finished first draft of scoped dplyr slides

uc-r · Dec 20, 2018 · 57647e6 · 57647e6
1 parent 560ef91
commit 57647e6
Show file tree

Hide file tree

Showing 48 changed files with 435 additions and 31 deletions.
diff --git a/README.Rmd b/README.Rmd
@@ -20,7 +20,7 @@ following is an outline of the material covered in this training:
 | :----------------------------------------------------------------------------- | :-----------: |
 | Breakfast / Social time                                                        |  8:00 - 9:00  |
 | [Introductions](https://uc-r.github.io/Intermediate-R/day-1a-intro.html)       |  9:00 - 9:30  |
-| Scoped variable transformations                                                |  9:30 - 10:45 |
+| [Scoped variable transformations](https://uc-r.github.io/Intermediate-R/day-1b-scoped-dplyr.html)  |  9:30 - 10:45 |
 | Break                                                                          | 10:45 - 11:00 |
 | Control flow                                                                   | 11:00 - 12:00 |
 | Lunch                                                                          | 12:00 - 1:00  |

diff --git a/README.md b/README.md
@@ -15,18 +15,18 @@ training:
 **Day
 1**
 
-| Topic                                                                    |     Time      |
-| :----------------------------------------------------------------------- | :-----------: |
-| Breakfast / Social time                                                  |  8:00 - 9:00  |
-| [Introductions](https://uc-r.github.io/Intermediate-R/day-1a-intro.html) |  9:00 - 9:30  |
-| Scoped variable transformations                                          | 9:30 - 10:45  |
-| Break                                                                    | 10:45 - 11:00 |
-| Control flow                                                             | 11:00 - 12:00 |
-| Lunch                                                                    | 12:00 - 1:00  |
-| Work flow                                                                |  1:00 - 2:30  |
-| Break                                                                    |  2:30 - 2:45  |
-| Case study                                                               |  2:45 - 4:00  |
-| Q\&A                                                                     |  4:00 - 4:30  |
+| Topic                                                                                             |     Time      |
+| :------------------------------------------------------------------------------------------------ | :-----------: |
+| Breakfast / Social time                                                                           |  8:00 - 9:00  |
+| [Introductions](https://uc-r.github.io/Intermediate-R/day-1a-intro.html)                          |  9:00 - 9:30  |
+| [Scoped variable transformations](https://uc-r.github.io/Intermediate-R/day-1b-scoped-dplyr.html) | 9:30 - 10:45  |
+| Break                                                                                             | 10:45 - 11:00 |
+| Control flow                                                                                      | 11:00 - 12:00 |
+| Lunch                                                                                             | 12:00 - 1:00  |
+| Work flow                                                                                         |  1:00 - 2:30  |
+| Break                                                                                             |  2:30 - 2:45  |
+| Case study                                                                                        |  2:45 - 4:00  |
+| Q\&A                                                                                              |  4:00 - 4:30  |
 
 **Day 2**
 

diff --git a/docs/day-1b-scoped-dplyr.Rmd b/docs/day-1b-scoped-dplyr.Rmd
@@ -600,7 +600,46 @@ flights %>%
 class: yourturn
 # Your Turn!
 
+.pull-left[
+
+### Challenge
+
+Using the `flights` data:
+
+1. convert month and day variables to type character
+2. group by month and day
+3. select all variables containing "time" and "delay"
+4. compute the mean of all "time" and "delay" variables
+
+```r
+# hint
+flights %>%
+  mutate(____) %>%
+  group_by(____) %>%
+  select(____) %>%
+  summarize_all(____) 
+```
+
+]
 
+--
+
+.pull-right[
+
+### Solution
+
+```{r}
+flights %>%
+  mutate(
+    month = as.character(month),
+    day   = as.character(day)
+  ) %>%
+  group_by(month, day) %>%
+  select(contains("time"), contains("delay")) %>%
+  summarize_all(mean, na.rm = TRUE)
+```
+
+]
 
 ---
 
@@ -661,7 +700,7 @@ Perform some operation on all variables that meet a condition
 
 # Transform .red[some] variables with .red[`*_if()`]
 
-Back to our problem of _wanting to standardize only .red[only numeric variables]_:
+Back to our problem of _wanting to standardize .red[only numeric variables]_:
 
 .font150.center[`df %>% mutate_if(.predicate, .funs, ...)`]
 
@@ -892,12 +931,6 @@ flights %>%
 
 ]
 
