From ad10eeca1c8c0f36dd764e576472becaf18bc0ca Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 2 Sep 2022 10:48:26 -0600
Subject: [PATCH 01/18] change `challenge.csv`

---
 inst/extdata/challenge.csv | 4002 ++++++++++++++++++------------------
 1 file changed, 2001 insertions(+), 2001 deletions(-)

diff --git a/inst/extdata/challenge.csv b/inst/extdata/challenge.csv
index d977e08b..4fa3c13a 100644
--- a/inst/extdata/challenge.csv
+++ b/inst/extdata/challenge.csv
@@ -1,2001 +1,2001 @@
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From bd3a4aaee22adbe2695c5832b9e963ddf2163bfc Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 2 Sep 2022 11:02:19 -0600
Subject: [PATCH 02/18] update text to reflect new example

---
 vignettes/readr.Rmd | 77 +++++++++++++++------------------------------
 1 file changed, 26 insertions(+), 51 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 91911789..ee956bb1 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -127,7 +127,7 @@ parse_number("$1,234")
 
 There are two parsers that will never be guessed: `col_skip()` and `col_factor()`. You will always need to supply these explicitly.
 
-You can see the specification that readr would generate for a column file by using `spec_csv()`, `spec_tsv()` and so on:
+You can see the specification that readr would generate for a file by using `spec_csv()`, `spec_tsv()` and so on.
 
 ```{r}
 x <- spec_csv(readr_example("challenge.csv"))
@@ -142,15 +142,6 @@ mtcars_spec
 cols_condense(mtcars_spec)
 ```
 
-
-By default readr only looks at the first 1000 rows. This keeps file parsing speedy, but can generate incorrect guesses. For example, in `challenge.csv` the column types change in row 1001, so readr guesses the wrong types. One way to resolve the problem is to increase the number of rows:
-
-```{r}
-x <- spec_csv(readr_example("challenge.csv"), guess_max = 1001)
-```
-
-Another way is to manually specify the `col_type`, as described below.
-
 ## Rectangular parsers
 
 readr comes with five parsers for rectangular file formats:
@@ -160,60 +151,44 @@ readr comes with five parsers for rectangular file formats:
 * `read_fwf()` for fixed-width files
 * `read_log()` for web log files
 
-Each of these functions firsts calls `spec_xxx()` (as described above), and then parses the file according to that column specification:
+If readr finds problems with parsing your data, it will emit a warning message.
+For example, in our `challenge.csv` file, the column type we expected from our data was a column of dates and a column of doubles.
 
 ```{r}
-df1 <- read_csv(readr_example("challenge.csv"))
+df1 <- read_csv(
+  readr_example("challenge.csv"),
+  col_types = cols(
+    date = col_date(),
+    value = col_double()
+  )
+)
 ```
 
-The rectangular parsing functions almost always succeed; they'll only fail if the format is severely messed up. Instead, readr will generate a data frame of problems. The first few will be printed out, and you can access them all with `problems()`:
+The data was successfully imported, but we get a warning message about parsing issues.
+To view more details about these parsing issues, call `problems()` on your data frame.
+This will generate a data frame with information about where the problem was found, and why.
 
 ```{r}
 problems(df1)
 ```
 
-You've already seen one way of handling bad guesses: increasing the number of rows used to guess the type of each column. 
-
-```{r}
-df2 <- read_csv(readr_example("challenge.csv"), guess_max = 1001)
-```
-
-Another approach is to manually supply the column specification.
-
-### Overriding the defaults
-
-In the previous examples, you may have noticed that readr printed the column specification that it used to parse the file:
-
-```{r}
-#> Parsed with column specification:
-#> cols(
-#>   x = col_integer(),
-#>   y = col_character()
-#> )
-```
-
-You can also access it after the fact using `spec()`:
-
-```{r}
-spec(df1)
-spec(df2)
-```
-
-(This also allows you to access the full column specification if you're reading a very wide file. By default, readr will only print the specification of the first 20 columns.)
-
-If you want to manually specify the column types, you can start by copying and pasting this code, and then tweaking it fix the parsing problems.
-
-```{r}
-df3 <- read_csv(
-  readr_example("challenge.csv"), 
-  col_types = list(
-    x = col_double(),
-    y = col_date(format = "")
+In this example, one of the dates is in an incorrect format.
+Since `problems()` gives us the row number from the original file, fixing this value in the original file could be easy, especially since there is only one problematic value.
+But data in the wild is rarely so clean.
+We can fix this parsing error by importing the date column as character and using the lubridate package to parse our dates.
+
+```{r, eval = FALSE}
+df1 <- read_csv(
+  readr_example("challenge.csv"),
+  col_types = cols(
+    date = col_character(),
+    value = col_double()
   )
 )
+df1$date <- lubridate::ymd(df1$date)
 ```
 
-In general, it's good practice to supply an explicit column specification. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use `stop_for_problems(df3)`. This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.
+In general, it's good practice to supply an explicit column specification to readr. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use `stop_for_problems(df1)`. This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.
 
 ### Available column specifications
 

From 025dae448e2913264742536171f55ea3ef86e80d Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 2 Sep 2022 11:09:20 -0600
Subject: [PATCH 03/18] restyle to match

---
 vignettes/readr.Rmd | 34 ++++++++++++++--------------------
 1 file changed, 14 insertions(+), 20 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index ee956bb1..2265814d 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -155,13 +155,10 @@ If readr finds problems with parsing your data, it will emit a warning message.
 For example, in our `challenge.csv` file, the column type we expected from our data was a column of dates and a column of doubles.
 
