Week 8: Apply it to your data 7

Import your data

MyData <- read_excel("MyData.xlsx")

Data Smaller

set.seed(1234) # for reproducible outcome
data_small <- MyData %>%
    
    # Select 3 Columns
    select(fiscal_year, citizenship, encounter_count) %>%
    
    # Randomly select 5 rows
    sample_n(5)

data_small

## # A tibble: 5 × 3
##   fiscal_year citizenship encounter_count
##         <dbl> <chr>                 <dbl>
## 1        2023 ROMANIA                  14
## 2        2021 COLOMBIA                  4
## 3        2022 HONDURAS                  4
## 4        2024 UKRAINE                   6
## 5        2024 MEXICO                  453

Pivoting

long to wide form

MyData_long <- data_small %>%
    
    pivot_longer(cols = c(fiscal_year, encounter_count), 
                 names_to = "year", 
                 values_to = "cases")

wide to long form

MyData_wide <- MyData_long %>%
    
    pivot_wider(names_from = year,
                values_from = cases)

Separating and Uniting

Unite two columns

data_unite <- data_small %>%
    
    unite(col = "Citizens", c(citizenship, encounter_count), sep = "/",)

Separate a column

data_unite %>%
     separate(col = Citizens, into = c("encounter_count", "citizenship"))

## # A tibble: 5 × 3
##   fiscal_year encounter_count citizenship
##         <dbl> <chr>           <chr>      
## 1        2023 ROMANIA         14         
## 2        2021 COLOMBIA        4          
## 3        2022 HONDURAS        4          
## 4        2024 UKRAINE         6          
## 5        2024 MEXICO          453

Missing Values

missing_values <- tibble(
  year   = c(2023, 2021, 2022, 2024, 2024),
  citizenship    = c("romania",  "colombia"  ,  "honduras",   "ukraine",  "mexico"),
  encounter_count = c(14, 4, 4, 6, 453)
)

missing_values %>%
    pivot_wider(names_from = year, values_from = encounter_count)

## # A tibble: 5 × 5
##   citizenship `2023` `2021` `2022` `2024`
##   <chr>        <dbl>  <dbl>  <dbl>  <dbl>
## 1 romania         14     NA     NA     NA
## 2 colombia        NA      4     NA     NA
## 3 honduras        NA     NA      4     NA
## 4 ukraine         NA     NA     NA      6
## 5 mexico          NA     NA     NA    453

```