Qualify namespaces in tests to avoid library() calls

tests/testthat.R

@@ -1,6 +1,4 @@
 library(testthat)
-library(gsDesign)
 library(gsDesign2)
-library(dplyr)
 
 test_check("gsDesign2")

tests/testthat/old_function/AHR_.R

@@ -189,37 +189,37 @@ AHR_ <- function(enrollRates = tibble::tibble(
     events <- NULL
     for (s in strata) {
       # subset to stratum
-      enroll <- enrollRates %>% filter(Stratum == s)
-      fail <- failRates %>% filter(Stratum == s)
+      enroll <- enrollRates %>% dplyr::filter(Stratum == s)
+      fail <- failRates %>% dplyr::filter(Stratum == s)
       # Control events
-      enrollc <- enroll %>% mutate(rate = rate * Qc)
+      enrollc <- enroll %>% dplyr::mutate(rate = rate * Qc)
       control <- eEvents_df_(enrollRates = enrollc, failRates = fail, totalDuration = td, simple = FALSE)
       # Experimental events
-      enrolle <- enroll %>% mutate(rate = rate * Qe)
-      fre <- fail %>% mutate(failRate = failRate * hr)
+      enrolle <- enroll %>% dplyr::mutate(rate = rate * Qe)
+      fre <- fail %>% dplyr::mutate(failRate = failRate * hr)
       experimental <- eEvents_df_(enrollRates = enrolle, failRates = fre, totalDuration = td, simple = FALSE)
       # Combine control and experimental; by period recompute HR, events, information
       events <-
         rbind(
-          control %>% mutate(Treatment = "Control"),
-          experimental %>% mutate(Treatment = "Experimental")
+          control %>% dplyr::mutate(Treatment = "Control"),
+          experimental %>% dplyr::mutate(Treatment = "Experimental")
         ) %>%
-        arrange(t, Treatment) %>%
-        ungroup() %>%
-        group_by(t) %>%
-        summarize(
+        dplyr::arrange(t, Treatment) %>%
+        dplyr::ungroup() %>%
+        dplyr::group_by(t) %>%
+        dplyr::summarize(
           Stratum = s, info = (sum(1 / Events))^(-1),
-          Events = sum(Events), HR = last(failRate) / first(failRate)
+          Events = sum(Events), HR = dplyr::last(failRate) / dplyr::first(failRate)
         ) %>%
         rbind(events)
     }
     rval <- rbind(
       rval,
       events %>%
-        mutate(Time = td, lnhr = log(HR), info0 = Events * Qc * Qe) %>%
-        ungroup() %>%
-        group_by(Time, Stratum, HR) %>%
-        summarize(
+        dplyr::mutate(Time = td, lnhr = log(HR), info0 = Events * Qc * Qe) %>%
+        dplyr::ungroup() %>%
+        dplyr::group_by(Time, Stratum, HR) %>%
+        dplyr::summarize(
           t = min(t),
           Events = sum(Events),
           info0 = sum(info0),
@@ -231,14 +231,14 @@ AHR_ <- function(enrollRates = tibble::tibble(
   if (!simple) {
     return(
       rval %>%
-        select(c("Time", "Stratum", "t", "HR", "Events", "info", "info0")) %>%
-        group_by(Time, Stratum) %>%
-        arrange(t, .by_group = TRUE)
+        dplyr::select(c("Time", "Stratum", "t", "HR", "Events", "info", "info0")) %>%
+        dplyr::group_by(Time, Stratum) %>%
+        dplyr::arrange(t, .by_group = TRUE)
     )
   }
   return(rval %>%
-    group_by(Time) %>%
-    summarize(
+    dplyr::group_by(Time) %>%
+    dplyr::summarize(
       AHR = exp(sum(log(HR) * Events) / sum(Events)),
       Events = sum(Events),
       info = sum(info),

