Week 8: Apply it to your data 7

Import your data

data = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-10-14/food_security.csv")

## Rows: 171232 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Area, Item, Unit, Flag, Note
## dbl (5): Year_Start, Year_End, Value, CI_Lower, CI_Upper
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

set.seed(1234)
data_small<- data %>%
    select("Item", "Value","Area") %>%
    sample_n(5)
data_small

## # A tibble: 5 × 3
##   Item                                                               Value Area 
##   <chr>                                                              <dbl> <chr>
## 1 Prevalence of undernourishment (percent) (3-year average)          NA    Sing…
## 2 Percentage of population using at least basic sanitation services… 94    Fiji 
## 3 Prevalence of undernourishment (percent) (annual value)             2.49 West…
## 4 Number of obese adults (18 years and older) (million)               1.2  Demo…
## 5 Percentage of population using at least basic drinking water serv… 99    Unit…

Pivoting

long to wide form

data_wide<- data_small %>%
pivot_wider(names_from = Area, 
            values_from = Value)

data_wide

## # A tibble: 5 × 6
##   Item                   Singapore  Fiji `Western Europe` Democratic Republic …¹
##   <chr>                      <dbl> <dbl>            <dbl>                  <dbl>
## 1 Prevalence of underno…        NA    NA            NA                      NA  
## 2 Percentage of populat…        NA    94            NA                      NA  
## 3 Prevalence of underno…        NA    NA             2.49                   NA  
## 4 Number of obese adult…        NA    NA            NA                       1.2
## 5 Percentage of populat…        NA    NA            NA                      NA  
## # ℹ abbreviated name: ¹​`Democratic Republic of the Congo`
## # ℹ 1 more variable: `United States of America` <dbl>

wide to long form

data_long <- data_wide %>%
    pivot_longer(cols = -Item, values_drop_na = TRUE) %>%
    select(Item, Value = value, Area = name)

data_long

## # A tibble: 4 × 3
##   Item                                                               Value Area 
##   <chr>                                                              <dbl> <chr>
## 1 Percentage of population using at least basic sanitation services… 94    Fiji 
## 2 Prevalence of undernourishment (percent) (annual value)             2.49 West…
## 3 Number of obese adults (18 years and older) (million)               1.2  Demo…
## 4 Percentage of population using at least basic drinking water serv… 99    Unit…

Separating and Uniting

Unite two columns

data_united <- data_small %>% unite(col = "Value_Area", c(Value, Area))
data_united

## # A tibble: 5 × 2
##   Item                                                                Value_Area
##   <chr>                                                               <chr>     
## 1 Prevalence of undernourishment (percent) (3-year average)           NA_Singap…
## 2 Percentage of population using at least basic sanitation services … 94_Fiji   
## 3 Prevalence of undernourishment (percent) (annual value)             2.49_West…
## 4 Number of obese adults (18 years and older) (million)               1.2_Democ…
## 5 Percentage of population using at least basic drinking water servi… 99_United…

Separate a column

data_seperated <- data_united %>% separate(col = Value_Area, into = c("Value", "Area"))

## Warning: Expected 2 pieces. Additional pieces discarded in 3 rows [3, 4, 5].

data_seperated

## # A tibble: 5 × 3
##   Item                                                               Value Area 
##   <chr>                                                              <chr> <chr>
## 1 Prevalence of undernourishment (percent) (3-year average)          NA    Sing…
## 2 Percentage of population using at least basic sanitation services… 94    Fiji 
## 3 Prevalence of undernourishment (percent) (annual value)            2     49   
## 4 Number of obese adults (18 years and older) (million)              1     2    
## 5 Percentage of population using at least basic drinking water serv… 99    Unit…

Missing Values

data_longer <- data_small %>%
    pivot_longer(cols =c("Area"),
                 names_to = ("Areas"), 
                 values_to = "Places",
                 values_drop_na = TRUE)

data_longer

## # A tibble: 5 × 4
##   Item                                                        Value Areas Places
##   <chr>                                                       <dbl> <chr> <chr> 
## 1 Prevalence of undernourishment (percent) (3-year average)   NA    Area  Singa…
## 2 Percentage of population using at least basic sanitation s… 94    Area  Fiji  
## 3 Prevalence of undernourishment (percent) (annual value)      2.49 Area  Weste…
## 4 Number of obese adults (18 years and older) (million)        1.2  Area  Democ…
## 5 Percentage of population using at least basic drinking wat… 99    Area  Unite…