Package 'ds4psy'

Description

base_digits provides numeral symbols (digits) for notational place-value systems with arbitrary bases (as a named character vector).

Usage

base_digits

Format

An object of class character of length 62.

Details

Note that the elements (digits) are character symbols (i.e., numeral digits "0"-"9", "A"-"F", etc.), whereas their names correspond to their numeric values (from 0 to length(base_digits) - 1).

Thus, the maximum base value in conversions by base2dec or dec2base is length(base_digits).

See Also

base2dec converts numerals in some base into decimal numbers; dec2base converts decimal numbers into numerals in another base; as.roman converts integers into Roman numerals.

Other numeric functions: base2dec(), dec2base(), is_equal(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Other utility functions: base2dec(), dec2base(), is_equal(), is_vect(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Examples

base_digits          # named character vector, zero-indexed names
length(base_digits)  # 62 (maximum base value)
base_digits[10]      # 10. element ("9" with name "9") 
base_digits["10"]    # named element "10" ("A" with name "10")
base_digits[["10"]]  # element named "10" ("A")

Description

base2dec converts a sequence of numeral symbols (digits) from its notation as positional numerals (with some base or radix) into standard decimal notation (using the base or radix of 10).

Usage

base2dec(x, base = 2)

Arguments

Details

The individual digits provided in x (e.g., from "0" to "9", "A" to "F") must be defined in the specified base (i.e., every digit value must be lower than the base or radix value). See base_digits for the sequence of default digits.

base2dec is the complement of dec2base.

Value

An integer number (in decimal notation).

See Also

dec2base converts decimal numbers into numerals in another base; as.roman converts integers into Roman numerals.

Other numeric functions: base_digits, dec2base(), is_equal(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Other utility functions: base_digits, dec2base(), is_equal(), is_vect(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Examples

# (a) single string input:
base2dec("11")   # default base = 2
base2dec("0101")
base2dec("1010")

base2dec("11", base = 3)
base2dec("11", base = 5)
base2dec("11", base = 10)

base2dec("11", base = 12)
base2dec("11", base = 14)
base2dec("11", base = 16)

# (b) numeric vectors as inputs:
base2dec(c(0, 1, 0))
base2dec(c(0, 1, 0), base = 3)

# (c) character vector as inputs:
base2dec(c("0", "1", "0"))
base2dec(c("0", "1", "0"), base = 3)

# (d) multi-digit vectors:
base2dec(c(1, 1))
base2dec(c(1, 1), base = 3)

# Extreme values:
base2dec(rep("1", 32))          # 32 x "1"
base2dec(c("1", rep("0", 32)))  # 2^32
base2dec(rep("1", 33))          # 33 x "1"
base2dec(c("1", rep("0", 33)))  # 2^33
 
# Non-standard inputs:
base2dec("  ", 2)      # no non-spaces: NA
base2dec(" ?! ", 2)    # no base digits: NA
base2dec(" 100  ", 2)  # remove leading and trailing spaces
base2dec("-  100", 2)  # handle negative inputs (value < 0)
base2dec("- -100", 2)  # handle double negations
base2dec("---100", 2)  # handle multiple negations

# Special cases:
base2dec(NA)
base2dec(0)
base2dec(c(3, 3), base = 3)  # Note message!

# Note: 
base2dec(dec2base(012340, base =  9), base =  9)
dec2base(base2dec(043210, base = 11), base = 11)

Description

Bushisms contains some phrases uttered by or attributed to U.S. president George W. Bush (the 43rd president of the United States of America, in office from January 2001 to January 2009).

Usage

Bushisms

Format

A vector of type character with length(Bushisms) = 22.

Source

Data based on https://en.wikipedia.org/wiki/Bushism.

See Also

Other datasets: Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

capitalize converts the first n initial characters of each element of a text string x (i.e., characters or words) to upper- or lowercase.

Usage

capitalize(x, n = 1, upper = TRUE, as_text = FALSE)

Arguments

Details

If as_text = TRUE, the input x is merged into one string of text and the arguments are applied to each word.

Value

A character vector.

See Also

caseflip for converting the case of all letters; words_to_text and text_to_words for converting character vectors and texts.

Other text objects and functions: Umlaut, caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

x <- c("Hello world!", "this is a TEST sentence.", "the end.")
capitalize(x)
capitalize(tolower(x))

# Options: 
capitalize(x, n = 3)                  # leaves strings intact
capitalize(x, n = 3, as_text = TRUE)  # treats strings as text
capitalize(x, n = 3, upper = FALSE)   # first n in lowercase

Description

caseflip flips the case of all characters in a string of text x.

Usage

caseflip(x)

Arguments

Details

Internally, caseflip uses the letters and LETTERS constants of base R and the chartr function for replacing characters in strings of text.

Value

A character vector.

See Also

capitalize for converting the case of initial letters; chartr for replacing characters in strings of text.

Other text objects and functions: Umlaut, capitalize(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

x <- c("Hello world!", "This is a 1st sentence.", "This is the 2nd sentence.", "The end.")
caseflip(x)

Description

cclass provides different character classes (as a named character vector).

Usage

cclass

Format

An object of class character of length 6.

Details

cclass allows illustrating matching character classes via regular expressions.

See ?base::regex for details on regular expressions and ?"'" for a list of character constants/quotes in R.

See Also

metachar for a vector of metacharacters.

Other text objects and functions: Umlaut, capitalize(), caseflip(), chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

cclass["hex"]  # select by name
writeLines(cclass["pun"])
grep("[[:alpha:]]", cclass, value = TRUE)

Description

change_time changes the time and time zone without changing the time display.

Usage

change_time(time, tz = "")

Arguments

Details

change_time expects inputs to time to be local time(s) (of the "POSIXlt" class) and a valid time zone argument tz (as a string) and returns the same time display (but different actual times) as calendar time(s) (of the "POSIXct" class).

Value

A calendar time of class "POSIXct".

See Also

change_tz function which preserves time but changes time display; Sys.time() function of base R.

Other date and time functions: change_tz(), cur_date(), cur_time(), days_in_month(), diff_dates(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

change_time(as.POSIXlt(Sys.time()), tz = "UTC")

# from "POSIXlt" time:
t1 <- as.POSIXlt("2020-01-01 10:20:30", tz = "Europe/Berlin")
change_time(t1, "Pacific/Auckland")
change_time(t1, "America/Los_Angeles")

# from "POSIXct" time:
tc <- as.POSIXct("2020-07-01 12:00:00", tz = "UTC")
change_time(tc, "Pacific/Auckland")

# from "Date":
dt <- as.Date("2020-12-31", tz = "Pacific/Honolulu")
change_time(dt, tz = "Pacific/Auckland")

# from time "string":
ts <- "2020-12-31 20:30:45"
change_time(ts, tz = "America/Los_Angeles")

# from other "string" times:
tx <- "7:30:45"
change_time(tx, tz = "Asia/Calcutta")
ty <- "1:30"
change_time(ty, tz = "Europe/London")

# convert into local times:
(l1 <- as.POSIXlt("2020-06-01 10:11:12"))
change_tz(change_time(l1, "Pacific/Auckland"), tz = "UTC")
change_tz(change_time(l1, "Europe/Berlin"), tz = "UTC")
change_tz(change_time(l1, "America/New_York"), tz = "UTC")

# with vector of "POSIXlt" times:
(l2 <- as.POSIXlt("2020-12-31 23:59:55", tz = "America/Los_Angeles"))
(tv <- c(l1, l2))              # uses tz of l1
change_time(tv, "America/Los_Angeles")  # change time and tz

Description

change_tz changes the nominal time zone (i.e., the time display) without changing the actual time.

Usage

change_tz(time, tz = "")

Arguments

Details

change_tz expects inputs to time to be calendar time(s) (of the "POSIXct" class) and a valid time zone argument tz (as a string) and returns the same time(s) as local time(s) (of the "POSIXlt" class).

Value

A local time of class "POSIXlt".

See Also

change_time function which preserves time display but changes time; Sys.time() function of base R.

Other date and time functions: change_time(), cur_date(), cur_time(), days_in_month(), diff_dates(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

change_tz(Sys.time(), tz = "Pacific/Auckland")
change_tz(Sys.time(), tz = "Pacific/Honolulu")

# from "POSIXct" time:
tc <- as.POSIXct("2020-07-01 12:00:00", tz = "UTC")
change_tz(tc, "Australia/Melbourne")
change_tz(tc, "Europe/Berlin")
change_tz(tc, "America/Los_Angeles")

# from "POSIXlt" time:
tl <- as.POSIXlt("2020-07-01 12:00:00", tz = "UTC")
change_tz(tl, "Australia/Melbourne")
change_tz(tl, "Europe/Berlin")
change_tz(tl, "America/Los_Angeles")

# from "Date":
dt <- as.Date("2020-12-31")
change_tz(dt, "Pacific/Auckland")
change_tz(dt, "Pacific/Honolulu")  # Note different date!

# with a vector of "POSIXct" times:
t2 <- as.POSIXct("2020-12-31 23:59:55", tz = "America/Los_Angeles")
tv <- c(tc, t2)
tv  # Note: Both times in tz of tc
change_tz(tv, "America/Los_Angeles")

Description

chars_to_text combines multi-element character inputs x into a single string of text (i.e., a character object of length 1), while preserving punctuation and spaces.

