Join the data set microorganisms easily to an existing data set or to a character vector.
inner_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
left_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
right_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
full_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
semi_join_microorganisms(x, by = NULL, ...)
anti_join_microorganisms(x, by = NULL, ...)
x | existing data set to join, or character vector. In case of a character vector, the resulting data.frame will contain a column 'x' with these values. |
---|---|
by | a variable to join by - if left empty will search for a column with class |
suffix | if there are non-joined duplicate variables in |
... | ignored, only in place to allow future extensions |
Note: As opposed to the join()
functions of dplyr
, character vectors are supported and at default existing columns will get a suffix "2"
and the newly joined columns will not get a suffix.
If the dplyr
package is installed, their join functions will be used. Otherwise, the much slower merge()
and interaction()
functions from base R will be used.
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.
left_join_microorganisms(as.mo("K. pneumoniae"))
left_join_microorganisms("B_KLBSL_PNMN")
# \donttest{
if (require("dplyr")) {
example_isolates %>%
left_join_microorganisms() %>%
colnames()
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-07"),
by = 1),
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
"E. coli", "E. coli", "E. coli")),
stringsAsFactors = FALSE)
colnames(df)
df_joined <- left_join_microorganisms(df, "bacteria")
colnames(df_joined)
}
# }