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(v0.7.0.9010) mo_synonyms, plot/barplot fixes
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@ -62,7 +62,7 @@ The function \code{n_rsi} is an alias of \code{count_all}. They can be used to c
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The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of S, I and R. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
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The function \code{rsi_df} works exactly like \code{count_df}, but add the percentage of S, I and R.
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The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R.
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}
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\section{Interpretation of S, I and R}{
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@ -20,6 +20,7 @@
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\alias{mo_year}
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\alias{mo_rank}
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\alias{mo_taxonomy}
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\alias{mo_synonyms}
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\alias{mo_info}
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\alias{mo_url}
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\title{Property of a microorganism}
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@ -60,6 +61,8 @@ mo_rank(x, ...)
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mo_taxonomy(x, language = get_locale(), ...)
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mo_synonyms(x, ...)
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mo_info(x, language = get_locale(), ...)
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mo_url(x, open = FALSE, ...)
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@ -147,6 +150,7 @@ mo_gramstain("E. coli") # "Gram-negative"
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mo_type("E. coli") # "Bacteria" (equal to kingdom, but may be translated)
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mo_rank("E. coli") # "species"
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mo_url("E. coli") # get the direct url to the online database entry
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mo_synonyms("E. coli") # get previously accepted taxonomic names
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## scientific reference
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mo_ref("E. coli") # "Castellani et al., 1919"
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@ -73,7 +73,7 @@ These functions are not meant to count isolates, but to calculate the portion of
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The function \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each group and each variable with class \code{"rsi"}.
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The function \code{rsi_df} works exactly like \code{portion_df}, but add the number of isolates.
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The function \code{rsi_df} works exactly like \code{portion_df}, but adds the number of isolates.
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\if{html}{
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\cr\cr
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To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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