 ```{r}
-df1 <- read_csv(
-  readr_example("challenge.csv"),
-  col_types = cols(
-    date = col_date(),
-    value = col_double()
-  )
-)
+df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
+  date = col_date(),
+  value = col_double()
+))
 ```
 
 The data was successfully imported, but we get a warning message about parsing issues.
@@ -178,13 +175,10 @@ But data in the wild is rarely so clean.
 We can fix this parsing error by importing the date column as character and using the lubridate package to parse our dates.
 
 ```{r, eval = FALSE}
-df1 <- read_csv(
-  readr_example("challenge.csv"),
-  col_types = cols(
-    date = col_character(),
-    value = col_double()
-  )
-)
+df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
+  date = col_character(),
+  value = col_double()
+))
 df1$date <- lubridate::ymd(df1$date)
 ```
 
@@ -228,12 +222,12 @@ Use the `col_types` argument to override the default choices. There are two ways
     
     ```r
       read_csv("iris.csv", col_types = list(
-      Sepal.Length = "d",
-      Sepal.Width = "d",
-      Petal.Length = "d",
-      Petal.Width = "d",
-      Species = col_factor(c("setosa", "versicolor", "virginica"))
-    ))
+        Sepal.Length = "d",
+        Sepal.Width = "d",
+        Petal.Length = "d",
+        Petal.Width = "d",
+        Species = col_factor(c("setosa", "versicolor", "virginica"))
+      ))
     ```
     
 Any omitted columns will be parsed automatically, so the previous call

From acb4690dc7919393b741b2f04965ccd20959d337 Mon Sep 17 00:00:00 2001
From: Shelby Bearrows <39840820+sbearrows@users.noreply.github.com>
Date: Wed, 7 Sep 2022 10:45:44 -0600
Subject: [PATCH 04/18] Apply Jennys review

Co-authored-by: Jennifer (Jenny) Bryan <jenny.f.bryan@gmail.com>
---
 vignettes/readr.Rmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 2265814d..a005854d 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -127,7 +127,7 @@ parse_number("$1,234")
 
 There are two parsers that will never be guessed: `col_skip()` and `col_factor()`. You will always need to supply these explicitly.
 
-You can see the specification that readr would generate for a file by using `spec_csv()`, `spec_tsv()` and so on.
+You can see the specification that readr would generate for a file by using `spec_csv()`, `spec_tsv()`, and so on.
 
 ```{r}
 x <- spec_csv(readr_example("challenge.csv"))
@@ -172,7 +172,7 @@ problems(df1)
 In this example, one of the dates is in an incorrect format.
 Since `problems()` gives us the row number from the original file, fixing this value in the original file could be easy, especially since there is only one problematic value.
 But data in the wild is rarely so clean.
-We can fix this parsing error by importing the date column as character and using the lubridate package to parse our dates.
+We can work around this parsing error by importing the date column as character and using the lubridate package to convert that column after the fact.
 
 ```{r, eval = FALSE}
 df1 <- read_csv(readr_example("challenge.csv"), col_types = list(

From 5bc1529025426015d155f032ebd011451fda7d37 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 7 Sep 2022 12:09:55 -0600
Subject: [PATCH 05/18] change code chunks to r chunks

---
 vignettes/readr.Rmd | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index a005854d..914abd62 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -208,32 +208,32 @@ Use the `col_types` argument to override the default choices. There are two ways
   
 * With a (named) list of col objects:
 
