LMLS 2020-05-14
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.3 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library()## Warning in library(): libraries '/usr/local/lib/R/site-library', '/usr/lib/R/
## site-library' contain no packages
setwd("~/Documentos/R/Stroke/")
stroke <- read.csv("stroke.csv")
glimpse(stroke)## Rows: 39,810
## Columns: 9
## $ year <int> 2012, 2012, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,…
## $ sexo <chr> "Men", "Men", "Women", "Men", "Men", "Women", "Women", "Men…
## $ edad <int> 75, 73, 70, 80, 66, 57, 56, 64, 64, 37, 90, 73, 98, 85, 92,…
## $ cid10 <chr> "I64", "I60", "I64", "I64", "I64", "I64", "I64", "I60", "I6…
## $ agecat <chr> "75+", "55-74", "55-74", "75+", "55-74", "55-74", "55-74", …
## $ level <chr> "I, II", "I, II", "I, II", "III", "III", "III", "III", "III…
## $ regions <chr> "Sierra", "Sierra", "Sierra", "Sierra", "Sierra", "Sierra",…
## $ los <int> 5, 5, 2, 28, 14, 2, 7, 10, 3, 12, 11, 12, 5, 4, 5, 8, 19, 5…
## $ condicion <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
strokerelos <- subset(stroke, select = c(year,regions,los))
levels(as.factor(strokerelos$regions))## [1] "Lima/Callao" "Resto Costa" "Selva" "Sierra"
strokerelos$regions <- ifelse(strokerelos$regions=="Lima/Callao" | strokerelos$regions=="Resto Costa", 0,
ifelse(strokerelos$regions=="Sierra" | strokerelos$regions=="Selva", 1, NA)) %>%
factor(strokerelos$regions, levels = c(0,1), labels = c("Lima-Restocosta", "Sierra y Selva"))
strokerelos[1:20,]## year regions los
## 1 2012 Sierra y Selva 5
## 2 2012 Sierra y Selva 5
## 3 2017 Sierra y Selva 2
## 4 2017 Sierra y Selva 28
## 5 2017 Sierra y Selva 14
## 6 2017 Sierra y Selva 2
## 7 2017 Sierra y Selva 7
## 8 2017 Sierra y Selva 10
## 9 2017 Sierra y Selva 3
## 10 2017 Sierra y Selva 12
## 11 2017 Sierra y Selva 11
## 12 2017 Sierra y Selva 12
## 13 2017 Sierra y Selva 5
## 14 2017 Sierra y Selva 4
## 15 2017 Sierra y Selva 5
## 16 2017 Sierra y Selva 8
## 17 2017 Sierra y Selva 19
## 18 2017 Sierra y Selva 5
## 19 2017 Sierra y Selva 6
## 20 2017 Sierra y Selva 6
stroke$cid10 <- as.factor(stroke$cid10)
levels(stroke$cid10)## [1] "I60" "I61" "I63" "I64"
kruskal.test(los ~ regions, data = stroke)##
## Kruskal-Wallis rank sum test
##
## data: los by regions
## Kruskal-Wallis chi-squared = 3273.8, df = 3, p-value < 2.2e-16
pairwise.wilcox.test(stroke$los, stroke$regions, p.adjust.method = "BH")##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: stroke$los and stroke$regions
##
## Lima/Callao Resto Costa Selva
## Resto Costa <2e-16 - -
## Selva <2e-16 <2e-16 -
## Sierra <2e-16 <2e-16 <2e-16
##
## P value adjustment method: BH
strokefacilities <- subset(stroke, select = c(level,los))
strokefacilities$level <- as.factor(strokefacilities$level)
levels(strokefacilities$level)## [1] "I, II" "III"
strokefacilities[1:20,]## level los
## 1 I, II 5
## 2 I, II 5
## 3 I, II 2
## 4 III 28
## 5 III 14
## 6 III 2
## 7 III 7
## 8 III 10
## 9 III 3
## 10 III 12
## 11 III 11
## 12 III 12
## 13 III 5
## 14 III 4
## 15 III 5
## 16 III 8
## 17 III 19
## 18 III 5
## 19 III 6
## 20 III 6
kruskal.test(los ~ level, data = strokefacilities)##
## Kruskal-Wallis rank sum test
##
## data: los by level
## Kruskal-Wallis chi-squared = 4636.8, df = 1, p-value < 2.2e-16
stroke$cid10 <- as.factor(stroke$cid10)
levels(stroke$cid10)## [1] "I60" "I61" "I63" "I64"
kruskal.test(los ~ cid10, data = stroke)##
## Kruskal-Wallis rank sum test
##
## data: los by cid10
## Kruskal-Wallis chi-squared = 2881, df = 3, p-value < 2.2e-16
pairwise.wilcox.test(stroke$los, stroke$year, p.adjust.method = "BH")##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: stroke$los and stroke$year
##
## 2002 2003 2004 2005 2006 2007 2008 2009 2010
## 2003 0.09685 - - - - - - - -
## 2004 0.89123 0.07174 - - - - - - -
## 2005 0.54892 0.01606 0.65627 - - - - - -
## 2006 0.40968 0.34814 0.32204 0.09508 - - - - -
## 2007 0.28606 0.49069 0.20669 0.05174 0.79998 - - - -
## 2008 0.08957 0.81729 0.05857 0.00739 0.39549 0.55035 - - -
## 2009 0.05580 0.94533 0.03880 0.00385 0.28606 0.40968 0.81006 - -
## 2010 2.4e-07 0.00238 4.8e-08 2.9e-11 1.2e-06 5.2e-06 4.3e-05 0.00010 -
## 2011 0.07781 0.87187 0.04900 0.00534 0.33979 0.51180 0.92623 0.89112 4.3e-05
## 2012 0.00050 0.16440 0.00016 2.3e-06 0.00385 0.00938 0.04661 0.07782 0.04661
## 2013 0.00018 0.14188 7.4e-05 5.7e-07 0.00215 0.00533 0.02914 0.05439 0.04093
## 2014 0.00595 0.52317 0.00333 9.9e-05 0.04661 0.08757 0.28121 0.38459 0.00238
## 2015 2.4e-05 0.03905 6.5e-06 3.8e-08 0.00016 0.00057 0.00356 0.00739 0.27769
## 2016 0.14844 0.66954 0.09492 0.01726 0.54339 0.76207 0.80957 0.63495 1.1e-05
## 2017 0.00534 0.55035 0.00302 8.9e-05 0.04661 0.09030 0.28918 0.40968 0.00138
## 2011 2012 2013 2014 2015 2016
## 2003 - - - - - -
## 2004 - - - - - -
## 2005 - - - - - -
## 2006 - - - - - -
## 2007 - - - - - -
## 2008 - - - - - -
## 2009 - - - - - -
## 2010 - - - - - -
## 2011 - - - - - -
## 2012 0.05122 - - - - -
## 2013 0.03701 0.93017 - - - -
## 2014 0.29833 0.36365 0.32333 - - -
## 2015 0.00385 0.40793 0.40968 0.06863 - -
## 2016 0.75276 0.02135 0.01163 0.16440 0.00141 -
## 2017 0.32577 0.32577 0.27769 0.92623 0.05247 0.17067
##
## P value adjustment method: BH