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<p>Graphically analyze using <code>ggvis</code> the 1970 Boston housing dataset. This dataset consists of 14 variables with 506 observations. The target variable, <code>medv</code>, is the median owner-occupied home value in 1000's of dollars. The Boston dataset is found in the <code>MASS</code> package. </p>
<pre><code class="r">library(MASS)
data(Boston)
Boston$chas <-factor(Boston$chas, levels = c(0,1),
labels = c("Not on River", "On River"))
Boston$rad <-factor(Boston$rad)
head(Boston)
</code></pre>
<pre><code>## crim zn indus chas nox rm age dis rad tax ptratio
## 1 0.00632 18 2.31 Not on River 0.538 6.575 65.2 4.0900 1 296 15.3
## 2 0.02731 0 7.07 Not on River 0.469 6.421 78.9 4.9671 2 242 17.8
## 3 0.02729 0 7.07 Not on River 0.469 7.185 61.1 4.9671 2 242 17.8
## 4 0.03237 0 2.18 Not on River 0.458 6.998 45.8 6.0622 3 222 18.7
## 5 0.06905 0 2.18 Not on River 0.458 7.147 54.2 6.0622 3 222 18.7
## 6 0.02985 0 2.18 Not on River 0.458 6.430 58.7 6.0622 3 222 18.7
## black lstat medv
## 1 396.90 4.98 24.0
## 2 396.90 9.14 21.6
## 3 392.83 4.03 34.7
## 4 394.63 2.94 33.4
## 5 396.90 5.33 36.2
## 6 394.12 5.21 28.7
</code></pre>
<p>The fit of a linear model to <code>log(medv)</code> using select predictors is:</p>
<pre><code class="r">attach(Boston)
</code></pre>
<pre><code>## The following objects are masked from Boston (pos = 6):
##
## age, black, chas, crim, dis, indus, lstat, medv, nox, ptratio,
## rad, rm, tax, zn
</code></pre>
<pre><code class="r">Boston.lm <- lm(log(medv) ~ crim + chas + rad + lstat)
summary(Boston.lm)
</code></pre>
<pre><code>##
## Call:
## lm(formula = log(medv) ~ crim + chas + rad + lstat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64352 -0.13733 -0.02115 0.10461 0.87218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.428419 0.051028 67.187 < 2e-16 ***
## crim -0.008905 0.001515 -5.876 7.73e-09 ***
## chasOn River 0.169177 0.039400 4.294 2.11e-05 ***
## rad2 0.214589 0.067157 3.195 0.001486 **
## rad3 0.204713 0.061187 3.346 0.000883 ***
## rad4 0.056412 0.054380 1.037 0.300072
## rad5 0.167914 0.053908 3.115 0.001948 **
## rad6 0.091301 0.066334 1.376 0.169330
## rad7 0.158928 0.073020 2.177 0.029990 *
## rad8 0.216818 0.067290 3.222 0.001356 **
## rad24 0.094849 0.058213 1.629 0.103879
## lstat -0.039079 0.001667 -23.443 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2213 on 494 degrees of freedom
## Multiple R-squared: 0.7134, Adjusted R-squared: 0.707
## F-statistic: 111.8 on 11 and 494 DF, p-value: < 2.2e-16
</code></pre>
<p>All variables are highly significant.</p>
<p>See the variable descriptions using <code>help</code> in the <code>MASS</code> package.</p>
<p>In each of the following plots, <strong>label the axes appropriately</strong>.</p>
<ol>
<li>Plot the boxplot distributions of the log of the median home values (<code>log_medv = log(medv)</code>) by the Charles River (<code>chas</code>) and separately by radial highway locations (<code>rad</code>). Draw horizontal lines at <code>mean(log(medv))</code> in each plot. Discuss each plot.</li>
</ol>
<pre><code class="r">library(dplyr, warn.conflicts = FALSE)
library(ggvis)
</code></pre>
<pre><code class="r">Boston %>% ggvis(~chas, ~log(medv)) %>% layer_boxplots(fill = ~chas)
</code></pre>
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<p>}
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ggvis.getPlot("plot_id755997269").parseSpec(plot_id755997269_spec);
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<pre><code class="r">mean.coords <- data.frame(y=mean(log(medv)), x=c(levels(Boston$chas)))
Boston %>% ggvis(~chas, ~log(medv)) %>% layer_boxplots(fill = ~chas) %>%
layer_paths(data=mean.coords, x = ~x, y = ~y, stroke := "red", strokeWidth := 3) %>%
scale_ordinal("x", domain = levels(chas))
</code></pre>
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