Usage

chars_to_text(x, sep = "")

Arguments

Details

chars_to_text is an inverse function of text_to_chars.

Note that using paste(x, collapse = "") would remove spaces. See collapse_chars for a simpler alternative.

Value

A character vector (of length 1).

See Also

collapse_chars for collapsing character vectors; text_to_chars for splitting text into a vector of characters; text_to_words for splitting text into a vector of words; strsplit for splitting strings.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

# (a) One string (with spaces and punctuation):
t1 <- "Hello world! This is _A   TEST_. Does this work?"
(cv <- unlist(strsplit(t1, split = "")))
(t2 <- chars_to_text(cv))
t1 == t2

# (b) Multiple strings (nchar from 0 to >1):
s <- c("Hi", " ", "", "there!", " ", "", "Does  THIS  work?")
chars_to_text(s)

# Note: Using sep argument: 
chars_to_text(c("Hi there!", "How are you today?"), sep = "  ")
chars_to_text(1:3, sep = " | ")

Description

coin generates a sequence of events that represent the results of flipping a fair coin n times.

Usage

coin(n = 1, events = c("H", "T"))

Arguments

Details

By default, the 2 possible events for each flip are "H" (for "heads") and "T" (for "tails").

See Also

Other sampling functions: dice(), dice_2(), sample_char(), sample_date(), sample_time()

Examples

# Basics: 
coin()
table(coin(n = 100))
table(coin(n = 100, events = LETTERS[1:3]))

# Note an oddity:
coin(10, events = 8:9)  # works as expected, but 
coin(10, events = 9:9)  # odd: see sample() for an explanation.

# Limits:
coin(2:3)
coin(NA)
coin(0)
coin(1/2)
coin(3, events = "X")
coin(3, events = NA)
coin(NULL, NULL)

Description

collapse_chars converts multi-element character inputs x into a single string of text (i.e., a character object of length 1), separating its elements by sep.

Usage

collapse_chars(x, sep = " ")

Arguments

Details

As collapse_chars is a wrapper around paste(x, collapse = sep). It preserves spaces within the elements of x.

The separator sep is only used when collapsing multi-element vectors and inserted between elements.

See chars_to_text for combining character vectors into text.

Value

A character vector (of length 1).

See Also

chars_to_text for combining character vectors into text; text_to_chars for splitting text into a vector of characters; text_to_words for splitting text into a vector of words; strsplit for splitting strings.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

collapse_chars(c("Hello", "world", "!"))
collapse_chars(c("_", " _ ", "  _  "), sep = "|")  # preserves spaces
writeLines(collapse_chars(c("Hello", "world", "!"), sep = "\n"))
collapse_chars(1:3, sep = "")

Description

count_chars provides frequency counts of the characters in a string of text x as a named numeric vector.

Usage

count_chars(x, case_sense = TRUE, rm_specials = TRUE, sort_freq = TRUE)

Arguments

Details

If rm_specials = TRUE (as per default), most special (or non-word) characters are removed and not counted. (Note that this currently works without using regular expressions.)

The quantification is case-sensitive and the resulting vector is sorted by name (alphabetically) or by frequency (per default).

Value

A named numeric vector.

See Also

count_words for counting the frequency of words; count_chars_words for counting both characters and words; plot_chars for a corresponding plotting function.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

# Default: 
x <- c("Hello world!", "This is a 1st sentence.", 
       "This is the 2nd sentence.", "THE END.")
count_chars(x)

# Options: 
count_chars(x, case_sense = FALSE)
count_chars(x, rm_specials = FALSE)
count_chars(x, sort_freq = FALSE)

Description

count_chars_words provides frequency counts of the characters and words of a string of text x on a per character basis.

Usage

count_chars_words(x, case_sense = TRUE, sep = "|", rm_sep = TRUE)

Arguments

Details

count_chars_words calls both count_chars and count_words and maps their results to a data frame that contains a row for each character of x.

The quantifications are case-sensitive. Special characters (e.g., parentheses, punctuation, and spaces) are counted as characters, but removed from word counts.

If input x consists of multiple text strings, they are collapsed with an added " " (space) between them.

Value

A data frame with 4 variables (char, char_freq, word, word_freq).

See Also

count_chars for counting the frequency of characters; count_words for counting the frequency of words; plot_chars for a character plotting function.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

s1 <- ("This test is to test this function.")
head(count_chars_words(s1))
head(count_chars_words(s1, case_sense = FALSE))

s3 <- c("A 1st sentence.", "The 2nd sentence.", 
        "A 3rd --- and also THE  FINAL --- SENTENCE.")
tail(count_chars_words(s3))
tail(count_chars_words(s3, case_sense = FALSE))

Description

count_words provides frequency counts of the words in a string of text x as a named numeric vector.

Usage

count_words(x, case_sense = TRUE, sort_freq = TRUE)

Arguments

Details

Special (or non-word) characters are removed and not counted.

The quantification is case-sensitive and the resulting vector is sorted by name (alphabetically) or by frequency (per default).

Value

A named numeric vector.

See Also

count_chars for counting the frequency of characters; count_chars_words for counting both characters and words; plot_chars for a character plotting function.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

# Default: 
s3 <- c("A first sentence.", "The second sentence.", 
        "A third --- and also THE FINAL --- SENTENCE.")
count_words(s3)  # case-sensitive, sorts by frequency 

# Options: 
count_words(s3, case_sense = FALSE)  # case insensitive
count_words(s3, sort_freq = FALSE)   # sorts alphabetically

Description

countries is a dataset containing the names of 197 countries (as a vector of text strings).

Usage

countries

Format

A vector of type character with length(countries) = 197.

Source

Data from https://www.gapminder.org: Original data at https://www.gapminder.org/data/documentation/gd004/.

See Also

Other datasets: Bushisms, Trumpisms, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

cur_date provides a relaxed version of Sys.time() that is sufficient for most purposes.

Usage

cur_date(rev = FALSE, as_string = TRUE, sep = "-")

Arguments

Details

By default, cur_date returns Sys.Date as a character string (using current system settings and sep for formatting). If as_string = FALSE, a "Date" object is returned.

Alternatively, consider using Sys.Date or Sys.time() to obtain the " format according to the ISO 8601 standard.

For more options, see the documentations of the date and Sys.Date functions of base R and the formatting options for Sys.time().

Value

A character string or object of class "Date".

See Also

what_date() function to print dates with more options; date() and today() functions of the lubridate package; date(), Sys.Date(), and Sys.time() functions of base R.

Other date and time functions: change_time(), change_tz(), cur_time(), days_in_month(), diff_dates(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

cur_date()
cur_date(sep = "/")
cur_date(rev = TRUE)
cur_date(rev = TRUE, sep = ".")

# return a "Date" object:
from <- cur_date(as_string = FALSE)
class(from)

Description

cur_time provides a satisficing version of Sys.time() that is sufficient for most purposes.

Usage

cur_time(seconds = FALSE, as_string = TRUE, sep = ":")

Arguments

Details

By default, cur_time returns a Sys.time() as a character string (in " using current system settings. If as_string = FALSE, a "POSIXct" (calendar time) object is returned.

For a time zone argument, see the what_time function, or the now() function of the lubridate package.

Value

A character string or object of class "POSIXct".

See Also

what_time() function to print times with more options; now() function of the lubridate package; Sys.time() function of base R.

Other date and time functions: change_time(), change_tz(), cur_date(), days_in_month(), diff_dates(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

cur_time() 
cur_time(seconds = TRUE)
cur_time(sep = ".")

# return a "POSIXct" object:
t <- cur_time(as_string = FALSE)
format(t, "%T %Z")

Description

data_1 is a fictitious dataset to practice importing data (from a DELIMITED file).

Usage

data_1

Format

A table with 100 cases (rows) and 4 variables (columns).

Source

See DELIMITED data at http://rpository.com/ds4psy/data/data_1.dat.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_2 is a fictitious dataset to practice importing data (from a FWF file).

Usage

data_2

Format

A table with 100 cases (rows) and 4 variables (columns).

Source

See FWF data at http://rpository.com/ds4psy/data/data_2.dat.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t1 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t1

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/data_t1.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t1_de is a fictitious dataset to practice importing data (from a CSV file, de/European style).

Usage

data_t1_de

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/data_t1_de.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t1_tab is a fictitious dataset to practice importing data (from a TAB file).

Usage

data_t1_tab

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See TAB-delimited data at http://rpository.com/ds4psy/data/data_t1_tab.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t2 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t2

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/data_t2.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t3 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t3

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/data_t3.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

data_t4 is a fictitious dataset to practice importing and joining data (from a CSV file).

Usage

data_t4

Format

A table with 20 cases (rows) and 4 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/data_t4.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

days_in_month computes the number of days in the months of given dates (provided as a date or time dt, or number/string denoting a 4-digit year).

Usage

days_in_month(dt = Sys.Date(), ...)

Arguments

Details

The function requires dt as "Dates", rather than month names or numbers, to check for leap years (in which February has 29 days).

Value

A named (numeric) vector.

See Also

is_leap_year to check for leap years; diff_tz for time zone-based time differences; days_in_month function of the lubridate package.

