Second, when we add the text in the third line of the code, we do not have pos=1, because we want to place the labels exactly where the points are. There are two differences in the code: First, we add type="n" to create the scatter plot without actually drawing any circles (an empty plot if you will). The scatter plot on the right is similar, but here we actually plot the labels instead of the dots. Plot(policy ~ perfor, bty="n", ylab="Policy Indicator", xlab="Performance", main="Policy and Performance") The argument pos=1 is there to tell R to draw the label underneath the point with pos=2 (etc.) we can change that position. To add the labels, we have text(), the first argument gives the X value of each point, the second argument the Y value (so R knows where to place the text) and the third argument is the corresponding label. Then we add the trend line with abline() and lm(). The third line here creates a string of characters “A” to “Y”, these are the labels!Ĭanton = sapply(65:89, function(x) rawToChar(as.raw(x)))įor the scatter plot on the left, we use plot(). In this example, we have 25 observations, for 25 units I call “cantons”. It is conventional to put the outcome variable on the Y axis and the predictor on the X axis, but in this example there’s no relationship to reality anyway… The reason I chose min and max values for the random variables here is that I jotted down this code as an explanation for a replication. In this fictitious example, I look at the relationship between a policy indicator and performance. You can position the legend anywhere on the scatterplot. Since the data are random, your plots will look different. To make the scatterplot more understandable, let us add a legend by using the function, legend( ). The basic function is text(), and here’s a reproducible example how you can use it to create these plots: Adding text to a scatter plot in Rįor the example, I’m creating random data. The ifelse( ) argument will be used to change the plot shape since we have only two shape choices.Adding text labels to a scatter plot in R is easy. At the same time, let us change the shape of the plots by denoting male patients with blue triangles and female patients with red circles. In this case, we will place it on the top-left corner. To make the scatterplot more understandable, let us add a legend by using the function, legend( ). If the patient is female, then the scatterplot will be red. That means, that if the patient is male, the scatterplot will be color blue. In the ifelse code, the first choice for the variable, sex, is 1, followed by the color blue. In our case, since we have only 2 color choices, we can use the ifelse( ) argument to assign the colors. Use the ifelse( ) argument when you only have 2 choices such as true or false, yes or no, etc. The ifelse( ) argument used above is the same as the if…else statement in programming but the ifelse( ) argument in R creates an if…else statement in one line of code. Plot(Melanoma $age, Melanoma $time, col = ifelse(Melanoma $sex = "1", "blue", "red"), main = "Survival Time from Malignant Melanoma", xlab = "Age (in years)", ylab = "Survival Time (in days)") More generally, it sounds like you need to spend some time with some. rstudio has a Workspace - import dataset menu Id recommend Rstudio, particularly if you are very new to R. You might want to use a program that lends a hand. 25.3 One-Sided Alternative Hypothesis Test If you do not know how to get data into R nor create a scatterplot, it sounds like you are very new to R.Their position on the X (horizontal) and Y (vertical) axis represents the values of the 2 variables. 25.1 Two-Sided Alternative Hypothesis Test A Scatterplot displays the relationship between 2 numeric variables.24.2 Hypothesis Test Using Value Differences.24.1 Hypothesis Test Using Paired Values.24 Inference on Two Dependent Sample Means.
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