Advanced quantitative data analysis

class: center, middle, inverse, title-slide

.title[
# Advanced quantitative data analysis
]
.subtitle[
## R basic II
]
.author[
### Mengni Chen
]
.institute[
### Department of Sociology, University of Copenhagen
]

---

#Packages today
- package used in this session
  - tidyverse
  - haven
  - janitor

```r
#we have installed "tidyverse" and "haven" last week
#now we need to install "janitor"
install.packages("janitor")
```

```r
#let use use the two package
library(tidyverse) 
#check packages embedded in the tidyverse: https://www.tidyverse.org/packages/
library(haven) # Read and handle SPSS, Stata & SAS data (no need to install)
library(janitor) #to provide some basic tabulations
```

---
#Import data

```r
# Create an object pairfam and assign the imported anchor1_50percent.dta to it
pairfam <- read_dta("anchor1_50percent_Eng.dta")
pairfam
```

```
## # A tibble: 6,201 × 1,458
##           id demodiff   wave    sample      pid parentidk1 parentidk2 parentidk3
##        <dbl> <dbl+lbl>  <dbl+l> <dbl+l>   <dbl> <dbl+lbl>  <dbl+lbl>  <dbl+lbl> 
##  1 267206000 0 [0 non-… 1 [1 2… 1 [1 p… NA             NA         NA  NA        
##  2 112963000 0 [0 non-… 1 [1 2… 1 [1 p… NA             NA         NA  NA        
##  3 327937000 0 [0 non-… 1 [1 2… 1 [1 p…  3.28e8        NA         NA  NA        
##  4 318656000 0 [0 non-… 1 [1 2… 1 [1 p…  3.19e8 318656101         NA  NA        
##  5 717889000 0 [0 non-… 1 [1 2… 1 [1 p…  7.18e8 717889101  717889101  NA        
##  6 222517000 0 [0 non-… 1 [1 2… 1 [1 p… NA             NA         NA  NA        
##  7 144712000 0 [0 non-… 1 [1 2… 1 [1 p… NA             NA         NA  NA        
##  8 659357000 0 [0 non-… 1 [1 2… 1 [1 p…  6.59e8        NA         NA  NA        
##  9 506367000 0 [0 non-… 1 [1 2… 1 [1 p…  5.06e8 506367101         NA  NA        
## 10  64044000 0 [0 non-… 1 [1 2… 1 [1 p… NA             NA         NA  NA        
## # ℹ 6,191 more rows
## # ℹ 1,450 more variables: parentidk4 <dbl+lbl>, parentidk5 <dbl+lbl>,
## #   parentidk6 <dbl+lbl>, parentidk7 <dbl+lbl>, parentidk8 <dbl+lbl>,
## #   parentidk9 <dbl+lbl>, parentidk10 <dbl+lbl>, parentidk11 <dbl+lbl>,
## #   parentidk12 <dbl+lbl>, parentidk13 <dbl+lbl>, parentidk14 <dbl+lbl>,
## #   parentidk15 <dbl+lbl>, sex_gen <dbl+lbl>, psex_gen <dbl+lbl>,
## #   k1sex_gen <dbl+lbl>, k2sex_gen <dbl+lbl>, k3sex_gen <dbl+lbl>, …
```
---
#Address single variable using **$** 
--

```r
# use $ to locate variable "age" 
pairfam$age #return the variable
```

```r
# Give a summary of numeric vector age contained in Dat.
summary(pairfam$age)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.00   17.00   26.00   25.84   35.00   38.00
```

---
#Address single variable using **$**

```r
# summarise a factor vector,i.e. a categorical variable sex_gen(variable name for gender).
summary(pairfam$sex_gen)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   1.000   2.000   1.512   2.000   2.000
```

```r
table(pairfam$sex_gen)
```

```
## 
##    1    2 
## 3029 3172
```
--
why R seems to treat sex_gen(var for gender) like a continuous variable? because it saves a lot space to store information in numbers.

