addresses #578 - use dplyr::select insstead of select_

R/ggcoxdiagnostics.R

@@ -99,9 +99,9 @@ ggcoxdiagnostics <- function (fit,
   else col_names <- names(stats::coef(fit))
   colnames(res) <- col_names
   res$xval <- xval
-  data2plot <- tidyr::gather_(res,
-                              key_col = "covariate", value_col = "res",
-                              gather_col = col_names)
+  data2plot <- tidyr::gather(res,
+                              key = "covariate", value = "res",
+                              col_names)
 
   gplot <- ggplot(aes(xval, res), data = data2plot) +
            geom_point(col = point.col, shape = point.shape,

R/ggsurvplot_combine.R

@@ -114,7 +114,7 @@ ggsurvplot_combine <- function(fit, data,
       all.levels <- c(all.levels, .levels(ss$strata))
     # convert strata into character before binding
     # avoid this warning: Unequal factor levels: coercing to character
-    grouped.d$survsummary <- map(grouped.d$survsummary,
+    grouped.d$survsummary <- purrr::map(grouped.d$survsummary,
                                  function(x){
                                    x$strata <- as.character(x$strata)
                                    x
@@ -161,9 +161,8 @@ ggsurvplot_combine <- function(fit, data,
       survtable$strata <- paste(fitname, "::", survtable$strata, sep = "") %>%
         factor(levels = strata.levels)
       survtable %>%
-        dplyr::select_( .dots = c("strata", "time", "n.risk", "pct.risk",
-                                  "n.event", "cum.n.event", "n.censor",
-                                  "cum.n.censor", "strata_size"))
+        dplyr::select(strata, time, n.risk, pct.risk, n.event, cum.n.event,
+                      n.censor, cum.n.censor, strata_size)
     }
 
     grouped.d <- grouped.d %>%
@@ -199,8 +198,8 @@ ggsurvplot_combine <- function(fit, data,
     #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
     if(keep.data){
-    res$data.survplot <- tibble::as.tibble(all.survsummary)
-    res$data.survtable <- tibble::as.tibble(all.survtable)
+    res$data.survplot <- tibble::as_tibble(all.survsummary)
+    res$data.survtable <- tibble::as_tibble(all.survtable)
     }
 
 

R/ggsurvplot_facet.R

@@ -179,10 +179,10 @@ ggsurvplot_facet <- function(fit, data, facet.by,
     pvalue <- surv_pvalue(grouped.d$fit, grouped.d$data, pval.coord = pval.coord,
                           pval.method.coord = pval.method.coord,...) %>%
       dplyr::bind_rows() %>%
-      tibble::as.tibble()
+      tibble::as_tibble()
     # Select the grouping variable columns and cbind the corresponding pvalue
     pvals.df <- grouped.d %>%
-      dplyr::select_( .dots = facet.by) %>%
+      dplyr::select(!!!syms(facet.by)) %>%
       dplyr::bind_cols(pvalue)
     pval.x <- pval.y <- pval.txt <- method.x <- method.y <- method <-  NULL
     p <- p +

R/surv_median.R

@@ -59,7 +59,7 @@ surv_median <- function(fit, combine = FALSE){
     .table$strata <- rownames(.table)
 
     .table <- .table %>%
-      dplyr::select_(.dots = c("strata",  "median", "`0.95LCL`", "`0.95UCL`"))
+      dplyr::select(strata, median, `0.95LCL`, `0.95UCL`)
     colnames(.table) <- c("strata", "median", "lower", "upper")
     rownames(.table) <- NULL
     .table

tests/testthat/setup-load_data.R

@@ -0,0 +1,2 @@
+library(survival)
+data('cancer')

tests/testthat/test-ggcoxdiagnostics.R

@@ -0,0 +1,16 @@
+test_that('ggcoxdiagnostics creates plot with all the observations', {
+  cph <- coxph(Surv(futime, fustat) ~ rx + age, data=ovarian)
+  p <- ggcoxdiagnostics(cph, type="deviance")
+  .build <- ggplot_build(p)
+  expect_equal(nrow(.build$data[[1]]), nrow(ovarian))
+})
+
+test_that('ggcoxdiagnostics with second type two rows for each observed event*term', {
+  cph <- coxph(Surv(futime, fustat) ~ rx + age, data=ovarian)
+  qty_terms <- length(attr(terms(cph$formula), "term.labels"))
+  qty_events <- sum(ovarian$fustat==1)
+  p <- ggcoxdiagnostics(cph, type="schoenfeld")
+  .build <- ggplot_build(p)
+  expect_equal(nrow(.build$data[[1]]), qty_terms*qty_events)
+})
+

tests/testthat/test-ggsurvplot_combine.R

@@ -0,0 +1,21 @@
+start_time <- 250
+fit1 <- survfit(Surv(time, status) ~ sex, data = lung)
+fit2 <- survfit(Surv(time, status) ~ sex, data = lung, start.time = start_time)
+
+test_that("survplot_combine plots successfully into 4 lines; second 2 fits have only (0,1) before start_time", {
+  p <- ggsurvplot_combine(list(
+      original=fit1, conditional=fit2
+    ), data = lung)
+  .build <- ggplot_build(p$plot)
+  .build_data <- .build$data[[1]]
+  expect_equal(length(unique(.build_data[['group']])), 4)
+  expect_lt(nrow(.build_data[(.build_data[['group']] >= 3) &
+                               (.build_data[['x']] < start_time), ]), 3)
+})
+
+test_that("survplot_combine includes dataframes when keep.data==TRUE", {
+  p <- ggsurvplot_combine(list(
+    original=fit1, conditional=fit2
+  ), data = lung, keep.data = TRUE)
+  expect_equal(length(names(p)), 3)
+})

tests/testthat/test-ggsurvplot_facet.R

@@ -0,0 +1,21 @@
+test_that("ggsurvplot_facet creates the correct quanitty of subplots", {
+  fit <- survfit(Surv(time, status) ~ sex, data=kidney)
+  p <- ggsurvplot_facet(fit, kidney, facet.by='disease')
+  .build <- ggplot_build(p)
+  expect_equal(nrow(.build$data[[2]]),
+               length(unique(kidney[['disease']])))
+
+  fit <- survfit(Surv(time, status) ~ disease, data=kidney)
+  p <- ggsurvplot_facet(fit, kidney, facet.by='sex')
+  .build <- ggplot_build(p)
+  expect_equal(nrow(.build$data[[2]]),
+               length(unique(kidney[['sex']])))
+})
+
+test_that("ggsurvplot_facet calculates pvalue for each facet", {
+  fit <- survfit(Surv(time, status) ~ sex, data=kidney)
+  p <- ggsurvplot_facet(fit, kidney, facet.by='disease', pval = TRUE)
+  .build <- ggplot_build(p)
+  expect_equal(nrow(.build$plot$layer[[4]][['data']]),
+               length(unique(kidney[['disease']])))
+})

tests/testthat/test-surv_median.R

@@ -0,0 +1,6 @@
+test_that("surv_median calculates medians", {
+  df_medians <- surv_median(survfit(Surv(time, status) ~ sex,
+                                    data=lung))
+  expect_equal(df_medians[['median']], c(270,426))
+  expect_lt(df_medians[['lower']][2], 426)
+})