Other date and time functions: change_time(), change_tz(), cur_date(), cur_time(), diff_dates(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

days_in_month() 

# Robustness: 
days_in_month(Sys.Date())    # Date
days_in_month(Sys.time())    # POSIXct
days_in_month("2020-07-01")  # string
days_in_month(20200901)      # number
days_in_month(c("2020-02-10 01:02:03", "2021-02-11", "2024-02-12"))  # vectors of strings

# For leap years:
ds <- as.Date("2020-02-20") + (365 * 0:4)  
days_in_month(ds)  # (2020/2024 are leap years)

Description

dec2base converts an integer from its standard decimal notation (i.e., using positional numerals with a base or radix of 10) into a sequence of numeric symbols (digits) in some other base. See base_digits for the sequence of default digits.

Usage

dec2base(x, base = 2)

Arguments

Details

To prevent erroneous interpretations of numeric outputs, dec2base returns a sequence of digits (as a character string).

dec2base is the complement of base2dec.

Value

A character string of digits (in base notation).

See Also

base2dec converts numerals in some base into decimal numbers; as.roman converts integers into Roman numerals.

Other numeric functions: base2dec(), base_digits, is_equal(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Other utility functions: base2dec(), base_digits, is_equal(), is_vect(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Examples

# (a) single numeric input:
dec2base(3)  # base = 2

dec2base(8, base = 2)
dec2base(8, base = 3)
dec2base(8, base = 7)

dec2base(100, base = 5)
dec2base(100, base = 10)
dec2base(100, base = 15)

dec2base(14, base = 14)
dec2base(15, base = 15)
dec2base(16, base = 16)

dec2base(15, base = 16)
dec2base(31, base = 16)
dec2base(47, base = 16)

# (b) single string input:
dec2base("7", base = 2)
dec2base("8", base = 3)

# Extreme values:
dec2base(base2dec(rep("1", 32)))          # 32 x "1"
dec2base(base2dec(c("1", rep("0", 32))))  # 2^32
dec2base(base2dec(rep("1", 33)))          # 33 x "1"
dec2base(base2dec(c("1", rep("0", 33))))  # 2^33

# Non-standard inputs:
dec2base("  ")          # only spaces: NA
dec2base("?")           # no decimal digits: NA
dec2base(" 10 ", 2)     # remove leading and trailing spaces
dec2base("-10", 2)      # handle negative inputs (in character strings)
dec2base(" -- 10", 2)   # handle multiple negations
dec2base("xy -10 ", 2)  # ignore non-decimal digit prefixes

# Note: 
base2dec(dec2base(012340, base =  9), base =  9)
dec2base(base2dec(043210, base = 11), base = 11)

Description

dice generates a sequence of events that represent the results of throwing a fair dice (with a given number of events or number of sides) n times.

Usage

dice(n = 1, events = 1:6)

Arguments

Details

By default, the 6 possible events for each throw of the dice are the numbers from 1 to 6.

See Also

Other sampling functions: coin(), dice_2(), sample_char(), sample_date(), sample_time()

Examples

# Basics:
dice()
table(dice(10^4))

# 5-sided dice:
dice(events = 1:5)
table(dice(100, events = 5))

# Strange dice:
dice(5, events = 8:9)
table(dice(100, LETTERS[1:3]))

# Note:
dice(10, 1)
table(dice(100, 2))

# Note an oddity:
dice(10, events = 8:9)  # works as expected, but 
dice(10, events = 9:9)  # odd: see sample() for an explanation.

# Limits:
dice(NA)
dice(0)
dice(1/2)
dice(2:3)
dice(5, events = NA)
dice(5, events = 1/2)
dice(NULL, NULL)

Description

dice_2 is a variant of dice that generates a sequence of events that represent the results of throwing a dice (with a given number of sides) n times.

Usage

dice_2(n = 1, sides = 6)

Arguments

Details

Something is wrong with this dice. Can you examine it and measure its problems in a quantitative fashion?

See Also

Other sampling functions: coin(), dice(), sample_char(), sample_date(), sample_time()

Examples

# Basics:
dice_2()
table(dice_2(100))

# 10-sided dice:
dice_2(sides = 10)
table(dice_2(100, sides = 10))

# Note:
dice_2(10, 1)
table(dice_2(5000, sides = 5))

# Note an oddity:
dice_2(n = 10, sides = 8:9)  # works, but 
dice_2(n = 10, sides = 9:9)  # odd: see sample() for an explanation.

Description

diff_dates computes the difference between two dates (i.e., from some from_date to some to_date) in human measurement units (periods).

Usage

diff_dates(
  from_date,
  to_date = Sys.Date(),
  unit = "years",
  as_character = TRUE
)

Arguments

Details

diff_dates answers questions like "How much time has elapsed between two dates?" or "How old are you?" in human time periods of (full) years, months, and days.

Key characteristics:

• If to_date or from_date are not "Date" objects, diff_dates aims to coerce them into "Date" objects.

• If to_date is missing (i.e., NA), to_date is set to today's date (i.e., Sys.Date()).

• If to_date is specified, any intermittent missing values (i.e., NA) are set to today's date (i.e., Sys.Date()). Thus, dead people (with both birth dates and death dates specified) do not age any further, but people still alive (with is.na(to_date), are measured to today's date (i.e., Sys.Date()).

• If to_date precedes from_date (i.e., from_date > to_date) computations are performed on swapped days and the result is marked as negative (by a character "-") in the output.

• If the lengths of from_date and to_date differ, the shorter vector is recycled to the length of the longer one.

By default, diff_dates provides output as (signed) character strings. For numeric outputs, use as_character = FALSE.

Value

A character vector or data frame (with dates, sign, and numeric columns for units).

See Also

Time spans (interval as.period) in the lubridate package.

Other date and time functions: change_time(), change_tz(), cur_date(), cur_time(), days_in_month(), diff_times(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

y_100 <- Sys.Date() - (100 * 365.25) + -1:1
diff_dates(y_100)

# with "to_date" argument: 
y_050 <- Sys.Date() - (50 * 365.25) + -1:1 
diff_dates(y_100, y_050)
diff_dates(y_100, y_050, unit = "d") # days (with decimals)

# Time unit and output format:
ds_from <- as.Date("2010-01-01") + 0:2
ds_to   <- as.Date("2020-03-01")  # (2020 is leap year)
diff_dates(ds_from, ds_to, unit = "y", as_character = FALSE)  # years
diff_dates(ds_from, ds_to, unit = "m", as_character = FALSE)  # months
diff_dates(ds_from, ds_to, unit = "d", as_character = FALSE)  # days

# Robustness:
days_cur_year <- 365 + is_leap_year(Sys.Date())
diff_dates(Sys.time() - (1 * (60 * 60 * 24) * days_cur_year))  # for POSIXt times
diff_dates("10-08-11", "20-08-10")   # for strings
diff_dates(20200228, 20200301)       # for numbers (2020 is leap year)

# Recycling "to_date" to length of "from_date":
y_050_2 <- Sys.Date() - (50 * 365.25)
diff_dates(y_100, y_050_2)

# Note maxima and minima: 
diff_dates("0000-01-01", "9999-12-31")  # max. d + m + y
diff_dates("1000-06-01", "1000-06-01")  # min. d + m + y

# If from_date == to_date:
diff_dates("2000-01-01", "2000-01-01")

# If from_date > to_date:
diff_dates("2000-01-02", "2000-01-01")  # Note negation "-"
diff_dates("2000-02-01", "2000-01-01", as_character = TRUE)
diff_dates("2001-02-02", "2000-02-02", as_character = FALSE)

# Test random date samples:
f_d <- sample_date(size = 10)
t_d <- sample_date(size = 10)
diff_dates(f_d, t_d, as_character = TRUE)

# Using 'fame' data:
dob <- as.Date(fame$DOB, format = "%B %d, %Y")
dod <- as.Date(fame$DOD, format = "%B %d, %Y")
head(diff_dates(dob, dod))  # Note: Deceased people do not age further.
head(diff_dates(dob, dod, as_character = FALSE))  # numeric outputs

Description

diff_times computes the difference between two times (i.e., from some from_time to some to_time) in human measurement units (periods).

Usage

diff_times(from_time, to_time = Sys.time(), unit = "days", as_character = TRUE)

Arguments

Details

diff_times answers questions like "How much time has elapsed between two dates?" or "How old are you?" in human time periods of (full) years, months, and days.

Key characteristics:

• If to_time or from_time are not "POSIXct" objects, diff_times aims to coerce them into "POSIXct" objects.

• If to_time is missing (i.e., NA), to_time is set to the current time (i.e., Sys.time()).

• If to_time is specified, any intermittent missing values (i.e., NA) are set to the current time (i.e., Sys.time()).

• If to_time precedes from_time (i.e., from_time > to_time) computations are performed on swapped times and the result is marked as negative (by a character "-") in the output.

• If the lengths of from_time and to_time differ, the shorter vector is recycled to the length of the longer one.

By default, diff_times provides output as (signed) character strings. For numeric outputs, use as_character = FALSE.

Value

A character vector or data frame (with times, sign, and numeric columns for units).

See Also

diff_dates for date differences; time spans (an interval as.period) in the lubridate package.

Other date and time functions: change_time(), change_tz(), cur_date(), cur_time(), days_in_month(), diff_dates(), diff_tz(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

t1 <- as.POSIXct("1969-07-13 13:53 CET")  # (before UNIX epoch)
diff_times(t1, unit = "years", as_character = TRUE)
diff_times(t1, unit = "secs", as_character = TRUE)

Description

diff_tz computes the time difference between two times t1 and t2 that is exclusively due to both times being in different time zones.