---
#Address single variable using **$** 
make sure that you treat categorical variables as factor in R

```r
pairfam$gender <- as_factor(pairfam$sex_gen)# make it as a factor
tabyl(pairfam$gender) #or you can use table()
```

```
##                     pairfam$gender    n   percent
##                -10 not in demodiff    0 0.0000000
##                 -7 Incomplete data    0 0.0000000
##  -4 Filter error / Incorrect entry    0 0.0000000
##                  -3 Does not apply    0 0.0000000
##                             1 Male 3029 0.4884696
##                           2 Female 3172 0.5115304
```

---
#Selection in R: 3 situations
- Situation 1: selecting variables, using `select()`
- Situation 2: build a subset of data based on values of certain variables, using `filter()`
- Situation 1+2: using `[,]`
- Situation 3: generate or  recode variables based on certain conditions, using `case_when()`

---
#Situation 1: use `select()` to select several variables
Select three variables to make a small dataset

```r
#select(dataset, var1, var2, var3,etc.)
pairfam1 <- select(pairfam, id, age, gender)
pairfam1
```

```
## # A tibble: 6,201 × 3
##           id age       gender  
##        <dbl> <dbl+lbl> <fct>   
##  1 267206000 16        2 Female
##  2 112963000 35        1 Male  
##  3 327937000 16        2 Female
##  4 318656000 27        2 Female
##  5 717889000 37        1 Male  
##  6 222517000 15        1 Male  
##  7 144712000 16        2 Female
##  8 659357000 17        2 Female
##  9 506367000 37        1 Male  
## 10  64044000 15        2 Female
## # ℹ 6,191 more rows
```

---
#`select()` to drop several variables

--
.pull-left[
drop variable age

```r
# remove gender
pairfam2 <- select(pairfam1, -gender)
pairfam2
```

```
## # A tibble: 6,201 × 2
##           id age      
##        <dbl> <dbl+lbl>
##  1 267206000 16       
##  2 112963000 35       
##  3 327937000 16       
##  4 318656000 27       
##  5 717889000 37       
##  6 222517000 15       
##  7 144712000 16       
##  8 659357000 17       
##  9 506367000 37       
## 10  64044000 15       
## # ℹ 6,191 more rows
```
]

.pull-right[
if you have more variables to drop

```r
# drop age and id
(pairfam3 <- select(pairfam1, -c(id,age)))
```

```
## # A tibble: 6,201 × 1
##    gender  
##    <fct>   
##  1 2 Female
##  2 1 Male  
##  3 2 Female
##  4 2 Female
##  5 1 Male  
##  6 1 Male  
##  7 2 Female
##  8 2 Female
##  9 1 Male  
## 10 2 Female
## # ℹ 6,191 more rows
```
]
---
# Situation 2: use `filter()` to select observations based on values in certain variables

```r
#filter(dataset, condition1 & condition2)
data_above30<- filter(pairfam1, age>30)
#keep respondents whose age is >30.
data_above30
```

```
## # A tibble: 2,015 × 3
##           id age       gender  
##        <dbl> <dbl+lbl> <fct>   
##  1 112963000 35        1 Male  
##  2 717889000 37        1 Male  
##  3 506367000 37        1 Male  
##  4 408364000 36        1 Male  
##  5 597606000 36        1 Male  
##  6 505725000 38        2 Female
##  7  19046000 35        2 Female
##  8  84655000 36        2 Female
##  9  36945000 35        2 Female
## 10 690012000 37        1 Male  
## # ℹ 2,005 more rows
```

---
# Situation 2: use `filter()` to select observations based on values in certain variables

```r
levels(pairfam1$gender)#check how female is coded,"2 Female"
```

```
## [1] "-10 not in demodiff"               "-7 Incomplete data"               
## [3] "-4 Filter error / Incorrect entry" "-3 Does not apply"                
## [5] "1 Male"                            "2 Female"
```

```r
data_female<- filter(pairfam1, gender=="2 Female")
data_female
```

```
## # A tibble: 3,172 × 3
##           id age       gender  
##        <dbl> <dbl+lbl> <fct>   
##  1 267206000 16        2 Female
##  2 327937000 16        2 Female
##  3 318656000 27        2 Female
##  4 144712000 16        2 Female
##  5 659357000 17        2 Female
##  6  64044000 15        2 Female
##  7 252002000 25        2 Female
##  8 215305000 17        2 Female
##  9 107033000 17        2 Female
## 10 505725000 38        2 Female
## # ℹ 3,162 more rows
```

