Stat 585 - The purrr package: split-apply-combine with lists

class: center, middle, inverse, title-slide

.title[
# Stat 585 - The <code>purrr</code> package: split-apply-combine with lists
]
.author[
### Heike Hofmann
]

---

## From Lab 2

Folder of csv files with data scraped from press logs:

```r
csvs <- dir("data", pattern="presslog-", full.names = TRUE)
csvs
```

```
##  [1] "data/presslog-20210209.csv" "data/presslog-20210214.csv"
##  [3] "data/presslog-20210425.csv" "data/presslog-20220122.csv"
##  [5] "data/presslog-20220407.csv" "data/presslog-20220607.csv"
##  [7] "data/presslog-20230213.csv" "data/presslog-20230216.csv"
##  [9] "data/presslog-20230220.csv" "data/presslog-20230223.csv"
```
--

Make this vector a part of a data set:

```r
results <- data.frame(origin = csvs)
```

Now we want to read the data inside the csv files. 
One file we would read as: `read_csv(origin[1])`

---

## List Variables

`purrr::map` applies function `.f` to each element of a variable:

```r
results <- results %>% mutate(
 data = origin %>% purrr::map(.f = readr::read_csv, 
 show_col_types = FALSE)
)

str(results)
```

```
## 'data.frame':	10 obs. of 2 variables:
## $ origin: chr "data/presslog-20210209.csv" "data/presslog-20210214.csv" "data/presslog-20210425.csv" "data/presslog-20220122.csv" ...
## $ data :List of 10
## ..$ : spc_tbl_ [223 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/9/2021 02:30AM" "2/9/2021 01:52AM" "2/9/2021 12:05AM" "2/8/2021 11:55PM" ...
## .. ..$ Incident ID : num 2.1e+08 2.1e+08 2.1e+08 2.1e+08 2.1e+08 ...
## .. ..$ How Call was Rec'd : chr "PHONE" "PHONE" "PHONE" "PHONE" ...
## .. ..$ Nature Code Description : chr "DISTURBANCE & NOISE PARTY" "PRIVATE TOW" "DISTURBANCE & NOISE PARTY" "SCAM" ...
## .. ..$ Location Address : chr "4211 LINCOLN SWING" "1206 S 4TH ST" "624-202 S 17TH ST" "1118 S DUFF AVE" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA 2.1e+07 2.1e+07 ...
## .. ..$ Closing Disposition or Cancel Code: chr "NAT" "PTOW" "NOR" "RPT" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [217 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/14/2021 04:10AM" "2/14/2021 02:39AM" "2/14/2021 02:31AM" "2/14/2021 02:24AM" ...
## .. ..$ Incident ID : num 2.1e+08 2.1e+08 2.1e+08 2.1e+08 2.1e+08 ...
## .. ..$ How Call was Rec'd : chr "PHONE" "PHONE" "PHONE" "PHONE" ...
## .. ..$ Nature Code Description : chr "GENERAL ALARM BANK / RESIDENTI" "PUBLIC INTOXICATION" "SUSPICIOUS PERSON/VEH/ACTIVITY" "DISORDERLY CONDUCT" ...
## .. ..$ Location Address : chr "710 S DUFF AVE" "100-BLK WELCH AVE" "1400-BLK IDAHO AVE" "400-BLK WELCH AVE" ...
## .. ..$ Report Number Assigned to Event : num NA 2.1e+07 NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "BIL" "ARR" "UTL" "UTL" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [261 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "4/25/2021 05:19AM" "4/25/2021 04:25AM" "4/25/2021 04:20AM" "4/25/2021 04:04AM" ...
## .. ..$ Incident ID : num 2.1e+08 2.1e+08 2.1e+08 2.1e+08 2.1e+08 ...
