Week 8: Apply it to your data 7

Import your data

setwd("~/Desktop/PSU_DAT3000_IntroToDA/05_module8/data/")
data <- read_excel("My_Data.xlsx")
data

## # A tibble: 1,302 × 9
##    Language   Endonym `World Region` Country `Global Speakers` `Language Family`
##    <chr>      <chr>   <chr>          <chr>               <dbl> <chr>            
##  1 Abakuá     Abakuá  Caribbean      "Cuba"                 NA <NA>             
##  2 Abaza      Абаза   Western Asia   "Turke…             49800 Abkhaz-Adyge     
##  3 Abruzzese… Abruzz… Southern Euro… "Italy"                NA Indo-European    
##  4 Abruzzese… Abruzz… Southern Euro… "Italy"                NA Indo-European    
##  5 Acehnese   Bahsa … Southeastern … "Indon…           3500000 Austronesian     
##  6 Acehnese   Bahsa … Southeastern … "Indon…           3500000 Austronesian     
##  7 Adjoukrou  <NA>    Western Africa "Ivory…            140000 Atlantic-Congo   
##  8 Adyghe     <NA>    Western Asia   "Turke…            117500 Abkhaz-Adyge     
##  9 Afenmai    Afenmai Western Africa "Niger…            270000 Atlantic-Congo   
## 10 African-A… Black … Northern Amer… "Unite…          45109521 Indo-European    
## # ℹ 1,292 more rows
## # ℹ 3 more variables: Location <chr>, Size <chr>, Status <chr>

data_long <- data %>%
    
    select(`World Region`, Language) %>%
    filter(`World Region` %in% c("Western Asia", "Southeastern Asia")) %>%
    distinct() 
data_long

## # A tibble: 108 × 2
##    `World Region`    Language            
##    <chr>             <chr>               
##  1 Western Asia      Abaza               
##  2 Southeastern Asia Acehnese            
##  3 Western Asia      Adyghe              
##  4 Southeastern Asia Aklanon             
##  5 Western Asia      Assyrian Neo-Aramaic
##  6 Western Asia      Avar                
##  7 Western Asia      Azeri               
##  8 Southeastern Asia Balinese            
##  9 Southeastern Asia Banguingui          
## 10 Southeastern Asia Banjarese           
## # ℹ 98 more rows

Pivoting

long to wide form

table4a_long <- data_long %>%
    
         pivot_longer(cols = c(`World Region`, Language),
                 names_to = "category",
                 values_to = "value") 
table4a_long

## # A tibble: 216 × 2
##    category     value               
##    <chr>        <chr>               
##  1 World Region Western Asia        
##  2 Language     Abaza               
##  3 World Region Southeastern Asia   
##  4 Language     Acehnese            
##  5 World Region Western Asia        
##  6 Language     Adyghe              
##  7 World Region Southeastern Asia   
##  8 Language     Aklanon             
##  9 World Region Western Asia        
## 10 Language     Assyrian Neo-Aramaic
## # ℹ 206 more rows

wide to long form

data_long %>%
  
    pivot_wider(names_from = `World Region`,
              values_from = Language)

## # A tibble: 1 × 2
##   `Western Asia` `Southeastern Asia`
##   <list>         <list>             
## 1 <chr [40]>     <chr [68]>

data_long

## # A tibble: 108 × 2
##    `World Region`    Language            
##    <chr>             <chr>               
##  1 Western Asia      Abaza               
##  2 Southeastern Asia Acehnese            
##  3 Western Asia      Adyghe              
##  4 Southeastern Asia Aklanon             
##  5 Western Asia      Assyrian Neo-Aramaic
##  6 Western Asia      Avar                
##  7 Western Asia      Azeri               
##  8 Southeastern Asia Balinese            
##  9 Southeastern Asia Banguingui          
## 10 Southeastern Asia Banjarese           
## # ℹ 98 more rows

Separating and Uniting

Separate a column

table3_sep <- data_long %>%
   
     separate(col = `World Region`, into = c("World Region", "Continent"))
table3_sep

## # A tibble: 108 × 3
##    `World Region` Continent Language            
##    <chr>          <chr>     <chr>               
##  1 Western        Asia      Abaza               
##  2 Southeastern   Asia      Acehnese            
##  3 Western        Asia      Adyghe              
##  4 Southeastern   Asia      Aklanon             
##  5 Western        Asia      Assyrian Neo-Aramaic
##  6 Western        Asia      Avar                
##  7 Western        Asia      Azeri               
##  8 Southeastern   Asia      Balinese            
##  9 Southeastern   Asia      Banguingui          
## 10 Southeastern   Asia      Banjarese           
## # ℹ 98 more rows

Unite two columns

table3_sep %>%
    
    unite(col = `World Region`, c("World Region", "Continent"), sep = "/", )

## # A tibble: 108 × 2
##    `World Region`    Language            
##    <chr>             <chr>               
##  1 Western/Asia      Abaza               
##  2 Southeastern/Asia Acehnese            
##  3 Western/Asia      Adyghe              
##  4 Southeastern/Asia Aklanon             
##  5 Western/Asia      Assyrian Neo-Aramaic
##  6 Western/Asia      Avar                
##  7 Western/Asia      Azeri               
##  8 Southeastern/Asia Balinese            
##  9 Southeastern/Asia Banguingui          
## 10 Southeastern/Asia Banjarese           
## # ℹ 98 more rows

Missing Values

data <- tibble(
  Language = c("Abaza", "Abaza", "Abaza", "Abaza","Abakua", "Abakua", "Abakua"),
  `World Region` = c(1, 2, 3, 4, 2, 3, 4),
  `Global Speakers` = c(1.88, 0.59, 0.35, NA, 0.92, 0.17, 2.66)
)

data %>%
  pivot_wider(names_from = Language, 
              values_from = `Global Speakers`)

## # A tibble: 4 × 3
##   `World Region` Abaza Abakua
##            <dbl> <dbl>  <dbl>
## 1              1  1.88  NA   
## 2              2  0.59   0.92
## 3              3  0.35   0.17
## 4              4 NA      2.66