---
#Situation 1+2: using `[]`

```r
name <- c("Agnes", "Martin", "Hakan", "Tu", "Thais")
age <- c(34, 22, 42, 12, 76)
conti <- factor(x = c("Europe", "Africa", "Africa", "Asia", "S. America"),
                 levels = c("Africa", "Asia", "Australia", "Europe", "N. America", "S. America"))
employed <- c(FALSE, TRUE, TRUE, TRUE, TRUE)
example <- data.frame(name,age,conti, employed)
example <- as_tibble(example)#a more user-friendly way of showing data
example
```

```
## # A tibble: 5 × 4
##   name     age conti      employed
##   <chr>  <dbl> <fct>      <lgl>   
## 1 Agnes     34 Europe     FALSE   
## 2 Martin    22 Africa     TRUE    
## 3 Hakan     42 Africa     TRUE    
## 4 Tu        12 Asia       TRUE    
## 5 Thais     76 S. America TRUE
```
---
#Situation 1+2: using `[,]`
.pull-left[

```r
example
```

```r
(example1 <- example[1, 3])
```

```
## # A tibble: 1 × 1
##   conti 
##   <fct> 
## 1 Europe
```
]

.pull-right[

```r
# Or use the index, to achieve the same.
(example2  <- example[1, ])
```

```
## # A tibble: 1 × 4
##   name    age conti  employed
##   <chr> <dbl> <fct>  <lgl>   
## 1 Agnes    34 Europe FALSE
```

```r
(example3 <- example[, 3])
```

```
## # A tibble: 5 × 1
##   conti     
##   <fct>     
## 1 Europe    
## 2 Africa    
## 3 Africa    
## 4 Asia      
## 5 S. America
```
]

---
#Situation 1+2: using `[rows, column]`

```r
#select variables
(example4 <- example[, c("age","conti")]) # [,"the column name to select"]
```

```
## # A tibble: 5 × 2
##     age conti     
##   <dbl> <fct>     
## 1    34 Europe    
## 2    22 Africa    
## 3    42 Africa    
## 4    12 Asia      
## 5    76 S. America
```
--

```r
#select observations based on values of certain variables
(example5 <- example[age>40, ]) 
```

```
## # A tibble: 2 × 4
##   name    age conti      employed
##   <chr> <dbl> <fct>      <lgl>   
## 1 Hakan    42 Africa     TRUE    
## 2 Thais    76 S. America TRUE
```
---
#Situation 1+2: using `[,]` 
use `[conditions,"var name"]` to select

```r
example
```

```r
(example6 <- example[age>40,c("age","conti")])#keep those aged>40 but only select two vars.
```

```
## # A tibble: 2 × 2
##     age conti     
##   <dbl> <fct>     
## 1    42 Africa    
## 2    76 S. America
```
---
#Generate or recode variables
To transform and recode simply use `$` to clarify which var from which dataset you are referring to.

```r
# Center age around the average age.
example$age_centered <- example$age - mean(example$age)
# Recode "Africa" to "Afrika!".
example$conti1 <- fct_recode(example$conti, "Afrika!" = "Africa")
example
```

```
## # A tibble: 5 × 6
##   name     age conti      employed age_centered conti1    
##   <chr>  <dbl> <fct>      <lgl>           <dbl> <fct>     
## 1 Agnes     34 Europe     FALSE           -3.20 Europe    
## 2 Martin    22 Africa     TRUE           -15.2  Afrika!   
## 3 Hakan     42 Africa     TRUE             4.8  Afrika!   
## 4 Tu        12 Asia       TRUE           -25.2  Asia      
## 5 Thais     76 S. America TRUE            38.8  S. America
```
---
#Generate or recode variables
If you want to transform and recode several variables at the same time, you can use `mutate()`.

```r
example_new1 <- mutate(
  example, # Use the data "example".
  age_z = age_centered / sd(age), # z-standardize age.
  conti2= fct_recode(conti,       # recode gender to gender2
                     "Europa!" = "Europe", # "Europe" to "Europa!".
                     "Asien!" = "Asia")    # "Asia" to "Asien!".
                     ) # Don't forget to close mutate's bracket ")"
example_new1
```

```
## # A tibble: 5 × 8
##   name     age conti      employed age_centered conti1      age_z conti2    
##   <chr>  <dbl> <fct>      <lgl>           <dbl> <fct>       <dbl> <fct>     
## 1 Agnes     34 Europe     FALSE           -3.20 Europe     -0.131 Europa!   
## 2 Martin    22 Africa     TRUE           -15.2  Afrika!    -0.620 Africa    
## 3 Hakan     42 Africa     TRUE             4.8  Afrika!     0.196 Africa    
## 4 Tu        12 Asia       TRUE           -25.2  Asia       -1.03  Asien!    
## 5 Thais     76 S. America TRUE            38.8  S. America  1.58  S. America
```
<span style="color:red;">**Attention: look how RStudio structures the brackets!**