-    ```r
-    read_csv("iris.csv", col_types = list(
-      Sepal.Length = col_double(),
-      Sepal.Width = col_double(),
-      Petal.Length = col_double(),
-      Petal.Width = col_double(),
-      Species = col_factor(c("setosa", "versicolor", "virginica"))
-    ))
-    ```
+```{r, eval = FALSE}
+read_csv("iris.csv", col_types = list(
+  Sepal.Length = col_double(),
+  Sepal.Width = col_double(),
+  Petal.Length = col_double(),
+  Petal.Width = col_double(),
+  Species = col_factor(c("setosa", "versicolor", "virginica"))
+))
+```
     
   Or, with their abbreviations:
     
-    ```r
-      read_csv("iris.csv", col_types = list(
-        Sepal.Length = "d",
-        Sepal.Width = "d",
-        Petal.Length = "d",
-        Petal.Width = "d",
-        Species = col_factor(c("setosa", "versicolor", "virginica"))
-      ))
-    ```
+```{r, eval = FALSE}
+read_csv("iris.csv", col_types = list(
+  Sepal.Length = "d",
+  Sepal.Width = "d",
+  Petal.Length = "d",
+  Petal.Width = "d",
+  Species = col_factor(c("setosa", "versicolor", "virginica"))
+))
+```
     
 Any omitted columns will be parsed automatically, so the previous call
 will lead to the same result as:
 
-```r
+```{r, eval = FALSE}
 read_csv("iris.csv", col_types = list(
   Species = col_factor(c("setosa", "versicolor", "virginica")))
 )
@@ -242,7 +242,7 @@ read_csv("iris.csv", col_types = list(
 You can also set a default type that will be used instead of
 relying on the automatic detection for columns you don't specify:
 
-```r
+```{r, eval = FALSE}
 read_csv("iris.csv", col_types = list(
   Species = col_factor(c("setosa", "versicolor", "virginica")),
   .default = col_double())
@@ -251,7 +251,7 @@ read_csv("iris.csv", col_types = list(
 
 If you only want to read specified columns, use `cols_only()`:
 
-```r
+```{r, eval = FALSE}
 read_csv("iris.csv", col_types = cols_only(
   Species = col_factor(c("setosa", "versicolor", "virginica")))
 )

From 948dc498d2c656e46714dd73189cc811ed2ca5d1 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 7 Sep 2022 12:10:34 -0600
Subject: [PATCH 06/18] add `spec()` example back

---
 vignettes/readr.Rmd | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 914abd62..e2d4763a 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -184,6 +184,12 @@ df1$date <- lubridate::ymd(df1$date)
 
 In general, it's good practice to supply an explicit column specification to readr. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use `stop_for_problems(df1)`. This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.
 
+To access the full column specification after importing, you can call `spec()` on your data frame. If you want to manually specify the column types, you can start by copying and pasting this code, and then tweaking it to fix the parsing problems.
+
+```{r}
+spec(df1)
+```
+
 ### Available column specifications
 
 The available specifications are: (with string abbreviations in brackets)

From e03899098eb07f6684190b94905849fe674c3382 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 7 Sep 2022 12:10:51 -0600
Subject: [PATCH 07/18] add `guess_max` information back

---
 vignettes/readr.Rmd | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index e2d4763a..d9de76dd 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -142,6 +142,14 @@ mtcars_spec
 cols_condense(mtcars_spec)
 ```
 
+When guessing column types, readr selects rows to use for guessing semi-randomly. The default number of rows it selects is 1000. If the column type guess is incorrect, increasing `guess_max` may or may not improve the results.
+
+```{r}
+x <- spec_csv(readr_example("challenge.csv"), guess_max = 1001)
+```
+
+The best solution is to manually specify the `col_type`, as described below.
+
 ## Rectangular parsers
 
 readr comes with five parsers for rectangular file formats:

From fcf0f81144a5071f0696404e979432b1190a5741 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 7 Sep 2022 12:11:14 -0600
Subject: [PATCH 08/18] change flow of `problems()` example