Usage

diff_tz(t1, t2, in_min = FALSE)

Arguments

Details

diff_tz ignores all differences in nominal times, but allows adjusting time-based computations for time shifts that are due to time zone differences (e.g., different locations, or changes to/from daylight saving time, DST), rather than differences in actual times.

Internally, diff_tz determines and contrasts the POSIX conversion specifications " (in numeric form).

If the lengths of t1 and t2 differ, the shorter vector is recycled to the length of the longer one.

Value

A character (in "HH:MM" format) or numeric vector (number of minutes).

See Also

days_in_month for the number of days in given months; is_leap_year to check for leap years.

Other date and time functions: change_time(), change_tz(), cur_date(), cur_time(), days_in_month(), diff_dates(), diff_times(), is_leap_year(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

# Time zones differences:
tm <- "2020-01-01 01:00:00"  # nominal time
t1 <- as.POSIXct(tm, tz = "Pacific/Auckland")
t2 <- as.POSIXct(tm, tz = "Europe/Berlin")
t3 <- as.POSIXct(tm, tz = "Pacific/Honolulu")

# as character (in "HH:MM"):
diff_tz(t1, t2)
diff_tz(t2, t3)
diff_tz(t1, t3)

# as numeric (in minutes):
diff_tz(t1, t3, in_min = TRUE)

# Compare local times (POSIXlt): 
t4 <- as.POSIXlt(Sys.time(), tz = "Pacific/Auckland")
t5 <- as.POSIXlt(Sys.time(), tz = "Europe/Berlin")
diff_tz(t4, t5)
diff_tz(t4, t5, in_min = TRUE)

# DSL shift: Spring ahead (on 2020-03-29: 02:00:00 > 03:00:00):
s6 <- "2020-03-29 01:00:00 CET"   # before DSL switch
s7 <- "2020-03-29 03:00:00 CEST"  # after DSL switch
t6 <- as.POSIXct(s6, tz = "Europe/Berlin")  # CET
t7 <- as.POSIXct(s7, tz = "Europe/Berlin")  # CEST

diff_tz(t6, t7)  # 1 hour forward
diff_tz(t6, t7, in_min = TRUE)

Description

The ds4psy package currently only contains a default vignette that provides general information and links.

Usage

ds4psy.guide()

Description

dt_10 contains precise DOB information of 10 non-existent, but definitely Danish people.

Usage

dt_10

Format

A table with 10 cases (rows) and 7 variables (columns).

Source

See CSV data file at http://rpository.com/ds4psy/data/dt_10.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

exp_num_dt is a fictitious set of data describing 1000 non-existing, but surprisingly friendly people.

Usage

exp_num_dt

Format

A table with 1000 cases (rows) and 15 variables (columns).

Details

Codebook The data characterize 1000 individuals (rows) in 15 variables (columns):

• 1. name: Participant initials.

• 2. gender: Self-identified gender (as a binary variable).

• 3. bday: Day (within month) of DOB.

• 4. bmonth: Month (within year) of DOB.

• 5. byear: Year of DOB.

• 6. height: Height (in cm).

• 7. blood_type: Blood type.

• 8. bnt_1 to 11. bnt_4: Correct response to corresponding BNT question? (1: correct, 0: incorrect).

• 12. g_iq and 13. s_iq: Scores from two IQ tests (general vs. social).

• 14. t_1 and 15. t_2: Study start and end time.

exp_num_dt was generated for practice purposes. It allows (1) converting data tables from wider into longer format, (2) dealing with date- and time-related variables, and (3) computing, analyzing, and visualizing test scores (e.g., numeracy, IQ).

The gender variable was converted into a binary variable (i.e., using 2 categories "female" and "not female").

Source

See CSV data files at http://rpository.com/ds4psy/data/numeracy.csv and http://rpository.com/ds4psy/data/dt.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_wide, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

exp_wide is a fictitious dataset to practice tidying data (here: converting from wide to long format).

Usage

exp_wide

Format

A table with 10 cases (rows) and 7 variables (columns).

Source

See CSV data at http://rpository.com/ds4psy/data/exp_wide.csv.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, falsePosPsy_all, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

falsePosPsy_all is a dataset containing the data from 2 studies designed to highlight problematic research practices within psychology.

Usage

falsePosPsy_all

Format

A table with 78 cases (rows) and 19 variables (columns):

Details

Simmons, Nelson and Simonsohn (2011) published a controversial article with a necessarily false finding. By conducting simulations and 2 simple behavioral experiments, the authors show that flexibility in data collection, analysis, and reporting dramatically increases the rate of false-positive findings.

study

Study ID.

id

Participant ID.

aged

Days since participant was born (based on their self-reported birthday).

aged365

Age in years.

female

Is participant a woman? 1: yes, 2: no.

dad

Father's age (in years).

mom

Mother's age (in years).

potato

Did the participant hear the song 'Hot Potato' by The Wiggles? 1: yes, 2: no.

when64

Did the participant hear the song 'When I am 64' by The Beatles? 1: yes, 2: no.

kalimba

Did the participant hear the song 'Kalimba' by Mr. Scrub? 1: yes, 2: no.

cond

In which condition was the participant? control: Subject heard the song 'Kalimba' by Mr. Scrub; potato: Subject heard the song 'Hot Potato' by The Wiggles; 64: Subject heard the song 'When I am 64' by The Beatles.

root

Could participant report the square root of 100? 1: yes, 2: no.

bird

Imagine a restaurant you really like offered a 30 percent discount for dining between 4pm and 6pm. How likely would you be to take advantage of that offer? Scale from 1: very unlikely, 7: very likely.

political

In the political spectrum, where would you place yourself? Scale: 1: very liberal, 2: liberal, 3: centrist, 4: conservative, 5: very conservative.

quarterback

If you had to guess who was chosen the quarterback of the year in Canada last year, which of the following four options would you choose? 1: Dalton Bell, 2: Daryll Clark, 3: Jarious Jackson, 4: Frank Wilczynski.

olddays

How often have you referred to some past part of your life as “the good old days”? Scale: 11: never, 12: almost never, 13: sometimes, 14: often, 15: very often.

feelold

How old do you feel? Scale: 1: very young, 2: young, 3: neither young nor old, 4: old, 5: very old.

computer

Computers are complicated machines. Scale from 1: strongly disagree, to 5: strongly agree.

diner

Imagine you were going to a diner for dinner tonight, how much do you think you would like the food? Scale from 1: dislike extremely, to 9: like extremely.

See https://bookdown.org/hneth/ds4psy/B.2-datasets-false.html for codebook and more information.

Source

Articles

• Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. doi: 10.1177/0956797611417632

• Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2014). Data from paper "False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant". Journal of Open Psychology Data, 2(1), e1. doi: 10.5334/jopd.aa

See files at https://openpsychologydata.metajnl.com/articles/10.5334/jopd.aa/ and the archive at https://zenodo.org/record/7664 for original dataset.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, fame, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

fame is a dataset to practice working with dates.

fame contains the names, areas, dates of birth (DOB), and — if applicable — the dates of death (DOD) of famous people.

Usage

fame

Format

A table with 67 cases (rows) and 4 variables (columns).

Source

Student solutions to exercises, dates mostly from https://www.wikipedia.org/.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, flowery, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

flowery contains versions and variations of Gertrude Stein's popular phrase "A rose is a rose is a rose".

Usage

flowery

Format

A vector of type character with length(flowery) = 60.

Details

The phrase stems from Gertrude Stein's poem "Sacred Emily" (written in 1913 and published in 1922, in "Geography and Plays"). The verbatim line in the poem actually reads "Rose is a rose is a rose is a rose".

See https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose for additional variations and sources.

Source

Data based on https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, fruits, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

fruits is a dataset containing the names of 122 fruits (as a vector of text strings).

Usage

fruits

Format

A vector of type character with length(fruits) = 122.

Details

Botanically, "fruits" are the seed-bearing structures of flowering plants (angiosperms) formed from the ovary after flowering.

In common usage, "fruits" refer to the fleshy seed-associated structures of a plant that taste sweet or sour, and are edible in their raw state.

Source

Data based on https://simple.wikipedia.org/wiki/List_of_fruits.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, i2ds_survey, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

get_set obtains a set of x/y coordinates and returns it (as a data frame).

Usage

get_set(n = 1)

Arguments

Details

Each set stems from Anscombe's Quartet (see datasets::anscombe, hence 1 <= n <= 4) and is returned as an 11 x 2 data frame.

Source

See ?datasets:anscombe for details and references.

See Also

Other data functions: make_grid()

Examples

get_set(1)
plot(get_set(2), col = "red")

Description

i2ds_survey contains pre-processed data from the i2ds online survey.

Usage

i2ds_survey

Format

On 2026-05-04, this data contains 104 participants (rows) and 119 variables (columns).

Details

Prefix codes

Many variable names have prefixes that indicate a particular type of variable:

• rv: A random variable

• c(#): A choice variable (with # alternatives)

• t: A text variable (with any input)

• tn: A text variable (with numeric input)

• crs: A course-related variable

• combined: A composite variable created by averaging either 4 or 5 individual Likert-scale items. Depending on the item set, the resulting score was normalized (i.e., divided by 4 or 5), and stored as a new variable.

