Fix markdown formatting

.remarkrc

@@ -12,6 +12,7 @@
     ["remark-lint-no-undefined-references", false],
     ["remark-lint-emphasis-marker", false],
     ["remark-lint-fenced-code-flag", false],
-    ["remark-lint-no-duplicate-headings", false]
+    ["remark-lint-no-duplicate-headings", false],
+    ["remark-lint-maximum-heading-length", false]
   ]
 }

NEWS.md

@@ -9,7 +9,6 @@
 -   Update code for Ex 5.7.7 to be cleaner (#41)
 -   Fix various typos (dongzhuoer #39, #40, #43, #45, #46, #47, #48)
 
-
 ## 2018-07-28
 
 -   Fix miscellaneous typos (dongzhuoer, #31, #33, #34, #35, #36, #37)

relational-data.Rmd

@@ -104,7 +104,7 @@ Identify the keys in the following datasets
 
 The answer to each part follows.
 
-1.  The primary key for `Lahman::Batting` is `playerID`, `yearID`, `stint`. 
+1.  The primary key for `Lahman::Batting` is `playerID`, `yearID`, `stint`.
     It is not simply `playerID`, `yearID` because players can have different stints in different leagues within the same year.
 
     ```{r}
@@ -114,7 +114,7 @@ The answer to each part follows.
       nrow()
     ```
 
-1.  The primary key for `babynames::babynames` is `year`, `sex`, `name`. 
+1.  The primary key for `babynames::babynames` is `year`, `sex`, `name`.
     It is not simply `year`, `name` since names can appear for both sexes with different counts.
 
     ```{r}
@@ -124,7 +124,7 @@ The answer to each part follows.
       nrow()
     ```
 
-1.   The primary key for `nasaweather::atmos` is the location and time of the measurement: `lat`, `long`, `year`, `month`.
+1.  The primary key for `nasaweather::atmos` is the location and time of the measurement: `lat`, `long`, `year`, `month`.
 
     ```{r}
     nasaweather::atmos %>%
@@ -294,11 +294,11 @@ Add the location of the origin and destination (i.e. the `lat` and `lon`) to `fl
 
 `r BeginAnswer()`
 
-You can perform one join after another. If duplicate variables are found, by default, dplyr will distinguish the two by adding `.x`, and `.y` to the ends of the variable names to solve naming conflicts. 
+You can perform one join after another. If duplicate variables are found, by default, dplyr will distinguish the two by adding `.x`, and `.y` to the ends of the variable names to solve naming conflicts.
 ```{r}
 airport_locations <- airports %>%
   select(faa, lat, lon)
-  
+
 flights %>%
     select(year:day, hour, origin, dest) %>%
   left_join(
@@ -314,7 +314,7 @@ This default can be over-ridden using the `suffix` argument.
 ```{r}
 airport_locations <- airports %>%
   select(faa, lat, lon)
-  
+
 flights %>%
     select(year:day, hour, origin, dest) %>%
   left_join(

strings.Rmd

@@ -249,8 +249,9 @@ The answer to each part follows.
     ```{r eval=is_html}
     str_view(stringr::words, "^y", match =TRUE)
     ```
-    
+
 1.  End with “x”
+
     ```{r eval=is_html}
     str_view(stringr::words, "x$", match = TRUE)
     ```
@@ -262,6 +263,7 @@ The answer to each part follows.
     ```
 
 1.  The words that have seven letters or more are
+
     ```{r eval=is_html}
     str_view(stringr::words, ".......", match = TRUE)
     ```
@@ -294,7 +296,7 @@ The answer to each part follows.
     ```
 