tests/testthat/old_function/eAccrual_.R

@@ -59,7 +59,7 @@ eAccrual_ <- function(x = 0:24,
   # check input enrollment rate assumptions
   if(!is.numeric(x)){stop("gsDesign2: x in `eAccrual()` must be a strictly increasing non-negative numeric vector")}
   if(!min(x) >= 0){stop("gsDesign2: x in `eAccrual()` must be a strictly increasing non-negative numeric vector")}
-  if(!min(lead(x,default=max(x)+1) - x) > 0){stop("gsDesign2: x in `eAccrual()` must be a strictly increasing non-negative numeric vector")}
+  if(!min(dplyr::lead(x,default=max(x)+1) - x) > 0){stop("gsDesign2: x in `eAccrual()` must be a strictly increasing non-negative numeric vector")}
 
   # check enrollment rate assumptions
   if(!is.data.frame(enrollRates)){stop("gsDesign2: enrollRates in `eAccrual()` must be a data frame")}
@@ -79,7 +79,7 @@ eAccrual_ <- function(x = 0:24,
   xvals <- sort(unique(c(x,cumsum(enrollRates$duration))))
   # make a tibble
   xx <- tibble::tibble(x=xvals,
-                       duration= xvals - lag(xvals,default = 0),
+                       duration= xvals - dplyr::lag(xvals,default = 0),
                        rate=ratefn(xvals), # enrollment rates at points (right continuous)
                        eAccrual=cumsum(rate*duration) # expected accrual
   )