## .. ..$ How Call was Rec'd : chr "SELF" "PHONE" "PHONE" "W911" ...
## .. ..$ Nature Code Description : chr "BURGLARY TO MOTOR VEHICLE" "SUSPICIOUS PERSON/VEH/ACTIVITY" "SUSPICIOUS PERSON/VEH/ACTIVITY" "MEDICAL ASSIST/AMBULANCE CALL" ...
## .. ..$ Location Address : chr "511 S 4TH ST" "312 HAYWARD AVE" "229 S KELLOGG AVE" "3115 GROVE AVE" ...
## .. ..$ Report Number Assigned to Event : num 2.1e+07 NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "RPT" "NOR" "NOR" "NRSP" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [281 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "1/22/2022 04:35AM" "1/22/2022 04:10AM" "1/22/2022 04:05AM" "1/22/2022 03:59AM" ...
## .. ..$ Incident ID : num 2.2e+08 2.2e+08 2.2e+08 2.2e+08 2.2e+08 ...
## .. ..$ How Call was Rec'd : chr "PHONE" "PHONE" "PHONE" "PHONE" ...
## .. ..$ Nature Code Description : chr "MEDICAL ASSIST/AMBULANCE CALL" "MISSING PERSON / RUNAWAY" "LAW DEPARTMENT ASSIST" "LAW DEPARTMENT ASSIST" ...
## .. ..$ Location Address : chr "3305-109 STANGE RD" "3006 WOODLAND ST" "1605-103 S DAYTON PL" "US HIGHWAY 30/S 500TH AVE" ...
## .. ..$ Report Number Assigned to Event : num NA NA 2.2e+07 NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "NRSP" "ASC" "ARR" "NOR" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [224 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "4/7/2022 05:46AM" "4/7/2022 02:31AM" "4/7/2022 02:15AM" "4/7/2022 01:58AM" ...
## .. ..$ Incident ID : num 2.2e+08 2.2e+08 2.2e+08 2.2e+08 2.2e+08 ...
## .. ..$ How Call was Rec'd : chr "E911" "PHONE" "PHONE" "E911" ...
## .. ..$ Nature Code Description : chr "DISORDERLY CONDUCT" "DISTURBANCE & NOISE PARTY" "DISORDERLY CONDUCT" "MEDICAL ASSIST/AMBULANCE CALL" ...
## .. ..$ Location Address : chr "133-13 BEEDLE DR" "4912-424 MORTENSEN RD" "119 MAIN ST" "3127 BURNHAM DR" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "CBC" "ASC" "ASC" "ASC" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [224 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "6/7/2022 05:17AM" "6/7/2022 05:02AM" "6/7/2022 05:00AM" "6/7/2022 04:00AM" ...
## .. ..$ Incident ID : num 2.21e+08 2.21e+08 2.21e+08 2.21e+08 2.21e+08 ...
## .. ..$ How Call was Rec'd : chr "SELF" "PHONE" "SELF" "SELF" ...
## .. ..$ Nature Code Description : chr "TRAFFIC STOP / ENFORCEMENT" "GENERAL ALARM BANK / RESIDENTI" "TRAFFIC STOP / ENFORCEMENT" "TRAFFIC STOP / ENFORCEMENT" ...
## .. ..$ Location Address : chr "148 MM US HIGHWAY 30 E" "3619 STANGE RD" "148 MM US HIGHWAY 30 E" "S DUFF AVE/S 3RD ST" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "WAR" "BIL" "WAR" "WAR" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [288 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/13/2023 05:52AM" "2/13/2023 03:12AM" "2/13/2023 01:22AM" "2/13/2023 12:37AM" ...
## .. ..$ Incident ID : num 2.3e+08 2.3e+08 2.3e+08 2.3e+08 2.3e+08 ...
## .. ..$ How Call was Rec'd : chr "SELF" "SELF" "W911" "PHONE" ...
## .. ..