---
#Situation 3: generate or  modify variables based on certain conditions
To transform/recode only among certain cases, use `case_when()`.

```r
example_new2 <- mutate(
  example, # Mutate variables contained in example data.
    work=NA,#create var work and make it missing in the begining
    work = case_when( # Start conditional recode of work,
      employed == FALSE  ~ "not working",  # 1. your condition ~ new value "not working" ,
      employed == TRUE  ~ "working" # 2. your condition ~ new value "working",              
                    ) # close case_when's bracket.
                      )
example_new2
```

```
## # A tibble: 5 × 7
##   name     age conti      employed age_centered conti1     work       
##   <chr>  <dbl> <fct>      <lgl>           <dbl> <fct>      <chr>      
## 1 Agnes     34 Europe     FALSE           -3.20 Europe     not working
## 2 Martin    22 Africa     TRUE           -15.2  Afrika!    working    
## 3 Hakan     42 Africa     TRUE             4.8  Afrika!    working    
## 4 Tu        12 Asia       TRUE           -25.2  Asia       working    
## 5 Thais     76 S. America TRUE            38.8  S. America working
```

---
#Situation 3: generate or  modify variables based on certain conditions
To transform/recode only among certain cases, use `case_when()`.

```r
example_new3 <- mutate(example, # dataset
      conti = case_when( # Start conditional recode of conti,
         employed == FALSE  ~ "Atlantis", # 1. complex condition ~ new value "Atlantis",
         age < 25 & employed == TRUE  ~ "Antarctica") # 2. complex condition ~ new value "Antarctica"
                      )
example_new3
```

---
##Conditional transform & recode 
Let us compare the following with the previous slide

```r
example_new3 <- mutate(example, # dataset
      conti = case_when( # Start conditional recode of conti,
         employed == FALSE  ~ "Atlantis", # 1. complex condition ~ new value "Atlantis",
         age < 25 & employed == TRUE  ~ "Antarctica", # 2. complex condition ~ new value "Antarctica",            
         TRUE ~ as.character(conti)) # close case_when's bracket.
                      )
example_new3
```

```
## # A tibble: 5 × 6
##   name     age conti      employed age_centered conti1    
##   <chr>  <dbl> <chr>      <lgl>           <dbl> <fct>     
## 1 Agnes     34 Atlantis   FALSE           -3.20 Europe    
## 2 Martin    22 Antarctica TRUE           -15.2  Afrika!   
## 3 Hakan     42 Africa     TRUE             4.8  Afrika!   
## 4 Tu        12 Antarctica TRUE           -25.2  Asia      
## 5 Thais     76 S. America TRUE            38.8  S. America
```
---
#Conditional transform & recode 
Let us see what the three lines of code in `case_when()` do
1. For all non-employed ~ recode conti to "Atlantis".
2. For all those who're aged< 25 and employed ~ recode conti to "Antarctica".
3. All remaining cases ~ use their original conti values, but transform them to a character vector.

Remember, R is class sensitive! It will not combine numeric information into a character vector. Because we give case_when() "Atlantis" and "Antarctica" as new values, it assumes that we want to make conti a character vector.

```r
  example_new3 <- mutate(
  example, # Mutate variables contained in Dat.
    conti = case_when( # Start conditional recode of conti,
      employed == FALSE  ~ "Atlantis", # 1. complex condition ~ new value "Atlantis",
      age < 25 & employed == TRUE  ~ "Antarctica", # 2. complex condition ~ new value "Antarctica",            
      TRUE ~ as.character(conti)
                     ) # close case_when's bracket.
                 )
```
---
#A task to do with pairfam data now?
Task: 
- 1) Construct a new dataset "small_pf" only containing variables: id, age, sex_gen, cohort, sat6; 
- 2) Create gender and cohort_group variables as factors to represent sex_gen and cohort as categorical.
- 3) Create a new variable called sat_missing: when sat6 is valid answer, use the original value; when sat6 is negative make it NA

