(v1.1.0.9007) lose dependencies

man/as.disk.Rd

@@ -38,6 +38,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
+\dontrun{
 # transform existing disk zones to the `disk` class
 library(dplyr)
 df <- data.frame(microorganism = "E. coli",
@@ -56,6 +57,7 @@ as.rsi(x = as.disk(18),
        
 as.rsi(df)
 }
+}
 \seealso{
 \code{\link[=as.rsi]{as.rsi()}}
 }

man/as.rsi.Rd

@@ -110,7 +110,7 @@ df <- data.frame(microorganism = "E. coli",
                  NIT = as.mic(32))
 as.rsi(df)
 
-\donttest{
+\dontrun{
 
 # the dplyr way
 library(dplyr)
@@ -157,6 +157,7 @@ is.rsi(rsi_data)
 plot(rsi_data)    # for percentages
 barplot(rsi_data) # for frequencies
 
+\dontrun{
 library(dplyr)
 example_isolates \%>\%
   mutate_at(vars(PEN:RIF), as.rsi)
@@ -173,6 +174,7 @@ example_isolates \%>\%
 is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
 is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
 }
+}
 \seealso{
 \code{\link[=as.mic]{as.mic()}}
 }

man/atc_online.Rd

@@ -75,7 +75,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
-\donttest{
+\dontrun{
 # oral DDD (Defined Daily Dose) of amoxicillin
 atc_online_property("J01CA04", "DDD", "O")
 # parenteral DDD (Defined Daily Dose) of amoxicillin

man/availability.Rd

@@ -36,6 +36,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 \examples{
 availability(example_isolates)
 
+\dontrun{
 library(dplyr)
 example_isolates \%>\% availability()
 
@@ -48,3 +49,4 @@ example_isolates \%>\%
   select_if(is.rsi) \%>\%
   availability()
 }
+}

man/count.Rd

@@ -158,7 +158,7 @@ count_susceptible(example_isolates$AMX)
 susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
 
 
-if (!require("dplyr")) {
+if (require("dplyr")) {
   example_isolates \%>\%
     group_by(hospital_id) \%>\%
     summarise(R  = count_R(CIP),

man/ggplot_rsi.Rd

@@ -147,44 +147,45 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
-library(dplyr)
-library(ggplot2)
-
-# get antimicrobial results for drugs against a UTI:
-ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
-  geom_rsi()
-
-# prettify the plot using some additional functions:
-df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
-ggplot(df) +
-  geom_rsi() +
-  scale_y_percent() +
-  scale_rsi_colours() +
-  labels_rsi_count() +
-  theme_rsi()
-
-# or better yet, simplify this using the wrapper function - a single command:
-example_isolates \%>\%
-  select(AMX, NIT, FOS, TMP, CIP) \%>\%
-  ggplot_rsi()
-
-# get only proportions and no counts:
-example_isolates \%>\%
-  select(AMX, NIT, FOS, TMP, CIP) \%>\%
-  ggplot_rsi(datalabels = FALSE)
-
-# add other ggplot2 parameters as you like:
-example_isolates \%>\%
-  select(AMX, NIT, FOS, TMP, CIP) \%>\%
-  ggplot_rsi(width = 0.5,
-             colour = "black",
-             size = 1,
-             linetype = 2,
-             alpha = 0.25)
-
-example_isolates \%>\%
-  select(AMX) \%>\%
-  ggplot_rsi(colours = c(SI = "yellow"))
+if (require("ggplot2") & require("dplyr")) {
+ 
+  # get antimicrobial results for drugs against a UTI:
+  ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
+    geom_rsi()
+ 
+  # prettify the plot using some additional functions:
+  df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
+  ggplot(df) +
+    geom_rsi() +
+    scale_y_percent() +
+    scale_rsi_colours() +
+    labels_rsi_count() +
+    theme_rsi()
+ 
+  # or better yet, simplify this using the wrapper function - a single command:
+  example_isolates \%>\%
+    select(AMX, NIT, FOS, TMP, CIP) \%>\%
+    ggplot_rsi()
+ 
+  # get only proportions and no counts:
+  example_isolates \%>\%
+    select(AMX, NIT, FOS, TMP, CIP) \%>\%
+    ggplot_rsi(datalabels = FALSE)
+ 
+  # add other ggplot2 parameters as you like:
+  example_isolates \%>\%
+    select(AMX, NIT, FOS, TMP, CIP) \%>\%
+    ggplot_rsi(width = 0.5,
+               colour = "black",
+               size = 1,
+               linetype = 2,
+               alpha = 0.25)
+ 
+  example_isolates \%>\%
+    select(AMX) \%>\%
+    ggplot_rsi(colours = c(SI = "yellow"))
+  
+}
   
