Given is the file survey.csv
survey <- read.csv("F:/R Course/Datasets/survey.csv")
library(tidyverse)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang## Registered S3 method overwritten by 'rvest':
## method from
## read_xml.response xml2## -- Attaching packages -------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.1
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ----------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()tbl_surv <- as_tibble(survey)
tbl_surv## # A tibble: 237 x 12
## Sex Wr.Hnd NW.Hnd W.Hnd Fold Pulse Clap Exer Smoke Height M.I
## <fct> <dbl> <dbl> <fct> <fct> <int> <fct> <fct> <fct> <dbl> <fct>
## 1 Fema~ 18.5 18 Right R on~ 92 Left Some Never 173 Metr~
## 2 Male 19.5 20.5 Left R on~ 104 Left None Regul 178. Impe~
## 3 Male 18 13.3 Right L on~ 87 Neit~ None Occas NA <NA>
## 4 Male 18.8 18.9 Right R on~ NA Neit~ None Never 160 Metr~
## 5 Male 20 20 Right Neit~ 35 Right Some Never 165 Metr~
## 6 Fema~ 18 17.7 Right L on~ 64 Right Some Never 173. Impe~
## 7 Male 17.7 17.7 Right L on~ 83 Right Freq Never 183. Impe~
## 8 Fema~ 17 17.3 Right R on~ 74 Right Freq Never 157 Metr~
## 9 Male 20 19.5 Right R on~ 72 Right Some Never 175 Metr~
## 10 Male 18.5 18.5 Right R on~ 90 Right Some Never 167 Metr~
## # ... with 227 more rows, and 1 more variable: Age <dbl>surv_Male <- tbl_surv %>% filter(Sex == "Male")
surv_Male## # A tibble: 118 x 12
## Sex Wr.Hnd NW.Hnd W.Hnd Fold Pulse Clap Exer Smoke Height M.I
## <fct> <dbl> <dbl> <fct> <fct> <int> <fct> <fct> <fct> <dbl> <fct>
## 1 Male 19.5 20.5 Left R on~ 104 Left None Regul 178. Impe~
## 2 Male 18 13.3 Right L on~ 87 Neit~ None Occas NA <NA>
## 3 Male 18.8 18.9 Right R on~ NA Neit~ None Never 160 Metr~
## 4 Male 20 20 Right Neit~ 35 Right Some Never 165 Metr~
## 5 Male 17.7 17.7 Right L on~ 83 Right Freq Never 183. Impe~
## 6 Male 20 19.5 Right R on~ 72 Right Some Never 175 Metr~
## 7 Male 18.5 18.5 Right R on~ 90 Right Some Never 167 Metr~
## 8 Male 21 21 Right R on~ 68 Left Freq Never NA <NA>
## 9 Male 16 15.5 Right R on~ 60 Right Some Never NA <NA>
## 10 Male 19.4 19.2 Left R on~ 74 Right Some Never 183. Impe~
## # ... with 108 more rows, and 1 more variable: Age <dbl>surv_Pul <- tbl_surv %>%
filter(Pulse > 80) %>%
select(Sex, Exer, Smoke, Pulse)
surv_Pul## # A tibble: 47 x 4
## Sex Exer Smoke Pulse
## <fct> <fct> <fct> <int>
## 1 Female Some Never 92
## 2 Male None Regul 104
## 3 Male None Occas 87
## 4 Male Freq Never 83
## 5 Male Some Never 90
## 6 Female Freq Never 89
## 7 Male Some Never 90
## 8 Male Some Never 90
## 9 Male Freq Regul 84
## 10 Male None Never 96
## # ... with 37 more rows