Note: sat6 asks the general life satisfaction (0=very unsatisfied, 10=very satisfied)

How will you handle this task? What kind of codes will you use?
---
#A task to do with pairfam data now?
What we can do:

```r
#Step1: import data
pairfam <- read_dta("anchor1_50percent_Eng.dta") 
#Step2: select variables and create a dataset named small_pf using select()
small_pf <- select(pairfam, 
                 id, 
                 age, 
                 sex_gen, 
                 cohort,
                 sat6) #select chozen variables
#Step3: treat categorical variables as factor
small_pf1 <- mutate(small_pf,
                 gender=as_factor(sex_gen),#make gender as a factor
                 cohort_group=as_factor(cohort) #make cohort_group as a factor
                )
#Step4: create sat_missing
small_pf2<- mutate(small_pf1,
                sat_missing=case_when(
                             sat6<0 ~ NA, #replace sat6 with NA when sat6<0
                             TRUE ~ sat6)
                   ) 
```
---
#Is there any more efficient way to do? Use pipe `%>%`
- If we do the task step by step as the previous slide shows, you will create 3 dataset as intermediate dataset.
- But, we can use pipe `%>%` to make it more efficient.
- The `%>%` operator pipes output of one function as input to the next function. You can basically say: `function(argument1 = value)` can be written as `value %>% function()`.

Or even easier, think of it as: "then"

```r
mean(sqrt(seq(from = 1, to = 13)))
```

```
## [1] 2.527274
```

```r
#use pipe
seq(from = 1, to = 13) %>% sqrt() %>% mean()
```

```
## [1] 2.527274
```
---
#The (forward) pipe ` %>% `
- Shortcut buttons for typing `%>%`: Windows: Ctrl+Shift+M; Mac: Cmd+Shift+M

```r
# Example 1: round(x=., digits =.)
#Both lines of code round 5.882 to have only one digit.
round(x = 5.882, digits = 1) 
```

```
## [1] 5.9
```

```r
5.882 %>% round(digits = 1)
```

```
## [1] 5.9
```

```r
#Example 2
2 %>% round(x = 5.882, digits = .)
```

```
## [1] 5.88
```
By default, %>% pipes into the first argument of a function. The . placeholder allows us to pipe into another argument.
---
# What pipe looks like?
.pull-left[
<img src="https://merlin-intro-r.netlify.app/5-piping/img/PipedEgg.jpg" width="190%" style="display: block; margin: auto;" >
]

.pull-right[
<img src="https://merlin-intro-r.netlify.app/5-piping/img/PipedEgg2.png" width="690%" style="display: block; margin: auto;">
]

---
#Advantages of piping & when not to pipe
- The advantages are
  - Legible code: We can structure code from left to right, as opposed to from inside and out.
  - Shorter code: You minimize the need for local/intermediate variables.
  - Easily mutable code: You can easily add steps anywhere in the sequence of operations.
  
- When not to pipe
  - If you have more than one or two major inputs, don't pipe.
  - If you have more than  ten steps, better make intermediate object.
  It helps you to debug (i.e., find mistakes) and is simply easier to read.
  
  
[Example video](https://www.youtube.com/watch?v=sohARFx6aTo)

