After R and ggplot2 update ggsurvplot no longer works fine. #694
Description
Previous installation:
> sessionInfo()
R version 4.0.1 (2020-06-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Italian_Italy.1252 LC_CTYPE=Italian_Italy.1252
[3] LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C
[5] LC_TIME=Italian_Italy.1252
attached base packages:
[1] grid splines stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] adjustedCurves_0.11.3 Matching_4.10-2
[3] EValue_4.1.4 rbounds_2.2
[5] AICcmodavg_2.3-1 orddom_3.1
[7] psych_2.2.3 sandwich_3.0-1
[9] car_3.0-12 lawstat_3.4
[11] sfsmisc_1.1-13 tree_1.0-41
[13] interactions_1.1.5 gvlma_1.0.0.3
[15] brglm_0.7.2 profileModel_0.6.1
[17] arm_1.12-2 gridExtra_2.3
[19] multcompView_0.1-8 RVAideMemoire_0.9-81-2
[21] lmerTest_3.1-3 lme4_1.1-27
[23] Matrix_1.3-4 fitdistrplus_1.1-8
[25] FSA_0.9.0 dynamichazard_1.0.1
[27] ClinicalTrialSummary_1.1.1 rms_6.2-0
[29] SparseM_1.81 Hmisc_4.5-0
[31] Formula_1.2-4 lattice_0.20-44
[33] flexsurv_2.1 coin_1.4-2
[35] coxphw_4.0.2 overlapping_2.1
[37] testthat_3.1.3 effects_4.2-1
[39] carData_3.0-5 nonnest2_0.5-5
[41] lmtest_0.9-40 zoo_1.8-9
[43] ordinal_2019.12-10 survminer_0.4.9
[45] ggpubr_0.4.0 survRM2_1.0-4
[47] emmeans_1.6.1 multcomp_1.4-17
[49] TH.data_1.0-10 MASS_7.3-54
[51] survival_3.2-11 mvtnorm_1.1-2
[53] forcats_0.5.1 stringr_1.4.0
[55] dplyr_1.0.6 purrr_0.3.4
[57] readr_2.1.2 tidyr_1.1.3
[59] tibble_3.1.2 ggplot2_3.3.5
[61] tidyverse_1.3.1 discSurv_2.0.0
[63] treeClust_1.1-7 cluster_2.1.2
[65] rpart_4.1-15 gam_1.20.1
[67] foreach_1.5.2
loaded via a namespace (and not attached):
[1] utf8_1.2.1 tidyselect_1.1.2 htmlwidgets_1.5.4
[4] ranger_0.13.1 jtools_2.1.3 munsell_0.5.0
[7] codetools_0.2-18 withr_2.5.0 colorspace_2.0-1
[10] muhaz_1.2.6.4 knitr_1.33 rstudioapi_0.13
[13] stats4_4.0.1 ggsignif_0.6.3 labeling_0.4.2
[16] Rdpack_2.3 mnormt_2.0.2 KMsurv_0.1-5
[19] farver_2.1.0 coda_0.19-4 vctrs_0.4.1
[22] generics_0.1.2 metafor_3.4-0 xfun_0.23
[25] geepack_1.3.3 markdown_1.1 R6_2.5.1
[28] VGAM_1.1-6 Kendall_2.2.1 assertthat_0.2.1
[31] scales_1.2.0 nnet_7.3-16 gtable_0.3.0
[34] conquer_1.0.2 rlang_1.0.2 MatrixModels_0.5-0
[37] rstatix_0.7.0 broom_0.8.0 checkmate_2.0.0
[40] unmarked_1.1.1 abind_1.4-5 modelr_0.1.8
[43] backports_1.2.1 gridtext_0.1.4 tools_4.0.1
[46] lavaan_0.6-11 ellipsis_0.3.2 raster_3.5-15
[49] RColorBrewer_1.1-3 plyr_1.8.7 Rcpp_1.0.8.3
[52] base64enc_0.1-3 deSolve_1.32 haven_2.4.1
[55] fs_1.5.2 survey_4.1-1 magrittr_2.0.1
[58] data.table_1.14.0 reprex_2.0.1 mvnfast_0.2.7
[61] tmvnsim_1.0-2 matrixStats_0.59.0 hms_1.1.1
