I have some data about the percentages of temperature for different time periods and I want to create a barplot showing those percentages and then add a linear regression line showing the trend. Although i manage to get the first graph, I fail to add a straight linear regression line
Basically I try to make a barplot with these tx_1 data
tx_1<-c(0.055,0.051,0.057,0.049,0.061,0.045)
mypath<-file.path("C:\\tx5\\1.jpeg")
jpeg(file = mypath,width = 1200, height = 600)
plot.dim<-barplot(get(name),
space= 2,
ylim=c(0,0.15),
main = "Percentage of days when Tmax < 5th percentile",
xlab = "Time Periods",
ylab = "Percentage",
names.arg = c("1975-1984", "1985-1990", "1991-1996", "1997-2002", "2003-2008", "2009-2014"),
col = "darkred",
horiz = FALSE)
dev.off()
I tried using ggplot also, but with no luck
Here i have included both a line connecting each observation and a overall best linear fit line. Hope this helps.
library(tidyverse)
year <- tribble(~ Year,~ Percent,
94,0.055,
95,0.051,
96,0.057,
97,0.049,
98,0.061,
99,0.045)
ggplot(year,aes(Year,Percent)) +
geom_bar(stat = "identity") +
geom_line() +
geom_smooth(method = "lm",se = F)
Related
I have a boxplot which summarizes ~60000 turbidity data points into quartiles, median, whiskers and sometimes outliers. Often a few outliers are so high up that the whole plot is compressed at the bottom, and I therefor choose to omit the outliers. However, I also have added averages to the plots as points, and I want these to be plotted always. The problem is that the y-axis of the boxplot does not adjust to the added average points, so when averages are far above the box they are simply plotted outside the chart window (see X-point for 2020, but none for 2021 or 2022). Normally with this parameter, the average will be between the whisker end and the most extreme outliers. This is normal, and expected in the data.
I have tried to capture the boxplot y-axis range to compare with the average, and then setting the ylim if needed, but I just don't know how to retrieve these axis ranges.
My code is just
boxplot(...)
points(...)
and works as far as plotting the points. Just not adjusting the y-axis.
Question 1: is it not possible to get the boxplot to redraw with the new points data? I thought this was standard in R plots.
Question 2: if not, how can I dynamically adjust the y-axis range?
Let's try to show a concrete example of the problem with some simulated data:
set.seed(1)
df <- data.frame(y = c(rexp(99), 150), x = rep(c("A", "B"), each = 50))
Here, group "B" has a single outlier at 150, even though most values are a couple of orders of magnitude lower. That means that if we try to draw a boxplot, the boxes get squished at the bottom of the plot:
boxplot(y ~ x, data = df, col = "lightblue")
If we remove outliers, the boxes plot nicely:
boxplot(y ~ x, data = df, col = "lightblue", outline = FALSE)
The problem comes when we want to add a point indicating the mean value for each boxplot, since the mean of "B" lies outside the plot limits. Let's calculate and plot the means:
mean_vals <- sapply(split(df$y, df$x), mean)
mean_vals
#> A B
#> 0.9840417 4.0703334
boxplot(y ~ x, data = df, col = "lightblue", outline = FALSE)
points(1:2, mean_vals, cex = 2, pch = 16, col = "red")
The mean for "B" is missing because it lies above the upper range of the plot.
The secret here is to use boxplot.stats to get the limits of the whiskers. By concatenating our vector of means to this vector of stats and getting its range, we can set our plot limits exactly where they need to be:
y_limits <- range(c(boxplot.stats(df$y)$stats, mean_vals))
Now we apply these limits to a new boxplot and draw it with the points:
boxplot(y ~ x, data = df, outline = FALSE, ylim = y_limits, col = "lightblue")
points(1:2, mean_vals, cex = 2, pch = 16, col = "red")
For comparison, you could do the whole thing in ggplot like this:
library(ggplot2)
ggplot(df, aes(x, y)) +
geom_boxplot(fill = "lightblue", outlier.shape = NA) +
geom_point(size = 3, color = "red", stat = "summary", fun = mean) +
coord_cartesian(ylim = range(c(range(c(boxplot.stats(df$y)$stats,
mean_vals))))) +
theme_classic(base_size = 16)
Created on 2023-02-05 with reprex v2.0.2
I want to only have labels every second number, but have the small ticks for every number in my graph. As you can see in the figure I added, the labels are every 2nd tick on the X-axis.