----
-class: yourturn
-
-# Your Turn!
-
-
 ---
 
 # `filter_*()` .red[and its helpers]
@@ -918,24 +951,143 @@ class: yourturn
 
 ---
 
-# Filter .red[all] rows
+# Filtering rows that meet certain conditions
+
+.pull-left[
+
+* The .red[`all_vars()`] function can be used to filter rows where .red[all variables meet the same logical condition]. <br><br>
+
+```{r filter-at-with-all-vars}
+# This will return rows where all variables containing "delay" are NA
+flights %>% 
+  filter_at(vars(contains("delay")), all_vars(is.na(.)))
+```
+
+]
+
+--
+
+.pull-right[
 
+* The .red[`any_vars()`] function can be used to filter rows where .red[at least one variable meets the logical condition]. 
 
+```{r filter-at-with-any-vars}
+# This will return rows where any variable containing "delay" is NA
+flights %>% 
+  filter_at(vars(contains("delay")), any_vars(is.na(.)))
+```
+
+]
+
+---
+
+# `group_by_*()` .red[and its helpers]
+
+Say we wanted to compute the median delay values for carriers by month and in doing so, we wanted to treat both `carrier` and `month` as factors.
 
 .pull-left[
 
+.center.font120.bold[Option A]
 
+```{r}
+flights %>%
+  mutate( 
+    carrier = as.factor(carrier),
+    month   = as.factor(month)
+  ) %>%
+  group_by(carrier, month) %>%
+  summarize_at(vars(contains("delay")), median, na.rm = TRUE)
+```
 
 ]
 
 --
 
 .pull-right[
 
+.center.font120.bold[Option B]
+
+```{r}
+flights %>%
+  group_by_at(vars(carrier, month), as.factor) %>%
+  summarize_at(vars(contains("delay")), median, na.rm = TRUE)
+```
 
 ]
 
 ---
+class: yourturn
 
-# `group_by_*()` .red[and its helpers]
+# Last Challenge!
+
+### Challenge
+
+Fill in the blanks and select the right .red[`filter_*()`] to filter for those flights where .red[either] departure .red[delay] (`dep_delay`) .red[or] arrival .red[delay] (`arr_delay`) exceeded the 99th percentile (hint: `quantile(x, .99)` provides the 99th percentile for variable `x`).
+
+```{r, eval=FALSE}
+flights %>%
+  filter_xxx(vars(contains("_____")), any_vars(___ > quantile(___, .99, na.rm = TRUE)))
+```
+
+---
+class: yourturn
+
+# Last Challenge!
+
+### Challenge
+
+Fill in the blanks and select the right .red[`filter_*()`] to filter for those flights where .red[either] departure .red[delay] (`dep_delay`) .red[or] arrival .red[delay] (`arr_delay`) exceeded the 99th percentile (hint: `quantile(x, .99)` provides the 99th percentile for variable `x`).
+
+```{r}
+flights %>%
+  filter_at(vars(contains("delay")), any_vars(. > quantile(., .99, na.rm = TRUE)))
+```
+
+---
+
+# Key things to remember
+
+.pull-left-60[
+
+* dplyr scoped variants:
+   - .bold[`*_all()`]: execute function(s) on all variables or...
+   - .bold[`*_if()`]: on variables that meet a certain condition or...
+   - .bold[`*_at()`]: for pre-specified variables
+
+* argument functions within scoped variants:
+   - .bold[`vars()`]: specify the variables to be executed on
+   - .bold[`funs()`]: specify the functions to be executed
+
+* helper functions for `filter_*()`
+   - .bold[`all_vars()`]: filter for rows where all variables meet the specified condition
+   - .bold[`any_vars()`]: filter for rows where at least one variable meets the specified condition
+
+]
+
+.pull-right-40[
+
+<br><br>
+```{r, echo=FALSE}
+knitr::include_graphics("images/information-overload.jpg")
+```
+
+]
+
+---
+
+# Key things to remember
+
+```{r, echo=FALSE, out.width="55%"}
+knitr::include_graphics("images/cheatsheet-dplyr.png")
+```
+
+.center[.content-box-gray[.bold[`Help >> Cheatsheets >> Data Transformation with dplyr`]]]
+
+---
+
+# Questions?
+
+<br>
 
+```{r questions-dplyr, echo=FALSE, out.height="450", out.width="450"}
+knitr::include_graphics("images/questions.png")