# Clean / Drop

#### Roland Krasser

#### 2025-06-24

    library(dplyr)
    library(explore)

## Rename variable

    data <- use_data_titanic(count = FALSE)
    glimpse(data)
    #> Rows: 2,201
    #> Columns: 4
    #> $ Class    <chr> "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd"~
    #> $ Sex      <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male~
    #> $ Age      <chr> "Child", "Child", "Child", "Child", "Child", "Child", "Child"~
    #> $ Survived <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "~

    data <- data %>% clean_var(Age, name = "age")
    glimpse(data)
    #> Rows: 2,201
    #> Columns: 4
    #> $ Class    <chr> "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd", "3rd"~
    #> $ Sex      <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male~
    #> $ age      <chr> "Child", "Child", "Child", "Child", "Child", "Child", "Child"~
    #> $ Survived <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "~

## Replace NA values

    data <- use_data_beer()
    data %>% describe(energy_kcal_100ml)
    #> variable = energy_kcal_100ml
    #> type     = double
    #> na       = 11 of 161 (6.8%)
    #> unique   = 34
    #> min|max  = 20 | 62
    #> q05|q95  = 24 | 56.65
    #> q25|q75  = 37 | 44
    #> median   = 42
    #> mean     = 39.89333

    data <- data %>% clean_var(energy_kcal_100ml, na = 42)
    data %>% describe(energy_kcal_100ml)
    #> variable = energy_kcal_100ml
    #> type     = double
    #> na       = 0 of 161 (0%)
    #> unique   = 33
    #> min|max  = 20 | 62
    #> q05|q95  = 24 | 55
    #> q25|q75  = 38 | 44
    #> median   = 42
    #> mean     = 40.03727

## Set min max values

    data <- create_data_person()
    data %>% describe(age)
    #> variable = age
    #> type     = integer
    #> na       = 0 of 1 000 (0%)
    #> unique   = 80
    #> min|max  = 16 | 95
    #> q05|q95  = 21 | 92
    #> q25|q75  = 37 | 76
    #> median   = 55
    #> mean     = 55.845

    data <- data %>% clean_var(age, min_val = 20, max_val = 80)
    data %>% describe(age)
    #> variable = age
    #> type     = integer
    #> na       = 0 of 1 000 (0%)
    #> unique   = 61
    #> min|max  = 20 | 80
    #> q05|q95  = 21 | 80
    #> q25|q75  = 37 | 76
    #> median   = 55
    #> mean     = 54.276

## Rescale 0 to 1

    data %>% describe(income)
    #> variable = income
    #> type     = double
    #> na       = 0 of 1 000 (0%)
    #> unique   = 228
    #> min|max  = 0 | 150
    #> q05|q95  = 6 | 123.025
    #> q25|q75  = 35 | 88.625
    #> median   = 62
    #> mean     = 61.5875

    data <- data %>% clean_var(income, rescale01 = TRUE)
    data %>% describe(income)
    #> variable = income
    #> type     = double
    #> na       = 0 of 1 000 (0%)
    #> unique   = 228
    #> min|max  = 0 | 1
    #> q05|q95  = 0.04 | 0.820167
    #> q25|q75  = 0.233333 | 0.590833
    #> median   = 0.4
    #> mean     = 0.410583

## Cleaning text

    data[1, "handset"] <- " android "
    data[2, "handset"] <- "ANDROID"
    data %>% describe(handset)
    #> variable = handset
    #> type     = character
    #> na       = 0 of 1 000 (0%)
    #> unique   = 5
    #>   android  = 1 (0.1%)
    #>  ANDROID   = 1 (0.1%)
    #>  Android   = 471 (47.1%)
    #>  Apple     = 430 (43%)
    #>  Other     = 97 (9.7%)

    data <- data %>% clean_var(handset, simplify_text = TRUE)
    data %>% describe(handset)
    #> variable = handset
    #> type     = character
    #> na       = 0 of 1 000 (0%)
    #> unique   = 3
    #>  ANDROID = 473 (47.3%)
    #>  APPLE   = 430 (43%)
    #>  OTHER   = 97 (9.7%)

## Drop variables

- `drop_var_no_variance()` Drop all variables with no variance
- `drop_var_not_numeric()` Drop all not numeric variables
- `drop_var_low_variance()` Drop all variables with low variance
- `drop_var_by_names()` Drop variables by name
- `drop_var_with_na()` Drop all variables with NA-values

    data <- use_data_beer()
    data %>% describe_tbl()
    #> 161 observations with 11 variables
    #> 19 observations containing missings (NA)
    #> 5 variables containing missings (NA)
    #> 1 variables with no variance

    data %>%
      drop_var_no_variance() %>%
      describe_tbl()
    #> 161 observations with 10 variables
    #> 19 observations containing missings (NA)
    #> 5 variables containing missings (NA)
    #> 0 variables with no variance

    data %>%
      drop_var_with_na() %>%
      describe_tbl()
    #> 161 observations with 6 variables
    #> 0 observations containing missings (NA)
    #> 0 variables containing missings (NA)
    #> 1 variables with no variance

## Drop observations

- `drop_obs_with_na()` Drop all observations with NA-values

    data %>%
      drop_obs_with_na() %>%
      describe_tbl()
    #> 142 observations with 11 variables
    #> 0 observations containing missings (NA)
    #> 0 variables containing missings (NA)
    #> 1 variables with no variance

- `drop_obs_if()` Drop all observations where expression is true

    data %>%
      count_pct(type)
    #> # A tibble: 3 x 4
    #>   type            n total   pct
    #>   <chr>       <int> <int> <dbl>
    #> 1 Alkoholfrei    27   161 16.8
    #> 2 Bock            8   161  4.97
    #> 3 Rest          126   161 78.3

    data %>%
      drop_obs_if(type == "Alkoholfrei") %>%
      count_pct(type)
    #> # A tibble: 2 x 4
    #>   type      n total   pct
    #>   <chr> <int> <int> <dbl>
    #> 1 Bock      8   134  5.97
    #> 2 Rest    126   134 94.0