diff --git a/code/07_workshop.R b/code/07_workshop.R
new file mode 100644
index 0000000..8eff6fe
--- /dev/null
+++ b/code/07_workshop.R
@@ -0,0 +1,79 @@
+
+library(dplyr)
+library(ggplot2)
+
+hours = c(1,2,2,3,4,5,7,8,9,10,
+  11,13,15,16,17,17,18,18,18,20,
+  20,21,21,24,27,29,30,37,40,40,
+  40,41,46,47,48,52,54,54, 55,55,
+  64,65,65,65,67,76,76,79,80,80,
+  82,86,87,89,94,96,100,101,102,104,
+  105,109,109,120,123,141,150,156,156,161,
+  164,167,170,178,181,191,193,206,211,212,
+  214,236,238,240,265,304,317,328,355,363,
+  365,369,389,404,427,435,500,522,547,889)
+
+
+# Try starting bin size of 55
+data.frame(
+  t = hours
+) %>%
+  mutate(label = cut_interval(t, length = 55)) %>%
+  group_by(label, .drop = FALSE) %>%
+  summarize(r_obs = n()) %>%
+  mutate(bin = as.numeric(label),
+         lower = (bin - 1) * 55,
+         upper = (bin * 55),
+         midpoint = (lower + upper) / 2) %>%
+  select(bin,label, midpoint,  r_obs)
+
+
+# Let's functionify this...
+crosstab = function(x, binsize = 55){
+  data.frame(
+    t = x
+  ) %>%
+    mutate(label = cut_interval(t, length = binsize)) %>%
+    group_by(label, .drop = FALSE) %>%
+    summarize(r_obs = n()) %>%
+    mutate(bin = as.numeric(label),
+           lower = (bin - 1) * binsize,
+           upper = (bin * binsize),
+           midpoint = (lower + upper) / 2) %>%
+    select(bin,label, midpoint,lower,upper, r_obs)
+}
+
+
+# try this bin size
+tab = crosstab(hours, binsize = 100) 
+
+# Edit your categories
+
+tab = tab %>%
+  mutate(label = case_when(
+    midpoint >= 450 ~ "(400,900]",
+    TRUE ~ label
+  ), bin = case_when(midpoint >= 450 ~ 5, TRUE ~ bin)) 
+
+tab
+tab = tab %>%
+  group_by(bin, label) %>%
+  summarize(r_obs = sum(r_obs)) %>%
+  mutate(lower = (bin - 1) * 100,
+            upper = (bin * 100),
+            midpoint = (lower + upper) / 2) %>%
+  select(bin,label, midpoint,lower,upper, r_obs)
+
+
+lambda_hat = 0.00725
+n = 100
+tab$r_obs %>% sum()
+f = function(t,lambda){ 1 - exp(-lambda*t)}
+ingredients = tab %>%
+  mutate(f2 = f(upper, lambda_hat),
+         f1 = f(lower, lambda_hat),
+         prob = f2 - f1,
+         r_exp = prob * n) 
+
+ingredients %>%
+  select(label, f2, f1, prob, r_exp)