---
title: "Lecture 6: Augmented vectors,  Factor + Labelled Variables"
subtitle:  ""
author: 
date: 
fontsize: 8pt
classoption: dvipsnames  # for colors
urlcolor: blue
output:
  beamer_presentation:
    keep_tex: true
    toc: false
    slide_level: 3
    theme: default # AnnArbor # push to header?
    #colortheme: "dolphin" # push to header?
    #fonttheme: "structurebold"
    highlight: default # Supported styles include "default", "tango", "pygments", "kate", "monochrome", "espresso", "zenburn", and "haddock" (specify null to prevent syntax highlighting); push to header
    df_print: default #default # tibble # push to header?    
    latex_engine: xelatex #  Available engines are pdflatex [default], xelatex, and lualatex; The main reasons you may want to use xelatex or lualatex are: (1) They support Unicode better; (2) It is easier to make use of system fonts.
    includes:
      in_header: ../beamer_header.tex
      #after_body: table-of-contents.txt 
---

```{r, echo=FALSE, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", highlight = TRUE)
  #comment = "#>" makes it so results from a code chunk start with "#>"; default is "##"
```

```{r, echo=FALSE, include=FALSE}
#THIS CODE DOWNLOADS THE MOST RECENT VERSION OF THE FILE beamder_header.tex AND SAVES IT TO THE DIRECTORY ONE LEVEL UP FROM THIS .RMD LECTURE FILE
download.file(url = 'https://raw.githubusercontent.com/ozanj/rclass/master/lectures/beamer_header.tex', 
              destfile = '../beamer_header.tex',
              mode = 'wb')
```

```{r, echo=FALSE, include=FALSE}
#DO NOT WORRY ABOUT THIS
if(!file.exists('data-structures-overview.png')){
  download.file(url = 'https://raw.githubusercontent.com/ozanj/rclass/master/lectures/lecture5/data-structures-overview.png', 
                destfile = 'data-structures-overview.png',
                mode = 'wb')
}
```

# Introduction


### Logistics

__Reading to do before next class:__

- GW 15.1 - 15.2 (factors) [this is like 2-3 pages]
- [OPTIONAL] GW 15.3 - 15.5 (remainder of "factors" chapter)
- [OPTIONAL] GW 20.6 - 20.7 (attributes and augmented vectors)
- [OPTIONAL] GW 10 (tibbles)

<!-- __Explanation about `beamer_header.tex` in YAML header:__ -->

<!-- - We are calling the beamer_header.tex file in the background to customize our slides. Without this LaTeX file, our slides would compile according to the default beamer presentation (PDF).   -->
<!--     - Why would we want to do this?   -->
<!--     - We can customize our slides with the beamer_header.tex LaTeX file to include page numbers, change heading options, or change slide colors (in addition to other things).  -->
<!-- - `includes` option in the YAML header customizes the beamer presentation slides   -->
<!--     - Here is a [link](https://bookdown.org/yihui/rmarkdown/pdf-document.html#latex-options) to a short description of the includes option in the YAML header.   -->



### What we will do today

\tableofcontents

```{r, eval=FALSE, echo=FALSE}
#Use this if you want TOC to show level 2 headings
\tableofcontents
#Use this if you don't want TOC to show level 2 headings
\tableofcontents[subsectionstyle=hide/hide/hide]
```

### Libraries we will use today

"Load" the package we will use today (output omitted)

- __you must run this code chunk after installing these packages__
```{r, message=FALSE}
library(tidyverse)
library(haven)
library(labelled)
library(lubridate)
```
__If package not yet installed__, then must install before you load. Install in "console" rather than .Rmd file

- Generic syntax: `install.packages("package_name")`
- Install "tidyverse": `install.packages("tidyverse")`

Note: when we load package, name of package is not in quotes; but when we install package, name of package is in quotes:

- `install.packages("tidyverse")`
- `library(tidyverse)`

# Augmented vectors

### Data we will use to introduce augmented vectors

```{r}
rm(list = ls()) # remove all objects

#load("../../data/prospect_list/western_washington_college_board_list.RData")
load(url("https://github.com/ozanj/rclass/raw/master/data/prospect_list/wwlist_merged.RData"))