---
 vignettes/readr.Rmd | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index d9de76dd..bea512f1 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -163,10 +163,7 @@ If readr finds problems with parsing your data, it will emit a warning message.
 For example, in our `challenge.csv` file, the column type we expected from our data was a column of dates and a column of doubles.
 
 ```{r}
-df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
-  date = col_date(),
-  value = col_double()
-))
+df1 <- read_csv(readr_example("challenge.csv"))
 ```
 
 The data was successfully imported, but we get a warning message about parsing issues.
@@ -178,9 +175,13 @@ problems(df1)
 ```
 
 In this example, one of the dates is in an incorrect format.
-Since `problems()` gives us the row number from the original file, fixing this value in the original file could be easy, especially since there is only one problematic value.
-But data in the wild is rarely so clean.
-We can work around this parsing error by importing the date column as character and using the lubridate package to convert that column after the fact.
+Since `problems()` gives us the row number from the original file, we can view that data in R with a combination of `readr::read_lines()` and `writeLines()`.
+
+```{r}
+writeLines(read_lines(readr_example("challenge.csv"), skip = 1325, n_max = 3))
+```
+
+We can work around this parsing error by importing the date column as character and converting it to a column of dates, after the fact. The lubridate package is a great option that we show here. But since readr doesn't depend on lubridate, we won't execute this code chunk.
 
 ```{r, eval = FALSE}
 df1 <- read_csv(readr_example("challenge.csv"), col_types = list(

From d1e80ebbcfd5f1a1c11be86a73693d73e00ee746 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Tue, 13 Sep 2022 15:37:13 -0600
Subject: [PATCH 09/18] done describe `guess_max` as random

---
 vignettes/readr.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index bea512f1..64a1dfd7 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -142,7 +142,7 @@ mtcars_spec
 cols_condense(mtcars_spec)
 ```
 
-When guessing column types, readr selects rows to use for guessing semi-randomly. The default number of rows it selects is 1000. If the column type guess is incorrect, increasing `guess_max` may or may not improve the results.
+When guessing column types, readr selects rows interspersed through-out the file and always includes the first and last row. The default number of rows it selects is 1000. If the column type guess is incorrect, increasing `guess_max` may or may not improve the results.
 
 ```{r}
 x <- spec_csv(readr_example("challenge.csv"), guess_max = 1001)

From 796c39453cf2383f6e43cb5a96a0ed2ee00a94fd Mon Sep 17 00:00:00 2001
From: Shelby Bearrows <39840820+sbearrows@users.noreply.github.com>
Date: Tue, 13 Sep 2022 16:36:48 -0600
Subject: [PATCH 10/18] incorporate Jennys feedback

Co-authored-by: Jennifer (Jenny) Bryan <jenny.f.bryan@gmail.com>
---
 vignettes/readr.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index bea512f1..b2506858 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -168,7 +168,7 @@ df1 <- read_csv(readr_example("challenge.csv"))
 