[64] xtable_1.8-4 jpeg_0.1-8.1 readxl_1.3.1
[67] compiler_4.0.1 functional_0.6 crayon_1.5.1
[70] minqa_1.2.4 htmltools_0.5.1.1 mgcv_1.8-40
[73] tzdb_0.3.0 ggtext_0.1.1 libcoin_1.0-9
[76] lubridate_1.8.0 DBI_1.1.2 dbplyr_2.1.1
[79] boot_1.3-28 brio_1.1.3 cli_3.2.0
[82] mitools_2.4 quadprog_1.5-8 rbibutils_2.2.8
[85] parallel_4.0.1 insight_0.18.0 pkgconfig_2.0.3
[88] km.ci_0.5-6 sp_1.4-7 numDeriv_2016.8-1.1
[91] foreign_0.8-81 terra_1.5-21 xml2_1.3.3
[94] pbivnorm_0.6.0 rngtools_1.5.2 estimability_1.3
[97] CompQuadForm_1.4.3 rvest_1.0.2 doRNG_1.8.6.2
[100] digest_0.6.27 MetaUtility_2.1.2 cellranger_1.1.0
[103] survMisc_0.5.6 htmlTable_2.4.0 quantreg_5.86
[106] modeltools_0.2-23 nloptr_1.2.2.2 lifecycle_1.0.1
[109] nlme_3.1-152 jsonlite_1.8.0 mstate_0.3.2
[112] fansi_0.5.0 pillar_1.7.0 httr_1.4.2
[115] glue_1.4.2 metadat_1.2-0 png_0.1-7
[118] iterators_1.0.14 pander_0.6.5 stringi_1.5.3
[121] polspline_1.1.19 latticeExtra_0.6-29 mathjaxr_1.6-0
[124] ucminf_1.1-4
df <-
structure(list(Group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), levels = c("Control", "Ozoile"), class = "factor"), H.T = c(45L,
45L, 45L, 75L, 90L, 60L, 45L, 60L, 30L, 60L, 75L, 60L, 45L, 45L,
60L, 60L, 60L, 90L, 90L, 90L, 45L, 60L, 60L, 90L, 75L, 75L, 90L,
90L, 75L, 60L, 60L, 90L, 60L, 90L, 45L, 45L, 90L, 60L, 60L, 90L,
60L, 75L, 60L, 60L, 45L, 60L, 90L, 60L, 45L, 75L, 75L, 60L, 90L,
90L, 60L, 90L, 75L, 60L, 75L, 60L, 60L, 75L, 45L, 75L, 75L, 60L,
45L, 75L, 60L, 75L, 30L, 60L, 45L, 90L, 60L, 90L, 45L, 75L, 90L,
60L, 30L, 60L, 75L, 75L, 90L, 75L, 60L, 90L, 60L, 90L, 60L, 60L,
90L, 75L, 90L, 45L, 60L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L), Status = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
)), class = "data.frame", row.names = c(NA, -200L))
library(survminer)
library(survival)
f3 <- survfit(Surv(I(H.T/15),Status) ~ Group, data=df)
windows(8,8); ggsurvplot(f3, cumevents = TRUE, cumevents.col = "strata", fun = "event", title="Healing times comparison by treatment", xlab = "Fortnight", ylab = "Healed patients' percentage", surv.scale="percent", conf.int=TRUE, cumevents.title="Healed patients", legend.title="", surv.median.line = "hv", legend.labs=c("Control","Ozoile"), pval=TRUE, pval.method=TRUE)
New installation:
> sessionInfo()
R version 4.4.0 (2024-04-24 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Italian_Italy.utf8 LC_CTYPE=Italian_Italy.utf8
[3] LC_MONETARY=Italian_Italy.utf8 LC_NUMERIC=C
[5] LC_TIME=Italian_Italy.utf8
