STATS 220

# STATS 220
## Data import⬇️/export⬆️

---

## Atomic vector (1d)

```r
dept <- c("Physics", "Mathematics", "Statistics", "Computer Science")
nstaff <- c(12L, 8L, 20L, 23L)
```

???

* an ensemble of scalars -> vectors

---

## 1d ➡️ 2d

```r
library(tibble)
sci_tbl <- tibble(
  department = dept, 
  count = nstaff, 
  percentage = count / sum(count))
sci_tbl
```

```
#> # A tibble: 4 x 3
#>   department       count percentage
#>   <chr>            <int>      <dbl>
#> 1 Physics             12      0.190
#> 2 Mathematics          8      0.127
#> 3 Statistics          20      0.317
#> 4 Computer Science    23      0.365
```
]

???

* an ensemble of vectors -> rect data/tabular data, like spreadsheet

---

## Beyond 1d vectors

### 1. Lists

### 2. Matrices and arrays

### 3. Data frames and tibbles

???

* Common data strs beyond 1d
* start with the most flex one
* briefly talk about mat
* focus on data frames, more specifically tibbles

---

.left-column[
## data strs
### - lists
]
.right-column[
An object contains elements of **different data types**.

```r
lst <- list( # list constructor/creator
  1:3, 
  "a", 
  c(TRUE, FALSE, TRUE), 
  c(2.3, 5.9)
)
lst
```

```
#> [[1]]
#> [1] 1 2 3
#> 
#> [[2]]
#> [1] "a"
#> 
#> [[3]]
#> [1]  TRUE FALSE  TRUE
#> 
#> [[4]]
#> [1] 2.3 5.9
```
]

???

* to create a list using `list()`
* put 4 atomic vectors inside my lst
* a list of 4 elements, or length of 4

---

.left-column[
## data strs
### - lists
]
.right-column[
<img src="https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png" width="100%">
.pull-left[
## data type

```r
typeof(lst) # primitive type
```

```
#> [1] "list"
```
## data class

```r
class(lst) # type + attributes
```

```
#> [1] "list"
```
]
.pull-right[
## data structure

```r
str(lst)
# el can be of diff lengths
```

```
#> List of 4
#>  $ : int [1:3] 1 2 3
#>  $ : chr "a"
#>  $ : logi [1:3] TRUE FALSE TRUE
#>  $ : num [1:2] 2.3 5.9
```
]
]

???

* vis rep: a container, 4 items inside
* primitive: original, cannot be modified
* class: type + attrs, can be modified
* rstudio values uses `str()`

---

```r
lst
```

```
#> [[1]]
#> [1] 1 2 3
#> 
#> [[2]]
#> [1] "a"
#> 
#> [[3]]
#> [1]  TRUE FALSE  TRUE
#> 
#> [[4]]
#> [1] 2.3 5.9
```
]
.pull-right[
.center[<img src="img/lst.png" height="100%">]
]
]

---

.left-column[
## data strs
### - lists
]
.right-column[
A list can contain other lists, i.e. **recursive**

```r
# a named list
str(list(first_el = lst, second_el = mtcars))
```

```
#> List of 2
#>  $ first_el :List of 4
#>   ..$ : int [1:3] 1 2 3
#>   ..$ : chr "a"
#>   ..$ : logi [1:3] TRUE FALSE TRUE
#>   ..$ : num [1:2] 2.3 5.9
#>  $ second_el:'data.frame':	32 obs. of  11 variables:
#>   ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#>   ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
#>   ..$ disp: num [1:32] 160 160 108 258 360 ...
#>   ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
#>   ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#>   ..$ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
#>   ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
#>   ..$ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
#>   ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
#>   ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
#>   ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
```
]

???

* most flex: put a list into a list
* a named list

---

```r
is.list(lst)
```

```
#> [1] TRUE
```
]
.pull-right[
Coerce to a list

```r
as.list(1:3)
```

```
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 2
#> 
#> [[3]]
#> [1] 3
```
]
]

???

* to test if an object is one type, funs prefixed `is`
* to coerce/convert from one type to another type, funs prefixed with `as`
* from a vector of integers to a list

---

```r
lst[1]
```

```
#> [[1]]
#> [1] 1 2 3
```
]
.pull-right[
Subset by `[[]]`

```r
lst[[1]]
```

```
#> [1] 1 2 3
```
]

---

.left-column[
## data strs
### - lists
### - matrices
]
.right-column[
2D structure of homogeneous data types
* `matrix()` to construct a matrix

```r
matrix(1:9, nrow = 3)
```

```
#>      [,1] [,2] [,3]
#> [1,]    1    4    7
#> [2,]    2    5    8
#> [3,]    3    6    9
```
* `as.matrix()` to coerce to a matrix
* `is.matrix()` to test for a matrix
]

???