List of variables

After pre-processing the raw data and re-arranging its variables (columns), the variable names and their contents in the i2ds_survey tibble are as follows:

1. Key person-related variables: c4_gender A categorical (character) variable indicating the participant’s gender identity, with possible values including "female", "male", "non-binary" or "do not wish to respond". This variable is used for demographic analysis.

2. tn_year A numeric (double) variable indicating the year of birth (e.g., 1999, 2000, 2001, etc.).

3. tn_month A numeric (double) variable indicating the participant’s birth month (1–12). This variable also supports demographic profiling.

4. tn_day A numeric (double) variable indicating the day of birth provided by the participant (1–31). Used for demographic purposes and potential exploratory analyses. DOB-related variables can be used to calculate age and analyze age-related trends.

5. t_height A character variable indicating a participant's self-described height, using various formats and units (e.g., "1.80", "180 cm", "1,80m", or "5'11"). This variable requires pre-processing for analysis.

6. t_pid An optional character variable capturing a participant ID, pseudonym, or other identifying entry. This variable allows participants to recognize their own data without disclosing their identity.

7. Variables indicating informed consent and willingness to share data: c2_informed_consent A logical variable indicating whether the participant provided informed consent before starting the study (TRUE = consent provided, FALSE = no consent provided). This variable is a pre-requisite for ethical compliance (i.e., should be TRUE for all participants).

8. c2_use_data_2 A logical variable indicating whether a participant still agrees to allow their data to be shared after having finished the survey (TRUE = consent provided, FALSE = no consent provided). This variable is a pre-requisite for data re-usability in research (and should be TRUE for all cases included here).

9. Variables indicating course membership: crs_i2ds_1 A logical variable indicating whether a participant is currently enrolled in the course Introduction to Data Science 1: Basics (i2ds 1: TRUE = enrolled).

10. crs_i2ds_2 A logical variable indicating whether a participant is enrolled in the course Introduction to Data Science 2: Applications (i2ds 2: TRUE = enrolled).

11. crs_ds4psy A logical variable indicating whether a participant is enrolled in the course Data Science for Psychology (ds4psy: TRUE = enrolled).

12. crs_viz A logical variable indicating whether a participant is enrolled in the course Data visualization for psychology research (vis4psy: TRUE = enrolled).

13. crs_diff_kn A logical variable indicating whether a participant is enrolled in a different course at the University of Konstanz (TRUE = yes).

14. crs_diff_else A logical variable indicating whether a participant is enrolled in a course not at the University of Konstanz (TRUE = yes). This variable helps identifying external learners.

15. crs_self_study A logical variable indicating whether a participant is engaging with course materials without formal enrollment (TRUE = yes). This variable reflects informal learning engagement.

16. crs_only_study A logical variable indicating whether a participant is taking the survey only, without engaging with course materials (TRUE = yes). This variable identifies participants not studying R or data science.

17. t_crs_other A character variable capturing free-text input describing any other course a participant is taking.

18. v_crs_other_dept A character variable indicating the department of the other course(s) mentioned in t_crs_other. This variable may facilitate grouping participants by academic discipline.

19. Variables indicating (randomized) survey conditions: rv_anchor_high_low A randomized (character) variable that indicates whether a person is to keep a relatively large or small number in memory (i.e., assignment to either 242 or 42, respectively). This manipulation is used to examine anchoring effects on later responses.

20. rv_scale_randomization A randomized (character) variable that indicates whether a person was asked to rate their personality (from "serious" to "humorous") on a 4-point or on a 5-point Likert scale. The variable controls for the influence of scale granularity on ratings.

21. rv_barnum_pos_neg A randomized (character) variable that indicates whether the participant is to receive a positive or negative Barnum statement ("positive" vs. "negative"). This is used to measure sensitivity to vague or generic personality feedback.

22. rv_sc_false_dicho_3 A randomized (character) variable indicating which version of the scale is to be shown: a dichotomous comparison between admiration vs. respect, fear vs. love, admiration vs. love and fear, or a single undivided scale (values: "admir_resp" "fear_love", "admir_love" fear_resp", "single_scale"). Used to examine how scale format affects evaluative judgments.

23. rv_wait_time A randomized (character) variable that indicates whether the participant waited 10 seconds ("short") or 30 seconds ("long") before continuing. This manipulation aims to examine whether a longer waiting period increases the perceived credibility or value of a following personality feedback, in line with mechanisms underlying the Barnum effect.

24. rv_political_orientation A randomized (character) variable indicating the order in which the two political orientation scales ("left–right" and "liberal–conservative") were presented. Possible values include "left_right, lib_cons", "left_cons, lib_right", etc. This variable is used to control for potential order effects in political self-placement tasks.

25. rv_thinkingstyle A randomized (character) variable that indicates the order in which pairs of thinking styles are to be presented ("deliberative vs. intuitive"; "reflective vs. spontaneous";" deliberative vs. spontaneous";"reflective vs. Intuitive"). The order is counterbalanced to reduce presentation bias in self-assessment tasks.

26. Binary choices on art preference: c2_img_sel_1 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 1. The binary variable indicates the participant's image preference:

    • 1 corresponds to the cubist painting Les Baigneurs (the bathers), by Roger de La Fresnaye, 1912

    • 2 corresponds to the expressionist painting Badende Mädchen (bathing girls), by August Macke, 1913

27. c2_img_sel_2 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 2. The binary variable indicates the participant's image preference:

    • 1 corresponds to the cubist painting Le Gouter (the taster, aka. tea time), by Jean Metzinger, 1911

    • 2 corresponds to the expressionist painting La petite Jeanne, by Amedeo Modigliani, 1909

28. c2_img_sel_3 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 3. The binary variable indicates the participant's image preference:

    • 1 corresponds to the cubist painting Edtaonisl Ecclesiastic (the 1st word being an acronym made by alternating the French words for 'star' and 'dance'), by Francis Picabia, 1913

    • 2 corresponds to the impressionist painting Femme avec parasol dans un jardin (woman with parasol in a garden), by Pierre-Auguste Renoir, 1875

29. c2_img_sel_4 A numeric (double) variable that represents the participant's preferred choice between 2 images in choice Set 4. The binary variable indicates the participant's image preference:

    • 1 corresponds to the expressionist painting Solitude, by Alexej von Jawlensky, 1912

    • 2 corresponds to the impressionist painting Pont dans le Jardin de Monet (bridge in Monet’s garden), by Claude Monet, 1895–96

30. Variables describing habits and preferences: c7_eating_habits A categorical (character) variable that indicates which dietary lifestyle an individual assigns to itself (1 = "vegetarian"; 2 = "omnivore"; 3 = "vegan"; 4 = "pescetarian"; 5 = "flexitarian"; 6 = "carnivore"; 7 = "other").

31. t_eating_habits_other A character variable intended to capture free-text input for other dietary descriptions; usually NA unless "other" was selected. May appear as logical if no responses were entered.

32. c7_apple A numeric (double) variable indicating how much a participant likes apples on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

33. c7_cherry A numeric (double) variable indicating how much a participant likes cherries on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

34. c7_broccoli A numeric (double) variable indicating how much a participant likes broccoli on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

35. c7_asparagus A numeric (double) variable indicating how much a participant likes asparagus on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

36. c7_spinach A numeric (double) variable indicating how much a participant likes spinach on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

37. c7_mud A numeric (double) variable indicating how much a participant likes mud on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

38. c7_banana A numeric (double) variable indicating how much a participant likes bananas on a 1-7 ranking scale (1 = highest preference, 7 = lowest preference, 0 if not ranked).

    Note: Variables c7_apple to c7_banana were derived from a sorting/ranking task in which each participant sorted/ranked food items by preference. Each item was subsequently coded as a numeric value between 1 and 7 (0 if not ranked).

39. Responses to binary choice items: c2_decsleep_instant A categorical (character) variable indicating whether a participant prefers to sleep before making important decisions ("sleep") or to make them instantly ("instant").

40. c2_shopperson_online A categorical (character) variable indicating whether a participant prefers shopping in person ("person") or online ("online").

41. c2_town_city A categorical (character) variable indicating whether a participant prefers living in a town ("town") or in a city ("city").

42. c2_club_house A categorical (character) variable indicating whether a participant prefers to party in a club ("club") or to attend an house party ("house").

43. c2_hotel_camping A categorical (character) variable capturing a participant's preference for staying in a hotel ("hotel") versus going camping ("camping").

44. c2_photo_being A categorical (character) variable indicating whether a participant prefers photographing ("photo") or being in a moment ("being").

45. c2_spring_fall A categorical (character) variable indicating whether a participant prefers the spring season ("spring") or the fall/autumn season ("fall").

46. c2_beach_mount A categorical (character) variable reflecting whether a participant prefers the beach ("beach") or the mountains ("mount").

47. c2_cats_dogs A categorical (character) variable indicating preference for cats ("cats") versus dogs ("dogs").

48. c2_indiv_team A categorical (character) variable indicating whether a participant prefers individual ("indiv") or team sports ("team").

49. c2_movies_books A categorical (character) variable indicating a participant's preference for movies ("movies") or books ("books").

50. c2_board_video A categorical (character) variable indicating whether a participant prefers board games ("board") or video games ("video").

51. c2_ios_android A categorical (character) variable indicating whether a participant prefers iOS ("ios") or Android ("android") as a mobile operating system.