 1.  Words that contain only consonants
-    
+
     ```{r eval=is_html}
     str_view(stringr::words, "^[^aeiou]+$", match=TRUE)
     ```
@@ -308,7 +310,7 @@ The answer to each part follows.
     ```
 
 1.  Words ending in `ing` or `ise`:
-    
+
     ```{r eval=is_html}
     str_view(stringr::words, "i(ng|se)$", match = TRUE)
     ```
@@ -592,11 +594,11 @@ The answer to each part follows.
     ```{r eval=is_html}
     str_view(words, "([[:letter:]]).*\\1")
     ```
-    
-    Note that these patterns are case sensitive. Use the 
+
+    Note that these patterns are case sensitive. Use the
     case insensitive flag if you want to check for repeated pairs
-    of letters with different capitalization. 
-    
+    of letters with different capitalization.
+
     The `\\1` is used to refer back to the first group (`(.)`) so that whatever letter is matched by `[A-Za-z]` is again matched.
 
 1.  This regex matches words that contain one letter repeated in at least three places.
@@ -655,7 +657,7 @@ The answer to each part follows.
     that contain at least one of each vowel. The regular expression
     would need to consider all possible orders in which the vowels
     could occur.
-    
+
     ```{r}
     pattern <-
       cross_n(rerun(5, c("a", "e", "i", "o", "u")),
@@ -668,14 +670,14 @@ The answer to each part follows.
     ```
 
     To check that this pattern works, test it on a pattern that
-    should match 
+    should match
     ```{r}
     str_subset("aseiouds", pattern)
     ```
-    
-    Using multiple `str_detect()` calls, one pattern for each vowel, 
+
+    Using multiple `str_detect()` calls, one pattern for each vowel,
     produces a much simpler and readable answer.
-    
+
     ```{r}
     str_subset(words, pattern)
 
@@ -685,15 +687,15 @@ The answer to each part follows.
           str_detect(words, "o") &
           str_detect(words, "u")]
     ```
-    
+
     There appear to be none.
 
 1.  The word with the highest number of vowels is
     ```{r}
     vowels <- str_count(words, "[aeiou]")
     words[which(vowels == max(vowels))]
     ```
-    
+
     The word with the highest proportion of vowels is
     ```{r}
     prop_vowels <- str_count(words, "[aeiou]") / str_length(words)
@@ -769,7 +771,7 @@ The answer to each part follows.
 1.  Finding all plurals cannot be correctly accoplished with regular expressions alone.
     Finding plural words would at least require morphological information about words in the language.
     See [WordNet](https://cran.r-project.org/web/packages/wordnet/index.html) for a resource that would do that.
-    However, identifying words that end in an "s" and with more than three characters, in order to remove "as", "is", "gas", etc., is 
+    However, identifying words that end in an "s" and with more than three characters, in order to remove "as", "is", "gas", etc., is
     a reasonable heuristic.
 
     ```{r}

tibble.Rmd

@@ -114,7 +114,6 @@ Practice referring to non-syntactic names in the following data frame by:
 For this example, I'll create a dataset called annoying with
 columns named `1` and `2`.
 
-
 ```{r}
 annoying <- tibble(
   `1` = 1:10,

transform.Rmd

@@ -384,14 +384,16 @@ Compare `air_time` with `arr_time - dep_time`. What do you expect to see? What d
 
 `r BeginAnswer()`
 
-Since `arr_time` and `dep_time` may be in different time zones, the `air_time` doesn't equal the difference.
-We would need to account for time-zones in these calculations.
+Since `arr_time` and `dep_time` may be in different time zones, the `air_time` does not equal the differences.
+
 ```{r}
-mutate(flights,
-       air_time2 = arr_time - dep_time,
-       air_time_diff = air_time2 - air_time) %>%
+air_times <- mutate(flights,
+       arr_time_min = arr_time %/% 100 * 60 + arr_time %% 100,
+       dep_time_min = dep_time %/% 100 * 60 + dep_time %% 100,
+       air_time_2 = (arr_time_min - dep_time_min + 1440) %% 1440,
+       air_time_diff = air_time_2 - air_time) %>%
   filter(air_time_diff != 0) %>%
-  select(air_time, air_time2, dep_time, arr_time, dest)
+  select(air_time, air_time_2, dep_time, arr_time, dest)
 ```
 
 `r EndAnswer()`