* we don't deal with matrix in 220, matrix for computational stats.

---

.left-column[
## data strs
### - lists
### - matrices
]
.right-column[
**array**: more than 2D matrix

```r
array(1:9, dim = c(1, 3, 3))
```

```
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    2    3
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    4    5    6
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    7    8    9
```
]

---

.left-column[
## data strs
### - lists
### - matrices
### - tibbles
]
.right-column[
A data frame is a **named list** of vectors of the **same length**.

```r
sci_df <- data.frame(
  department = dept, 
  count = nstaff)
sci_df
```

```
#>         department count
#> 1          Physics    12
#> 2      Mathematics     8
#> 3       Statistics    20
#> 4 Computer Science    23
```
]

---

.left-column[
## data strs
### - lists
### - matrices
### - tibbles
]
.right-column[
The underlying data type is a list.

```r
typeof(sci_df)
```

```
#> [1] "list"
```
.pull-left[
.center[data class]

```r
class(sci_df)
```

```
#> [1] "data.frame"
```
]
.pull-right[
.center[data attributes (meta info)]

```r
attributes(sci_df)
```

```
#> $names
#> [1] "department" "count"     
#> 
#> $class
#> [1] "data.frame"
#> 
#> $row.names
#> [1] 1 2 3 4
```
]
]

???

* `data.frame` represents tabular data in R
* attributes: colnames and rownames

---

.left-column[
## data strs
### - lists
### - matrices
### - tibbles
]
.right-column[
A tibble is a **modern reimagining** of the data frame.

```r
library(tibble)
sci_tbl <- tibble(
  department = dept, 
  count = nstaff, 
  percentage = count / sum(count))
sci_tbl
```

```
#> # A tibble: 4 x 3
#>   department       count percentage
#>   <chr>            <int>      <dbl>
#> 1 Physics             12      0.190
#> 2 Mathematics          8      0.127
#> 3 Statistics          20      0.317
#> 4 Computer Science    23      0.365
```
* `as_tibble()` to coerce to a tibble
* `is_tibble()` to test for a tibble
]

???

* why we call it `tibble`

---

.left-column[
## data strs
### - lists
### - matrices
### - tibbles
]
.right-column[
.center[
<img src="https://d33wubrfki0l68.cloudfront.net/9ec5e1f8982238a413847eb5c9bbc5dcf44c9893/bc590/diagrams/vectors/summary-tree-s3-2.png" width="250">
]

```r
typeof(sci_tbl) # list in essence
```

```
#> [1] "list"
```

```r
class(sci_tbl) # tibble is a special class of data.frame
```

```
#> [1] "tbl_df"     "tbl"        "data.frame"
```
]

???

* multi cls: left to right, specific to more general

---

## Why tibble not data frame?

```r
sci_df <- data.frame(
  department = dept, 
  count = nstaff)
sci_df
```

```
#>         department count
#> 1          Physics    12
#> 2      Mathematics     8
#> 3       Statistics    20
#> 4 Computer Science    23
```
]
.pull-right[

```r
sci_tbl <- tibble(
  department = dept, 
  count = nstaff, 
* percentage = count / sum(count))
sci_tbl
```

```
*#> # A tibble: 4 x 3
#>   department       count percentage
*#>   <chr>            <int>      <dbl>
#> 1 Physics             12      0.190
#> 2 Mathematics          8      0.127
#> 3 Statistics          20      0.317
#> 4 Computer Science    23      0.365
```
]

???

* tibble's display: friendly & informative

---

## Glimpse data

```r
glimpse(sci_tbl) # to replace str()
```

```
#> Rows: 4
#> Columns: 3
#> $ department <chr> "Physics", "Mathematics", "Statistics",…
#> $ count      <int> 12, 8, 20, 23
#> $ percentage <dbl> 0.1904762, 0.1269841, 0.3174603, 0.3650…
```

Data types and their abbreviations

.pull-left[
* `chr`: character
* `dbl`: double
* `int`: integer
* `lgl`: logical
]
.pull-right[
* `fct`: factor
* `date`: date
* `dttm`: date-time
* more [column data types](https://tibble.tidyverse.org/articles/types.html)
]

???

