Check the percentages of missing value
check_percentage_missing.RdThe function will flag if a survey for its missing values. The missing values column can be created with add_percentage_missing and the values are flagged with check_outliers.
Usage
check_percentage_missing(
dataset,
uuid_column = "uuid",
column_to_check = "percentage_missing",
strongness_factor = 2,
log_name = "percentage_missing_log"
)Arguments
- dataset
a dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset".
- uuid_column
uuid column in the dataset. Default is "uuid".
- column_to_check
string character with the name of the columns to check. Default is "percentage_missing"
- strongness_factor
Strongness factor define how strong your outliers will be. The default is 3.
- log_name
name of the log of flagged value, default is percentage_missing_log
Value
return a list with the dataset checked stored as checked_dataset and a dataframe with the flagged values log
Examples
# Adding the percentage missing first
data_example <- data.frame(
uuid = letters[1:3],
col_1 = c(1:3),
col_2 = c(NA, NA, "expenditures"),
col_3 = c("with need", NA, "with need"),
col_4 = c("food health school", NA, "food"),
col_4.food = c(1, NA, 1),
col_4.health = c(1, NA, 0),
col_4.school = c(1, NA, 0)
)
data_example <- data_example %>%
add_percentage_missing()
data_example %>% check_percentage_missing()
#> [1] "checking_percentage_missing"
#> $checked_dataset
#> uuid col_1 col_2 col_3 col_4 col_4.food col_4.health
#> 1 a 1 <NA> with need food health school 1 1
#> 2 b 2 <NA> <NA> <NA> NA NA
#> 3 c 3 expenditures with need food 1 0
#> col_4.school percentage_missing
#> 1 1 0.125
#> 2 NA 0.750
#> 3 0 0.000
#>
#> $percentage_missing_log
#> # A tibble: 0 × 4
#> # ℹ 4 variables: uuid <chr>, issue <chr>, question <chr>, old_value <chr>
#>
# With a dataset that already has a percentage missing
data_example2 <- data.frame(
uuid = letters,
any_cols = LETTERS,
any_number = 1:26,
percentage_missing = c(rep(.05, 25), .99)
)
data_example2 %>% check_percentage_missing()
#> [1] "checking_percentage_missing"
#> $checked_dataset
#> uuid any_cols any_number percentage_missing
#> 1 a A 1 0.05
#> 2 b B 2 0.05
#> 3 c C 3 0.05
#> 4 d D 4 0.05
#> 5 e E 5 0.05
#> 6 f F 6 0.05
#> 7 g G 7 0.05
#> 8 h H 8 0.05
#> 9 i I 9 0.05
#> 10 j J 10 0.05
#> 11 k K 11 0.05
#> 12 l L 12 0.05
#> 13 m M 13 0.05
#> 14 n N 14 0.05
#> 15 o O 15 0.05
#> 16 p P 16 0.05
#> 17 q Q 17 0.05
#> 18 r R 18 0.05
#> 19 s S 19 0.05
#> 20 t T 20 0.05
#> 21 u U 21 0.05
#> 22 v V 22 0.05
#> 23 w W 23 0.05
#> 24 x X 24 0.05
#> 25 y Y 25 0.05
#> 26 z Z 26 0.99
#>
#> $percentage_missing_log
#> # A tibble: 1 × 4
#> uuid issue question old_value
#> <chr> <chr> <chr> <chr>
#> 1 z Percentages of missing values from this survey is di… percent… 0.99
#>