time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] grid splines stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] WeightIt_1.5.1 pammtools_0.7.3
[3] riskRegression_2025.09.17 discSurv_2.0.0
[5] adjustedCurves_0.11.3 Matching_4.10-15
[7] EValue_4.1.4 rbounds_2.2
[9] AICcmodavg_2.3-4 orddom_3.1
[11] psych_2.5.6 sandwich_3.1-1
[13] car_3.1-3 lawstat_3.6
[15] sfsmisc_1.1-23 tree_1.0-45
[17] interactions_1.2.0 gvlma_1.0.0.3
[19] brglm_0.7.3 profileModel_0.6.1
[21] arm_1.14-4 gridExtra_2.3
[23] multcompView_0.1-10 RVAideMemoire_0.9-83-12
[25] lmerTest_3.1-3 lme4_1.1-37
[27] Matrix_1.7-0 fitdistrplus_1.2-4
[29] FSA_0.10.0 lattice_0.22-6
[31] ClinicalTrialSummary_1.1.1 rms_8.1-0
[33] Hmisc_5.2-4 flexsurv_2.3.2
[35] coin_1.4-3 coxphw_4.0.3
[37] overlapping_2.2 testthat_3.3.0
[39] effects_4.2-4 carData_3.0-5
[41] nonnest2_0.5-8 lmtest_0.9-40
[43] zoo_1.8-14 ordinal_2023.12-4.1
[45] survminer_0.5.1 ggpubr_0.6.2
[47] survRM2_1.0-4 emmeans_2.0.0
[49] multcomp_1.4-29 TH.data_1.1-5
[51] MASS_7.3-60.2 survival_3.5-8
[53] mvtnorm_1.3-3 lubridate_1.9.4
[55] forcats_1.0.1 stringr_1.6.0
[57] dplyr_1.1.4 purrr_1.2.0
[59] readr_2.1.6 tidyr_1.3.1
[61] tibble_3.3.0 ggplot2_4.0.1
[63] tidyverse_2.0.0 treeClust_1.1-7.1
[65] cluster_2.1.6 rpart_4.1.23
[67] gam_1.22-6 foreach_1.5.2
loaded via a namespace (and not attached):
[1] ggtext_0.1.2 MetaUtility_2.1.2 cmprsk_2.2-12
[4] matrixStats_1.5.0 RColorBrewer_1.1-3 insight_1.4.3
[7] numDeriv_2016.8-1.1 tools_4.4.0 doRNG_1.8.6.2
[10] backports_1.5.0 R6_2.6.1 metafor_4.8-0
[13] lazyeval_0.2.2 mgcv_1.9-1 withr_3.0.2
[16] quantreg_6.1 cli_3.6.5 labeling_0.4.3
[19] survMisc_0.5.6 S7_0.2.1 polspline_1.1.25
[22] pbivnorm_0.6.0 foreign_0.8-86 R.utils_2.13.0
[25] parallelly_1.45.1 VGAM_1.1-13 rstudioapi_0.17.1
[28] generics_0.1.4 shape_1.4.6.1 scam_1.2-20
[31] metadat_1.4-0 abind_1.4-8 R.methodsS3_1.8.2
[34] lifecycle_1.0.4 CompQuadForm_1.4.4 mstate_0.3.3
[37] mathjaxr_1.8-0 functional_0.6 lavaan_0.6-20
[40] crayon_1.5.3 cowplot_1.2.0 jtools_2.3.0
[43] pillar_1.11.1 knitr_1.50 boot_1.3-30
[46] estimability_1.5.1 future.apply_1.20.0 codetools_0.2-20
[49] glue_1.8.0 data.table_1.17.8 vctrs_0.6.5
[52] Rdpack_2.6.4 gtable_0.3.6 xfun_0.54
[55] rbibutils_2.4 prodlim_2025.04.28 libcoin_1.0-10
[58] survey_4.4-8 coda_0.19-4.1 reformulas_0.4.2
[61] Kendall_2.2.1 iterators_1.0.14 KMsurv_0.1-6
[64] lava_1.8.2 statmod_1.5.1 nlme_3.1-164
[67] pbkrtest_0.5.5 colorspace_2.1-2 DBI_1.2.3
[70] nnet_7.3-19 mnormt_2.1.1 tidyselect_1.2.1
[73] compiler_4.4.0 glmnet_4.1-10 htmlTable_2.4.3
[76] SparseM_1.84-2 xml2_1.5.0 unmarked_1.5.1