tests/testthat/old_function/eEvents_df_.R

@@ -90,15 +90,15 @@ NULL
 #' gsDesign2:::eEvents_df_(totalDuration = .5)
 #' # Single time period example
 #' gsDesign2:::eEvents_df_(
-#'   enrollRates = tibble(duration = 10, rate = 10),
-#'   failRates = tibble(duration = 100, failRate = log(2) / 6, dropoutRate = .01),
+#'   enrollRates = tibble::tibble(duration = 10, rate = 10),
+#'   failRates = tibble::tibble(duration = 100, failRate = log(2) / 6, dropoutRate = .01),
 #'   totalDuration = 22,
 #'   simple = FALSE
 #' )
 #' # Single time period example, multiple enrolment periods
 #' gsDesign2:::eEvents_df_(
-#'   enrollRates = tibble(duration = c(5, 5), rate = c(10, 20)),
-#'   failRates = tibble(duration = 100, failRate = log(2) / 6, dropoutRate = .01),
+#'   enrollRates = tibble::tibble(duration = c(5, 5), rate = c(10, 20)),
+#'   failRates = tibble::tibble(duration = 100, failRate = log(2) / 6, dropoutRate = .01),
 #'   totalDuration = 22,
 #'   simple = FALSE
 #' )
@@ -165,18 +165,18 @@ eEvents_df_ <- function(enrollRates = tibble::tibble(
     failRate = failRates$failRate,
     dropoutRate = failRates$dropoutRate
   )
-  df_2 <- if (last(cumsum(failRates$duration)) < totalDuration) df_2[-nrow(df_2), ] else df_2[df_2$startEnroll > 0, ] # we will use start of failure rate periods repeatedly below
+  df_2 <- if (dplyr::last(cumsum(failRates$duration)) < totalDuration) df_2[-nrow(df_2), ] else df_2[df_2$startEnroll > 0, ] # we will use start of failure rate periods repeatedly below
   startFail <- c(0, cumsum(failRates$duration))
   # Step function to define failure rates over time
   sf.failRate <- stepfun(startFail,
-    c(0, failRates$failRate, last(failRates$failRate)),
+    c(0, failRates$failRate, dplyr::last(failRates$failRate)),
     right = FALSE
   )
   # Step function to define dropout rates over time
   sf.dropoutRate <- stepfun(startFail,
     c(
       0, failRates$dropoutRate,
-      last(failRates$dropoutRate)
+      dplyr::last(failRates$dropoutRate)
     ),
     right = FALSE
   )
@@ -197,25 +197,25 @@ eEvents_df_ <- function(enrollRates = tibble::tibble(
   )
   # Put everything together as laid out in vignette
   # "Computing expected events by interval at risk"
-  df_join <- full_join(df_1, df_2, by = c("startEnroll", "endFail")) %>%
-    arrange(endFail) %>%
-    mutate(
-      endEnroll = lag(startEnroll, default = as.numeric(totalDuration)),
-      startFail = lag(endFail, default = 0),
+  df_join <- dplyr::full_join(df_1, df_2, by = c("startEnroll", "endFail")) %>%
+    dplyr::arrange(endFail) %>%
+    dplyr::mutate(
+      endEnroll = dplyr::lag(startEnroll, default = as.numeric(totalDuration)),
+      startFail = dplyr::lag(endFail, default = 0),
       duration = endEnroll - startEnroll,
       failRate = sf.failRate(startFail),
       dropoutRate = sf.dropoutRate(startFail),
       enrollRate = sf.enrollRate(startEnroll),
       q = exp(-duration * (failRate + dropoutRate)),
-      Q = lag(cumprod(q), default = 1)
+      Q = dplyr::lag(cumprod(q), default = 1)
     ) %>%
-    arrange(desc(startFail)) %>%
-    mutate(
+    dplyr::arrange(dplyr::desc(startFail)) %>%
+    dplyr::mutate(
       g = enrollRate * duration,
-      G = lag(cumsum(g), default = 0)
+      G = dplyr::lag(cumsum(g), default = 0)
     ) %>%
-    arrange(startFail) %>%
-    mutate(
+    dplyr::arrange(startFail) %>%
+    dplyr::mutate(
       d = ifelse(failRate == 0, 0, Q * (1 - q) * failRate / (failRate + dropoutRate)),
       nbar = ifelse(failRate == 0, 0,
         G * d + (failRate * Q * enrollRate) / (failRate + dropoutRate) * (duration - (1 - q) / (failRate + dropoutRate))
@@ -225,12 +225,12 @@ eEvents_df_ <- function(enrollRates = tibble::tibble(
     return(as.numeric(sum(df_join$nbar)))
   }
   df_join %>%
-    transmute(
+    dplyr::transmute(
       t = endFail, failRate = failRate, Events = nbar,
       startFail = sf.startFail(startFail)
     ) %>%
-    group_by(startFail) %>%
-    summarize(failRate = first(failRate), Events = sum(Events)) %>%
-    mutate(t = startFail) %>%
-    select("t", "failRate", "Events")
+    dplyr::group_by(startFail) %>%
+    dplyr::summarize(failRate = dplyr::first(failRate), Events = sum(Events)) %>%
+    dplyr::mutate(t = startFail) %>%
+    dplyr::select("t", "failRate", "Events")
 }

tests/testthat/old_function/gs_design_ahr_.R

@@ -145,10 +145,10 @@ gs_design_ahr_ <- function(enrollRates = tibble::tibble(
                            binding = FALSE,
                            upper = gs_b,
                            # Default is Lan-DeMets approximation of
-                           upar = gsDesign(
+                           upar = gsDesign::gsDesign(
                              k = 3, test.type = 1,
                              n.I = c(.25, .75, 1),
-                             sfu = sfLDOF, sfupar = NULL
+                             sfu = gsDesign::sfLDOF, sfupar = NULL
                            )$upper$bound,
                            lower = gs_b,
                            lpar = c(qnorm(.1), -Inf, -Inf), # Futility only at IA1
@@ -191,7 +191,7 @@ gs_design_ahr_ <- function(enrollRates = tibble::tibble(
             tEvents_(enrollRates, failRates,
               targetEvents = IF[K - i] * finalEvents, ratio = ratio,
               interval = c(.01, nextTime)
-            ) %>% mutate(theta = -log(AHR), Analysis = K - i),
+            ) %>% dplyr::mutate(theta = -log(AHR), Analysis = K - i),
             y
           )
       } else if (IF[K - i] > IFalt[K - i]) {
@@ -235,16 +235,16 @@ gs_design_ahr_ <- function(enrollRates = tibble::tibble(
     tol = tol
   ) %>%
     # Add Time, Events, AHR, N from gs_info_ahr call above
-    full_join(y %>% select(-c(info, info0, theta)), by = "Analysis") %>%
-    select(c("Analysis", "Bound", "Time", "N", "Events", "Z", "Probability", "AHR", "theta", "info", "info0")) %>%
-    arrange(desc(Bound), Analysis)
+    dplyr::full_join(y %>% dplyr::select(-c(info, info0, theta)), by = "Analysis") %>%
+    dplyr::select(c("Analysis", "Bound", "Time", "N", "Events", "Z", "Probability", "AHR", "theta", "info", "info0")) %>%
+    dplyr::arrange(dplyr::desc(Bound), Analysis)
   bounds$Events <- bounds$Events * bounds$info[K] / y$info[K]
   bounds$N <- bounds$N * bounds$info[K] / y$info[K]
 
