(~H1_EtnTotaal + ~H2_Roken + ~H1_geslacht, scales = "free_y") +
theme_classic()
t + labs(x = "Age (years)", y = "Mean IMT (mm)", title ="IMT", caption = "Figure 2: mean IMT", color = "cardiovascular risk factors", fil = "cardiovascular risk factors")
I think it might be generally easier to reshape the data to a long format for plotting with ggplot2. If you want seperate legends for each of the categories, you can use the {ggnewscale} package to do so. Is this (approximately) what you're looking for?
library(ggnewscale)
library(ggplot2)
# Dummy data
Dataset <- data.frame(
H2_lft = runif(100, 18, 90),
MeanIMT_alg = rnorm(100),
H1_EtnTotaal = sample(LETTERS[1:5], 100, replace = TRUE),
H2_Roken = sample(LETTERS[6:8], 100, replace = TRUE),
H1_geslacht = sample(c("M", "F"), 100, replace = TRUE)
)
# Reshape data to long format
new <- tidyr::pivot_longer(Dataset, c(H1_EtnTotaal, H2_Roken, H1_geslacht))
ggplot(new, aes(H2_lft, MeanIMT_alg, group = value)) +
geom_smooth(
data = ~ subset(.x, name == "H1_EtnTotaal"),
aes(colour = value),
se = FALSE
) +
scale_colour_discrete(name = "EtnTotaal") +
new_scale_colour() +
geom_smooth(
data = ~ subset(.x, name == "H1_geslacht"),
aes(colour = value),
se = FALSE
) +
scale_colour_discrete(name = "geslacht") +
new_scale_colour() +
geom_smooth(
data = ~ subset(.x, name == "H2_Roken"),
aes(colour = value),
se = FALSE
) +
scale_colour_discrete(name = "Roken") +
geom_smooth(
method = lm, se = FALSE,
aes(group = NULL)
) +
facet_wrap(~ name)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
Created on 2022-11-04 by the reprex package (v2.0.0)
I have a dataframe (dat) with two columns 1) Month and 2) Value. I would like to highlight that the x-axis is not continuous in my boxplot by interrupting the x-axis with two angled lines on the x-axis that are empty between the angled lines.
Example Data and Boxplot
library(ggplot2)
set.seed(321)
dat <- data.frame(matrix(ncol = 2, nrow = 18))
x <- c("Month", "Value")
colnames(dat) <- x
dat$Month <- rep(c(1,2,3,10,11,12),3)
dat$Value <- rnorm(18,20,2)
ggplot(data = dat, aes(x = factor(Month), y = Value)) +
geom_boxplot() +
labs(x = "Month") +
theme_bw() +
theme(panel.grid = element_blank(),
text = element_text(size = 16),
axis.text.x = element_text(size = 14, color = "black"),
axis.text.y = element_text(size = 14, color = "black"))
The ideal figure would look something like below. How can I make this discontinuous axis in ggplot?
You could make use of the extended axis guides in the ggh4x package. Alas, you won't easily be able to create the "separators" without a hack similar to the one suggested by user Zhiqiang Wang
guide_axis_truncated accepts vectors to define lower and upper trunks. This also works for units, by the way, then you have to pass the vector inside the unit function (e.g., trunc_lower = unit(c(0,.45), "npc") !
library(ggplot2)
library(ggh4x)
set.seed(321)
dat <- data.frame(matrix(ncol = 2, nrow = 18))
x <- c("Month", "Value")
colnames(dat) <- x
dat$Month <- rep(c(1,2,3,10,11,12),3)
dat$Value <- rnorm(18,20,2)
# this is to make it slightly more programmatic
x1end <- 3.45
x2start <- 3.55
p <-
ggplot(data = dat, aes(x = factor(Month), y = Value)) +
geom_boxplot() +
labs(x = "Month") +
theme_classic() +
theme(axis.line = element_line(colour = "black"))
p +
guides(x = guide_axis_truncated(
trunc_lower = c(-Inf, x2start),
trunc_upper = c(x1end, Inf)
))
Created on 2021-11-01 by the reprex package (v2.0.1)
The below is taking user Zhiqiang Wang's hack a step further. You will see I am using simple trigonometry to calculate the segment coordinates. in order to make the angle actually look as it is defined in the function, you would need to set coord_equal.