$ Nature Code Description : chr "TRAFFIC STOP / ENFORCEMENT" "TRESPASS" "MEDICAL ASSIST/AMBULANCE CALL" "EQUIPMENT/SIGN MALFUNCTION" ...
## .. ..$ Location Address : chr "DAYTON AVE/E LINCOLN WAY" "1605 6TH ST" "2143 FRILEY RD" "515 CLARK AVE" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "CIT" "NOR" "NRSP" "INFO" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [229 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/16/2023 05:53AM" "2/16/2023 05:06AM" "2/16/2023 03:53AM" "2/16/2023 03:40AM" ...
## .. ..$ Incident ID : num 2.3e+08 2.3e+08 2.3e+08 2.3e+08 2.3e+08 ...
## .. ..$ How Call was Rec'd : chr "W911" "E911" "SELF" "PHONE" ...
## .. ..$ Nature Code Description : chr "MEDICAL ASSIST/AMBULANCE CALL" "MEDICAL ASSIST/AMBULANCE CALL" "TRAFFIC STOP / ENFORCEMENT" "PRIVATE TOW" ...
## .. ..$ Location Address : chr "3018 OAKLAND ST" "3218 ORION DR" "UNIVERSITY BLVD/LINCOLN WAY" "1206 S 4TH" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA 2.3e+07 ...
## .. ..$ Closing Disposition or Cancel Code: chr "NRSP" "NRSP" "WAR" "PTOW" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [311 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/20/2023 05:15AM" "2/20/2023 02:25AM" "2/20/2023 01:46AM" "2/19/2023 11:30PM" ...
## .. ..$ Incident ID : num 2.3e+08 2.3e+08 2.3e+08 2.3e+08 2.3e+08 ...
## .. ..$ How Call was Rec'd : chr "W911" "SELF" "W911" "PHONE" ...
## .. ..$ Nature Code Description : chr "911 HANG UP / INCOMPLETE CALL" "SUSPICIOUS PERSON/VEH/ACTIVITY" "MEDICAL ASSIST/AMBULANCE CALL" "PRIVATE TOW" ...
## .. ..$ Location Address : chr "1407 FLORIDA AVE" "5205 GRAND AVE" "225 S KELLOGG AVE" "5310 MORTENSEN RD" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "ASC" "NOR" "NAT" "PTOW" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr> 
## ..$ : spc_tbl_ [265 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## .. ..$ Call Received Date/Time : chr "2/22/2023 05:37AM" "2/22/2023 05:15AM" "2/22/2023 04:58AM" "2/22/2023 12:30AM" ...
## .. ..$ Incident ID : num 2.3e+08 2.3e+08 2.3e+08 2.3e+08 2.3e+08 ...
## .. ..$ How Call was Rec'd : chr "W911" "PHONE" "W911" "SELF" ...
## .. ..$ Nature Code Description : chr "MEDICAL ASSIST/AMBULANCE CALL" "TRAFFIC HAZARD" "MEDICAL ASSIST/AMBULANCE CALL" "TRAFFIC STOP / ENFORCEMENT" ...
## .. ..$ Location Address : chr "1501 MAXWELL AVE" "LINCOLN WAY/N ELM AVE" "203-606 S 5TH ST" "100-BLK S 5TH ST" ...
## .. ..$ Report Number Assigned to Event : num NA NA NA NA NA ...
## .. ..$ Closing Disposition or Cancel Code: chr "NRSP" "ASC" "NRSP" "CIT" ...
## .. ..- attr(*, "spec")=
## .. .. .. cols(
## .. .. .. `Call Received Date/Time` = col_character(),
## .. .. .. `Incident ID` = col_double(),
## .. .. .. `How Call was Rec'd` = col_character(),
## .. .. .. `Nature Code Description` = col_character(),
## .. .. .. `Location Address` = col_character(),
## .. .. .. `Report Number Assigned to Event` = col_double(),
## .. .. .. `Closing Disposition or Cancel Code` = col_character()
## .. .. .. )
## .. ..- attr(*, "problems")=<externalptr>
```

