update dependencies

man/rsi.Rd

@@ -34,14 +34,14 @@ This function uses the \code{\link{rsi_df}} function internally.
 tbl \%>\%
   group_by(hospital) \%>\%
   summarise(cipr = rsi(cipr))
-  
+
 tbl \%>\%
   group_by(year, hospital) \%>\%
   summarise(
     isolates = n(),
     cipro = rsi(cipr \%>\% as.rsi(), percent = TRUE),
     amoxi = rsi(amox \%>\% as.rsi(), percent = TRUE))
-    
+
 rsi(as.rsi(isolates$amox))
 
 rsi(as.rsi(isolates$amcl), interpretation = "S")

man/rsi_df.Rd

@@ -40,7 +40,7 @@ rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
 library(dplyr)
 # calculate current empiric therapy of Helicobacter gastritis:
 my_table \%>\%
-  filter(first_isolate == TRUE, 
+  filter(first_isolate == TRUE,
          genus == "Helicobacter") \%>\%
   rsi_df(ab = c("amox", "metr"))
 }

man/rsi_predict.Rd

@@ -39,7 +39,7 @@ Create a prediction model to predict antimicrobial resistance for the next years
 # use it directly:
 rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
             col_ab = "amcl", col_date = "date")
-  
+
 # or with dplyr so you can actually read it:
 library(dplyr)
 tbl \%>\%
@@ -53,22 +53,22 @@ tbl \%>\%
 library(dplyr)
 septic_patients \%>\%
   # get bacteria properties like genus and species
-  left_join_microorganisms("bactid") \%>\% 
+  left_join_microorganisms("bactid") \%>\%
   # calculate first isolates
-  mutate(first_isolate = 
+  mutate(first_isolate =
            first_isolate(.,
                          "date",
                          "patient_id",
                          "bactid",
                          col_specimen = NA,
-                         col_icu = NA)) \%>\% 
+                         col_icu = NA)) \%>\%
   # filter on first E. coli isolates
-  filter(genus == "Escherichia", 
-         species == "coli", 
+  filter(genus == "Escherichia",
+         species == "coli",
          first_isolate == TRUE) \%>\%
   # predict resistance of cefotaxime for next years
-  rsi_predict(col_ab = cfot,
-              col_date = date,
+  rsi_predict(col_ab = "cfot",
+              col_date = "date",
               year_max = 2025,
               preserve_measurements = FALSE)