Tidy up strings with the R package metan

Getting started

In this quick tip, I show you how to tidy character strings with the package metan. If the package is not yet installed, you can download it from CRAN. Install the released version of metan from CRAN with:

install.packages("metan")

For the latest release notes on this development version, see the NEWS file.

Then, load it with:

library(metan)

metan's function tidy_strings() can be used to tidy up characters strings by putting all word in upper case, replacing any space, tabulation, punctuation characters by _ (underscore), and putting _ between lower and upper case.

A simple example

Suppose that we have a character string, say, str = c("Env1", "env 1", "env.1"). By definition str should represent a unique level in plant breeding trials, e.g., environment 1, but in fact it has three levels.

str <- c("Env1", "env 1", "env.1")
levels(factor(str))
# [1] "env 1" "env.1" "Env1"

Bad idea!

We can use tidy_strings() to tidy up this string as follows:

tidy_strings(str)
# [1] "ENV_1" "ENV_1" "ENV_1"

Great! We have now the unique level we should have before.

More examples

All of the following will be translated into "ENV_1".

messy_env <- c("ENV 1", "Env   1", "Env1", "env1", "Env.1", "Env_1")
tidy_strings(messy_env)
# [1] "ENV_1" "ENV_1" "ENV_1" "ENV_1" "ENV_1" "ENV_1"

All of the following will be translated into "GEN_*".

messy_gen <- c("GEN1", "gen 2", "Gen.3", "gen-4", "Gen_5", "GEN_6")
tidy_strings(messy_gen)
# [1] "GEN_1" "GEN_2" "GEN_3" "GEN_4" "GEN_5" "GEN_6"

All of the following will be translated into "ENV_GEN"

messy_int <- c("EnvGen", "Env_Gen", "env gen", "Env Gen", "ENV.GEN", "ENV_GEN")
tidy_strings(messy_int)
# [1] "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN"

Tidy up a whole data frame

We can also tidy up strings of a whole data frame. By default the separator character is _. To change this default use the argument sep.

library(tibble)
df <- tibble(Env = messy_env,
             gen = messy_gen,
             Env_Gen = interaction(Env, gen),
             y = rnorm(6, 300, 10))
df
# # A tibble: 6 x 4
#   Env     gen   Env_Gen           y
#   <chr>   <chr> <fct>         <dbl>
# 1 ENV 1   GEN1  ENV 1.GEN1     304.
# 2 Env   1 gen 2 Env   1.gen 2  308.
# 3 Env1    Gen.3 Env1.Gen.3     301.
# 4 env1    gen-4 env1.gen-4     295.
# 5 Env.1   Gen_5 Env.1.Gen_5    294.
# 6 Env_1   GEN_6 Env_1.GEN_6    303.
tidy_strings(df, sep = "")
# # A tibble: 6 x 4
#   Env   gen   Env_Gen      y
#   <chr> <chr> <chr>    <dbl>
# 1 ENV1  GEN1  ENV1GEN1  304.
# 2 ENV1  GEN2  ENV1GEN2  308.
# 3 ENV1  GEN3  ENV1GEN3  301.
# 4 ENV1  GEN4  ENV1GEN4  295.
# 5 ENV1  GEN5  ENV1GEN5  294.
# 6 ENV1  GEN6  ENV1GEN6  303.

To select variables to tidy up, simply type the variable name. Here, we also put all column names to upper case

tidy_strings(df, Env) %>% 
  colnames_to_upper()
# # A tibble: 6 x 4
#   ENV   GEN   ENV_GEN           Y
#   <chr> <chr> <fct>         <dbl>
# 1 ENV_1 GEN1  ENV 1.GEN1     304.
# 2 ENV_1 gen 2 Env   1.gen 2  308.
# 3 ENV_1 Gen.3 Env1.Gen.3     301.
# 4 ENV_1 gen-4 env1.gen-4     295.
# 5 ENV_1 Gen_5 Env.1.Gen_5    294.
# 6 ENV_1 GEN_6 Env_1.GEN_6    303.