```

## Review data types and structures

### __Vectors__ are the primary data structures in R

\medskip

Two types of vectors:

1. __atomic vectors__
1. __lists__

\medskip

![Overview of data structures (Grolemund and Wickham, 2018)](data-structures-overview.png){width=60%}

### Review data structures: atomic vectors


\medskip An __atomic vector__ is a collection of values

- each value in an atomic vector is an __element__
- all elements within vector must have same __data type__

```{r}
(a <- c(1,2,3)) # parentheses () assign and print object in one step
length(a)
typeof(a)
str(a)
```

Can assign __names__ to vector elements, creating a __named atomic vector__

```{r}
(b <- c(v1=1,v2=2,v3=3))
length(b)
typeof(b)
str(b)
```

### Review data structures: lists

\medskip

- Like atomic vectors, __lists__ are objects that contain __elements__
- However, __data type__ can differ across elements within a list
    - an element of a list can be another list


```{r}
list_a <- list(1,2,"apple")
typeof(list_a)
length(list_a)
str(list_a)

list_b <- list(1, c("apple", "orange"), list(1, 2))
length(list_b)
str(list_b)
```

### Review data structures: lists

Like atomic vectors, elements within a list can be named, thereby creating a __named list__

```{r}
# not named
str(list_b) 

# named
list_c <- list(v1=1, v2=c("apple", "orange"), v3=list(1, 2, 3))
str(list_c) 
```

### Review data structures: a data frame is a list

A __data frame__ is a list with the following characteristics:

- All the elements must be __vectors__ with the same __length__
- Data frames are __augmented lists__ because they have additional __attributes__ [described later]

```{r}
#a regular list
list_d <- list(col_a = c(1,2,3), col_b = c(4,5,6), col_c = c(7,8,9))
typeof(list_d)
str(list_d)

#a data frame
df_a <- data.frame(col_a = c(1,2,3), col_b = c(4,5,6), col_c = c(7,8,9))
typeof(df_a)
str(df_a)
```
## Attributes and augmented vectors

### Atomic vectors versus augmented vectors

__Atomic vectors__ [our focus so far]

- I think of atomic vectors as "just the data"
- Atomic vectors are the building blocks for augmented vectors

__Augmented vectors__

- __Augmented vectors__ are atomic vectors with additional __attributes__ attached

__Attributes__

- __Attributes__ are additional "metadata" that can be attached to any object (e.g., vector or list)
- Examples of some important attributes in R:
    - __Names__: name the elements of a vector (e.g., variable names)
    - __value labels__: character labels (e.g., "Charter School") attached to numeric values
    - __Object class__: How object should be treated by object oriented programming language [discussed below]

__Main takaway__:

- Augmented vectors are atomic vectors (just the data) with additional attributes attached

### Attributes in vectors

Identify attributes in any object using the `attributes()` function

```{r}
#vector with no attributes
vector1 <- c(1,2,3,4)
vector1
attributes(vector1)

#vector with name attributes
vector2 <- c(a = 1, b= 2, c= 3, d = 4)
vector2
attributes(vector2)
```


### Attributes in lists

```{r}
#no attributes
list1 <- list(c(1,2,3), c(4,5,6))
attributes(list1)

#list with attributes
list2 <- list(col_a = c(1,2,3), col_b = c(4,5,6))
str(list2)
attributes(list2)

#data frame with attributes
list3 <- data.frame(col_a = c(1,2,3), col_b = c(4,5,6))
str(list3)
attributes(list3)
```
## Object class

### Object class

\medskip 
Every object in R has a __class__

- Object class defines rules for how object can be treated by object oriented programming language (e.g., which functions you can apply to object)
- class is an __attribute__ of an object

Identify the class of an object using the `class()` function
```{r}
(vector2 <- c(a = 1, b= 2, c= 3, d = 4))
class(vector2)
```

When I encounter a new object I often investigate object by applying `typeof()`, `class()`, and `attributes()` functions to that object
```{r}
vector2
typeof(vector2)
class(vector2)
attributes(vector2)
```

### Object class

Why is __class__ important?

