diff --git a/transform.Rmd b/transform.Rmd
index 5028b2a..3f45a07 100644
--- a/transform.Rmd
+++ b/transform.Rmd
@@ -125,7 +125,7 @@ filter(flights, !(arr_delay > 120 | dep_delay > 120))
 filter(flights, arr_delay <= 120, dep_delay <= 120)
 ```
 
-As well as `&` and `|`, R also has `&&` and `||`. Don't use them here! You'll when you should use them in [conditional execution].
+As well as `&` and `|`, R also has `&&` and `||`. Don't use them here! You'll learn when you should use them in [conditional execution].
 
 Sometimes you want to find all rows after the first `TRUE`, or all rows until the first `FALSE`. The window functions `cumany()` and `cumall()` allow you to find these values:
 
@@ -309,7 +309,7 @@ select(flights, time_hour, air_time, everything())
     vars <- c("year", "month", "day", "dep_delay", "arr_delay")
     ```
     
-1.  Does the result of running the following code suprise you?  How do the
+1.  Does the result of running the following code surprise you?  How do the
     select helpers deal with case by default? How can you change that default?
 
     ```{r, eval = FALSE}
@@ -784,7 +784,7 @@ daily <- group_by(flights, year, month, day)
 (per_year  <- summarise(per_month, flights = sum(flights)))
 ```
 
-Be careful when progressively rolling up summaries: it's OK for sums and counts, but you need to think about weighting means and variances, and it's not possible to do it exactly for rank-based statistics like the median. In otherwords, the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median.
+Be careful when progressively rolling up summaries: it's OK for sums and counts, but you need to think about weighting means and variances, and it's not possible to do it exactly for rank-based statistics like the median. In other words, the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median.
 
 ### Ungrouping
 
@@ -814,7 +814,7 @@ daily %>%
     Which is more important: arrival delay or departure delay?
 
 1.  Our definition of cancelled flights (`!is.na(dep_delay) & !is.na(arr_delay)`
-    ) is slightly sup-optimal. Why? Which is the most important column?
+    ) is slightly suboptimal. Why? Which is the most important column?
 
 1.  Look at the number of cancelled flights per day. Is there a pattern?
     Is the proportion of cancelled flights related to the average delay?
@@ -874,7 +874,7 @@ Functions that work most naturally in grouped mutates and filters are known as
 1.  Delays are typically temporally correlated: even once the problem that
     caused the initial delay has been resolved, later flights are delayed 
     to allow earlier flights to leave. Using `lag()` explore how the delay
-    of a flight is related to the delay of the immediately preceeding flight.
+    of a flight is related to the delay of the immediately preceding flight.
     
 1.  Look at each destination. Can you find flights that are suspiciously
     fast? (i.e. flights that represent a potential data entry error). Compute