---

## List Variables

`results$data` is a list column (or list variable).

List-columns are columns of a
data frame where each element is
a list or vector instead of an atomic
value.

Specifically, each element of `results$data` is a data set. This makes `results` a **nested data set**:

```r
head(results$data[[1]])
```

```
## # A tibble: 6 × 7
## `Call Received Date/Time` Incident I…¹ How C…² Natur…³ Locat…⁴ Repor…⁵ Closi…⁶
## <chr> <dbl> <chr> <chr> <chr> <dbl> <chr> 
## 1 2/9/2021 02:30AM 210202114 PHONE DISTUR… 4211 L… NA NAT 
## 2 2/9/2021 01:52AM 210202108 PHONE PRIVAT… 1206 S… NA PTOW 
## 3 2/9/2021 12:05AM 210202101 PHONE DISTUR… 624-20… NA NOR 
## 4 2/8/2021 11:55PM 210202100 PHONE SCAM 1118 S… 2.10e7 RPT 
## 5 2/8/2021 11:19PM 210202095 W911 BURGLA… 2919 O… 2.10e7 RPT 
## 6 2/8/2021 10:39PM 210202091 PHONE CIVIL … 219-11… NA NOR 
## # … with abbreviated variable names ¹`Incident ID`, ²`How Call was Rec'd`,
## # ³`Nature Code Description`, ⁴`Location Address`,
## # ⁵`Report Number Assigned to Event`, ⁶`Closing Disposition or Cancel Code`
```
---

## Mapping

- `purrr` [cheat sheet](https://github.com/rstudio/cheatsheets/raw/main/purrr.pdf)

take each element of a list (or vector) and apply function `.f` to it

Then return a

| Return | Function |
|---|---|
| list | map |
| numeric vector | map_dbl |
| logical vector | map_lgl |
| data frame | map_df |

`purrr` also implements very powerful ways of reshaping lists

---
class: inverse

## Your turn

We have been looking at the object `got_chars` in the `repurrrsive` package.

- What happens, when you apply the function `purrr::transpose` on the list?

- What does the length of the transposed object signify?

- Can we turn the transposed object into a data frame? into a tibble?

---

A tibble it is! How do we get the lists out of the data? - we unnest!

```r
library(repurrrsive)
transpose(got_chars) %>% names()
```

```
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"
```

```r
got_chars_tbl <- as_tibble(transpose(got_chars))
got_chars_tbl
```

```
## # A tibble: 30 × 18
## url id name gender culture born died alive titles aliases father
## <list> <list> <lis> <list> <list> <lis> <lis> <lis> <list> <list> <list>
## 1 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 2 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 3 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 4 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 5 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 6 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 7 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 8 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 9 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## 10 <chr [1]> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> 
## # … with 20 more rows, and 7 more variables: mother <list>, spouse <list>,
## # allegiances <list>, books <list>, povBooks <list>, tvSeries <list>,
## # playedBy <list>
```

---

# unnesting

`nest` and `unnest` are functions in the package `tidyr` to work with [nested data frames](https://tidyr.tidyverse.org/articles/nest.html)

`tidyr` 1.3.0 has just been released (Jan 24 2023) https://tidyr.tidyverse.org/news/index.html

which brings some syntactic changes with it: [new syntax](https://tidyr.tidyverse.org/reference/nest.html#new-syntax)

```r
got_chars_tbl %>% unnest(cols=1:6)
```

```
## # A tibble: 30 × 18
## url id name gender culture born died alive titles aliases father
## <chr> <int> <chr> <chr> <chr> <chr> <lis> <lis> <list> <list> <list>
## 1 https://w… 1022 Theo… Male "Ironb… "In … <chr> <lgl> <chr> <chr> <chr> 
## 2 https://w… 1052 Tyri… Male "" "In … <chr> <lgl> <chr> <chr> <chr> 
## 3 https://w… 1074 Vict… Male "Ironb… "In … <chr> <lgl> <chr> <chr> <chr> 
## 4 https://w… 1109 Will Male "" "" <chr> <lgl> <chr> <chr> <chr> 
## 5 https://w… 1166 Areo… Male "Norvo… "In … <chr> <lgl> <chr> <chr> <chr> 
## 6 https://w… 1267 Chett Male "" "At … <chr> <lgl> <chr> <chr> <chr> 
## 7 https://w… 1295 Cres… Male "" "In … <chr> <lgl> <chr> <chr> <chr> 
## 8 https://w… 130 Aria… Female "Dorni… "In … <chr> <lgl> <chr> <chr> <chr> 
## 9 https://w… 1303 Daen… Female "Valyr… "In … <chr> <lgl> <chr> <chr> <chr> 
## 10 https://w… 1319 Davo… Male "Weste… "In … <chr> <lgl> <chr> <chr> <chr> 
## # … with 20 more rows, and 7 more variables: mother <list>, spouse <list>,
## # allegiances <list>, books <list>, povBooks <list>, tvSeries <list>,
## # playedBy <list>
```