[79] checkmate_2.3.3 scales_1.4.0 pec_2025.06.24
[82] quadprog_1.5-8 digest_0.6.38 mvnfast_0.2.8
[85] minqa_1.2.8 rmarkdown_2.30 htmltools_0.5.8.1
[88] pkgconfig_2.0.3 base64enc_0.1-3 fastmap_1.2.0
[91] rlang_1.1.6 htmlwidgets_1.6.4 farver_2.1.2
[94] broom.mixed_0.2.9.6 R.oo_1.27.1 magrittr_2.0.4
[97] modeltools_0.2-24 Formula_1.2-5 Rcpp_1.1.0
[100] geepack_1.3.13 ucminf_1.2.2 furrr_0.3.1
[103] chk_0.10.0 stringi_1.8.7 brio_1.1.5
[106] parallel_4.4.0 listenv_0.10.0 mets_1.3.8
[109] gridtext_0.1.5 pander_0.6.6 hms_1.1.4
[112] timereg_2.0.7 ranger_0.17.0 ggsignif_0.6.4
[115] rngtools_1.5.2 stats4_4.4.0 evaluate_1.0.5
[118] mitools_2.4 deSolve_1.40 nloptr_2.2.1
[121] tzdb_0.5.0 MatrixModels_0.5-4 muhaz_1.2.6.4
[124] future_1.68.0 km.ci_0.5-6 broom_1.0.10
[127] xtable_1.8-4 rstatix_0.7.3 timechange_0.3.0
[130] globals_0.18.0
df <-
structure(list(Group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), levels = c("Control", "Ozoile"), class = "factor"), H.T = c(45L,
45L, 45L, 75L, 90L, 60L, 45L, 60L, 30L, 60L, 75L, 60L, 45L, 45L,
60L, 60L, 60L, 90L, 90L, 90L, 45L, 60L, 60L, 90L, 75L, 75L, 90L,
90L, 75L, 60L, 60L, 90L, 60L, 90L, 45L, 45L, 90L, 60L, 60L, 90L,
60L, 75L, 60L, 60L, 45L, 60L, 90L, 60L, 45L, 75L, 75L, 60L, 90L,
90L, 60L, 90L, 75L, 60L, 75L, 60L, 60L, 75L, 45L, 75L, 75L, 60L,
45L, 75L, 60L, 75L, 30L, 60L, 45L, 90L, 60L, 90L, 45L, 75L, 90L,
60L, 30L, 60L, 75L, 75L, 90L, 75L, 60L, 90L, 60L, 90L, 60L, 60L,
90L, 75L, 90L, 45L, 60L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L), Status = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
)), class = "data.frame", row.names = c(NA, -200L))
library(survminer)
library(survival)
f3 <- survfit(Surv(I(H.T/15),Status) ~ Group, data=df)
windows(8,8); ggsurvplot(f3, cumevents = TRUE, cumevents.col = "strata", fun = "event", title="Healing times comparison by treatment", xlab = "Fortnight", ylab = "Healed patients' percentage", surv.scale="percent", conf.int=TRUE, cumevents.title="Healed patients", legend.title="", surv.median.line = "hv", legend.labs=c("Control","Ozoile"), pval=TRUE, pval.method=TRUE)
Error:
! Problem while converting geom to grob.
ℹ Error occurred in the 3rd layer.
Caused by error in `draw_group()`:
! Unable to check the capabilities of the windows device.
Run `rlang::last_trace()` to see where the error occurred.
Messaggio di avvertimento:
Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
ℹ The deprecated feature was likely used in the ggpubr package.
Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
It seems the issue is due to ggpubr.