- Specific functions usually work with only particular __classes__ of objects
    - e.g., "date"" functions usually only work on objects with a date class
    - "string" functions usually only work with on objects with a character class
    - Functions that do mathematical computation usually work on objects with a numeric class
- Note: functions care about object __class__, not object __type__

object with `numeric` class (output omitted)
```{r, eval=FALSE}
str(wwlist)

typeof(wwlist$med_inc_zip)
class(wwlist$med_inc_zip)
sum(wwlist$med_inc_zip[1:10], na.rm = TRUE) # numeric function

# load library with date functions
library(lubridate)
#Sys.setenv(TZ="America/Los_Angeles") #setting time zone to Los Angeles time
year(wwlist$med_inc_zip[1:10]) # date function
```
### Object class

Why is __class__ important?

- Specific functions usually work with only particular __classes__ of objects
- Note: functions care about object __class__, not object __type__

Object with `character` class
```{r, eval=FALSE}
str(wwlist$hs_city)
typeof(wwlist$hs_city)
class(wwlist$hs_city)

tolower(wwlist$hs_city[1:10]) # string function
sum(wwlist$hs_city, na.rm = TRUE) # numeric function
```


Object with a date class
```{r, eval=FALSE}
typeof(wwlist$receive_date)
class(wwlist$receive_date)

year(wwlist$receive_date[1:10]) # date function
sum(wwlist$receive_date) # numeric function

```

### Class and object oriented programming

Definition of object oriented programming from this [LINK](https://www.webopedia.com/TERM/O/object_oriented_programming_OOP.html)

> "Object-oriented programming (OOP) refers to a type of computer programming in which programmers define not only the data type of a data structure, but also the types of operations (functions) that can be applied to the data structure."

Object __class__ is fundamental to object oriented programming because:

- object class determines which functions can be applied to the object
- object class also determines what those functions do to the object

Many different object classes exist in R

- we can also create our own classes
- but in this course we will work with classes that have been created by others

## Class == factor

### Factors

__Factors__ are an object _class_ used to display categorical data (e.g., marital status)

- A factor is an __augmented vector__ built by attaching a "levels" attribute to an (atomic) integer vectors

Usually, we would prefer a categorical variable (e.g., race, school type) to be a factor variable rather than a character variable

- So far in the course I have made all categorical variables character variables because we had not introduced factors yet

Below, I'll create a factor version of the character variable `ethn_code` 

- (don't worry about understanding this code; I'll explain it later)
```{r}
str(wwlist$ethn_code)
class(wwlist$ethn_code)
# create factor var; tidyverse approach
wwlist <- wwlist %>% mutate(ethn_code_fac = factor(ethn_code)) 
#wwlist$ethn_code_fac <- factor(wwlist$ethn_code) # base r approach

str(wwlist$ethn_code_fac)
```
### Factors

A factor is an __augmented vector__ built by attaching a "levels" attribute to an (atomic) integer vector

Compare (character) `ethn_code` to (factor) `ethn_code_fac` (output omitted)
```{r, results="hide"}
#character var
typeof(wwlist$ethn_code)
class(wwlist$ethn_code)
str(wwlist$ethn_code)
attributes(wwlist$ethn_code)

#factor var
typeof(wwlist$ethn_code_fac)
class(wwlist$ethn_code_fac)
str(wwlist$ethn_code_fac)
attributes(wwlist$ethn_code_fac)
```

__Main takeaway__

- `ethn_code_fac` has `type=integer` and `class=factor` because the variable has a "levels" attribute
- Underlying data are integers but levels attribute is used to display the data.
```{r}
wwlist$ethn_code_fac[1:4] # print first few obs of ethn_code_fac
```



### Working with factor variables

```{r, results='hide'}
attributes(wwlist$ethn_code_fac)
```
Refer to categories of a factor by the values of the __level attribute__ rather than the underlying values of the variable

__Task__

- count the number of prospects in object `wwlist` who identify as "white"
```{r}
# referring to variable value; this doesn't work
wwlist %>% filter(ethn_code_fac==10) %>% count 

#referring to value of level attribute; this works
wwlist %>% filter(ethn_code_fac=="white") %>% count 