# a simple function to help make the segments
add_separators <- function(x, y = 0, angle = 45, length = .1){
add_y <- length * sin(angle * pi/180)
add_x <- length * cos(angle * pi/180)
## making the list for your segments
myseg <- list(x = x - add_x, xend = x + add_x,
y = rep(y - add_y, length(x)), yend = rep(y + add_y, length(x)))
## this function returns an annotate layer with your segment coordinates
annotate("segment",
x = myseg$x, xend = myseg$xend,
y = myseg$y, yend = myseg$yend)
}
# you will need to set limits for correct positioning of your separators
# I chose 0.05 because this is the expand factor by default
y_sep <- min(dat$Value) -0.05*(min(dat$Value))
p +
guides(x = guide_axis_truncated(
trunc_lower = c(-Inf, x2start),
trunc_upper = c(x1end, Inf)
)) +
add_separators(x = c(x1end, x2start), y = y_sep, angle = 70) +
# you need to set expand to 0
scale_y_continuous(expand = c(0,0)) +
## to make the angle look like specified, you would need to use coord_equal()
coord_cartesian(clip = "off", ylim = c(y_sep, NA))
I think it is possible to get what you want. It may take some work.
Here is your graph:
library(ggplot2)
set.seed(321)
dat <- data.frame(matrix(ncol = 2, nrow = 18))
x <- c("Month", "Value")
colnames(dat) <- x
dat$Month <- rep(c(1,2,3,10,11,12),3)
dat$Value <- rnorm(18,20,2)
p <- ggplot(data = dat, aes(x = factor(Month), y = Value)) +
geom_boxplot() +
labs(x = "Month") +
theme_bw() +
theme(panel.grid = element_blank(),
text = element_text(size = 16),
axis.text.x = element_text(size = 14, color = "black"),
axis.text.y = element_text(size = 14, color = "black"))
Here is my effort:
p + annotate("segment", x = c(3.3, 3.5), xend = c(3.6, 3.8), y = c(14, 14), yend = c(15, 15))+
coord_cartesian(clip = "off", ylim = c(15, 25))
Get something like this:
If you want to go further, it may take several tries to get it right:
p + annotate("segment", x = c(3.3, 3.5), xend = c(3.6, 3.8), y = c(14, 14), yend = c(15, 15))+
annotate("segment", x = c(0, 3.65), xend = c(3.45, 7), y = c(14.55, 14.55), yend = c(14.55, 14.55)) +
coord_cartesian(clip = "off", ylim = c(15, 25)) +
theme_classic()+
theme(axis.line.x = element_blank())
Just replace axis with two new lines. This is a rough idea, it may take some time to make it perfect.
You could use facet_wrap. If you assign the first 3 months to one group, and the other months to another, then you can produce two plots that are side by side and use a single y axis.
It's not exactly what you want, but it will show the data effectively, and highlights the fact that the x axis is not continuous.
dat$group[dat$Month %in% c("1", "2", "3")] <- 1
dat$group[dat$Month %in% c("10", "11", "12")] <- 2
ggplot(data = dat, aes(x = factor(Month), y = Value)) +
geom_boxplot() +
labs(x = "Month") +
theme_bw() +
theme(panel.grid = element_blank(),
text = element_text(size = 16),
axis.text.x = element_text(size = 14, color = "black"),
axis.text.y = element_text(size = 14, color = "black")) +
facet_wrap(~group, scales = "free_x")
* Differences in the plot are likely due to using different versions of R where the set.seed gives different result
So I've created a time series event. I'd like to plot my NS data on the y axes. Unfortunately it's not working and I can't figure out why. I'd like to plot it as a bar plot. The x value input should be my newly created DateTime. Any notes?