text in pink suggest links

---

## Subsetting tibble

```r
sci_tbl[["count"]] # col name
```

```
#> [1] 12  8 20 23
```

```r
sci_tbl[[2]] # col pos
```

```
#> [1] 12  8 20 23
```

```r
sci_tbl$count # col name
```

```
#> [1] 12  8 20 23
```
]

---

## Subsetting tibble

```r
sci_tbl["count"]
```

```
#> # A tibble: 4 x 1
#>   count
#>   <int>
#> 1    12
#> 2     8
#> 3    20
#> 4    23
```
]
.pull-right[

```r
sci_tbl[2] # sci_tbl[, 2]
```

```
#> # A tibble: 4 x 1
#>   count
#>   <int>
#> 1    12
#> 2     8
#> 3    20
#> 4    23
```
]
]

---

## Subsetting tibble

```r
sci_tbl[c(1, 3), ]
```

```
#> # A tibble: 2 x 3
#>   department count percentage
#>   <chr>      <int>      <dbl>
#> 1 Physics       12      0.190
#> 2 Statistics    20      0.317
```
]
.pull-right[

```r
sci_tbl[-c(2, 4), ]
```

```
#> # A tibble: 2 x 3
#>   department count percentage
#>   <chr>      <int>      <dbl>
#> 1 Physics       12      0.190
#> 2 Statistics    20      0.317
```
]
]

---

## Subsetting tibble

.left-column[
### - to 1d
### - by columns
### - by rows
### - by cols & rows
]
.right-column[
* with `[row, col]`

```r
sci_tbl[1:3, 2]
## sci_tbl[-4, 2]
## sci_tbl[1:3, "count"]
## sci_tbl[c(rep(TRUE, 3), FALSE), 2]
```

```
#> # A tibble: 3 x 1
#>   count
#>   <int>
#> 1    12
#> 2     8
#> 3    20
```
]

---

## Subsetting tibble

* Use `[[` to extract 1d vectors from 2d tibbles
* Use `[` to subset tibbles to a new tibble
  + numbers (positive/negative) as indices
  + characters (column names) as indices
  + logicals as indices

```r
sci_tbl[1:3, 2]
sci_tbl[-4, 2]
sci_tbl[1:3, "count"]
sci_tbl[c(rep(TRUE, 3), FALSE), 2]
```

---

## The [tidyverse](https://www.tidyverse.org) is an opinionated [collection of R packages](https://www.tidyverse.org/packages/) designed for data science. *All packages share an underlying design philosophy, grammar, and data structures.*

---

## Use {tidyverse}

```r
library(tidyverse)
```

```
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
```

```
#> ✔ ggplot2 3.3.3     ✔ purrr   0.3.4
#> ✔ tibble  3.1.0     ✔ dplyr   1.0.5
#> ✔ tidyr   1.1.3     ✔ stringr 1.4.0
#> ✔ readr   1.4.0     ✔ forcats 0.5.1
```

```
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
```

---

# Data import ⬇️

---

???

* 3M students from more than 90 countries
* conducted every 3 yrs

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/readr.png" width="60%">]
]
.right-column[
## Reading plain-text rectangular files
### .small[(a.k.a. flat or spreadsheet-like files)]
* delimited text files with `read_delim()`
  + `.csv`: comma separated values with `read_csv()`
  + `.tsv`: tab separated values `read_tsv()`
* `.fwf`: fixed width files with `read_fwf()`
<hr>

```bash
head -4 data/pisa/pisa-student.csv # shell command, not R
```

```
#> year,country,school_id,student_id,mother_educ,father_educ,gender,computer,internet,math,read,science,stu_wgt,desk,room,dishwasher,television,computer_n,car,book,wealth,escs
#> 2000,ALB,1001,1,NA,NA,female,NA,no,324.35,397.87,345.66,2.16,yes,no,no,1,3+,1,11-50,-0.6,0.10575582991490981
#> 2000,ALB,1001,3,NA,NA,female,NA,no,NA,368.41,385.83,2.16,yes,yes,no,2,0,0,1-10,-1.84,-1.424044581128788
#> 2000,ALB,1001,6,NA,NA,male,NA,no,NA,294.17,327.94,2.16,yes,yes,no,2,0,0,1-10,-1.46,-1.306683855365612
```
]