 The data was successfully imported, but we get a warning message about parsing issues.
 To view more details about these parsing issues, call `problems()` on your data frame.
-This will generate a data frame with information about where the problem was found, and why.
+This will generate a data frame with information about the parsing problems, with one row per problem.
 
 ```{r}
 problems(df1)

From 1c6cdaa532067a9c4039e436831b54bc087f2906 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 14 Sep 2022 11:17:06 -0600
Subject: [PATCH 11/18] fix indenting

---
 vignettes/readr.Rmd | 68 ++++++++++++++++++++++-----------------------
 1 file changed, 34 insertions(+), 34 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 4bb6c73e..2c18228c 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -223,54 +223,54 @@ Use the `col_types` argument to override the default choices. There are two ways
   
 * With a (named) list of col objects:
 
-```{r, eval = FALSE}
-read_csv("iris.csv", col_types = list(
-  Sepal.Length = col_double(),
-  Sepal.Width = col_double(),
-  Petal.Length = col_double(),
-  Petal.Width = col_double(),
-  Species = col_factor(c("setosa", "versicolor", "virginica"))
-))
-```
+  ```{r, eval = FALSE}
+  read_csv("iris.csv", col_types = list(
+    Sepal.Length = col_double(),
+    Sepal.Width = col_double(),
+    Petal.Length = col_double(),
+    Petal.Width = col_double(),
+    Species = col_factor(c("setosa", "versicolor", "virginica"))
+  ))
+  ```
     
   Or, with their abbreviations:
     
-```{r, eval = FALSE}
-read_csv("iris.csv", col_types = list(
-  Sepal.Length = "d",
-  Sepal.Width = "d",
-  Petal.Length = "d",
-  Petal.Width = "d",
-  Species = col_factor(c("setosa", "versicolor", "virginica"))
-))
-```
+  ```{r, eval = FALSE}
+  read_csv("iris.csv", col_types = list(
+    Sepal.Length = "d",
+    Sepal.Width = "d",
+    Petal.Length = "d",
+    Petal.Width = "d",
+    Species = col_factor(c("setosa", "versicolor", "virginica"))
+  ))
+  ```
     
 Any omitted columns will be parsed automatically, so the previous call
 will lead to the same result as:
 
-```{r, eval = FALSE}
-read_csv("iris.csv", col_types = list(
-  Species = col_factor(c("setosa", "versicolor", "virginica")))
-)
-```
+  ```{r, eval = FALSE}
+  read_csv("iris.csv", col_types = list(
+    Species = col_factor(c("setosa", "versicolor", "virginica")))
+  )
+  ```
 
 You can also set a default type that will be used instead of
 relying on the automatic detection for columns you don't specify:
 
-```{r, eval = FALSE}
-read_csv("iris.csv", col_types = list(
-  Species = col_factor(c("setosa", "versicolor", "virginica")),
-  .default = col_double())
-)
-```
+  ```{r, eval = FALSE}
+  read_csv("iris.csv", col_types = list(
+    Species = col_factor(c("setosa", "versicolor", "virginica")),
+    .default = col_double())
+  )
+  ```
 
 If you only want to read specified columns, use `cols_only()`:
 
-```{r, eval = FALSE}
-read_csv("iris.csv", col_types = cols_only(
-  Species = col_factor(c("setosa", "versicolor", "virginica")))
-)
-```
+  ```{r, eval = FALSE}
+  read_csv("iris.csv", col_types = cols_only(
+    Species = col_factor(c("setosa", "versicolor", "virginica")))
+  )
+  ```
 
 ### Output
 

From 2c0d715f36e4bf4c780bf923efec15f64a39c1c1 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 14 Sep 2022 12:25:45 -0600
Subject: [PATCH 12/18] incorporate Jennys feedback

---
 vignettes/readr.Rmd | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 2c18228c..1446d404 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -175,13 +175,14 @@ problems(df1)
 ```
 
 In this example, one of the dates is in an incorrect format.
-Since `problems()` gives us the row number from the original file, we can view that data in R with a combination of `readr::read_lines()` and `writeLines()`.
+Since `problems()` gives us the row number from the original file, we can view that area of data in R with a combination of `readr::read_lines()` and `writeLines()`.  
 
 ```{r}
-writeLines(read_lines(readr_example("challenge.csv"), skip = 1325, n_max = 3))
+problem_area <- problems(df1)$row - 2
+writeLines(read_lines(readr_example("challenge.csv"), skip = problem_area, n_max = 3))
 ```
 
-We can work around this parsing error by importing the date column as character and converting it to a column of dates, after the fact. The lubridate package is a great option that we show here. But since readr doesn't depend on lubridate, we won't execute this code chunk.
+We can work around this parsing error by importing the date column as character and converting it to a column of dates, after the fact. Importing as character and using a more specialized package to parse the data post-import is a common solution to parsing tricky columns. In this case, the lubridate package can parse our column back into dates.
 
 ```{r, eval = FALSE}
 df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
@@ -189,6 +190,13 @@ df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
   value = col_double()
 ))
 df1$date <- lubridate::ymd(df1$date)
+df1[1325:1327, ]
+#> # A tibble: 3 × 2
+#>   date       value
+#>   <date>     <dbl>
+#> 1 2008-03-16   762
+#> 2 2008-02-17   206
+#> 3 2019-01-28  1333
 ```
 
 In general, it's good practice to supply an explicit column specification to readr. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use `stop_for_problems(df1)`. This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.