   # Document design enrollment, failure rates, and bounds
   return(list(
     enrollRates = enrollRates %>%
-      mutate(rate = rate * bounds$info[K] / y$info[K]),
+      dplyr::mutate(rate = rate * bounds$info[K] / y$info[K]),
     failRates = failRates,
     bounds = bounds
   ))

tests/testthat/old_function/gs_design_npe_.R

@@ -129,7 +129,7 @@ NULL
 #'   theta = rep(0, 3), info = design$info0[1:3],
 #'   upar = design$Z[1:3], lpar = rep(-Inf, 3)
 #' ) %>%
-#'   filter(Bound == "Upper")
+#'   dplyr::filter(Bound == "Upper")
 #'
 #' # Spending bound examples
 #'
@@ -186,7 +186,7 @@ NULL
 #'
 #' # Re-use these bounds under alternate hypothesis
 #' # Always use binding = TRUE for power calculations
-#' upar <- (xx %>% filter(Bound == "Upper"))$Z
+#' upar <- (xx %>% dplyr::filter(Bound == "Upper"))$Z
 #' gsDesign2:::gs_design_npe_(
 #'   theta = c(.1, .2, .3), info = (1:3) * 40,
 #'   binding = TRUE,

tests/testthat/old_function/gs_power_ahr_.R

@@ -62,7 +62,7 @@ NULL
 #' library(gsDesign2)
 #' library(dplyr)
 #'
-#' gs_power_ahr() %>% filter(abs(Z) < Inf)
+#' gs_power_ahr() %>% dplyr::filter(abs(Z) < Inf)
 #'
 #' # 2-sided symmetric O'Brien-Fleming spending bound
 #' # NOT CURRENTLY WORKING
@@ -93,10 +93,10 @@ gs_power_ahr_ <- function(enrollRates = tibble::tibble(
                           binding = FALSE,
                           upper = gs_b,
                           # Default is Lan-DeMets approximation of
-                          upar = gsDesign(
+                          upar = gsDesign::gsDesign(
                             k = length(events), test.type = 1,
                             n.I = events, maxn.IPlan = max(events),
-                            sfu = sfLDOF, sfupar = NULL
+                            sfu = gsDesign::sfLDOF, sfupar = NULL
                           )$upper$bound,
                           lower = gs_b,
                           lpar = c(qnorm(.1), rep(-Inf, length(events) - 1)), # Futility only at IA1
@@ -117,7 +117,7 @@ gs_power_ahr_ <- function(enrollRates = tibble::tibble(
     test_upper = test_upper, test_lower = test_lower,
     r = r, tol = tol
   ) %>%
-    right_join(x %>% select(-c(info, info0, theta)), by = "Analysis") %>%
-    select(c(Analysis, Bound, Time, Events, Z, Probability, AHR, theta, info, info0)) %>%
-    arrange(desc(Bound), Analysis))
+    dplyr::right_join(x %>% dplyr::select(-c(info, info0, theta)), by = "Analysis") %>%
+    dplyr::select(c(Analysis, Bound, Time, Events, Z, Probability, AHR, theta, info, info0)) %>%
+    dplyr::arrange(dplyr::desc(Bound), Analysis))
 }