---
#Use pipe `%>%` to do the task
But now: way 2 using ` %>%`

```r
#Step1: import data
pairfam <- read_dta("anchor1_50percent_Eng.dta")

#Step2: to make it all done
small_pf <- pairfam %>%
  select(id, 
         age, 
         sex_gen,
         cohort,
         sat6) %>%
  mutate(gender=as_factor(sex_gen), #make gender as a factor;
         cohort_group=as_factor(cohort), #make cohort_group as a factor;
         sat_missing=case_when(
                     sat6<0 ~ NA, #replace sat6 with NA when sat6<0
                     TRUE ~ sat6)
                   ) 
```
---
#Use pipe `%>%` and `transmute()` to do the task
- Oooh, what is `transmute()`?

```r
#compare data1 and data2
data1 <- mutate(pairfam,
                gender=as_factor(sex_gen), #make gender as a factor;
                cohort_group=as_factor(cohort) #make cohort_group as a factor; 
                )
data2 <- transmute(pairfam,
                gender=as_factor(sex_gen), #make gender as a factor;
                cohort_group=as_factor(cohort) #make cohort_group as a factor;
                )
```
`transmute()` only keep variables you specify in the `transmute()`
[Difference between `mutate()` and `transmute()`](https://www.youtube.com/watch?v=vvIhRginelA)
---
#Use pipe `%>%` and `transmute()` to do the task
But now: way 3 using ` %>%` and `transmute()`

```r
#Step1: import data
pairfam <- read_dta("anchor1_50percent_Eng.dta") 
#Step2: to make it all done
small_pf <- pairfam %>%
transmute(id, 
         age, 
         sex_gen, 
         cohort,
         sat6,
         gender=as_factor(sex_gen), #make sex_gen as a factor, named gender
         cohort_group=as_factor(cohort), #make cohort as a factor, named cohort_group
         sat_missing=case_when(
                     sat6<0 ~ NA, #replace sat6 with NA when sat6<0
                     TRUE ~ sat6)
          ) 
```

---
#Try another task using `%>%`
Task: generate a nice two-way table on cohort_group and gender

```r
tabyl(small_pf, cohort_group, gender)#tabyl(dataset, var1, var2)
```

```
##                      cohort_group -10 not in demodiff -7 Incomplete data
##                -7 Incomplete data                   0                  0
##  0 former capikid first interview                   0                  0
##                       1 1991-1993                   0                  0
##                       2 1981-1983                   0                  0
##                       3 1971-1973                   0                  0
##                       4 2001-2003                   0                  0
##     9 former capikid re-interview                   0                  0
##  -4 Filter error / Incorrect entry -3 Does not apply 1 Male 2 Female
##                                  0                 0      0        0
##                                  0                 0      0        0
##                                  0                 0   1112     1061
##                                  0                 0   1000     1013
##                                  0                 0    917     1098
##                                  0                 0      0        0
##                                  0                 0      0        0
```
oooh, no! Not really can be said "nice"!

---
#Try another task using `%>%`
.pull-left[

```r
small_pf %>% 
  mutate(
    gender=fct_drop(gender),
    cohort_group=fct_drop(cohort_group)
  )%>%
  tabyl(cohort_group, gender)
```

```
##  cohort_group 1 Male 2 Female
##   1 1991-1993   1112     1061
##   2 1981-1983   1000     1013
##   3 1971-1973    917     1098
```
]
.pull-right[

```r
small_pf %>% 
  mutate(gender=fct_drop(gender),
        cohort_group=fct_drop(cohort_group))%>%
  tabyl(cohort_group, gender)%>%
  adorn_totals("row") %>% #add row total
  adorn_percentages("row") %>% #add row %
  adorn_pct_formatting() %>% #format the percentage
  adorn_ns(position="front")%>% #add absolute n in front
  adorn_title() %>% #add title
  knitr::kable() #generate a table
```

|             |gender        |              |
|:------------|:-------------|:-------------|
|cohort_group |1 Male        |2 Female      |
|1 1991-1993  |1,112 (51.2%) |1,061 (48.8%) |
|2 1981-1983  |1,000 (49.7%) |1,013 (50.3%) |
|3 1971-1973  |917 (45.5%)   |1,098 (54.5%) |
|Total        |3,029 (48.8%) |3,172 (51.2%) |
]

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#Take home
1. The function of `$` for identifying a variable in a dataset
2. Three situations of selection in R
  - select() to select several variables
  - filter() to select observations based on values in certain variables
  - [] can also be used for select both variables and observations
  - case_when() to recode or create variables based on conditions
4. Use pipe ` %>% ` to make coding more efficient

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# Important code
- as_tibble(): take a data frame and make it a Tibble.
- summary(): Give a summary of an object.
- select(): select several variables from a data frame/Tibble.
- filter(): filter cases based on values in certain variables.
- case_when(): Conditional recode for cases filtered in complex ways.
- mutate(): Adds new variables and preserves existing. Good for recoding several variables.
- transmute(): similar to mutate but only keep variables you specify in the ()
- as_factor(): Make a labelled Stata/SPSS variable a factor.
- tabyl(): Make a two-way table

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class: center, middle
#[Exercise](https://rpubs.com/fancycmn/1343533)