From bbbc833615053cd19043ebb1957b2172533ed80c Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Wed, 14 Sep 2022 12:57:00 -0600
Subject: [PATCH 13/18] fix indenting

---
 vignettes/readr.Rmd | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 1446d404..e291129c 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -256,29 +256,29 @@ Use the `col_types` argument to override the default choices. There are two ways
 Any omitted columns will be parsed automatically, so the previous call
 will lead to the same result as:
 
-  ```{r, eval = FALSE}
-  read_csv("iris.csv", col_types = list(
-    Species = col_factor(c("setosa", "versicolor", "virginica")))
-  )
-  ```
+```{r, eval = FALSE}
+read_csv("iris.csv", col_types = list(
+  Species = col_factor(c("setosa", "versicolor", "virginica")))
+)
+```
 
 You can also set a default type that will be used instead of
 relying on the automatic detection for columns you don't specify:
 
-  ```{r, eval = FALSE}
-  read_csv("iris.csv", col_types = list(
-    Species = col_factor(c("setosa", "versicolor", "virginica")),
-    .default = col_double())
-  )
-  ```
+```{r, eval = FALSE}
+read_csv("iris.csv", col_types = list(
+  Species = col_factor(c("setosa", "versicolor", "virginica")),
+  .default = col_double())
+)
+```
 
 If you only want to read specified columns, use `cols_only()`:
 
-  ```{r, eval = FALSE}
-  read_csv("iris.csv", col_types = cols_only(
-    Species = col_factor(c("setosa", "versicolor", "virginica")))
-  )
-  ```
+```{r, eval = FALSE}
+read_csv("iris.csv", col_types = cols_only(
+  Species = col_factor(c("setosa", "versicolor", "virginica")))
+)
+```
 
 ### Output
 

From 583e32e4f09a71260bb89de9b508db7f79916448 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 16 Sep 2022 12:56:01 -0600
Subject: [PATCH 14/18] revert challenge.csv

---
 inst/extdata/challenge.csv | 4002 ++++++++++++++++++------------------
 1 file changed, 2001 insertions(+), 2001 deletions(-)

diff --git a/inst/extdata/challenge.csv b/inst/extdata/challenge.csv
index 4fa3c13a..d977e08b 100644
--- a/inst/extdata/challenge.csv
+++ b/inst/extdata/challenge.csv
@@ -1,2001 +1,2001 @@
-date,value
-2020-02-17,990
-2012-11-06,802
-2007-05-20,1929
-2021-04-11,251
-2000-09-25,1332
-2009-10-05,1547
-2011-10-09,217
-2013-10-07,1755
-2021-06-14,1731
-2021-10-15,665
-2005-08-01,1948
-2012-11-15,778
-2010-03-22,847
-2014-08-20,967
-2019-07-17,1874
-2014-07-24,1662
-2019-03-05,285
-2003-11-06,1790
-2012-09-29,1229
-2021-05-27,1312
-1999-10-28,612
-2016-11-18,330
-2006-03-21,982
-2009-06-16,373
-2000-09-02,1448
-2000-12-29,120
-2003-09-15,297
-2003-02-17,1494
-2009-06-02,1976
-2012-06-07,860
-2015-12-25,159
-2004-06-06,1438
-2018-12-20,138
-2014-10-26,1819
-2015-03-25,1879
-2011-11-14,1563
-2012-04-02,167
-2001-06-28,61
-2012-09-06,1100
-2021-04-25,161
-2001-03-01,1423
-1999-07-13,1688
-2018-06-27,1630
-2019-06-03,1580
-2003-04-18,914
-2021-01-05,1509
-2005-03-29,1767
-2010-08-04,497
-1999-11-04,1318
-2014-12-19,966
-2003-06-21,455
-1999-11-24,658
-2020-06-04,542
-2001-06-02,331
-2003-04-10,1884
-2020-04-02,155
-2017-12-02,1687
-2014-01-30,1372
-2021-10-12,459
-2017-03-16,1742
-2001-02-19,1473
-2005-08-04,1032
-2009-12-01,960
-2018-01-28,1559
-2013-11-25,28
-2019-12-15,479
-2003-09-25,107
-2012-07-29,1921
-2008-04-06,336
-2019-04-28,1344
-2012-03-27,1456
-2012-06-04,659
-2004-02-24,989
-1999-01-24,1088
-2011-07-07,60
-2015-11-24,1184
-2015-05-17,1808
-2017-09-16,1465
-2004-07-22,740
-2014-11-01,1253
-2016-09-12,1592
-2004-04-27,1517
-2010-03-14,1259
-2011-06-25,133
-2002-12-01,39
-2003-08-22,308
-2011-08-03,1729
-2016-09-28,1117
-2010-10-12,1158
-1999-03-15,1891
-2014-10-28,1276
-2000-05-30,1877
-2012-07-18,1171
-2020-05-02,1537
-2012-04-22,1937
-2016-02-14,1200
-2018-09-01,36
-2019-07-12,129
-2020-11-20,1085
-1999-03-21,867
-2016-05-21,26
-2011-02-12,1458
-2010-07-30,1108
-2016-05-07,1135
-2020-05-31,140
-2001-02-02,1162
-2018-07-16,1901
-2004-09-10,76
-2016-04-28,626
-2004-02-05,1261
-2016-07-11,969
-2000-04-08,1936
-2005-10-21,1881
-2019-01-16,1677
-2018-12-06,1429
-2017-08-16,1524
-2007-06-13,1636
-2017-01-22,589
-2008-04-07,1564
-2018-11-23,1384
-2008-10-22,1000
-2019-05-09,1065
-1999-05-20,160
-2006-07-21,641
-2016-04-20,1862
-2005-09-17,191
-2014-12-11,1444
-2000-10-31,803
-2013-02-01,861
-2016-05-02,1358
-2010-12-18,908
-2020-09-06,996
-2001-10-30,1221
-2012-09-30,1573
-2017-01-20,1692
-2013-10-15,1298
-2000-10-18,527
-2011-04-11,1293
-2010-04-29,1869
-2007-03-16,639
-2010-11-28,587
-2004-11-01,1750
-2019-09-27,565
-2018-09-24,1146
-2006-01-29,1791
-2014-10-21,96
-2002-01-26,1598
-2012-12-20,1307
-2019-03-09,357
-2002-04-05,1040
-2007-06-05,172
-2021-10-28,634
-2020-05-19,1904
-2002-11-09,8
-2014-11-09,236
-2014-03-19,1070
-2017-05-10,151
-2011-05-08,1109
-1999-08-06,53
-2001-05-08,1751
-2019-08-08,1319
-2018-12-13,1403
-2003-01-13,33
-2000-12-11,628
-2002-11-30,1322
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From 5afee27d6c09502b4a4a71f0996b1ed3af940aa7 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 16 Sep 2022 15:09:25 -0600
Subject: [PATCH 15/18] new data for `problems()`