52. c2_text_voice A categorical (character) variable indicating whether a participant prefers texting ("text") or sending voice messages ("voice").

53. c2_cook_bake A categorical (character) variable indicating whether a participant prefers cooking ("cook") or baking ("bake").

54. c2_pinapple_no A categorical (character) variable that records whether a participant likes pineapple on pizza ("yes") or not ("no").

55. c2_ketchup_mayo A categorical (character) variable indicating whether a participant prefers ketchup ("ketchup") or mayonnaise ("mayo").

56. c2_coffee_tea A categorical (character) variable indicating whether a participant prefers coffee ("coffee") or tea ("tea").

57. c2_math_lang A categorical (character) variable indicating whether a participant prefers mathematics ("math") or language-related subjects ("lang").

58. c2_odd_even A categorical (character) variable indicating whether a participant prefers odd numbers ("odd") or even numbers ("even").

59. c3_diff_bin A categorical (character) variable indicating how difficult it was for a participant to make their previous preference decisions (items 22–41) . Response options include "yes", "a little", and "no". This item captures perceived decisional difficulty and may serve as an indicator of response certainty, thinking style, or task engagement.

60. Variables on political opinions: politics_left A numeric (double) variable representing the participant’s self-placement on a left–right political spectrum. Values range from 1 (left) to 6 (right).

61. politics_liberal A numeric (double) variable representing self-placement on a liberal to conservative scale, ranging from 1 (liberal) to 6 (conservative).

62. Miscellaneous estimates, choices, opinions, and preferences: tn_estimate_sun A numeric (double) variable capturing the participant’s estimate of how many times larger the sun’s diameter is compared to that of the earth. This item serves as a manipulation check for the anchoring effect, based on previously presented numeric anchors (e.g., 42 or 242).

63. t_att_check_1 A character variable containing the participant’s open-text response to an attention check prompt ("Please type: 'I read the instructions'"). This attention check allows detecting inattentive or automated responses.

64. c2_fly_invisible A categorical (character) variable indicating whether the participant would prefer the superpower of flying ("fly") or becoming invisible ("invisible").

65. t_fly_invisible_explain A character variable where participants explain their choice between flying and invisibility. This free text answer allows for qualitative analysis of a participant's justifications and motivations.

66. combined_c_ser_hum_self A numeric (double) variable reflecting a participant’s self-assessment on a "serious vs. humorous" scale. The score is based on a 4-point or 5-point Likert scale, depending on random assignment. This variable is used to test how perspective (self vs. others) and scale format (presence vs. absence of a middle option) influences self-ratings.

67. combined_c_ser_hum_others A combined numeric (double) variable reflecting how humorous or serious participants believe others to perceive them. This score is derived from either a 4-point or 5-point scale and is used to examine the effect of perspective and scale design on perceived external ratings.

68. c4_chronotype A categorical (character) variable indicating whether the participant identifies as a morning person ("morning"), evening person ("evening") mid-day person ("mid-day") or a never person ("never").

69. tn_sleep A numeric (double) variable indicating the typical number of hours the participant typically sleeps per night.

70. tn_bedtime A character variable representing the participant’s usual bedtime, to be entered in 24-hour format (e.g., "22:30", "00:00").

71. tn_anchor_recall_1 A numeric (double) variable recording the number (either 42 or 242) that the participant was previously asked to memorize and later recall. It is used to test memory for the anchor manipulation.

72. combined_admired A combined numeric (double) variable reflecting how much a participant wants to be admired by others, rated on a 1–6 Likert scale (1 = not at all, 6 = very much).

73. combined_feared A combined numeric (double) variable reflecting how much a participant wants to be feared by others, rated on a 1–6 Likert scale (1 = not at all, 6 = very much).

74. combined_loved A combined numeric (double) variable reflecting how much a participant wants to be loved by others, rated on a 1–6 Likert scale (1 = not at all, 6 = very much).

75. combined_respected A combined numeric (double) variable reflecting how much a participant wants to be respected by others, rated on a 1–6 Likert scale (1 = not at all, 6 = very much).

76. c7_pess_opti A numeric (double) variable capturing a participant’s self-rated tendency toward pessimism versus optimism, on a 7-point scale (1 = very pessimistic, 7 = very optimistic).

77. c7_story_list A numeric (double) variable indicating how much a participant enjoys listening to or reading stories, rated from 1 (not at all) to 7 (very much).

78. c7_stab_adv A numeric (double) variable indicating a participant’s self-assessed position on a stability versus adventurousness spectrum, rated on a scale from 1 (very stable) to 7 (very adventurous). This variable may indicate personality traits related to risk-taking.

79. think_reflect A numeric (double) variable representing a participant’s placement on a bipolar scale ranging from 1 ("reflective") to 6 (either "spontaneous" or " intuitive"). The specific version of the 2nd scale anchor is randomly assigned.

80. think_delib A numeric (double) variable representing a participant’s placement on a bipolar scale ranging from 1 ("deliberative") to 6 (either "intuitive" or " spontaneous". The specific version of the 2nd scale anchor is randomly assigned.

81. c4_intro_extrovert A categorical (character) variable indicating a participant's self-rated social orientation: "introverted", "extroverted", or mixed variants such as "extro-intro" or "intro-extro".

82. tn_favorit_number A numeric (double) variable capturing a participant’s favorite number, in free answer format.

83. c3_cutlery A categorical (character) variable indicating which piece of cutlery a participant most identifies with. The 3 possible values include "knife", "fork", and "spoon".

84. c3_rock_paper_scissors A categorical (character) variable capturing a participant's selection in a rock–paper–scissors scenario. The 3 possible values are "rock", "paper", or "scissors".

85. c5_att_check_2 A numeric (double) variable used as an attention check. Participants were asked to select the number that most resembles the shape of a circle. The correct response is 0, which corresponds to scale option 5. Responses deviating from this may indicate inattentiveness.

86. c6_barnum_accuracy A numeric (double) variable indicating how accurately a participant rated a generic personality description (i.e., a Barnum statement), on a scale from 1 (poor) to 6 (perfect). This variable is used to assess susceptibility to the so-called Barnum effect (i.e., the tendency to perceive vague and general statements as highly accurate).

87. t_anchor_recall_2 A numeric (double) variable recording whether a participant correctly remembered a previously presented number (either 42 or 242). This assesses memory and anchoring manipulation success (for a 2nd time).

88. Other person-related variables: c9_occupation A categorical (character) variable indicating a participant’s current occupational status (e.g., "student", "employed", "other"). This variable may be used for demographic segmentation.

89. t_occupation_other A logical variable for free-text input if a participant selected "other" for occupation. This variable captures detailed occupational descriptions not covered by the pre-defined options.

90. c7_education A categorical (character) variable indicating a participant’s highest completed education level (e.g., "high school", "bachelor", "master"). This variable may be used for demographic segmentation.

91. t_education_other A logical variable to allow participants to enter their education level in free text (if "other" was selected). This variable enables open-format responses for less common education paths.

92. c3_current_degree A categorical (character) variable indicating the type of academic degree a participant is currently pursuing (e.g, "bachelor", "master"). This variable provides educational context for other academic measures.

93. tn_semester A numeric (double) variable indicating the current semester of study reported by a participant (e.g., 1, 6, 10). This variable helps contextualize course experience and academic progress.

94. c14_studyfield A categorical (character) variable indicating the participant’s field of study (e.g., "psychology", "data science"). This variable is used to examine field-specific attitudes and skills.

95. t_studyfield_other A character variable capturing free-text responses if the participant selected "other" as their study field. This variable allows classification of less common disciplines.

96. Preferences for course contents: c5_pref_stats A numeric (double) variable indicating a participant’s interest in preparing data for statistical analysis, rated on a scale from 1 (no interest) to 5 (absolutely essential).

97. c5_pref_visualize A numeric (double) variable indicating a participant's interest in data visualization in R, rated on a scale from 1 (no interest) to 5 (absolutely essential).

98. c5_pref_sims A numeric (double) variable indicating a participant’s interest in using R for simulations and modeling, rated on a scale from 1 (no interest) to 5 (absolutely essential).

99. c5_pref_shiny A numeric (double) variable capturing how essential a participant considers learning to build interactive web applications using R Shiny. Responses range from 1 (no interest) to 5 (absolutely essential).

100. c5_pref_scrape A numeric (double) variable capturing how essential a participant considers learning web scraping with R. Responses range from 1 (no interest) to 5 (absolutely essential).

101. c5_pref_arts A numeric (double) variable capturing how essential a participant considers exploring artistic or creative aspects of data science (e.g., generative art in R). Responses range from 1 (no interest) to 5 (absolutely essential).

102. Course-related expectations and worries: t_crs_expect_i2ds_1 A character variable containing free-text input describing a participant’s expectations and hopes for the course Introduction to Data Science 1: Basics (i2ds 1).

103. t_crs_worry_i2ds_1 A character variable capturing free-text responses describing a participant’s worries and reservations related to the course Introduction to Data Science 1: Basics (i2ds 1).

104. t_crs_expect_i2ds_2 A character variable containing free-text input describing a participant’s expectations and hopes for the course Introduction to Data Science 2: Applications (i2ds 2).

105. t_crs_worry_i2ds_2 A character variable capturing free-text input describing a participant’s worries and reservations concerns related to the course Introduction to Data Science 2: Applications (i2ds 2).