But I want to achieve the result that's on the Y-axis:
With ggplot, this is possible with ggh4x and if_elfse. But I can't find a way how to do this in ggsurvplot. This is my code, for the first picture. The code for the second picture is found here: Code 2
ggsurvplot(fit, data = d,
conf.int = F,
censor = F,
palette = c("green", "purple", "red"),
legend.labs = c("Reference water (pH 7.3)\n(N = 66)",
"Acidic al-poor (pH 5.8)\n(N = 66)",
"Acidic al-rich (pH 5.8)\n(N = 66)"),
legend.title = "Water quality",
xlab = "Days",
xlim = c(1,23),
break.time.by = 2
)
Thank you in advance for yor help.
As ggsurvplot returns a list containing the plot as a ggplot2 object you could achieve your desired result using ggh2x by overriding the x scale as in the example code by #tjebo from Adding minor tick marks to the x axis in ggplot2 (with no labels).
Making use of the default example from ?ggsruvplot:
library(survminer)
library(survival)
library(ggh4x)
fit<- survfit(Surv(time, status) ~ sex, data = lung)
p <- ggsurvplot(fit, data = lung, main = "Survival curve",
xlab = "Days",
xlim = c(1,23))
p$plot +
scale_x_continuous(minor_breaks = seq(0, 24, 1), breaks = seq(0, 24, 2), guide = "axis_minor") +
theme(ggh4x.axis.ticks.length.minor = rel(1))
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
I'm trying to figure out how to modify a scatter-plot that contains two groups of data along a continuum separated by a large gap. The graph needs a break on the x-axis as well as on the regression line.
This R code using the ggplot2 library accurately presents the data, but is unsightly due to the vast amount of empty space on the graph. Pearson's correlation is -0.1380438.
library(ggplot2)
p <- ggplot(, aes(x = dis, y = result[, 1])) + geom_point(shape = 1) +
xlab("X-axis") +
ylab("Y-axis") + geom_smooth(color = "red", method = "lm", se = F) + theme_classic()
p + theme(plot.title = element_text(hjust = 0.5, size = 14))
This R code uses gap.plot to produce the breaks needed, but the regression line doesn't contain a break and doesn't reflect the slope properly. As you can see, the slope of the regression line isn't as sharp as the graph above and there needs to be a visible distinction in the slope of the line between those disparate groups.
library(plotrix)
gap.plot(
x = dis,
y = result[, 1],
gap = c(700, 4700),
gap.axis = "x",
xlab = "X-Axis",
ylab = "Y-Axis",
xtics = seq(0, 5575, by = 200)
)
abline(v = seq(700, 733) , col = "white")
abline(lm(result[, 1] ~ dis), col = "red", lwd = 2)
axis.break(1, 716, style = "slash")
Using MS Paint, I created an approximation of what the graph should look like. Notice the break marks on the top as well as the discontinuity between on the regression line between the two groups.
One solution is to plot the regression line in two pieces, using ablineclip to limit what's plotted each time. (Similar to #tung's suggestion, although it's clear that you want the appearance of a single graph rather than the appearance of facets.) Here's how that would work:
library(plotrix)
# Simulate some data that looks roughly like the original graph.
dis = c(rnorm(100, 300, 50), rnorm(100, 5000, 100))
result = c(rnorm(100, 0.6, 0.1), rnorm(100, 0.5, 0.1))
# Store the location of the gap so we can refer to it later.
x.axis.gap = c(700, 4700)
# gap.plot() works internally by shifting the location of the points to be
# plotted based on the gap size/location, and then adjusting the axis labels
# accordingly. We'll re-compute the second half of the regression line in the
# same way; these are the new values for the x-axis.
dis.alt = dis - x.axis.gap[1]
# Plot (same as before).
gap.plot(
x = dis,
y = result,
gap = x.axis.gap,
gap.axis = "x",
xlab = "X-Axis",
ylab = "Y-Axis",
xtics = seq(0, 5575, by = 200)
)
abline(v = seq(700, 733), col = "white")
axis.break(1, 716, style = "slash")
# Add regression line in two pieces: from 0 to the start of the gap, and from
# the end of the gap to infinity.
ablineclip(lm(result ~ dis), col = "red", lwd = 2, x2 = x.axis.gap[1])
ablineclip(lm(result ~ dis.alt), col = "red", lwd = 2, x1 = x.axis.gap[1] + 33)
I am using the following code in R to a plot a linear regression with confidence interval bands (95%) around the regression line.