```

### Working with factor variables

__Task__

- count the number of prospects in object `wwlist` who identify as "white"

If you want to refer to underlying values, then apply `as.integer()` function to the factor variable
```{r}
attributes(wwlist$ethn_code_fac)
wwlist %>% filter(as.integer(ethn_code_fac)==10) %>% count 
```
### How to identify the variable values associated with factor levels

Let's create a factor version of the character variable `psat_range`
```{r}
wwlist <- wwlist %>% mutate(psat_range_fac = factor(psat_range)) # create factor var;
```

Run below code in console rather than code chunk to see values associated with each factor
```{r, results="hide"}
wwlist %>% count(psat_range_fac)
attributes(wwlist$psat_range_fac)
levels(wwlist$psat_range_fac) #starts at 1
nlevels(wwlist$psat_range_fac) #7 levels total
levels(wwlist$psat_range_fac)[5] #prints levels 1-3

```

Once you know values associated with factor, you can filter based on values
```{r}
wwlist %>% filter(as.integer(psat_range_fac)==4 | as.integer(psat_range_fac)==5) %>% count()
```

Or you can just filter based on value of __factor levels__
```{r}
wwlist %>% filter(psat_range=="1270-1520") %>% count()
```

### Creating factor variables from character variables or from integer variables

See Appendix

### Factor student exercise   

1. After running the code below, use `typeof`, `class`, `str`, and `attributes` functions to check the new variable `receive_year`  
2. Create a factor variable from the input variable `receive_year` and name it `receive_year_fac`  
3. Run the same functions (`typeof`, `class`, etc.) from the first question using the new variable you created  
4. Get a count of `receive_year_fac`. __hint:__ you could also run this in the console to see values associated with each factor

Run this code to create a year variable from the input variable "receive_date"
```{r, results="hide", message=FALSE}
#wwlist %>% glimpse()

library(lubridate) #load library if you haven't already
wwlist <- wwlist %>%
  mutate(receive_year = year(receive_date)) #creating year variable with the lubridate package

#Check variable
wwlist %>% 
  count(receive_year)

wwlist %>%
  group_by(receive_year) %>% 
  count(receive_date)

```


### Factor student exercise solutions   
1. Use `typeof`, `class`, `str`, and `attributes` functions to check the new variable `receive_year`  
```{r}
typeof(wwlist$receive_year)
class(wwlist$receive_year)
str(wwlist$receive_year)
attributes(wwlist$receive_year) 
```

### Factor student exercise solutions  
2. Now create a factor variable from the input variable `receive_year` and name it `receive_year_fac` 
```{r}
# create factor var; tidyverse approach
wwlist <- wwlist %>%
  mutate(receive_year_fac = factor(receive_year))  

```

### Factor student exercise solutions 
3. Run the same functions (`typeof`, `class`, etc.) from the first question using the new variable you created  
```{r}
typeof(wwlist$receive_year_fac)
class(wwlist$receive_year_fac)
str(wwlist$receive_year_fac)
attributes(wwlist$receive_year_fac)   
```

### Factor student exercise solutions 
4. Get a count of `receive_year_fac`. __hint:__ you could also run this in the console to see values associated with each factor
```{r}
wwlist %>%
  count(receive_year_fac)
```


## Class == labelled

### Data we will use to introduce `labelled` class

High school longitudinal surveys from National Center for Education Statistics (NCES)

- Follow U.S. students from high school through college, labor market

We will be working with [High School Longitudinal Study of 2009 (HSLS:09)](https://nces.ed.gov/surveys/hsls09/index.asp)

- Follows 9th graders from 2009
- Data collection waves

      - Base Year (2009)
      - First Follow-up (2012)
      - 2013 Update (2013)
      - High School Transcripts (2013-2014)
      - Second Follow-up (2016)    

### `haven` package

[`haven`](https://haven.tidyverse.org/), which is part of __tidyverse__, "enables R to read and write various data formats" from the following statistical packages: 

- SAS
- SPSS
- Stata

When using `haven` to read data, resulting R objects have these characteristics:

