| 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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