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/readr.png" width="60%">]
]
.right-column[
## Reading comma delimited files

```r
library(readr) # library(tidyverse)
pisa <- read_csv("data/pisa/pisa-student.csv", n_max = 2929621)
pisa
```

```
#> # A tibble: 2,929,621 x 22
#>    year country school_id student_id mother_educ father_educ
#>   <dbl> <chr>       <dbl>      <dbl> <lgl>       <lgl>      
#> 1  2000 ALB          1001          1 NA          NA         
#> 2  2000 ALB          1001          3 NA          NA         
#> 3  2000 ALB          1001          6 NA          NA         
#> 4  2000 ALB          1001          8 NA          NA         
#> 5  2000 ALB          1001         11 NA          NA         
#> 6  2000 ALB          1001         12 NA          NA         
#> # … with 2,929,615 more rows, and 16 more variables:
#> #   gender <chr>, computer <lgl>, internet <chr>,
#> #   math <dbl>, read <dbl>, science <dbl>, stu_wgt <dbl>,
#> #   desk <chr>, room <chr>, dishwasher <chr>,
#> #   television <chr>, computer_n <chr>, car <chr>,
#> #   book <chr>, wealth <dbl>, escs <dbl>
```
]

???

* from external files in a disk to a tibble obj in R

---

## Let's talk about the file path again!

```r
pisa <- read_csv("data/pisa/pisa-student.csv", n_max = 2929621)
```

`data/pisa/pisa-student.csv` relative to the top-level (or root) directory:

* `stats220.Rproj`
* `data/`
  * `pisa/pisa-student.csv`

If you don't like `/`, you can use `here::here()` instead.

```r
read_csv(here::here("data", "pisa", "pisa-student.csv"))
```

.footnote[NOTE: I use the `here()` function from the {here} package using `pkg::fun()`, without calling `library(here)` the ususal way.]

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/readr.png" width="60%">]
]
.right-column[
## `read_csv()` arguments with [`?read_csv()`](https://readr.tidyverse.org/reference/read_delim.html)

```r
read_csv(
  file,
  col_names = TRUE,
  col_types = NULL,
  locale = default_locale(),
  na = c("", "NA"),
  quoted_na = TRUE,
  quote = "\"",
  comment = "",
  trim_ws = TRUE,
  skip = 0,
  n_max = Inf,
  guess_max = min(1000, n_max),
  progress = show_progress(),
  skip_empty_rows = TRUE
)
```
]

???

* w/o using arguments, readr makes smart guesses, which means take a little longer
* more specific, speed up the reading

---

.left-column[
.center[<img src="https://github.com/r-lib/vroom/blob/master/man/figures/logo.png?raw=true" width="75%">]
]
.right-column[
## Faster delimited reader at **1.4GB/sec**

```r
library(vroom)
pisa <- vroom("data/pisa/pisa-student.csv", n_max = 2929621)
```
]

???

* {readr} as toyota, {vroom} sports car
* super optimized for fast reading, likely have edge cases, better not for production
* when {vroom} moves to a more stable lifecylce, backend {readr}

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/readxl.png" width="60%">]
]
.right-column[
## Reading proprietary binary files
* Microsoft Excel
  + `.xls`: MSFT Excel 2003 and earlier
  + `.xlsx`: MSFT Excel 2007 and later

```r
library(readxl)
time_use <- read_xlsx("data/time-use-oecd.xlsx")
time_use
```

```
#> # A tibble: 461 x 3
#>   Country   Category  `Time (minutes)`
#>   <chr>     <chr>                <dbl>
#> 1 Australia Paid work             211.
#> 2 Austria   Paid work             280.
#> 3 Belgium   Paid work             194.
#> 4 Canada    Paid work             269.
#> 5 Denmark   Paid work             200.
#> 6 Estonia   Paid work             231.
#> # … with 455 more rows
```
]

???

* contrasting to plain-text, binary files have to be opened by a certain app

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/haven.png" width="60%">]
]
.right-column[
## Reading proprietary binary files
* SAS
  + `.sas7bdat` with `read_sas()`
* Stata
  + `.dta` with `read_dta()`
* SPSS
  + `.sav` with `read_sav()`

```r
library(haven)
pisa2018 <- read_spss("data/pisa/CY07_MSU_STU_QQQ.sav")
```
<hr>
Raw PISA data is made available in SAS and SPSS data formats.
.footnote[
data source: [https://www.oecd.org/pisa/data/2018database/](https://www.oecd.org/pisa/data/2018database/)
]
]

---

## Your turn

> What is the R data format for a single object? What is its file extension?

