LMLS 2020-05-13
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()
stroke <- read.csv("stroke.csv")
stroke_sexo <- subset(stroke, select = c(year,sexo))
stroke_s1 <- stroke_sexo %>%
group_by(year) %>%
summarise(total = n())stroke_sexo[1:16,]## year sexo
## 1 2012 Men
## 2 2012 Men
## 3 2017 Women
## 4 2017 Men
## 5 2017 Men
## 6 2017 Women
## 7 2017 Women
## 8 2017 Men
## 9 2017 Men
## 10 2017 Men
## 11 2017 Women
## 12 2017 Women
## 13 2017 Women
## 14 2017 Women
## 15 2017 Men
## 16 2017 Men
stroke_s1[1:16,]## # A tibble: 16 x 2
## year total
## <int> <int>
## 1 2002 1424
## 2 2003 1503
## 3 2004 1625
## 4 2005 2192
## 5 2006 2285
## 6 2007 2269
## 7 2008 2425
## 8 2009 2518
## 9 2010 2988
## 10 2011 2834
## 11 2012 2836
## 12 2013 3154
## 13 2014 3250
## 14 2015 2953
## 15 2016 2599
## 16 2017 2955
strokefemale <- subset(stroke, stroke$sexo=="Women", select = c(year))
strokefemale <- strokefemale%>%group_by(year)%>%dplyr::summarise(women = n())
strokemale <- subset(stroke, stroke$sexo=="Men", select = c(year))
strokemale <- strokemale%>%group_by(year)%>%dplyr::summarise(men = n())
stroketrendsex <- full_join(stroke_s1,strokefemale)## Joining, by = "year"
stroketrendsex <- full_join(stroketrendsex, strokemale)## Joining, by = "year"
stroketrendsex <- stroketrendsex %>%
mutate(percent1=women/total) %>%
mutate(percent2=men/total)
stroke14 <- stroketrendsex %>% filter(year %in% c("2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014"))
stroke14$percent1 <- as.ts(stroke14$percent1)
stroke14$percent2 <- as.ts(stroke14$percent2)
stroke15 <- stroketrendsex %>% filter(year %in% c("2015","2016","2017"))
stroke15$percent1 <- as.ts(stroke15$percent1)
stroke15$percent2 <- as.ts(stroke15$percent2)
stroke14## # A tibble: 13 x 6
## year total women men percent1 percent2
## <int> <int> <int> <int> <ts> <ts>
## 1 2002 1424 740 684 0.5196629 0.4803371
## 2 2003 1503 746 757 0.4963407 0.5036593
## 3 2004 1625 826 799 0.5083077 0.4916923
## 4 2005 2192 1171 1021 0.5342153 0.4657847
## 5 2006 2285 1219 1066 0.5334792 0.4665208
## 6 2007 2269 1200 1069 0.5288673 0.4711327
## 7 2008 2425 1272 1153 0.5245361 0.4754639
## 8 2009 2518 1254 1264 0.4980143 0.5019857
## 9 2010 2988 1497 1491 0.5010040 0.4989960
## 10 2011 2834 1413 1421 0.4985886 0.5014114
## 11 2012 2836 1395 1441 0.4918900 0.5081100
## 12 2013 3154 1579 1575 0.5006341 0.4993659
## 13 2014 3250 1531 1719 0.4710769 0.5289231
stroke15## # A tibble: 3 x 6
## year total women men percent1 percent2
## <int> <int> <int> <int> <ts> <ts>
## 1 2015 2953 1472 1481 0.4984761 0.5015239
## 2 2016 2599 1265 1334 0.4867257 0.5132743
## 3 2017 2955 1434 1521 0.4852792 0.5147208
library(trend)
partial.cor.trend.test(stroke14$percent1,stroke14$percent2, method = "spearman")##
## Spearman's Partial Correlation Trend Test
##
## data: t AND stroke14$percent1 . stroke14$percent2
## t = 0, df = 11, p-value = 1
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## r(tstroke14$percent1.stroke14$percent2)
## 0
partial.cor.trend.test(stroke15$percent1,stroke15$percent2, method = "spearman")##
## Spearman's Partial Correlation Trend Test
##
## data: t AND stroke15$percent1 . stroke15$percent2
## t = 0, df = 1, p-value = 1
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## r(tstroke15$percent1.stroke15$percent2)
## 0