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The goal of df2yaml is to simplify the process of
converting dataframe to YAML. The dataframe with multiple key columns
and one value column (this column can also contain key-value pair(s))
will be converted to multi-level hierarchy.
df2yaml is an R package distributed as part of the CRAN. To install the package,
start R and enter:
# install via CRAN
install.package("df2yaml")
# install via Github
# install.package("remotes") #In case you have not installed it.
remotes::install_github("showteeth/df2yaml")In general, it is recommended to install from Github repository (update more timely).
# library
library(df2yaml)
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'tibble'
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'pillar'
# load test file
test_file <- system.file("extdata", "df2yaml_l3.txt", package = "df2yaml")
test_data = read.table(file = test_file, header = T, sep = "\t")
head(test_data)
#> paras subcmd values
#> 1 picard insert_size MINIMUM_PCT: 0.5
#> 2 picard markdup CREATE_INDEX: true; VALIDATION_STRINGENCY: SILENT
#> 3 preseq -r 100 -seg_len 100000000
#> 4 qualimap --java-mem-size=20G -outformat HTML
#> 5 rseqc mapq: 30; percentile-floor: 5; percentile-step: 5
# output yaml string
yaml_res = df2yaml(df = test_data, key_col = c("paras", "subcmd"), val_col = "values")
cat(yaml_res)
#> preseq: -r 100 -seg_len 100000000
#> qualimap: --java-mem-size=20G -outformat HTML
#> rseqc:
#> mapq: 30
#> percentile-floor: 5
#> percentile-step: 5
#> picard:
#> insert_size:
#> MINIMUM_PCT: 0.5
#> markdup:
#> CREATE_INDEX: true
#> VALIDATION_STRINGENCY: SILENTPlease note that the df2yaml project is released with a
Contributor
Code of Conduct. By contributing to this project, you agree to abide
by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.