This is the code I used:
resources <- read.csv("https://raw.githubusercontent.com/umbertomig/intro-prob-stat-FGV/master/datasets/resources.csv")
res <- subset(resources, select = c("cty_name", "year", "regime",
"oil", "logGDPcp", "illit"))
resNoNA <- na.omit(res)
resNoNAS <- scale(resNoNA[, 3:6])
colMeans(resNoNA[, 3:6])
apply(resNoNA[, 3:6], 2, sd)
cluster2 <- kmeans(resNoNAS, centers = 2)
table(cluster2$cluster)
## this gives standardized answer, which is hard to interpret
cluster2$centers
## better to subset the original data and then compute means
g1 <- resNoNA[cluster2$cluster == 1, ]
colMeans(g1[, 3:6])
g2 <- resNoNA[cluster2$cluster == 2, ]
colMeans(g2[, 3:6])
plot(x = resNoNA$logGDPcp, y = resNoNA$illit, main = "Illiteracy v GDP",
xlab = "GDP per Capita", ylab = "Illiteracy",
col = cluster2$cluster, cex = resNoNA$oil)
but I wanted to make the circles smaller in order to fit within the limits of the graph
You control the circle diameter with cex= here.
plot(x = resNoNA$logGDPcp, y = resNoNA$illit, main = "Illiteracy v GDP",
xlab = "GDP per Capita", ylab = "Illiteracy",
col = cluster2$cluster, cex = resNoNA$oil)
plot(x = resNoNA$logGDPcp, y = resNoNA$illit, main = "Illiteracy v GDP",
xlab = "GDP per Capita", ylab = "Illiteracy",
col = cluster2$cluster, cex = resNoNA$oil/3)
plot(x = resNoNA$logGDPcp, y = resNoNA$illit, main = "Illiteracy v GDP",
xlab = "GDP per Capita", ylab = "Illiteracy",
col = cluster2$cluster, cex = resNoNA$oil/5)
Realize, however, that if you are using this in some automated report generator (e.g., rmarkdown, shiny), then you may need to adjust the dimensions of the plot to control it from the other angle: update xlim and ylim.
Related
I compare two treatments A and B. The objective is to show that A is not inferior to B. The non inferiority margin delta =-2
After comparing Treatment A - Treatment B I have these results
Mean difference and 95% CI = -0.7 [-2.1, 0.8]
I would like to plot this either with a package or manually. I have no idea how to do it.
Welch Two Sample t-test
data: mydata$outcome[mydata$traitement == "Bras S"] and mydata$outcome[mydata$traitement == "B"]
t = 0.88938, df = 258.81, p-value = 0.3746
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.133224 0.805804
sample estimates:
mean of x mean of y
8.390977 9.054688
I want to create this kind of plot:
You could abstract the relevant data from the t.test results and then plot in base R using segments and points to plot the data and abline to draw in the relevant vertical lines. Since there were no reproducible data, I made some up but the process is generally the same.
#sample data
set.seed(123)
tres <- t.test(runif(10), runif(10))
# get values to plot from t test results
ci <- tres$conf.int
ests <- tres$estimate[1] - tres$estimate[2]
# plot
plot(x = ci, ylim = c(0,2), xlim = c(-4, 4), type = "n", # blank plot
bty = "n", xlab = "Treatment A - Treatment B", ylab = "",
axes = FALSE)
points(x = ests, y = 1, pch = 20) # dot for point estimate
segments(x0 = ci[1], x1 = ci[2], y0 = 1) #CI line
abline(v = 0, lty = 2) # vertical line, dashed
abline(v = 2, lty = 1, col = "darkblue") # vertical line, solid, blue
axis(1, col = "darkblue") # add in x axis, blue
EDIT:
If you wanted to more accurately recreate your figure with the x axis in descending order and using your statement "Mean difference and 95% CI = -0.7 [-2.1, 0.8]", you can do the following manipulations to the above approach:
diff <- -0.7
ci <- c(-2.1, 0.8)
# plot
plot(1, xlim = c(-4, 4), type = "n",
bty = "n", xlab = "Treatment A - Treatment B", ylab = "",
axes = FALSE)
points(x = -diff, y = 1, pch = 20)
segments(x0 = -ci[2], x1 = -ci[1], y0 = 1)
abline(v = 0, lty = 2)
abline(v = 2, lty = 1, col = "darkblue")
axis(1, at = seq(-4,4,1), labels = seq(4, -4, -1), col = "darkblue")
I am running the following R code:
plot(datereal, casesimm8lock7,
type = "l", lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
And I want the y-axis, which currently displays 50000 100000 150000 etc. to display 50,000 100,000 150,000 etc.
I am trying to use the scales package but haven't figured it out yet.
Start by making an empty plot with no y axis. Then plot the data.
Then plot the y axis with axis(2, ...). In order to have the labels formatted like the question asks for, use help("formatC").