106. t_crs_expect_ds4psy A logical variable containing free-text input describing a participant’s expectations and hopes for the course Data Science for Psychology (ds4psy).

107. t_crs_worry_ds4psy A logical variable describing a participant’s worries and reservations regarding the course Data Science for Psychology (ds4psy), in free text format.

108. t_crs_expect_viz A logical variable containing free-text input describing a participant’s expectations and hopes for the course Data visualization for psychology research (vis4psy).

109. t_crs_worry_viz A logical variable describing a participant’s worries and reservations regarding the course Data visualization for psychology research (vis4psy), in free text format.

110. Variables on prior experience: c6_exp_math A numeric (double) variable indicating a participant’s self-assessed experience with mathematics, rated on a scale from 1 (no experience) to 6 (extremely experienced).

111. c6_exp_statistics A numeric (double) variable measuring a participant’s self-assessed experience with statistics, rated on a scale from 1 (no experience) to 6 (extremely experienced).

112. c6_exp_program A numeric (double) variable indicating a participant’s experience with programming (any programming language), rated on a scale from 1 (no experience) to 6 (extremely experienced).

113. c6_exp_r A numeric (double) variable indicating a participant’s experience with R programming, rated on a scale from 1 (no experience) to 6 (extremely experienced).

114. c6_exp_datavisual A numeric (double) variable capturing a participant’s prior experience with data visualization, rated on a scale from 1 (no experience) to 6 (extremely experienced).

115. Survey feedback: t_feedback An optional character variable containing general feedback provided by the participant regarding the survey or course. This is an open-ended text field for final comments, impressions, or suggestions.

116. Session info: referer URL of referring page.

117. datetime Date and time of initial survey access.

118. duration Session duration (in seconds).

119. date_of_last_access Date and time of final survey access.

See the codebook and print version for additional coding details.

Missing values are represented as NA values in the data. These can be due to a participant not providing a response to an item or to an item not being applicable to this participant.

Source

See online survey at https://ww3.unipark.de/uc/i2ds_survey/.

See Also

Other datasets: Bushisms, Trumpisms, countries, data_1, data_2, data_t1, data_t1_de, data_t1_tab, data_t2, data_t3, data_t4, dt_10, exp_num_dt, exp_wide, falsePosPsy_all, fame, flowery, fruits, outliers, pi_100k, posPsy_AHI_CESD, posPsy_long, posPsy_p_info, posPsy_wide, t3, t4, t_1, t_2, t_3, t_4, table6, table7, table8, table9, tb

Description

invert_rules allows decoding messages that were encoded by a set of rules x.

Usage

invert_rules(x)

Arguments

Details

x is assumed to be a named vector.

invert_rules replaces the elements of x by the names of x, and vice versa.

A message is issued if the elements of x are repeated (i.e., decoding is non-unique).

Value

A character vector.

See Also

transl33t for encoding text (e.g., into leet slang); l33t_rul35 for default rules used.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

invert_rules(l33t_rul35)  # Note repeated elements

# Encoding and decoding a message:
(txt_0 <- "Hello world! How are you doing today?")             # message
(txt_1 <- transl33t(txt_0, rules = l33t_rul35))                # encoding 
(txt_2 <- transl33t(txt_1, rules = invert_rules(l33t_rul35)))  # decoding

Description

is_equal tests if two vectors x and y are pairwise equal.

Usage

is_equal(x, y, ...)

Arguments

Details

If both x and y are numeric, is_equal calls num_equal(x, y, ...) (allowing for a tolerance threshold tol). Otherwise, x and y are compared by x == y.

is_equal provides a wrapper around num_equal (for numeric objects x and y) and == (otherwise).

See Also

num_equal function for comparing numeric vectors; all.equal function of the R base package; near of the dplyr package.

Other numeric functions: base2dec(), base_digits, dec2base(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Other utility functions: base2dec(), base_digits, dec2base(), is_vect(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Examples

# numeric data: 
is_equal(2, sqrt(2)^2)
is_equal(2, sqrt(2)^2, tol = 0)
is_equal(c(2, 3), c(sqrt(2)^2, sqrt(3)^2, 4/2, 9/3))

# other data types:
is_equal((1:3 > 1), (1:3 > 2))                         # logical
is_equal(c("A", "B", "c"), toupper(c("a", "b", "c")))  # character
is_equal(as.Date("2023-10-30"), Sys.Date())            # dates

# factors:
is_equal((1:3 > 1), as.factor((1:3 > 2)))  
is_equal(c(1, 2, 3), as.factor(c(1, 2, 3)))
is_equal(c("A", "B", "C"), as.factor(c("A", "B", "C")))

Description

is_leap_year checks whether a given year (provided as a date or time dt, or number/string denoting a 4-digit year) lies in a so-called leap year (i.e., a year containing a date of Feb-29).

Usage

is_leap_year(dt)

Arguments

Details

When dt is not recognized as "Date" or "POSIXt" object(s), is_leap_year aims to parse a string dt as describing year(s) in a "dddd" (4-digit year) format, as a valid "Date" string (to retrieve the 4-digit year "%Y"), or a numeric dt as 4-digit integer(s).

is_leap_year then solves the task by verifying the numeric definition of a "leap year" (see https://en.wikipedia.org/wiki/Leap_year).

An alternative solution that tried using as.Date() for defining a "Date" of Feb-29 in the corresponding year(s) was removed, as it evaluated NA values as FALSE.

Value

Boolean vector.

Source

See https://en.wikipedia.org/wiki/Leap_year for definition.

See Also

days_in_month for the number of days in given months; diff_tz for time zone-based time differences; leap_year function of the lubridate package.

Other date and time functions: change_time(), change_tz(), cur_date(), cur_time(), days_in_month(), diff_dates(), diff_times(), diff_tz(), what_date(), what_month(), what_time(), what_wday(), what_week(), what_year(), zodiac()

Examples

is_leap_year(2020)
(days_this_year <- 365 + is_leap_year(Sys.Date()))

# from dates:
is_leap_year(Sys.Date())
is_leap_year(as.Date("2022-02-28"))

# from times:
is_leap_year(Sys.time())
is_leap_year(as.POSIXct("2022-10-11 10:11:12"))
is_leap_year(as.POSIXlt("2022-10-11 10:11:12"))

# from non-integers:
is_leap_year(2019.5)

# For vectors:
is_leap_year(2020:2028)

# with dt as strings:
is_leap_year(c("2020", "2021"))
is_leap_year(c("2020-02-29 01:02:03", "2021-02-28 01:02"))

# Note: Invalid date string yields error: 
# is_leap_year("2021-02-29")

Description

is_vect tests if x is a vector.

Usage

is_vect(x)

Arguments

Details

is_vect does what the base R function is.vector is not designed to do:

• is_vect() returns TRUE if x is an atomic vector or a list (irrespective of its attributes).

• is.vector() returns TRUE if x is a vector of the specified mode having no attributes other than names, otherwise FALSE.

Internally, the function is a wrapper for is.atomic(x) | is.list(x).

Note that data frames are also vectors.

See the is_vector function of the purrr package and the base R functions is.atomic, is.list, and is.vector, for details.

See Also

is_vect function of the purrr package; is.atomic function of the R base package; is.list function of the R base package; is.vector function of the R base package.

Other utility functions: base2dec(), base_digits, dec2base(), is_equal(), is_wholenumber(), num_as_char(), num_as_ordinal(), num_equal()

Examples

# Define 3 types of vectors:
v1 <- 1:3  # (a) atomic vector
names(v1) <- LETTERS[v1]  # with names

v2 <- v1   # (b) copy vector
attr(v2, "my_attr") <- "foo"  # add an attribute
ls <- list(1, 2, "C")  # (c) list

# Compare:
is.vector(v1)
is.list(v1)
is_vect(v1)

is.vector(v2)  # FALSE
is.list(v2)
is_vect(v2)  # TRUE

is.vector(ls)
is.list(ls)
is_vect(ls)

# Data frames are also vectors: 
df <- as.data.frame(1:3)
is_vect(df)  # is TRUE

Description

is_wholenumber tests if x contains only integer numbers.

Usage

is_wholenumber(x, tol = .Machine$double.eps^0.5)

Arguments

Details

is_wholenumber does what the base R function is.integer is not designed to do:

• is_wholenumber() returns TRUE or FALSE depending on whether its numeric argument x is an integer value (i.e., a "whole" number).

• is.integer() returns TRUE or FALSE depending on whether its argument is of integer type, and FALSE if its argument is a factor.

See the documentation of is.integer for definition and details.

See Also

is.integer function of the R base package.

Other numeric functions: base2dec(), base_digits, dec2base(), is_equal(), num_as_char(), num_as_ordinal(), num_equal()

Other utility functions: base2dec(), base_digits, dec2base(), is_equal(), is_vect(), num_as_char(), num_as_ordinal(), num_equal()

Examples

is_wholenumber(1)    # is TRUE
is_wholenumber(1/2)  # is FALSE
x <- seq(1, 2, by = 0.5)
is_wholenumber(x)

# Compare:
is.integer(1+2) 
is_wholenumber(1+2)

Description

l33t_rul35 specifies rules for translating characters into other characters (typically symbols) to mimic leet/l33t slang (as a named character vector).

Usage

l33t_rul35

Format

An object of class character of length 13.

Details

Old (i.e., to be replaced) characters are paste(names(l33t_rul35), collapse = "").