Average <- c(0.298,0.783429,0.2295,0.3725,0.598,0.892,2.4816,2.79975,
1.716368,0.4845,0.974133,0.824,0.936846,1.54905,0.8166,1.83535,
1.6902,1.292667,0.2325,0.801,0.516,2.06645,2.64965,2.04785,0.55075,
0.698615,1.285,2.224118,2.8576,2.42905,1.138143,1.94225,2.467357,0.6615,
0.75,0.547,0.4518,0.8002,0.5936,0.804,0.7,0.6415,0.702182,0.7662,0.847)
Area <-c(8.605,16.079,4.17,5.985,12.419,10.062,50.271,61.69,30.262,11.832,25.099,
8.594,17.786,36.995,7.473,33.531,30.97,30.894,4.894,8.572,5.716,45.5,69.431,
40.736,8.613,14.829,4.963,33.159,66.32,37.513,27.302,47.828,39.286,9.244,19.484,
11.877,9.73,11.542,12.603,9.988,7.737,9.298,14.918,17.632,15)
lm.out <- lm (Area ~ Average)
newx = seq(min(Average), by = 0.05)
conf_interval <- predict(lm.out, newdata = data.frame(Average = newx), interval ="confidence",
level = 0.95)
plot(Average, Area, xlab ="Average", ylab = "Area", main = "Regression")
abline(lm.out, col = "lightblue")
lines(newx, conf_interval[,2], col = "blue", lty ="dashed")
lines(newx, conf_interval[,3], col = "blue", lty ="dashed")
I am stuck because the graph I got reports the bands just for the first part pf the line, leaving out all the remaining line (you find the link to the image at the bottom of the message). What is going wrong? I would also like to shade the area of the confidence interval (not just the lines corresponding to the limits) but I can't understand how to do it.
Any help would be really appreciated, I am completely new in R.
This is very easy with the ggplot2 -library. Here is the code:
library(ggplot2)
data = data.frame(Average, Area)
ggplot(data=data, aes(x=Area, y=Average))+
geom_smooth(method="lm", level=0.95)+
geom_point()
Code to install the library:
install.packages("ggplot2")
I am new in plotting time series. I downloaded a time series data and calculated a linear equation and I would like to add it in the time series plot. I want to show the year in the plot so I used index(stk) as x-axis input.
code:
library(quantmod)
stk <- suppressWarnings(getSymbols("AAPL", auto.assign = FALSE,
src = "yahoo", periodicity = "daily"))
stk <- na.omit(stk)
stk.lm1 <- lm(log(Cl(stk)) ~ c(1:nrow(stk)), data = stk)
plot(index(stk), log(Cl(stk)), type = "l", lwd = 3, las = 1)
abline(coefficients(stk.lm1)[1], coefficients(stk.lm1)[2], col="blue")
I know it is the plot using index(stk), how can I do to keep the x axis of plot in date and can I use plot.xts or other like ggplot2 to do the same things? Please advise, thank you very much.
It isn't dificult to do the plot that you want in base r plot or ggplot2 here is what you what:
plot(index(stk), log(Cl(stk)), type="l", lwd=3, las=1)
lines(x = index(stk.lm1$fitted.values), y = stk.lm1$fitted.values,col = "blue")
for the base r plot I added a line with the fitted values of the linear regression that I extracted with the $ signed and the dates of theme. Take into account that lm respect the structure of the data so the results are xts
library(ggplot2)
ggplot(stk, aes(x = index(stk), y = as.numeric(log(Cl(stk)))))+geom_line(lwd=1)+
geom_line(aes(x = index(stk.lm1$fitted.values), y = stk.lm1$fitted.values),col = "blue")+
labs(x = "Date", y = "Log Price")
For ggplot2 is quite similar. First you have to initiate the plot with ggplot where you defined the data and aesthetics (aes), then you add a line with geom_line and for the extra line I used the this command and define the new line in a new aes the same way I did it with the base r function.
Here's a ggplot solution. You shouldn't have to calculate the linear regression coefficients yourself:
# convert stk to data frame & specify your x-axis variable explicitly
stk.df <- as.data.frame(stk)
stk.df$Date <- as.Date(rownames(stk.df))
# plot
ggplot(stk.df,
aes(x = Date, y = log(AAPL.Close))) +
geom_line() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Year", y = "log(AAPL's closing value)") +
theme_bw()
The geom_smooth line takes care of the regression. Set se = TRUE if you want to include a confidence interval around the line.