- Are __tibbles__, a particular type of data frame we discuss in future weeks
- Transform variables with "value labels" into the `labelled()` class [our focus today]
    - `labelled` is an object __class__ created by folks who created `haven` package
    - `labelled` is an object class, just like `factor` is an object class
    - `labelled` and `factor` classes are both viable alternatives for categorical variables
    - Helpful description of `labelled` class  [HERE](https://haven.tidyverse.org/articles/semantics.html)
- Dates and times converted to R date/time classes
- Character vectors not converted to factors

### `haven` package

Use `read_dta()` function from `haven` to import Stata dataset into R
```{r, results="hide"}
hsls <- read_dta(file="https://github.com/ozanj/rclass/raw/master/data/hsls/hsls_stu_small.dta")
```

Let's examine the data [you __must__ run this code chunk]
```{r, results="hide"}
names(hsls)
names(hsls) <- tolower(names(hsls)) # convert names to lowercase
names(hsls)

str(hsls) # ugh

str(hsls$s3classes)
attributes(hsls$s3classes)
typeof(hsls$s3classes)
class(hsls$s3classes)
```

### `labelled` package

Purpose of the `labelled` package is to work with data imported from SPSS/Stata/SAS using the `haven` package. 

- In particular, `labelled` package creates functions to work with objects that have `labelled` class
- From package documentation: "purpose of the `labelled` package is to provide functions to manipulate _metadata_ as variable labels, value labels and defined missing values using the `labelled` class and the `label` attribute introduced in `haven` package.
- More info on the `labelled` package: [LINK](https://cran.r-project.org/web/packages/labelled/vignettes/intro_labelled.html)

Functions in `labelled` package

- [Full list](https://www.rdocumentation.org/packages/labelled/versions/1.1.0)
- A couple relevant functions
    - `val_labels`: get or set variable _value labels_
    - `var_label`: get or set a _variable label_

```{r, results="hide"}
attributes(hsls$s3classes)

hsls %>% select(s3classes) %>% var_label()
hsls %>% select(s3classes) %>% val_labels()
```
<!-- ### Core concepts for understanding `labelled` class [SKIP] -->

<!-- __atomic vectors (and lists)__ the underlying data -->

<!-- - data structures: vector or list -->
<!-- - data type: numeric (integer or double); character; logical -->
<!-- ```{r} -->
<!-- typeof(hsls$s3classes) -->
<!-- ``` -->

<!-- __augmented vectors__ are atomic vectors with __attributes__ attached -->

<!-- __attributes__ are "metadata" attached to an object. Examples -->

<!-- - __names__: names of elements of a vector or list (e.g., variable names) -->
<!-- - __levels__: display output associated with values of a factor variable -->
<!-- - __class__: e.g., factor, labelled -->

<!-- ```{r, results="hide"} -->
<!-- attributes(hsls$s3classes) -->
<!-- ``` -->
<!-- __class__ is an object oriented programming concept. The `class` of an object determines which functions can be applied to the object and what those functions do -->

<!-- - e.g., can't apply `sum()` to an object where `class=character` -->

### What is `labelled` class?

\medskip

- `labelled` is an object class created by the `haven` package for importing variables from SAS/SPSS/Stata that have __value labels__
- __value labels__ [in Stata] are labels attached to specific values of a variable:
    - e.g., variable value `1` attached to value label "married", `2`="single", `3`="divorced"
- Variables in an R data frame with `class==labelled`:
    - data `type` can be numeric(double) or character
    - To see `value labels` associated with each value:
        - `attr(data_frame_name$variable_name,"labels")`
        - e.g., `attr(hsls$s3classes,"labels")`

Let's investigate the attributes of `hsls$s3classes`
```{r, results="hide"}
typeof(hsls$s3classes)
class(hsls$s3classes)
str(hsls$s3classes)
attributes(hsls$s3classes)
```
use `attr(object_name,"attribute_name")` to refer to each attribute
```{r, results="hide"}
attr(hsls$s3classes,"label")
attr(hsls$s3classes,"labels")
attr(hsls$s3classes,"class")
attr(hsls$s3classes,"format.stata")
```