 \dontrun{
 

man/key_antibiotics.Rd

@@ -136,6 +136,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 # `example_isolates` is a dataset available in the AMR package.
 # See ?example_isolates.
 
+\dontrun{
 library(dplyr)
 # set key antibiotics to a new variable
 my_patients <- example_isolates \%>\%
@@ -150,7 +151,7 @@ my_patients <- example_isolates \%>\%
 # Check the difference, in this data set it results in 7\% more isolates:
 sum(my_patients$first_regular, na.rm = TRUE)
 sum(my_patients$first_weighted, na.rm = TRUE)
-
+}
 
 # output of the `key_antibiotics` function could be like this:
 strainA <- "SSSRR.S.R..S"

man/like.Rd

@@ -68,11 +68,13 @@ a \%like\% b
 #> TRUE TRUE TRUE
 
 # get isolates whose name start with 'Ent' or 'ent'
+\dontrun{
 library(dplyr)
 example_isolates \%>\%
   filter(mo_name(mo) \%like\% "^ent") \%>\% 
   freq(mo)
 }
+}
 \seealso{
 \code{\link[base:grep]{base::grep()}}
 }

man/mdro.Rd

@@ -206,7 +206,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
-\donttest{
+\dontrun{
 library(dplyr)
 
 example_isolates \%>\%

man/proportion.Rd

@@ -160,8 +160,7 @@ proportion_I(example_isolates$AMX)
 proportion_IR(example_isolates$AMX)
 proportion_R(example_isolates$AMX)
 
-if (!require("dplyr")) {
-  library(dplyr)
+if (require("dplyr")) {
   example_isolates \%>\%
     group_by(hospital_id) \%>\%
     summarise(r = resistance(CIP),
@@ -218,7 +217,9 @@ if (!require("dplyr")) {
     select(hospital_id, AMX, CIP) \%>\%
     group_by(hospital_id) \%>\%
     proportion_df(translate = FALSE)
- 
+}
+
+\dontrun{  
   # calculate current empiric combination therapy of Helicobacter gastritis:
   my_table \%>\%
     filter(first_isolate == TRUE,

man/resistance_predict.Rd

@@ -132,11 +132,12 @@ x <- resistance_predict(example_isolates,
                         year_min = 2010,
                         model = "binomial")
 plot(x)
-ggplot_rsi_predict(x)
+if (require("ggplot2")) {
+  ggplot_rsi_predict(x)
+}
 
 # using dplyr:
-if (!require("dplyr")) {
-  library(dplyr)
+if (require("dplyr")) {
   x <- example_isolates \%>\%
     filter_first_isolate() \%>\%
     filter(mo_genus(mo) == "Staphylococcus") \%>\%
@@ -149,7 +150,7 @@ if (!require("dplyr")) {
 }
 
 # create nice plots with ggplot2 yourself
-if (!require(ggplot2) & !require("dplyr")) {
+if (require(ggplot2) & require("dplyr")) {
 
   data <- example_isolates \%>\%
     filter(mo == as.mo("E. coli")) \%>\%