utils_num_str | R Documentation |
all_lower_case()
: Translate all non-numeric strings of a data frame
to lower case.
all_upper_case()
: Translate all non-numeric strings of a data frame
to upper case.
all_title_case()
: Translate all non-numeric strings of a data frame
to title case.
first_upper_case
: Translate the first word of a string to upper
case.
extract_number()
: Extract the number(s) of a string.
extract_string()
: Extract all strings, ignoring case.
find_text_in_num()
: Find text characters in a numeric sequence and
return the row index.
has_text_in_num()
: Inspect columns looking for text in numeric
sequence and return a warning if text is found.
remove_space()
: Remove all blank spaces of a string.
remove_strings()
: Remove all strings of a variable.
replace_number()
: Replace numbers with a replacement.
replace_string()
: Replace all strings with a replacement, ignoring
case.
round_cols()
: Round a selected column or a whole data frame to
significant figures.
tidy_strings()
: Tidy up characters strings, non-numeric columns, or
any selected columns in a data frame by putting all word in upper case,
replacing any space, tabulation, punctuation characters by '_'
, and
putting '_'
between lower and upper case. Suppose that str = c("Env1", "env 1", "env.1")
(which by definition should represent a unique
level in plant breeding trials, e.g., environment 1) is subjected to
tidy_strings(str)
: the result will be then c("ENV_1", "ENV_1", "ENV_1")
. See Examples section for more examples.
all_upper_case(.data, ...) all_lower_case(.data, ...) all_title_case(.data, ...) first_upper_case(.data, ...) extract_number(.data, ..., pattern = NULL) extract_string(.data, ..., pattern = NULL) find_text_in_num(.data, ...) has_text_in_num(.data) remove_space(.data, ...) remove_strings(.data, ...) replace_number( .data, ..., pattern = NULL, replacement = "", ignore_case = FALSE ) replace_string( .data, ..., pattern = NULL, replacement = "", ignore_case = FALSE ) round_cols(.data, ..., digits = 2) tidy_strings(.data, ..., sep = "_")
.data |
A data frame |
... |
The argument depends on the function used.
|
pattern |
A string to be matched. Regular Expression Syntax is also allowed. |
replacement |
A string for replacement. |
ignore_case |
If |
digits |
The number of significant figures. |
sep |
A character string to separate the terms. Defaults to "_". |
Tiago Olivoto tiagoolivoto@gmail.com
library(metan) ################ Rounding numbers ############### # All numeric columns round_cols(data_ge2, digits = 1) # Round specific columns round_cols(data_ge2, EP, digits = 1) ########### Extract or replace numbers ########## # Extract numbers extract_number(data_ge, GEN) # Replace numbers replace_number(data_ge, GEN) replace_number(data_ge, GEN, pattern = 1, replacement = "_one") ########## Extract, replace or remove strings ########## # Extract strings extract_string(data_ge, GEN) # Replace strings replace_string(data_ge, GEN) replace_string(data_ge, GEN, pattern = "G", replacement = "GENOTYPE_") # Remove strings remove_strings(data_ge) remove_strings(data_ge, ENV) ############ Find text in numeric sequences ########### mixed_text <- data.frame(data_ge) mixed_text[2, 4] <- "2..503" mixed_text[3, 4] <- "3.2o75" find_text_in_num(mixed_text, GY) ############# upper, lower and title cases ############ gen_text <- c("This is the first string.", "this is the second one") all_lower_case(gen_text) all_upper_case(gen_text) all_title_case(gen_text) first_upper_case(gen_text) # A whole data frame all_lower_case(data_ge) ############### Tidy up messy text string ############## messy_env <- c("ENV 1", "Env 1", "Env1", "env1", "Env.1", "Env_1") tidy_strings(messy_env) messy_gen <- c("GEN1", "gen 2", "Gen.3", "gen-4", "Gen_5", "GEN_6") tidy_strings(messy_gen) messy_int <- c("EnvGen", "Env_Gen", "env gen", "Env Gen", "ENV.GEN", "ENV_GEN") tidy_strings(messy_int) library(tibble) # Or a whole data frame df <- tibble(Env = messy_env, gen = messy_gen, Env_GEN = interaction(Env, gen), y = rnorm(6, 300, 10)) df tidy_strings(df)
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