R Notebook

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Let’s load some data to play with:

summary(Boston$crim)

    Min.  1st Qu.   Median     Mean  3rd Qu. 
 0.00632  0.08204  0.25651  3.61352  3.67708 
    Max. 
88.97620 

ggplot(Boston, aes(x=indus, y=medv)) +
    geom_point(shape=19, alpha=1/4) +
    geom_smooth(method=lm)

sort(Boston$nox)[1:100]

  [1] 0.3850 0.3890 0.3920 0.3920 0.3940
  [6] 0.3980 0.3980 0.4000 0.4000 0.4000
 [11] 0.4000 0.4010 0.4010 0.4010 0.4030
 [16] 0.4030 0.4030 0.4040 0.4040 0.4040
 [21] 0.4050 0.4050 0.4050 0.4090 0.4090
 [26] 0.4090 0.4100 0.4100 0.4100 0.4110
 [31] 0.4110 0.4110 0.4110 0.4110 0.4110
 [36] 0.4130 0.4130 0.4130 0.4130 0.4130
 [41] 0.4130 0.4150 0.4150 0.4161 0.4161
 [46] 0.4161 0.4220 0.4260 0.4260 0.4260
 [51] 0.4260 0.4280 0.4280 0.4280 0.4280
 [56] 0.4280 0.4280 0.4280 0.4280 0.4290
 [61] 0.4290 0.4290 0.4310 0.4310 0.4310
 [66] 0.4310 0.4310 0.4310 0.4310 0.4310
 [71] 0.4310 0.4310 0.4330 0.4330 0.4330
 [76] 0.4350 0.4370 0.4370 0.4370 0.4370
 [81] 0.4370 0.4370 0.4370 0.4370 0.4370
 [86] 0.4370 0.4370 0.4370 0.4370 0.4370
 [91] 0.4370 0.4370 0.4370 0.4379 0.4379
 [96] 0.4390 0.4390 0.4390 0.4390 0.4420

stem(Boston$nox)

  The decimal point is 2 digit(s) to the left of the |

  38 | 5922488
  40 | 000011133344455599900011111133333355666
  42 | 26666888888889991111111111333577777777777777777889999
  44 | 222333355555777778888888889999333333888
  46 | 00044444444992222
  48 | 4488888888999999999999999333333339999
  50 | 4444444477777777770000000555555558
  52 | 0000000000044444442222288888888888888888888888
  54 | 4444444444447777777770000
  56 | 3333355
  58 | 0000111111133334444444455555555777777
  60 | 55555555555555999994444444
  62 | 44444444444444411111
  64 | 777777777755599
  66 | 888111111199999999
  68 | 33333333333333
  70 | 00000000000333333333333333333888888
  72 | 
  74 | 0000000000000
  76 | 00000000
  78 | 
  80 | 
  82 | 
  84 | 
  86 | 1111111111111111

ggplot(Boston, aes(x=rm)) +
    geom_histogram(aes(y=..density..),      # Histogram with density instead of count on y-axis
      binwidth=.5, colour="black", fill="white") +
    geom_vline(aes(xintercept=mean(rm, na.rm=T)),   # Ignore NA values for mean
               color="red", linetype="dashed", size=1)+
geom_density(alpha=.2, fill="#FF6666")  # Overlay with transparent density plot

# A basic box with the conditions colored
ggplot(Boston, aes(x=ptratio, y=medv, fill=ptratio>mean(ptratio))) + geom_boxplot()