Here is a sample of my tibble
protein patient value
<chr> <chr> <dbl>
1 BOD1L2 RF0064_Case-9-d- 10.4
2 PPFIA2 RF0064_Case-20-d- 7.83
3 STAT4 RF0064_Case-11-d- 11.0
4 TOM1L2 RF0064_Case-29-d- 13.0
5 SH2D2A RF0064_Case-2-d- 8.28
6 TIGD4 RF0064_Case-49-d- 9.71
In the "patient" column the "d" as in "Case-x-d" represents the a number of days. What I would like to do is create a new column stating whether the strings in the "patient" column contain values less than 14d.
I have managed to do this using the following command:
under14 <- "-1d|-2d|-3d|-4d|-4d|-5d|-6d|-7d|-8d|-9d|-11d|-12d|-13d|-14d"
data <- data %>%
mutate(case=ifelse(grepl(under14,data$patient),'under14days','over14days'))
However this seems extremely clunky and actually took way to long to type. I will have to be changing my search term many times so would like a quicker way to do this? Perhaps using some kind of regex is the best option, but I don't really know where to start with this.
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_NZ.UTF-8/en_NZ.UTF-8/en_NZ.UTF-8/C/en_NZ.UTF-8/en_NZ.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.1.0 Rmisc_1.5 plyr_1.8.4 lattice_0.20-35 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.5 purrr_0.2.5
[9] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2 ggplot2_2.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 cellranger_1.1.0 pillar_1.2.3 compiler_3.5.0 bindr_0.1.1 tools_3.5.0 lubridate_1.7.4
[8] jsonlite_1.5 nlme_3.1-137 gtable_0.2.0 pkgconfig_2.0.1 rlang_0.2.1 psych_1.8.4 cli_1.0.0
[15] rstudioapi_0.7 yaml_2.1.19 parallel_3.5.0 haven_1.1.1 bindrcpp_0.2.2 xml2_1.2.0 httr_1.3.1
[22] hms_0.4.2 grid_3.5.0 tidyselect_0.2.4 glue_1.2.0 R6_2.2.2 foreign_0.8-70 modelr_0.1.2
[29] reshape2_1.4.3 magrittr_1.5 scales_0.5.0 rvest_0.3.2 assertthat_0.2.0 mnormt_1.5-5 colorspace_1.3-2
[36] utf8_1.1.4 stringi_1.2.3 lazyeval_0.2.1 munsell_0.5.0 broom_0.4.4 crayon_1.3.4
>
One possibility is to use tidyr::separate
library(tidyverse)
df %>%
separate(patient, into = c("ID1", "Days", "ID2"), sep = "-", extra = "merge", remove = F) %>%
mutate(case = ifelse(as.numeric(Days) <= 14, "under14days", "over14days")) %>%
select(-ID1, -ID2)
# protein patient Days value case
#1 BOD1L2 RF0064_Case-9-d- 9 10.40 under14days
#2 PPFIA2 RF0064_Case-20-d- 20 7.83 over14days
#3 STAT4 RF0064_Case-11-d- 11 11.00 under14days
#4 TOM1L2 RF0064_Case-29-d- 29 13.00 over14days
#5 SH2D2A RF0064_Case-2-d- 2 8.28 under14days
#6 TIGD4 RF0064_Case-49-d- 49 9.71 over14days
df <-read.table(text =
" protein patient value
1 BOD1L2 RF0064_Case-9-d- 10.4
2 PPFIA2 RF0064_Case-20-d- 7.83
3 STAT4 RF0064_Case-11-d- 11.0
4 TOM1L2 RF0064_Case-29-d- 13.0
5 SH2D2A RF0064_Case-2-d- 8.28
6 TIGD4 RF0064_Case-49-d- 9.71 ", header = T, row.names = 1)
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