---
 data-raw/my_dates.R       |    7 +
 inst/extdata/my_dates.csv | 1002 +++++++++++++++++++++++++++++++++++++
 2 files changed, 1009 insertions(+)
 create mode 100644 data-raw/my_dates.R
 create mode 100644 inst/extdata/my_dates.csv

diff --git a/data-raw/my_dates.R b/data-raw/my_dates.R
new file mode 100644
index 00000000..23f4d2d1
--- /dev/null
+++ b/data-raw/my_dates.R
@@ -0,0 +1,7 @@
+dates <- tibble::tibble(dates = as.character(sample(seq(as.Date("2020/01/01"),
+                                           as.Date("2022/12/31"), by = "day"), 1001)))
+
+# readr/vroom will use rows 1-999 + 1001 for guessing
+dates[1000,] <- "20220907"
+write_csv(dates, "inst/extdata/my_dates.csv")
+df1 <- read_csv("inst/extdata/my_dates.csv")
diff --git a/inst/extdata/my_dates.csv b/inst/extdata/my_dates.csv
new file mode 100644
index 00000000..1b6f9b24
--- /dev/null
+++ b/inst/extdata/my_dates.csv
@@ -0,0 +1,1002 @@
+dates
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From bd459c77fd1cd02a9fbc5cfc3bfe68ebdfaad90c Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 16 Sep 2022 15:09:49 -0600
Subject: [PATCH 16/18] updates for new problematic data

---
 vignettes/readr.Rmd | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index e291129c..77dd23c1 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -130,7 +130,7 @@ There are two parsers that will never be guessed: `col_skip()` and `col_factor()
 You can see the specification that readr would generate for a file by using `spec_csv()`, `spec_tsv()`, and so on.
 
 ```{r}
-x <- spec_csv(readr_example("challenge.csv"))
+x <- spec_csv(readr_example("my_dates.csv"))
 ```
 
 For bigger files, you can often make the specification simpler by changing the default column type using `cols_condense()`
@@ -145,7 +145,7 @@ cols_condense(mtcars_spec)
 When guessing column types, readr selects rows interspersed through-out the file and always includes the first and last row. The default number of rows it selects is 1000. If the column type guess is incorrect, increasing `guess_max` may or may not improve the results.
 
 ```{r}
-x <- spec_csv(readr_example("challenge.csv"), guess_max = 1001)
+x <- spec_csv(readr_example("my_dates.csv"), guess_max = 1001)
 ```
 