---
class: inverse
## Your Turn

The tibble `got_chars_tbl` is created by transposing the `got_chars` object and casting it into a tibble:

```r
got_chars_tbl <- as_tibble(transpose(got_chars))
```

> The `unnest` function takes all elements out of a list object and inserts them into the data set:

```r
got_chars_tbl %>% unnest("url")
```

Why does `got_chars_tbl %>% unnest("books")` not work?

---
class: inverse
## Your Turn

We fix the empty list in element 11 of `books` and change it to a list of an empty string instead:

```r
got_chars_tbl$books[11] <- list("")
```

What does `got_chars_tbl %>% unnest("books")` do now?

---
class: inverse
## Your Turn

Write a function `chars_to_char` that takes a vector of characters and returns a single character .... does that sound like `paste`?

Apply that function to the `books` variable of `got_chars_tbl`

What does `got_chars_tbl %>% unnest("books")` do now?

Can we apply this idea to other variables, such as `allegiances`?

---
class: inverse
## Your Turn

What is the difference between

```
got_chars_tbl %>% pluck("books") %>% reduce(.f = paste)
```

and

```
got_chars_tbl %>% pluck("books") %>% 
   map(.f = function(x) reduce(x, .f = paste, sep=", ", .init=""))
```

and how can we get rid of the leading comma?

---

# Back to Lab 2

The following code extracts the call codes from one of the press logs published by Ames PD:

```r
library(tabulizer)
codes <- locate_areas("presslogs/PressLog-2023-02-20.pdf", pages=1)

#codes <- c(474.79543, 20.19263, 546.14273, 747.12743)
#names(codes) <- c("top", "left", "bottom", "right")
# codes <- list(codes)

call_codes <- extract_tables("presslogs/PressLog-2023-02-20.pdf", pages=1, area = codes, guess = FALSE)

# turn all the codes into a single string
call_codes <- as.vector(paste(call_codes[[1]][,1], collapse = ""))

call_codes
```

---
class: inverse
## Your Turn

Take the `call_codes` object and create a data frame as shown below.

Make sure to use functionality of the `purrr` package!

```
##   code                 description
## 1  ACC  Accidently Chose New Event
## 2  ARR                      Arrest
## 3  ASC         Assignment Complete
## 4 ASST                      Assist
## 5  BIL              Billable Alarm
## 6 CALR        Canceled Alarm Reset
```

---

```r
# split the string along each comma:
codes_list <- str_split(call_codes, pattern=",")[[1]]

# get rid of leading and trailing white spaces
codes_list <- trimws(codes_list)

# now split the string along each equal symbol:
codes <- str_split(codes_list, pattern="=")
codes_df <- codes %>% purrr::map(.f = function(x) {
 list(code = x[1], description = x[2])
 }) %>% transpose() %>% as_tibble %>% unnest(cols = 1:2)

write.csv(codes_df, "data/call_codes.csv", row.names = FALSE)
```

---
class: middle, center

## A bigger example

---

## Example: the gapminder project

Part of a quantitative world is our responsibility to make data accessible

- Hans Rosling (2017✝) was at the fore-front of providing World Health Statistics (gapminder)

- in R through the package `gapminder` by Jenny Bryan

- Hans Rosling's [TED talk](https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen?language=en)

Can we describe countries by the way that their life expectancy develops over time?

