Visualising the distribution for different subgroups

Question

I'm using "d.pizza" data. There is variable called "delivery_min" which is delivery time (in minutes) and there is variable called "area" which can be one of three areas (Camden, Westminster and Brent). I want to draw a density plot that visualises the distribution of delivery time for these three areas.

I tried

 plot.ecdf(pizza_d$delivery_min)

this code works, but how can I do it for each area?

head(d.pizza)=

index       date week weekday        area count rabate  price operator  driver delivery_min
1 1     1 01.03.2014    9       6      Camden     5   TRUE 65.655   Rhonda  Taylor         20.0
2 2     2 01.03.2014    9       6 Westminster     2  FALSE 26.980   Rhonda Butcher         19.6
3 3     3 01.03.2014    9       6 Westminster     3  FALSE 40.970  Allanah Butcher         17.8
4 4     4 01.03.2014    9       6       Brent     2  FALSE 25.980  Allanah  Taylor         37.3
5 5     5 01.03.2014    9       6       Brent     5   TRUE 57.555   Rhonda  Carter         21.8
6 6     6 01.03.2014    9       6      Camden     1  FALSE 13.990  Allanah  Taylor         48.7
  temperature wine_ordered wine_delivered wrongpizza quality
1        53.0            0              0      FALSE  medium
2        56.4            0              0      FALSE    high
3        36.5            0              0      FALSE    <NA>
4          NA            0              0      FALSE    <NA>
5        50.0            0              0      FALSE  medium
6        27.0            0              0      FALSE     low

Answer 1

You could do:

library(DescTools)

data(d.pizza)

plot.ecdf(subset(d.pizza, area == "Camden")$delivery_min, 
          col = "red", main = "ECDF for pizza deliveries")
plot.ecdf(subset(d.pizza, area == "Westminster")$delivery_min, 
          add = TRUE, col = "blue")
plot.ecdf(subset(d.pizza, area == "Brent")$delivery_min, 
          add = TRUE, col = "green")

Answer 2

library(DescTools)

data(d.pizza)
summary(d.pizza$delivery_min)

plot(NULL,ylab='',xlab='', xlim=c(5,66), ylim=0:1)
for(A in 1:3) {
    plot.ecdf(d.pizza$delivery_min[d.pizza$area == levels(d.pizza$area)[A]], 
        pch=20, col=A+1, add=T)
}
legend("bottomright", legend=levels(d.pizza$area), 
        bty='n', pch=20, col=2:4)

Answer 3

I'd recommend the ggplot2 library for data visualization in R. Here's some code using ggplot2 that can create a density plot with the three groups overlaid:

library(ggplot2)

# make example dataframe
d.pizza <- data.frame(delivery_min = rnorm(n=30), area = rep(c("Camden", "Westminster", "Brent"), 10))

# plot data in ggplot2
ggplot(d.pizza, aes(x = delivery_min, fill = area, color = area)) + geom_density(alpha = 0.5)

If you want a histogram, that can be done too:

ggplot(d.pizza, aes(x = delivery_min, fill = area, color = area)) + geom_histogram(alpha = 0.5, position = 'identity')