---

## Well, SQL!

* **Structured Query Language** for accessing and manipulating databases.
* Relational database management systems
  + [SQLite](https://www.sqlite.org/index.html)
  + [MySQL](https://www.mysql.com)
  + PostgresSQL
  + BigQuery
  + Spark SQL

### However, 220 is all about R!

---

```r
library(RSQLite)
con <- dbConnect(SQLite(), dbname = "data/pisa/pisa-student.db")
dbListTables(con)
```

```
#> [1] "pisa"
```

```r
dbListFields(con, "pisa")
```

```
#>  [1] "year"        "country"     "school_id"   "student_id"  "mother_educ"
#>  [6] "father_educ" "gender"      "computer"    "internet"    "math"       
#> [11] "read"        "science"     "stu_wgt"     "desk"        "room"       
#> [16] "dishwasher"  "television"  "computer_n"  "car"         "book"       
#> [21] "wealth"      "escs"
```
.footnote[NOTE: slides marked with `*` are not examinable.]
]

???

* dbi: database interface, communicating b/t R and db
* connecting to SQLite
* multi tables typically: students, schools
* fields = column names

---

```r
pisa <- dbReadTable(con, "pisa")
```

* writing SQL queries to read chunks

```r
res <- dbSendQuery(con, "SELECT * FROM pisa WHERE year = 2018")
pisa2018 <- dbFetch(res)
```

* closing connection

```r
dbDisconnect(con)
```
]

---

.left-column[
.center[<img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/readr.png" width="60%">]
]
.right-column[
## Reading chunks for larger than memory data*

```r
chunked <- function(x, pos) {
  dplyr::filter(x, year == 2018)
}
pisa2018 <- read_csv_chunked("data/pisa/pisa-student.csv",
  callback = DataFrameCallback$new(chunked))
```
]

???

* GPU, disk size, RAM
* data files in disk
* R obj in RAM
* crashed, blow up my RAM for reading pisa twice

---

* object: `{}`
* array: `[]`
* value: string/character, number, object, array, logical, `null`

.pull-left[
### JSON
```json
{
  "firstName": "Earo",
  "lastName": "Wang",
  "address": {
    "city": "Auckland",
    "postalCode": 1010
  }
  "logical": [true, false]
}
```
]
.pull-right[
### R list
```r
list(
  firstName = "Earo",
  lastName = "Wang",
  address = list(
    city = "Auckland",
    postalCode = 1010
  ),
  logical = c(TRUE, FALSE)
)
```
]
]

???

* a lightweight text format
* easy for humans to read and write
* easy for machines to parse and generate
* annologue to list
* `null` is `NA`

---

```r
library(jsonlite)
url <- "https://vega.github.io/vega-editor/app/data/movies.json"
movies <- read_json(url)
length(movies)
```

```
#> [1] 3201
```

```r
movies[[1]]
```

```
#> $Title
#> [1] "The Land Girls"
#> 
#> $US_Gross
#> [1] 146083
#> 
#> $Worldwide_Gross
#> [1] 146083
#> 
#> $US_DVD_Sales
#> NULL
#> 
#> $Production_Budget
#> [1] 8000000
#> 
#> $Release_Date
#> [1] "12-Jun-98"
#> 
#> $MPAA_Rating
#> [1] "R"
#> 
#> $Running_Time_min
#> NULL
#> 
#> $Distributor
#> [1] "Gramercy"
#> 
#> $Source
#> NULL
#> 
#> $Major_Genre
#> NULL
#> 
#> $Creative_Type
#> NULL
#> 
#> $Director
#> NULL
#> 
#> $Rotten_Tomatoes_Rating
#> NULL
#> 
#> $IMDB_Rating
#> [1] 6.1
#> 
#> $IMDB_Votes
#> [1] 1071
```
]

???

* read from url
* but url is temporary
* labs/assignments must use relative path, no web url accepted

---

```r
movies_tbl <- as_tibble(read_json(url, simplifyVector = TRUE))
movies_tbl
```

```
#> # A tibble: 3,201 x 16
#>   Title                US_Gross Worldwide_Gross US_DVD_Sales
#>   <chr>                   <int>           <dbl>        <int>
#> 1 The Land Girls         146083          146083           NA
#> 2 First Love, Last Ri…    10876           10876           NA
#> 3 I Married a Strange…   203134          203134           NA
#> 4 Let's Talk About Sex   373615          373615           NA
#> 5 Slam                  1009819         1087521           NA
#> 6 Mississippi Mermaid     24551         2624551           NA
#> # … with 3,195 more rows, and 12 more variables:
#> #   Production_Budget <int>, Release_Date <chr>,
#> #   MPAA_Rating <chr>, Running_Time_min <int>,
#> #   Distributor <chr>, Source <chr>, Major_Genre <chr>,
#> #   Creative_Type <chr>, Director <chr>,
#> #   Rotten_Tomatoes_Rating <int>, IMDB_Rating <dbl>,
#> #   IMDB_Votes <int>
```
]