New (i.e., replaced) characters are paste(l33t_rul35, collapse = "").

See https://en.wikipedia.org/wiki/Leet for details.

See Also

transl33t for a corresponding function.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), map_text_chars(), map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Description

make_grid generates a grid of x/y coordinates and returns it (as a data frame).

Usage

make_grid(x_min = 0, x_max = 2, y_min = 0, y_max = 1)

Arguments

See Also

Other data functions: get_set()

Examples

make_grid()
make_grid(x_min = -3, x_max = 3, y_min = -2, y_max = 2)

Description

map_text_chars parses text (from a text string x) into a table that contains a row for each character and x/y-coordinates corresponding to the character positions in x.

Usage

map_text_chars(x, flip_y = FALSE)

Arguments

Details

map_text_chars creates a data frame with 3 variables: Each character's x- and y-coordinates (from top to bottom) and a variable char for the character at these coordinates.

Note that map_text_chars was originally a part of read_ascii, but has been separated to enable independent access to separate functionalities.

Note that map_text_chars is replaced by the simpler map_text_coord function.

Value

A data frame with 3 variables: Each character's x- and y-coordinates (from top to bottom) and a variable char for the character at this coordinate.

See Also

read_ascii for parsing text from file or user input; plot_chars for a character plotting function.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_coord(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Description

map_text_coord parses text (from a text string x) into a table that contains a row for each character and x/y-coordinates corresponding to the character positions in x.

Usage

map_text_coord(x, flip_y = FALSE, sep = "")

Arguments

Details

map_text_coord creates a data frame with 3 variables: Each character's x- and y-coordinates (from top to bottom) and a variable char for the character at these coordinates.

Note that map_text_coord was originally a part of read_ascii, but has been separated to enable independent access to separate functionalities.

Value

A data frame with 3 variables: Each character's x- and y-coordinates (from top to bottom) and a variable char for the character at this coordinate.

See Also

map_text_regex for mapping text to a character table and matching patterns; plot_charmap for plotting character maps; plot_chars for creating and plotting character maps; read_ascii for parsing text from file or user input.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_regex(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

map_text_coord("Hello world!")             # 1 line of text
map_text_coord(c("Hello", "world!"))       # 2 lines of text
map_text_coord(c("Hello", " ", "world!"))  # 3 lines of text
 

## Read text from file:

## Create a temporary file "test.txt":
# cat("Hello world!", "This is a test.",
#     "Can you see this text?", "Good! Please carry on...",
#      file = "test.txt", sep = "\n")
 
# txt <- read_ascii("test.txt")
# map_text_coord(txt)

# unlink("test.txt")  # clean up (by deleting file).

Description

map_text_regex parses text (from a file or user input) into a data frame that contains a row for each character of x.

Usage

map_text_regex(
  x = NA,
  file = "",
  lbl_hi = NA,
  lbl_lo = NA,
  bg_hi = NA,
  bg_lo = "[[:space:]]",
  lbl_rotate = NA,
  case_sense = TRUE,
  lbl_tiles = TRUE,
  col_lbl = "black",
  col_lbl_hi = pal_ds4psy[[1]],
  col_lbl_lo = pal_ds4psy[[9]],
  col_bg = pal_ds4psy[[7]],
  col_bg_hi = pal_ds4psy[[4]],
  col_bg_lo = "white",
  col_sample = FALSE,
  rseed = NA,
  angle_fg = c(-90, 90),
  angle_bg = 0
)

Arguments

Details

map_text_regex allows for regular expression (regex) to match text patterns and create corresponding variables (e.g., for color or orientation).

Five regular expressions and corresponding color and angle arguments allow identifying, marking (highlighting or de-emphasizing), and rotating those sets of characters (i.e, their text labels or fill colors). that match the provided patterns.

The plot generated by plot_chars is character-based: Individual characters are plotted at equidistant x-y-positions and the aesthetic settings provided for text labels and tile fill colors.

map_text_regex returns a plot description (as a data frame). Using this output as an input to plot_charmap plots text in a character-based fashion (i.e., individual characters are plotted at equidistant x-y-positions). Together, both functions replace the over-specialized plot_chars and plot_text functions.

Value

A data frame describing a plot.

See Also

map_text_coord for mapping text to a table of character coordinates; plot_charmap for plotting character maps; plot_chars for creating and plotting character maps; plot_text for plotting characters and color tiles by frequency; read_ascii for reading text inputs into a character string.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), metachar, read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

## (1) From text string(s): 
ts <- c("Hello world!", "This is a test to test this splendid function",
        "Does this work?", "That's good.", "Please carry on.")
sum(nchar(ts))

# (a) simple use:
map_text_regex(ts) 

# (b) matching patterns (regex):
map_text_regex(ts, lbl_hi = "\\b\\w{4}\\b", bg_hi = "[good|test]",
               lbl_rotate = "[^aeiou]", angle_fg = c(-45, +45))

## (2) From user input:
# map_text_regex()  # (enter text in Console)
 
## (3) From text file:
# cat("Hello world!", "This is a test file.",
#     "Can you see this text?",
#     "Good! Please carry on...",
#     file = "test.txt", sep = "\n")
# 
# map_text_regex(file = "test.txt")  # default
# map_text_regex(file = "test.txt", lbl_hi = "[[:upper:]]", lbl_lo = "[[:punct:]]",
#                col_lbl_hi = "red", col_lbl_lo = "blue")
# 
# map_text_regex(file = "test.txt", lbl_hi = "[aeiou]", col_lbl_hi = "red",
#                col_bg = "white", bg_hi = "see")  # mark vowels and "see" (in bg)
# map_text_regex(file = "test.txt", bg_hi = "[aeiou]", col_bg_hi = "gold")  # mark (bg of) vowels
# 
# # Label options:
# map_text_regex(file = "test.txt", bg_hi = "see", lbl_tiles = FALSE)
# map_text_regex(file = "test.txt", angle_bg = c(-20, 20))
# 
# unlink("test.txt")  # clean up (by deleting file).

Description

metachar provides the metacharacters of extended regular expressions (as a character vector).

Usage

metachar

Format

An object of class character of length 12.

Details

metachar allows illustrating the notion of meta-characters in regular expressions (and provides corresponding exemplars).

See ?base::regex for details on regular expressions and ?"'" for a list of character constants/quotes in R.

See Also

cclass for a vector of character classes.

Other text objects and functions: Umlaut, capitalize(), caseflip(), cclass, chars_to_text(), collapse_chars(), count_chars(), count_chars_words(), count_words(), invert_rules(), l33t_rul35, map_text_chars(), map_text_coord(), map_text_regex(), read_ascii(), text_to_chars(), text_to_sentences(), text_to_words(), transl33t(), words_to_text()

Examples

metachar
length(metachar)  # 12
nchar(paste0(metachar, collapse = ""))  # 12

Description

num_as_char converts a number into a character sequence (of a specific length).

Usage

num_as_char(x, n_pre_dec = 2, n_dec = 2, sym = "0", sep = ".")

Arguments

Details

The arguments n_pre_dec and n_dec set a number of desired digits before and after the decimal separator sep. num_as_char tries to meet these digit numbers by adding zeros to the front and end of x. However, when n_pre_dec is lower than the number of relevant (pre-decimal) digits, all relevant digits are shown.

n_pre_dec also works for negative numbers, but the minus symbol is not counted as a (pre-decimal) digit.

Caveat: Note that this function illustrates how numbers, characters, for loops, and paste() can be combined when writing functions. It is not written efficiently or well.

See Also

Other numeric functions: base2dec(), base_digits, dec2base(), is_equal(), is_wholenumber(), num_as_ordinal(), num_equal()

Other utility functions: base2dec(), base_digits, dec2base(), is_equal(), is_vect(), is_wholenumber(), num_as_ordinal(), num_equal()

Examples

num_as_char(1)
num_as_char(10/3)
num_as_char(1000/6) 

# rounding down:
num_as_char((1.3333), n_pre_dec = 0, n_dec = 0)
num_as_char((1.3333), n_pre_dec = 2, n_dec = 0)
num_as_char((1.3333), n_pre_dec = 2, n_dec = 1)

# rounding up: 
num_as_char(1.6666, n_pre_dec = 1, n_dec = 0)
num_as_char(1.6666, n_pre_dec = 1, n_dec = 1)
num_as_char(1.6666, n_pre_dec = 2, n_dec = 2)
num_as_char(1.6666, n_pre_dec = 2, n_dec = 3)

# Note: If n_pre_dec is too small, actual number is kept:
num_as_char(11.33, n_pre_dec = 0, n_dec = 1)
num_as_char(11.66, n_pre_dec = 1, n_dec = 1)

# Note:
num_as_char(1, sep = ",")
num_as_char(2, sym = " ")
num_as_char(3, sym = " ", n_dec = 0)

# for vectors:
num_as_char(1:10/1, n_pre_dec = 1, n_dec = 1)
num_as_char(1:10/3, n_pre_dec = 2, n_dec = 2)

# for negative numbers (adding relevant pre-decimals):
mix <- c(10.33, -10.33, 10.66, -10.66)
num_as_char(mix, n_pre_dec = 1, n_dec = 1)
num_as_char(mix, n_pre_dec = 1, n_dec = 0)

# Beware of bad inputs:
num_as_char(4, sym = "8")
num_as_char(5, sym = "99")