library(ggplot2)
head(diamonds)
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
By the way, the number above was calculated 'inline' with the code, 53940 (It seems that you can't make something verbatim in the verbatem context.)
set.seed(1000) # make sample reproducible
dsmall <- diamonds[sample(nrow(diamonds), 100), ]
head(dsmall)
## carat cut color clarity depth table price x y z
## 17686 1.23 Ideal H VS2 62.2 55 7130 6.81 6.85 4.25
## 40932 0.30 Ideal E SI1 61.7 58 499 4.29 4.30 2.65
## 6146 0.90 Good H VS2 61.9 58 3989 6.14 6.18 3.81
## 37258 0.31 Ideal G VVS1 62.8 57 977 4.33 4.30 2.71
## 27853 0.31 Ideal G VS2 61.5 56 652 4.34 4.36 2.68
## 3654 1.01 Ideal F I1 62.2 54 3439 6.44 6.42 4.00
The defaults of qplot() are 'x, y, data ='
qplot(carat, price, data = dsmall)
qplot accepts functions of variable as arguments so we can look at the logged variables to see if the relationship is closer to linear when the data is transformed.