# test data
datereal <- Sys.Date() - 10:0
casesimm8lock7 <- seq(0,250000, length.out = 11)
# the plot
plot(datereal, casesimm8lock7, type = "n", yaxt = "n")
lines(datereal, casesimm8lock7,
lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
axis(2, at = seq(0, 250000, by = 50e3),
labels = formatC(seq(0, 250000, by = 50e3),
format = "d", big.mark = ","))
With package scales, it could be with either label_comma, like below, or label_number. Thse functions return a labeller function, to be applied to the vector of axis marks.
labeller <- scales::label_comma()
plot(datereal, casesimm8lock7, type = "n", yaxt = "n")
lines(datereal, casesimm8lock7,
lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
axis(2, at = seq(0, 250000, by = 50e3),
labels = labeller(seq(0, 250000, by = 50e3)))
I have a 3D scatter plot that looks like this
and the code associated with it is as follows
nr = c(114,114,1820,100,100)
acc = c(70.00,45.00,98.89,82.00,74.90)
ti = c(25.00,87.50,0.25,41.40,51.30)
label = c(1, 2, 3, 4, 5)
data = data.frame(nr, acc, ti, label)
library(scatterplot3d)
scatterplot3d(data$nr, data$acc, data$ti, main = "3D Plot - Requirements, Accuracy & Time", xlab = "Number of requirements", ylab = "Accuracy", zlab = "Time", pch = data$label, angle = 45)
Now, I want to add a legend to the bottom right to indicate what those symbols mean
tech <- c('BPL','W','RT','S','WSM')
For instance, the triangle stands for BPL, + for RT and so on
You can try this:
library(scatterplot3d)
# define a plot
s3d <-scatterplot3d(data$nr, data$acc, data$ti, main = "3D Plot - Requirements, Accuracy & Time",
xlab = "Number of requirements", ylab = "Accuracy", zlab = "Time", pch = data$label, angle = 45)
# add a legend
legend("topright",s3d$xyz.convert(18, 0, 12), pch = data$label, yjust=0,
# here you define the labels in the legend
legend = c('BPL','W','RT','S','WSM'), cex = 1.1
)
This is probably a very basic question, but I am struggling to change the graphics parameters in the xyplot (below). The objective is to change the colour of the regression line to red and the data-points to blue.
I have basicaly tried the code below but when I add pch=19, and col="blue" to the code the colours don't change.
If anyone can help, I would be deeply appreciative.
R code for the XY plot
##Create a vector
Z3 <- as.vector(as.matrix(Pairs[, c("Lighting",
"Distance_Coast",
"Temp_Canopy",
"Temp_Open",
"T.max.open",
"T.min.open",
"T.min.canopy",
"T.max.canopy")]))
#Setup the data in vector format for the xyplot
Y10 <- rep(Pairs$Canopy_index, 8)
MyNames <-names(Pairs[,c("Lighting",
"Distance_Coast",
"Temp_Canopy",
"Temp_Open",
"T.max.open",
"T.min.open",
"T.min.canopy",
"T.max.canopy")])
ID10 <- rep(MyNames, each = length(Pairs$Canopy_index))
ID11 <- factor(ID10, labels = c("Lighting",
"Distance_Coast",
"Temp_Canopy",
"Temp_Open",
"T.max.open",
"T.min.open",
"T.min.canopy",
"T.max.canopy"),
levels = c("Lighting",
"Distance_Coast",
"Temp_Canopy",
"Temp_Open",
"T.max.open",
"T.min.open",
"T.min.canopy",
"T.max.canopy"))
##XY Plot
xyplot(Y10 ~ Z3 | ID11, col = 1,
strip = function(bg='white',...) strip.default(bg='white',...),
scales = list(alternating = T,
x = list(relation = "free"),
y = list(relation = "same")),
xlab = "Explanatory Variables",
par.strip.text = list(cex = 0.8),
ylab = "Canopy Index",
panel=function(x, y, subscripts,...){
panel.grid(h =- 1, v = 2)
panel.points(x, y, col = 1, pch = 16)
if(ID10[subscripts][1] != "Minutes.After.Sunset") {panel.loess(x,y,col=1,lwd=2)}
})
XY Plot
Heres the answer:
##XY Plot
xyplot(Y10 ~ Z3 | ID11, col = 1,
strip = function(bg='white',...) strip.default(bg='white',...),
scales = list(alternating = T,
x = list(relation = "free"),
y = list(relation = "same")),
xlab = "Explanatory Variables",
par.strip.text = list(cex = 0.8),
ylab = "Canopy Index",
panel=function(x, y, subscripts,...){
panel.grid(h =- 1, v = 2)
panel.points(x, y, col = "blue", pch = 19)
if(ID10[subscripts][1] != "Minutes.After.Sunset"){panel.loess(x, y, col="red", lwd=2)}
})
XY Plot
how to
Combine a bar chart and line in single plot in R (from different data sources)?
Say I have two data sources as:
barData<-c(0.1,0.2,0.3,0.4) #In percentage
lineData<-c(100,22,534,52,900)
Note that they may not be in the same scale.
Can I plot both barData and LineData in one plot and make them good looking ?
I cant use ggplot in this case so this is not a duplicated question..
Something like the following:
Maybe this helps as a starting point:
par(mar = rep(4, 4))
barData<-c(0.1,0.2,0.3,0.4) * 100
y <- lineData<-c(100,22,534,900);
x <- barplot(barData,
axes = FALSE,
col = "blue",
xlab = "",
ylab = "",
ylim = c(0, 100) )[, 1]
axis(1, at = x, labels = c("Julia", "Pat", "Max", "Norman"))
ats <- c(seq(0, 100, 15), 100); axis(4, at = ats, labels = paste0(ats, "%"), las = 2)
axis(3, at = x, labels = NA)
par(new = TRUE)
plot(x = x, y = y, type = "b", col = "red", axes = FALSE, xlab = "", ylab = "")
axis(2, at = c(pretty(lineData), max(lineData)), las = 2)
mtext(text="Lines of code by Programmer", side = 3, line = 1)
box()