### Working with `labelled` class data

\medskip

Show variable labels (`var_label`); and show value labels (`val_labels`)
```{r, results="hide"}
hsls %>% select(s3classes,s3clglvl) %>% var_label #show variable label
hsls %>% select(s3classes,s3clglvl) %>% val_labels #show value labels
```

Create frequency tables with `labelled` class variables using `count()`

- Default setting is to show variable __values__ not __value labels__
```{r, results="hide"}
hsls %>% count(s3classes)
#investigate the object created
hsls_freq_temp <- hsls %>% count(s3classes)
hsls_freq_temp
rm(hsls_freq_temp)
```

To make frequency table show __value labels__ add `%>% as_factor()` to pipe

- `as_factor()` is function from `haven` that converts an object to a factor
```{r, results="hide"}
hsls %>% count(s3classes) %>% as_factor()
#investigate the object created
hsls_freq_temp <- hsls %>% count(s3classes)  %>% as_factor()
hsls_freq_temp
rm(hsls_freq_temp)
```
### Working with `labelled` class data

To isolate values of `labelled` class variables in `filter()` function:

- refer to variable __value__, not the __value label__

__Task__ 

- how many observations in var `s3classes` associated with "Unit non-response"
- how many observations in var `s3classes` associated with "Yes"

General steps to follow: 

1. investigate object
1. use filter to isolate desired observations

Investigate object
```{r, results="hide"}
class(hsls$s3classes)
hsls %>% select(s3classes,s3clglvl) %>% var_label #show variable label
hsls %>% select(s3classes,s3clglvl) %>% val_labels #show value label
hsls %>% count(s3classes) # freq table, values
hsls %>% count(s3classes) %>% as_factor() # freq table, value labels

```

filter specific values
```{r, results="hide"}
hsls %>% filter(s3classes==-8) %>% count() # -8 = unit non-response
hsls %>% filter(s3classes==1) %>% count() # 1 = yes
```

### Labelled student exercise

1. Get variable and value labels of `s3hs`  
2. Get a count of the variable showing the values and the value labels. __hint__ use factor()  
3. Filter if value is associated with "Missing"  
4. Filter if value is associated with "Missing" or "Unit non-response"

### Labelled student exercise solutions
1. Get variable and value labels of `s3hs` 
```{r}
hsls %>% 
  select(s3hs) %>% 
  var_label() 

hsls %>% 
  select(s3hs) %>% 
  val_labels()
```

### Labelled student exercise solutions
2. Get a count of the variable `s3hs` showing the value labels. __hint__ use factor()

```{r}
hsls %>% 
  count(s3hs) 

hsls %>% 
  count(s3hs) %>% 
  as_factor() 

```

### Labelled student exercise solutions
3. Filter if value is associated with "Missing"
```{r}
hsls %>%
  filter(s3hs== -9) %>% 
  count()
```

### Labelled student exercise solutions  
4. Filter if value is associated with "Missing" or "Unit non-response"
```{r}
hsls %>%
  filter(s3hs== -9 | s3hs== -8) %>% 
  count()
```


## Comparing labelled class to factor class

### Comparing `class==labelled` to `class==factor`

|  | `class==labelled` | `class==factor`
|---|----------|-------------|
| data type  |    numeric or character   | integer |
| name of value label attribute | labels | levels |
| refer to data using | variable values | levels attribute |

    
### Converting `class==labelled` to `class==factor`

The `as_factor()` function from `haven` package converts variables with `class==labelled` to `class==factor`

- Can be used for descriptive statistics
```{r, results="hide"}
hsls %>% select(s3classes) %>% count(s3classes) %>% as_factor()
```

- Can create object with some or all `labelled` vars converted to `factor`
```{r}
hsls_f <- as_factor(hsls,only_labelled = TRUE)
```

Let's examine this object
```{r, results="hide"}
glimpse(hsls_f)
hsls_f %>% select(s3classes,s3clglvl) %>% str()
typeof(hsls_f$s3classes)
class(hsls_f$s3classes)
attributes(hsls_f$s3classes)

hsls_f %>% select(s3classes) %>% var_label()
hsls_f %>% select(s3classes) %>% val_labels()
```

