Join microorganisms to a data set

Join the data set microorganisms easily to an existing table or 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, ...)

Arguments

x	existing table to join, or character vector
by	a variable to join by - if left empty will search for a column with class mo (created with as.mo()) or will be "mo" if that column name exists in x, could otherwise be a column name of x with values that exist in microorganisms$mo (like by = "bacteria_id"), or another column in microorganisms (but then it should be named, like by = c("my_genus_species" = "fullname"))
suffix	if there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
...	other parameters to pass on to dplyr::join()

Details

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.

These functions rely on merge(), a base R function to do joins.

Stable lifecycle

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 parameter 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.

Read more on our website!

On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

Examples

left_join_microorganisms(as.mo("K. pneumoniae"))
left_join_microorganisms("B_KLBSL_PNE")

if (FALSE) {
library(dplyr)
example_isolates %>% left_join_microorganisms()

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)
}