---

## First Look: gapminder

```r
library(gapminder)

gg <- gapminder %>% ggplot(aes(x = year, y = lifeExp)) + geom_line(aes(group= country)) + facet_wrap(~continent) 
plotly::ggplotly(gg)
```

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--

Most  countries show an increase in life expectancy over time, but not all

---

## Split-Apply-Combine for lists

- `nest` creates list of data frames (split)

- `map` applies function to list object (apply)

- `unnest` moves list results back into a data frame (combine)

Usually a good idea to develop code for one example, then generalize

---

## First Model: US only

```r
gapminder %>% filter(country == "United States") %>% 
 ggplot(aes(x = year, y =lifeExp)) + geom_line() + geom_point() +
  geom_smooth(method="lm", se=FALSE)
```

```
## `geom_smooth()` using formula = 'y ~ x'
```

![](02b_purrr_files/figure-html/unnamed-chunk-14-1.png)

---

## Model of the US

```r
lm(lifeExp~I(year-1950), data = filter(gapminder, country == "United States"))
```

```
## 
## Call:
## lm(formula = lifeExp ~ I(year - 1950), data = filter(gapminder, 
##     country == "United States"))
## 
## Coefficients:
##    (Intercept)  I(year - 1950)  
##        68.0455          0.1842
```

---
class: inverse
## Your turn:  another country

Pick another country in the gapminder data and derive estimates.

How do the estimates compare to the US?

---

# Now we want to apply this to all countries

Nest by country:

```r
country_nest <- gapminder %>% 
 mutate(year = year - 1950) %>%
 group_by(continent, country) %>% nest()
```

Creates a `data` variable with a dataset for each country

```r
country_nest$data[[1]] %>% head()
```

```
## # A tibble: 6 × 4
## year lifeExp pop gdpPercap
## <dbl> <dbl> <int> <dbl>
## 1 2 28.8 8425333 779.
## 2 7 30.3 9240934 821.
## 3 12 32.0 10267083 853.
## 4 17 34.0 11537966 836.
## 5 22 36.1 13079460 740.
## 6 27 38.4 14880372 786.
```

---

# List variables: stepping through

The `map` function of the `purrr` package allows us to apply a function to each element of a list.
`map_dbl` returns a double value, `map_df` a data frame. `map` itself returns a list.

```r
country_nest <- country_nest %>% mutate(
 model = purrr::map(data, function(d) lm(lifeExp~year, data = d))
)
```

---

```r
country_nest <- country_nest %>% 
 mutate(
 intercept = model %>% purrr::map_dbl(.f = function(m) m$coef[1])
 )

country_nest %>% head()
```

```
## # A tibble: 6 × 5
## # Groups: continent, country [6]
## country continent data model intercept
## <fct> <fct> <list> <list> <dbl>
## 1 Afghanistan Asia <tibble [12 × 4]> <lm> 29.4
## 2 Albania Europe <tibble [12 × 4]> <lm> 58.6
## 3 Algeria Africa <tibble [12 × 4]> <lm> 42.2
## 4 Angola Africa <tibble [12 × 4]> <lm> 31.7
## 5 Argentina Americas <tibble [12 × 4]> <lm> 62.2
## 6 Australia Oceania <tibble [12 × 4]> <lm> 67.9
```

---

![](02b_purrr_files/figure-html/unnamed-chunk-20-1.png)

---
class: inverse
## Your Turn

- Work through the previous example and expand the object `country_nest` by adding the slope from each model as an additional variable

- Plot a scatterplot of slope and  intercept and compare.

- Advanced: Jenny Bryan is using pretty color schemes for these data at https://github.com/jennybc/gapminder
Can you get those to work for your example? Test with ggplotly.

---
class: inverse
## Your Turn

Find the data for Canada from `country_nest`

Why does `country_nest$data[[country_nest$country=="Canada"]]` not work?

---

## Nested data frames (cont'd)

We can use the list elements as data inputs:

```r
lm(lifeExp~year, data=country_nest$data[[10]])
```

```
## 
## Call:
## lm(formula = lifeExp ~ year, data = country_nest$data[[10]])
## 
## Coefficients:
## (Intercept)         year  
##     67.4738       0.2091
```

---
class: inverse
## Your Turn (8 mins)

The goal of this your turn is for you to try out nesting operations on various datasets.

