+<p>Note that you could also do this pretty easily with a call to <code>group_by</code> and <code>summarize</code> in the tidyverse:</p>
+<pre class="r"><code>grads %>%
+ group_by(cohort) %>%
+ summarize(
+ n = n(),
+ min = min(income),
+ mean = mean(income),
+ max = max(income),
+ sd = sd(income)
+ )</code></pre>
+<pre><code>## # A tibble: 13 x 6
+## cohort n min mean max sd
+## <fct> <int> <dbl> <dbl> <dbl> <dbl>
+## 1 cohort_01 142 27.0 54.3 86.4 16.8
+## 2 cohort_02 142 25.4 54.3 78.0 16.8
+## 3 cohort_03 142 20.2 54.3 95.3 16.8
+## 4 cohort_04 142 22.0 54.3 98.3 16.8
+## 5 cohort_05 142 30.4 54.3 89.5 16.8
+## 6 cohort_06 142 17.9 54.3 96.1 16.8
+## 7 cohort_07 142 31.1 54.3 85.4 16.8
+## 8 cohort_08 142 22.3 54.3 98.2 16.8
+## 9 cohort_09 142 15.6 54.3 91.6 16.8
+## 10 cohort_10 142 27.4 54.3 77.9 16.8
+## 11 cohort_11 142 19.3 54.3 91.7 16.8
+## 12 cohort_12 142 21.9 54.3 85.7 16.8
+## 13 cohort_13 142 18.1 54.3 95.6 16.8</code></pre>
+<pre class="r"><code>grads %>%
+ group_by(cohort) %>%
+ summarize(
+ n = n(),
+ min = min(gpa),
+ mean = mean(gpa),
+ max = max(gpa),
+ sd = sd(gpa)
+ )</code></pre>
+<pre><code>## # A tibble: 13 x 6
+## cohort n min mean max sd
+## <fct> <int> <dbl> <dbl> <dbl> <dbl>
+## 1 cohort_01 142 14.4 47.8 92.2 26.9
+## 2 cohort_02 142 15.8 47.8 94.2 26.9
+## 3 cohort_03 142 5.65 47.8 99.6 26.9
+## 4 cohort_04 142 10.5 47.8 90.5 26.9
+## 5 cohort_05 142 2.73 47.8 99.7 26.9
+## 6 cohort_06 142 14.9 47.8 87.2 26.9
+## 7 cohort_07 142 4.58 47.8 97.8 26.9
+## 8 cohort_08 142 2.95 47.8 99.5 26.9
+## 9 cohort_09 142 0.0151 47.8 97.5 26.9
+## 10 cohort_10 142 0.217 47.8 99.3 26.9
+## 11 cohort_11 142 9.69 47.8 85.9 26.9
+## 12 cohort_12 142 16.3 47.8 85.6 26.9
+## 13 cohort_13 142 0.304 47.8 99.6 26.9</code></pre>
+<p>Huh. Those are remarkably similar values for the group means and the group standard deviations…weird.</p>
projects / stats_class_2020.git / blobdiff