---

.left-column[
.center[<img src="https://github.com/r-spatial/sf/raw/master/man/figures/logo.png" width="80%">]
]
.right-column[
## Reading spatial data*

```r
library(sf)
akl_bus <- st_read("data/BusService/BusService.shp")
```

```
#> Reading layer `BusService' from data source `/Users/wany568/Teaching/stats220/lectures/data/BusService/BusService.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 509 features and 7 fields
#> geometry type:  MULTILINESTRING
#> dimension:      XY
#> bbox:           xmin: 1727652 ymin: 5859539 xmax: 1787138 ymax: 5982575
#> projected CRS:  NZGD2000_New_Zealand_Transverse_Mercator_2000
```

.footnote[data source: [**Auckland Transport Open GIS Data**](https://data-atgis.opendata.arcgis.com/datasets/bus-route/data?geometry=169.841%2C-37.610%2C179.685%2C-36.072)]
]

???

* sf: simple features
* spatial data: points, lines from a to b (bus routes), polygons

---

.left-column[
.center[<img src="https://github.com/r-spatial/sf/raw/master/man/figures/logo.png" width="80%">]
]
.right-column[
## Reading spatial data*

```r
library(sf)
akl_bus <- st_read("data/BusService/BusService.shp")
```

.footnote[data source: [**Auckland Transport Open GIS Data**](https://data-atgis.opendata.arcgis.com/datasets/bus-route/data?geometry=169.841%2C-37.610%2C179.685%2C-36.072)]
]

---

.left-column[
.center[<img src="https://github.com/r-spatial/sf/raw/master/man/figures/logo.png" width="80%">]
]
.right-column[
## Reading spatial data*

```r
akl_bus[1:4, ]
```

```
#> Simple feature collection with 4 features and 7 fields
#> geometry type:  MULTILINESTRING
#> dimension:      XY
#> bbox:           xmin: 1751253 ymin: 5915245 xmax: 1758019 ymax: 5921383
#> projected CRS:  NZGD2000_New_Zealand_Transverse_Mercator_2000
#>   OBJECTID ROUTEPATTE AGENCYNAME                                 ROUTENAME
#> 1   343077      02005        NZB St Lukes To Wynyard Quarter Via Kingsland
#> 2   343078      02006        NZB Wynyard Quarter To St Lukes Via Kingsland
#> 3   343079      02209        NZB  Avondale To City Centre Via New North Rd
#> 4   343080      02208        NZB  City Centre To Avondale Via New North Rd
#>   ROUTENUMBE MODE Shape__Len                       geometry
#> 1         20  Bus   7948.418 MULTILINESTRING ((1755487 5...
#> 2         20  Bus   7919.198 MULTILINESTRING ((1756321 5...
#> 3        22A  Bus  11419.588 MULTILINESTRING ((1757613 5...
#> 4        22A  Bus  11607.711 MULTILINESTRING ((1757346 5...
```
]

---

.left-column[
.center[<img src="https://github.com/r-spatial/sf/raw/master/man/figures/logo.png" width="80%">]
]
.right-column[
## Spatial visualisation*

```r
library(ggplot2)
ggplot() +
  geom_sf(data = akl_bus, aes(colour = AGENCYNAME))
```
]
]
]
]

???

* rich data fmts: audio, images, etc
* seen an unseen file type: google that type and the corresponding r function

---

# Data export ⬆️

---

## From `read_*()` to `write_*()`

```r
write_csv(movies_tbl, file = "data/movies.csv")
write_sas(movies_tbl, path = "data/movies.sas7bdat")
write_json(movies_tbl, path = "data/movies.json")
```

---

## Reading

.pull-left[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png" height="320px">](https://r4ds.had.co.nz)]
* [Tibbles](https://r4ds.had.co.nz/tibbles.html)
* [Data import](https://r4ds.had.co.nz/data-import.html)
]
.pull-right[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="320px">](https://adv-r.hadley.nz)]
* [Subsetting](https://adv-r.hadley.nz/subsetting.html#subset-single)
]