- Nest the `ChickWeight` data in different ways: what are the results of

```
ChickWeight %>% nest(-Diet)
ChickWeight %>% nest(weight)
ChickWeight %>% nest(Time, weight)
```

- `chickwts` is yet another data set on feeding chicks. Nest it by different feeds.

- `nest` respects grouping structure introduced by `group_by`. Use `group_by` to nest the `iris` dataset by species.

---

# Extracting model estimates

```r
country_nest %>%
  mutate(
    intercept = model %>% purrr::map_dbl(.f = function(m) coef(m)[1]),
    slope = model %>% purrr::map_dbl(.f = function(m) coef(m)[2])
  ) %>% head(5)
```

```
## # A tibble: 5 × 6
## # Groups: continent, country [5]
## country continent data model intercept slope
## <fct> <fct> <list> <list> <dbl> <dbl>
## 1 Afghanistan Asia <tibble [12 × 4]> <lm> 29.4 0.275
## 2 Albania Europe <tibble [12 × 4]> <lm> 58.6 0.335
## 3 Algeria Africa <tibble [12 × 4]> <lm> 42.2 0.569
## 4 Angola Africa <tibble [12 × 4]> <lm> 31.7 0.209
## 5 Argentina Americas <tibble [12 × 4]> <lm> 62.2 0.232
```

---

## The broom package

Now we have all these models ...

---

## The `broom` package

`broom` allows to extract values from models on three levels:

- for each model: `broom::glance`

- for each coefficient in the model: `broom::tidy`

- for each value in the dataset: `broom::augment`

```r
library(broom)
broom::glance(country_nest$model[[1]])
```

```
## # A tibble: 1 × 12
## r.squ…¹ adj.r…² sigma stati…³ p.value df logLik AIC BIC devia…⁴ df.re…⁵
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0.948 0.942 1.22 181. 9.84e-8 1 -18.3 42.7 44.1 15.0 10
## # … with 1 more variable: nobs <int>, and abbreviated variable names
## # ¹r.squared, ²adj.r.squared, ³statistic, ⁴deviance, ⁵df.residual
```

---

```r
broom::tidy(country_nest$model[[1]])
```

```
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 29.4 0.699 42.0 1.40e-12
## 2 year 0.275 0.0205 13.5 9.84e- 8
```

---

```r
broom::augment(country_nest$model[[1]])
```

```
## # A tibble: 12 × 8
## lifeExp year .fitted .resid .hat .sigma .cooksd .std.resid
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 28.8 2 29.9 -1.11 0.295 1.21 0.243 -1.08 
## 2 30.3 7 31.3 -0.952 0.225 1.24 0.113 -0.884 
## 3 32.0 12 32.7 -0.664 0.169 1.27 0.0360 -0.595 
## 4 34.0 17 34.0 -0.0172 0.127 1.29 0.0000165 -0.0151
## 5 36.1 22 35.4 0.674 0.0991 1.27 0.0185 0.581 
## 6 38.4 27 36.8 1.65 0.0851 1.15 0.0923 1.41 
## 7 39.9 32 38.2 1.69 0.0851 1.15 0.0967 1.44 
## 8 40.8 37 39.5 1.28 0.0991 1.21 0.0667 1.10 
## 9 41.7 42 40.9 0.754 0.127 1.26 0.0317 0.660 
## 10 41.8 47 42.3 -0.534 0.169 1.27 0.0233 -0.479 
## 11 42.1 52 43.7 -1.54 0.225 1.15 0.299 -1.43 
## 12 43.8 57 45.1 -1.22 0.295 1.19 0.296 -1.19
```