library(ggplot2)
library(scales)
Versuch3 <- data.frame(date = c("1.1.2015", "2.1.2015", "3.1.2015"),
time = c("06:12:03", "08:23:45", "15:40:32"),
NS = c("0.3", "0.4", "0.6"),
status = c("0", "1", "0"))
Versuch3$DateTime <- paste(Versuch3$date,Versuch3$time,sep =" ")
Versuch3$DateTime <- as.POSIXct(strptime(Versuch3$DateTime, "%d.%m.%Y %H:%M:%S"))
Versuch3$NS <- as.integer(Versuch3$NS)
dtLimits <- c(as.POSIXct("2014-12-01 00:00:00"), as.POSIXct("2015-07-01 00:00:00"))
#plot
ggplot(data = Versuch3, aes(x=DateTime, y=NS)) +
geom_bar(stat="identity", aes(fill=status), width = 3) +
scale_x_datetime(date_breaks = "1 month",
labels = date_format("%d.%m.%Y %H:%M:%S"),
limits = dtLimits) +
theme(axis.text.x = element_text(angle = 90, vjust = .5))
I have set up a line graph in shiny. The x axis has dates covering 2014 to current date.
I have set up various vertical lines using geom_vline() to highlight points in the data.
I'm using dateRangeInput() so the user can choose the start/end date range to look at on the graph.
One of my vertical lines is in Feb 2014. If the user uses the dateRangeInput() to look at dates from say Jan 2016 the vertical line for Feb 2014 is still showing on the graph. This is also causing the x axis to go from 2014 even though the data line goes from Jan 2016 to current date.
Is there a way to stop this vertical line showing on the graph when it's outside of the dataRangeInput()? Maybe there's an argument in geom_vline() to deal with this?
library(shiny)
library(tidyr)
library(dplyr)
library(ggplot2)
d <- seq(as.Date("2014-01-01"),Sys.Date(),by="day")
df <- data.frame(date = d , number = seq(1,length(d),by=1))
lines <- data.frame(x = as.Date(c("2014-02-07","2017-10-31", "2017-08-01")),
y = c(2500,5000,7500),
lbl = c("label 1", "label 2", "label 3"))
#UI
ui <- fluidPage(
#date range select:
dateRangeInput(inputId = "date", label = "choose date range",
start = min(df$date), end = max(df$date),
min = min(df$date), max = max(df$date)),
#graph:
plotOutput("line")
)
#SERVER:
server <- function(input, output) {
data <- reactive({ subset(df, date >= input$date[1] & date <= input$date[2])
})
#graph:
output$line <- renderPlot({
my_graph <- ggplot(data(), aes(date, number )) + geom_line() +
geom_vline(data = lines, aes(xintercept = x, color = factor(x) )) +
geom_label(data = lines, aes(x = x, y = y,
label = lbl, colour = factor(x),
fontface = "bold" )) +
scale_color_manual(values=c("#CC0000", "#6699FF", "#99FF66")) +
guides(colour = "none", size = "none")
return(my_graph)
})
}
shinyApp(ui = ui, server = server)
As mentioned by Aimée in a different thread:
In a nutshell, ggplot2 will always plot all of the data that you provide and the axis limits are based on that unless you specify otherwise. So because you are telling it to plot the line & label, they will appear on the plot even though the rest of the data doesn't extend that far.
You can resolve this by telling ggplot2 what you want the limits of your x axis to be, using the coord_cartesian function.
# Set the upper and lower limit for the x axis
dateRange <- c(input$date[1], input$date[2])
my_graph <- ggplot(df, aes(date, number)) + geom_line() +
geom_vline(data = lines, aes(xintercept = x, color = factor(x) )) +
geom_label(data = lines, aes(x = x, y = y,
label = lbl, colour = factor(x),
fontface = "bold" )) +
scale_color_manual(values=c("#CC0000", "#6699FF", "#99FF66")) +
guides(colour = "none", size = "none") +
coord_cartesian(xlim = dateRange)
My input data looks like:
COMPANY DOMAIN REVIEW PROGRESS
Company A Service Good +
Company A Response Good +
Company A Delay Very Good
Company A Cost Poor -
Company B Service Poor -
Company B Delay Average
Company B Cost Good +
Company C Service Very Poor +
Company C Cost Average
I produced a heat map in which I add some text (value of the "PROGRESS" variable - i.e. plus or minus sign).
