Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.
Create some data
The data set contains the value of weight by sex for 200 individuals.
set.seed(1234) x <- c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5)) head(x)
##  48.96467 56.38715 60.42221 43.27151 57.14562 57.53028
Create histogram plots: hist()
- A histogram can be created using the function hist(), which simplified format is as follow:
hist(x, breaks = "Sturges")
- x: a numeric vector
- breaks: breakpoints between histogram cells.
- Create histograms
hist(x, col = "steelblue", frame = FALSE)
# Change the number of breaks hist(x, col = "steelblue", frame = FALSE, breaks = 30)
Create density plots: density()
The function density() is used to estimate kernel density.
# Compute the density data dens <- density(mtcars$mpg) # plot density plot(dens, frame = FALSE, col = "steelblue", main = "Density plot of mpg")
# Fill the density plot using polygon() plot(dens, frame = FALSE, col = "steelblue", main = "Density plot of mpg") polygon(dens, col = "steelblue")
This analysis has been performed using R statistical software (ver. 3.2.4).
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