---

##Extract values for each coefficient

Extract coefficients for all countries automatically (hello `map` again!)

```r
# works, but we lose the context
coefs = country_nest$model %>% purrr::map(.f=broom::tidy)
head(coefs)
```

```
## [[1]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 29.4 0.699 42.0 1.40e-12
## 2 year 0.275 0.0205 13.5 9.84e- 8
## 
## [[2]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 58.6 1.13 51.7 1.79e-13
## 2 year 0.335 0.0332 10.1 1.46e- 6
## 
## [[3]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 42.2 0.756 55.8 8.22e-14
## 2 year 0.569 0.0221 25.7 1.81e-10
## 
## [[4]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 31.7 0.804 39.4 2.63e-12
## 2 year 0.209 0.0235 8.90 4.59e- 6
## 
## [[5]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 62.2 0.167 372. 4.80e-22
## 2 year 0.232 0.00489 47.4 4.22e-13
## 
## [[6]]
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 67.9 0.355 192. 3.70e-19
## 2 year 0.228 0.0104 21.9 8.67e-10
```

---

##Extract values for each coefficient (cont'd)

```r
# better, but everything is still stuffed into this strange format
country_nest <- country_nest %>% mutate(
 coefs = model %>% purrr::map(.f=broom::tidy)
) 
country_nest %>% head()
```

```
## # A tibble: 6 × 7
## # Groups: continent, country [6]
## country continent data model intercept slope coefs 
## <fct> <fct> <list> <list> <dbl> <dbl> <list> 
## 1 Afghanistan Asia <tibble [12 × 4]> <lm> 29.4 0.275 <tibble>
## 2 Albania Europe <tibble [12 × 4]> <lm> 58.6 0.335 <tibble>
## 3 Algeria Africa <tibble [12 × 4]> <lm> 42.2 0.569 <tibble>
## 4 Angola Africa <tibble [12 × 4]> <lm> 31.7 0.209 <tibble>
## 5 Argentina Americas <tibble [12 × 4]> <lm> 62.2 0.232 <tibble>
## 6 Australia Oceania <tibble [12 × 4]> <lm> 67.9 0.228 <tibble>
```

---

## Ready to fly!

The opposite of `nest` is `unnest`:

```r
coefs <- country_nest %>% select(country, continent, coefs) %>% 
 unnest(cols = coefs)
coefs
```

```
## # A tibble: 284 × 7
## # Groups: continent, country [142]
## country continent term estimate std.error statistic p.value
## <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia (Intercept) 29.4 0.699 42.0 1.40e-12
## 2 Afghanistan Asia year 0.275 0.0205 13.5 9.84e- 8
## 3 Albania Europe (Intercept) 58.6 1.13 51.7 1.79e-13
## 4 Albania Europe year 0.335 0.0332 10.1 1.46e- 6
## 5 Algeria Africa (Intercept) 42.2 0.756 55.8 8.22e-14
## 6 Algeria Africa year 0.569 0.0221 25.7 1.81e-10
## 7 Angola Africa (Intercept) 31.7 0.804 39.4 2.63e-12
## 8 Angola Africa year 0.209 0.0235 8.90 4.59e- 6
## 9 Argentina Americas (Intercept) 62.2 0.167 372. 4.80e-22
## 10 Argentina Americas year 0.232 0.00489 47.4 4.22e-13
## # … with 274 more rows
```

---

## Reshaping

```r
coefsLong <- coefs %>% pivot_longer(names_to="Statistic", values_to = "Value", 4:7)
coefsTerm <- coefsLong %>% pivot_wider(names_from="term", values_from="Value")
coefsTerm %>% filter(Statistic == "estimate") %>% 
 ggplot(aes(x = `(Intercept)`, y = year)) + 
 geom_point(aes(colour = continent)) + ylab("Slope (year)")
```

![](02b_purrr_files/figure-html/unnamed-chunk-29-1.png)

---
class: inverse
## Your turn (10 mins)

- Extract other model diagnostics: find the R square value for each model. Draw a visualization of R square by country - can you see a pattern? What does the pattern mean?

- Extract residuals and fitted values for each of the models and store them in a dataset together with country and continent information.

- Plot residuals across the years and fit a smooth. What does the pattern mean?

- Include predicted Life Expectancy for 2017 into the data set. Then compare predicted life expectancy in 2017 with that in 1950.