Week 8: Code Along 7

Tidy Data

Pivoting

long to wide form

table4a_long <- table4a %>%
    pivot_longer(cols = c('1999' ,'2000' ),
                 names_to = "year" ,
                 values_to = "cases")

wide to long form

table4a_long %>% 
    pivot_wider(names_from = year,
    values_from = cases)

## # A tibble: 3 × 3
##   country     `1999` `2000`
##   <chr>        <dbl>  <dbl>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766

##Seperating and Uniting ### seperate a column

table3_sep <- table3 %>%
    separate(col = rate,into = c("cases", "population"))

unite a column

table3_sep %>% 
    unite(col = "rate", cases:population, sep = "/",)

## # A tibble: 6 × 3
##   country      year rate             
##   <chr>       <dbl> <chr>            
## 1 Afghanistan  1999 745/19987071     
## 2 Afghanistan  2000 2666/20595360    
## 3 Brazil       1999 37737/172006362  
## 4 Brazil       2000 80488/174504898  
## 5 China        1999 212258/1272915272
## 6 China        2000 213766/1280428583

Missing Values

Non-Tidy Data