tests/testthat/old_function/gs_power_npe_.R

@@ -89,7 +89,7 @@ NULL
 #' library(dplyr)
 #'
 #' # Default (single analysis; Type I error controlled)
-#' gsDesign2:::gs_power_npe_(theta = 0) %>% filter(Bound == "Upper")
+#' gsDesign2:::gs_power_npe_(theta = 0) %>% dplyr::filter(Bound == "Upper")
 #'
 #' # Fixed bound
 #' gsDesign2:::gs_power_npe_(
@@ -113,7 +113,7 @@ NULL
 #'     sfu = gsDesign::sfLDOF
 #'   )$upper$bound,
 #'   lpar = rep(-Inf, 3)
-#' ) %>% filter(Bound == "Upper")
+#' ) %>% dplyr::filter(Bound == "Upper")
 #'
 #' # Fixed bound with futility only at analysis 1;
 #' # efficacy only at analyses 2, 3
@@ -192,7 +192,7 @@ NULL
 #'
 #' # Re-use these bounds under alternate hypothesis
 #' # Always use binding = TRUE for power calculations
-#' upar <- (xx %>% filter(Bound == "Upper"))$Z
+#' upar <- (xx %>% dplyr::filter(Bound == "Upper"))$Z
 #' gsDesign2:::gs_power_npe_(
 #'   theta = c(.1, .2, .3),
 #'   info = (1:3) * 40,

tests/testthat/test-developer-ahr.R

@@ -1,8 +1,6 @@
 source_files <- list.files("./old_function/", "*.R$")
 sapply(paste0("./old_function/", source_files), source)
 
-library(dplyr)
-
 test_that("unstratified population", {
   enroll_rate <- define_enroll_rate(
     duration = c(2, 10, 4, 4, 8),
@@ -21,11 +19,11 @@ test_that("unstratified population", {
     total_duration = c(15, 30)
   )
   x2 <- AHR_( # old version
-    enrollRates = enroll_rate %>% rename(Stratum = stratum),
-    failRates = fail_rate %>% rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
+    enrollRates = enroll_rate %>% dplyr::rename(Stratum = stratum),
+    failRates = fail_rate %>% dplyr::rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
     totalDuration = c(15, 30)
   ) %>%
-    rename(time = Time, ahr = AHR, event = Events)
+    dplyr::rename(time = Time, ahr = AHR, event = Events)
   expect_equal(as.data.frame(x1), as.data.frame(x2))
 })
 
@@ -48,10 +46,10 @@ test_that("stratified population", {
     total_duration = c(15, 30)
   )
   x2 <- AHR_( # old version
-    enrollRates = enroll_rate %>% rename(Stratum = stratum),
-    failRates = fail_rate %>% rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
+    enrollRates = enroll_rate %>% dplyr::rename(Stratum = stratum),
+    failRates = fail_rate %>% dplyr::rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
     totalDuration = c(15, 30)
   ) %>%
-    rename(time = Time, ahr = AHR, event = Events)
+    dplyr::rename(time = Time, ahr = AHR, event = Events)
   expect_equal(as.data.frame(x1), as.data.frame(x2))
 })

tests/testthat/test-developer-expected_event.R

@@ -1,5 +1,3 @@
-library(dplyr)
-
 source_files <- list.files("./old_function/", "*.R$")
 sapply(paste0("./old_function/", source_files), source)
 
@@ -16,8 +14,8 @@ test_that("expected event vs gsDesign", {
     T = total_duration
   )$d
   x2 <- eEvents_df_( # gsDesign2 old version
-    enrollRates = enroll_rate %>% rename(Stratum = stratum),
-    failRates = fail_rate %>% rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
+    enrollRates = enroll_rate %>% dplyr::rename(Stratum = stratum),
+    failRates = fail_rate %>% dplyr::rename(Stratum = stratum, failRate = fail_rate, dropoutRate = dropout_rate),
     total_duration,
     simple = TRUE
   )