 The best solution is to manually specify the `col_type`, as described below.
@@ -163,7 +163,7 @@ If readr finds problems with parsing your data, it will emit a warning message.
 For example, in our `challenge.csv` file, the column type we expected from our data was a column of dates and a column of doubles.
 
 ```{r}
-df1 <- read_csv(readr_example("challenge.csv"))
+df1 <- read_csv(readr_example("my_dates.csv"))
 ```
 
 The data was successfully imported, but we get a warning message about parsing issues.
@@ -179,24 +179,23 @@ Since `problems()` gives us the row number from the original file, we can view t
 
 ```{r}
 problem_area <- problems(df1)$row - 2
-writeLines(read_lines(readr_example("challenge.csv"), skip = problem_area, n_max = 3))
+writeLines(read_lines(readr_example("my_dates.csv"), skip = problem_area, n_max = 3))
 ```
 
-We can work around this parsing error by importing the date column as character and converting it to a column of dates, after the fact. Importing as character and using a more specialized package to parse the data post-import is a common solution to parsing tricky columns. In this case, the lubridate package can parse our column back into dates.
+We can work around this parsing error by importing the date column as character and converting it to a column of dates, after the fact. Importing as character and using a more specialized package to parse the data post-import, is a common solution to parsing tricky columns. In this case, the lubridate package can parse our column back into dates.
 
 ```{r, eval = FALSE}
-df1 <- read_csv(readr_example("challenge.csv"), col_types = list(
-  date = col_character(),
-  value = col_double()
+df1 <- read_csv(readr_example("my_dates.csv"), col_types = list(
+  dates = col_character()
 ))
-df1$date <- lubridate::ymd(df1$date)
-df1[1325:1327, ]
-#> # A tibble: 3 × 2
-#>   date       value
-#>   <date>     <dbl>
-#> 1 2008-03-16   762
-#> 2 2008-02-17   206
-#> 3 2019-01-28  1333
+df1$dates <- lubridate::ymd(df1$dates)
+df1[problem_area:(problem_area + 2), ]
+#> # A tibble: 3 × 1
+#>   dates     
+#>   <date>    
+#> 1 2020-02-04
+#> 2 2022-09-07
+#> 3 2022-07-30
 ```
 
 In general, it's good practice to supply an explicit column specification to readr. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use `stop_for_problems(df1)`. This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.

From 2c4c842e651f1a90b49cbddbbac4704e0b41915d Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 16 Sep 2022 15:12:40 -0600
Subject: [PATCH 17/18] restyle

---
 data-raw/my_dates.R | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/data-raw/my_dates.R b/data-raw/my_dates.R
index 23f4d2d1..0e52116c 100644
--- a/data-raw/my_dates.R
+++ b/data-raw/my_dates.R
@@ -1,7 +1,12 @@
-dates <- tibble::tibble(dates = as.character(sample(seq(as.Date("2020/01/01"),
-                                           as.Date("2022/12/31"), by = "day"), 1001)))
+data <- as.character(sample(
+  seq(
+    as.Date("2020/01/01"),
+    as.Date("2022/12/31"),
+    by = "day"
+  ), 1001
+))
 
-# readr/vroom will use rows 1-999 + 1001 for guessing
-dates[1000,] <- "20220907"
+dates <- tibble::tibble(dates = data)
+
+dates[1000, ] <- "20220907"
 write_csv(dates, "inst/extdata/my_dates.csv")
-df1 <- read_csv("inst/extdata/my_dates.csv")

From 675088c2e4ef45a50f4a675d34339a5b63565718 Mon Sep 17 00:00:00 2001
From: shelby <scbearrows@gmail.com>
Date: Fri, 16 Sep 2022 15:36:47 -0600
Subject: [PATCH 18/18] remove mention of `challenge.csv`

---
 vignettes/readr.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/readr.Rmd b/vignettes/readr.Rmd
index 77dd23c1..a6cc343a 100644
--- a/vignettes/readr.Rmd
+++ b/vignettes/readr.Rmd
@@ -160,7 +160,7 @@ readr comes with five parsers for rectangular file formats:
 * `read_log()` for web log files
 
 If readr finds problems with parsing your data, it will emit a warning message.
-For example, in our `challenge.csv` file, the column type we expected from our data was a column of dates and a column of doubles.
+For example, in our aptly named `my_dates.csv` file, the column type we expected from our data was a column of dates.
 
 ```{r}
 df1 <- read_csv(readr_example("my_dates.csv"))