### Working with `class==factor` data

Showing values associated with factor levels
```{r}
hsls_f %>% count(s3classes)
```

In code, refer `level` attribute not variable value
```{r}
hsls_f %>% filter(s3classes=="Yes") %>% count(s3classes)
```


# Creating factor variables

### Create factors [from string variables]

To create a factor variable from string variable

1. create a character vector containing underlying data
1. create a vector containing valid levels
3. Attach levels to the data using the `factor()` function

```{r}
#underlying data: months my fam is born
x1 <- c("Jan", "Aug", "Apr", "Mar")
#create vector with valid levels
month_levels <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", 
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
#attach levels to data
x2 <- factor(x1, levels = month_levels)
```
Note how attributes differ
```{r}
str(x1)
str(x2)
```
Sorting differs
```{r}
sort(x1)
sort(x2)
```

### Create factors [from string variables]

Let's create a character version of variable `hs_state` and then turn it into a factor

```{r, eval=FALSE}
#wwlist %>%
#  count(hs_state)
#Subset obs to West Coast states 
wwlist_temp <- wwlist %>%
  filter(hs_state %in% c("CA", "OR", "WA"))

#Create character version of high school state for West Coast states only
wwlist_temp$hs_state_char <- as.character(wwlist_temp$hs_state)

#investigate character variable
str(wwlist_temp$hs_state_char)
class(wwlist_temp$hs_state_char)
table(wwlist_temp$hs_state_char)

#create new variable that assigns levels
wwlist_temp$hs_state_fac <- factor(wwlist_temp$hs_state_char, levels = c("CA","OR","WA"))
str(wwlist_temp$hs_state_fac)
attributes(wwlist_temp$hs_state_fac)

#wwlist_temp %>%
#  count(hs_state_fac)
rm(wwlist_temp)

wwlist$hs_state_fac <- as_factor(wwlist_temp$hs_state_char)


```

### Create factors [from string variables]
How the `levels` argument works when underlying data is character

- Matches value of underlying data to value of the level attribute
- Converts underlying data to integer, with level attribute attached

\medskip See chapter 15 of Wickham for more on factors (e.g., modifying factor order, modifying factor levels)

### Creating factors [from integer vectors]

Factors are just integer vectors with level attributes attached to them. So, to create a factor:

1. create a vector for the underlying data
1. create a vector that has level attributes
3. Attach levels to the data using the `factor()` function

```{r}
a1 <- c(1,1,1,0,1,1,0) #a vector of data
a2 <- c("zero","one") #a vector of labels

#attach labels to values
a3 <- factor(a1, labels = a2)
a3
str(a3)

```

Note: By default, `factor()` function attached "zero" to the lowest value of vector `a1` because "zero" was the first element of vector `a2`

### Creating factors [from integer vectors]

Let's turn an integer variable into a factor variable in the `wwlist` data frame

Create integer version of `receive_year`
```{r}
#typeof(wwlist_temp$receive_year)

wwlist_temp <- wwlist %>%
  filter(zip5 %in% c("98103", "98030", "98290"))

wwlist_temp$zip_int <- as.integer(wwlist_temp$zip5)
str(wwlist_temp$zip_int)
typeof(wwlist_temp$zip_int)
```


### Creating factors [from integer vectors]


Assign levels to values of integer variable 
```{r}

# variable zip_int is coded 98103, 98030 or 98290
# we want to attach value labels 98103=nine-eight-one-zero-three, 98030=nine-eight-zero-three-zero, 98290=nine-eight-two-nine-zero


wwlist_temp$zip_fac <- factor(wwlist_temp$zip_int, 
      levels = c(98103, 98030, 98290),
      labels = c("nine-eight-one-zero-three", "nine-eight-zero-three-zero", "nine-eight-two-nine-zero"))

str(wwlist_temp$zip_fac)
str(wwlist_temp$zip5)

#Check variable
wwlist_temp %>%
  count(zip_fac)

wwlist_temp %>%
  count(zip_int)
```