Overview
The supportR package is an amalgam of distinct functions
I’ve written to accomplish small data wrangling, quality control, or
visualization tasks. These functions tend to be short and
narrowly-defined. An additional consequence of the motivation for
creating them is that they tend to not be inter-related or united by a
common theme. If this vignette feels somewhat scattered because of that,
I hope it doesn’t negatively affect how informative it is or your
willingness to adopt supportR into your scripts!
This vignette describes the main functions of supportR
using the examples included in each function.
Identify Differences Between Two Vectors
In terms of quality control functions, diff_check
compares two vectors and reports back what is in the first but not the
second (i.e., what is “lost”) and what is in the second but not the
first (i.e., what is “gained”). I find this most useful (A) when
comparing the index columns of two data objects I intend to join
together and (B) to ensure no columns are unintentionally removed during
lengthy tidyverse-style pipes (%>%).
diff_check also includes optional logical arguments
sort and return that will respectively either
sort the difference in both vectors and return a two-element if set to
TRUE.
# Make two vectors
vec1 <- c("x", "a", "b")
vec2 <- c("y", "z", "a")
# Compare them!
supportR::diff_check(old = vec1, new = vec2, sort = TRUE, return = TRUE)
#> Following element(s) found in old object but not new:
#> [1] "b" "x"
#> Following element(s) found in new object but not old:
#> [1] "y" "z"
#> $lost
#> [1] "b" "x"
#>
#> $gained
#> [1] "y" "z"Identify Non-Numbers
This package also includes the function num_check that
identifies all values of a column that would be coerced to
NA if as.numeric was run on the column. Once
these non-numbers are identified you can handle that in whatever way you
feel is most appropriate. num_check is intended only to
flag these for your attention, not to attempt a fix using a
method you may or may not support.
# Make a dataframe with non-numbers in a number column
fish <- data.frame("species" = c("salmon", "bass", "halibut", "eel"),
"count" = c(1, "14x", "_23", 12))
# Use `num_check` to identify non-numbers
num_check(data = fish, col = "count")
#> For 'count', 2 non-numbers identified: '14x' | '_23'
#> $count
#> [1] "14x" "_23"Identify Malformed Dates
date_check does a similar operation but is checking a
column for entries that would be coerced to NA by
as.Date instead. Note that if a date is sufficiently badly
formatted as.Date will throw an error instead of coercing
to NA so date_check will do the same
thing.
# Make a dataframe including malformed dates
sites <- data.frame("site" = c("LTR", "GIL", "PYN", "RIN"),
"visit" = c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx"))
# Now we can use our function to identify bad dates
supportR::date_check(data = sites, col = "visit")
#> For 'visit', 3 non-dates identified: '2021-01-0w' | '1990' | '2020-10-xx'
#> $visit
#> [1] "2021-01-0w" "1990" "2020-10-xx"Both num_check and date_check can accept
multiple column names to the col argument (as of version
1.1.1) and all columns are checked separately.
Deduce Date Formats from Ambiguous Dates
Another date column quality control function is
date_format_guess. This This function checks a column of
dates (stored as characters!) and tries to guess the format of the date
(i.e., month/day/year, day/month/year, etc.).
It can make a more informed guess if there is a grouping column because it can use the frequency of the “date” entries within those groups to guess whether a given number is the day or the month. This is based on the assumption that sampling occurs more often within months than across them so the number that occurs in more rows within the grouping values is most likely month.
Recognizing that assumption may be uncomfortable for some users, the
groups argument can be set to FALSE and it
will do the clearer judgment calls (i.e., if a number is >12 it is
day, etc.). Note that dates that cannot be guessed by my function will
return “FORMAT UNCERTAIN” so that you can handle them using your
knowledge of the system (or by returning to your raw data if need
be).
# Make a dataframe with dates in various formats and a grouping column
my_df <- data.frame("data_enterer" = c("person A", "person B",
"person B", "person B",
"person C", "person D",
"person E", "person F",
"person G"),
"bad_dates" = c("2022.13.08", "2021/2/02",
"2021/2/03", "2021/2/04",
"1899/1/15", "10-31-1901",
"26/11/1901", "08.11.2004",
"6/10/02"))
# Now we can invoke the function!
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
group_col = "data_enterer", return = "dataframe")
#> Returning dataframe of data format guesses
#> data_enterer bad_dates format_guess
#> 1 person A 2022.13.08 year/day/month
#> 2 person B 2021/2/02 year/month/day
#> 3 person B 2021/2/03 year/month/day
#> 4 person B 2021/2/04 year/month/day
#> 5 person C 1899/1/15 year/month/day
#> 6 person D 10-31-1901 month/day/year
#> 7 person E 26/11/1901 day/month/year
#> 8 person F 08.11.2004 FORMAT UNCERTAIN
#> 9 person G 6/10/02 FORMAT UNCERTAIN
# If preferred, do it without groups and return a vector
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
groups = FALSE, return = "vector")
#> Defining 'groups' is strongly recommended! If none exist, consider adding a single artificial group shared by all rows then re-run this function
#> Returning vector of data format guesses
#> [1] "year/day/month" "FORMAT UNCERTAIN" "FORMAT UNCERTAIN" "FORMAT UNCERTAIN"
#> [5] "year/month/day" "month/day/year" "day/month/year" "FORMAT UNCERTAIN"
#> [9] "FORMAT UNCERTAIN"