Here is my code:
require("ggplot2")
graph <- read.table("input.tab", header=T, sep="\t")
ggplot(data=graph, aes(x=COMPANY, y=DOMAIN, group=REVIEW, fill=REVIEW)) +
geom_tile() +
geom_text(aes(x=COMPANY, y=DOMAIN, label=PROGRESS)) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
geom_vline(xintercept=seq(1.5, length(graph$COMPANY)+0.5)) +
geom_hline(yintercept=seq(1.5, length(graph$DOMAIN)+0.5)) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank(),
plot.background = element_blank(),
axis.title=element_blank(),
axis.text.x = element_text(angle=45, size=12, hjust=1)
)
However I am struggling adding (see figure modified manually below):
(1) the following "PROGRESS" legend as part of the color code already listed:
+ Better
- Worse
(2) the count of data available on each row between the right side of the plot and the legend
(3) the count of data available on each column on top of the plot
Any advice?
Here's my proposed solution, I added comments in the code for you to understand what I did. There is probably a better way of generating the grid, though. Hope it helps.
graph <- read_csv(
"COMPANY ,DOMAIN ,REVIEW ,PROGRESS
Company A ,Service ,Good ,+
Company A ,Response ,Good ,+
Company A ,Delay ,Very Good ,
Company A ,Cost ,Poor ,-
Company B ,Service ,Poor ,-
Company B ,Delay ,Average ,
Company B ,Cost ,Good ,+
Company C ,Service ,Very Poor ,+
Company C ,Cost ,Average ,")
ggplot() +
# moved aesthetics and data to each geom,
# if you keep them in the ggplot call,
# you have to specify `inherit.aes = FALSE` in the rest of the geoms
geom_tile(data = graph,
aes(x = COMPANY,
y = DOMAIN,
fill = REVIEW)) +
# changed from `geom_text` to `geom_point` with custom shapes
geom_point(data = graph,
aes(x = COMPANY,
y = DOMAIN,
shape = factor(PROGRESS, labels = c("Worse", "Better"))),
size = 3) +
# custom shape scale
scale_shape_manual(name = "", values = c("-", "+")) +
# calculate marginal totals "on the fly"
# top total
geom_text(data = summarize(group_by(graph, COMPANY),
av_data = length(!is.na(PROGRESS))),
aes(x = COMPANY,
y = length(unique(graph$DOMAIN)) + 0.7,
label = av_data)) +
# right total
geom_text(data = summarize(group_by(graph, DOMAIN),
av_data = length(!is.na(PROGRESS))),
aes(x = length(unique(graph$COMPANY)) + 0.7,
y = DOMAIN, label = av_data)) +
# expand the plotting area to accomodate for the marginal totals
scale_x_discrete(expand = c(0, 0.8)) +
scale_y_discrete(expand = c(0, 0.8)) +
# changed to `geom_segment` to generate the grid, otherwise grid extends
# beyond the heatmap
# horizontal lines
geom_segment(aes(y = rep(0.5, 1 + length(unique(graph$COMPANY))),
yend = rep(length(unique(graph$DOMAIN)) + 0.5,
1 + length(unique(graph$COMPANY))),
x = seq(0.5, 1 + length(unique(graph$COMPANY))),
xend = seq(0.5, 1 + length(unique(graph$COMPANY))))) +
# vertical lines
geom_segment(aes(x = rep(0.5, 1 + length(unique(graph$DOMAIN))),
xend = rep(length(unique(graph$COMPANY)) + 0.5,
1 + length(unique(graph$DOMAIN))),
y = seq(0.5, 1 + length(unique(graph$DOMAIN))),
yend = seq(0.5, 1 + length(unique(graph$DOMAIN))))) +
# custom legend order
guides(fill = guide_legend(order = 1),
shape = guide_legend(order = 2)) +
# theme tweaks
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank(),
plot.background = element_blank(),
axis.title = element_blank(),
axis.text.x = element_text(angle = 45,
size = 12,
hjust = 1,
# move text up 20 pt
margin = margin(-20,0,0,0, "pt")),
# move text right 20 pt
axis.text.y = element_text(margin = margin(0,-20,0,0, "pt"))
)