This is part of My notes on R programming on my site https://dataz4s.com
# Read in data via read_excel
library(readxl)
LungCapData <- read_excel("C:/Users/Usuario/Documents/dataZ4s/R/MarinLectures/LungCapData.xlsx",
col_types = c("numeric", "numeric", "numeric",
"text", "text", "text"))
# R reads in Smoke, Gender and Caesarean as "text". Needs change to "factor"
# Change Smoke, Gender and Caesarean to factors with as.factor() command
LungCapData$Smoke <- as.factor(LungCapData$Smoke)
LungCapData$Gender <- as.factor(LungCapData$Gender)
LungCapData$Caesarean <- as.factor(LungCapData$Caesarean)
# attach(LungCapData)
attach(LungCapData)# Checking names
head(LungCapData)## # A tibble: 6 x 6
## LungCap Age Height Smoke Gender Caesarean
## <dbl> <dbl> <dbl> <fct> <fct> <fct>
## 1 6.48 6 62.1 no male no
## 2 10.1 18 74.7 yes female no
## 3 9.55 16 69.7 no female yes
## 4 11.1 14 71 no male no
## 5 4.8 5 56.9 no male no
## 6 6.22 11 58.7 no female noAge[1:5]## [1] 6 18 16 14 5# Is the age more than 14
# Shown by TRUE/FALSE
# 2nd, 3rd and 9th rows are > 14
temp <- Age>14
temp[1:10]## [1] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE# Replacing TRUE/FALSE with 0/1
# Change to numeric with as.numeric command
temp2 <- as.numeric(Age>15)
temp2[1:10]## [1] 0 1 1 0 0 0 0 0 0 0# View the first 10 rows
LungCapData[1:10, ]## # A tibble: 10 x 6
## LungCap Age Height Smoke Gender Caesarean
## <dbl> <dbl> <dbl> <fct> <fct> <fct>
## 1 6.48 6 62.1 no male no
## 2 10.1 18 74.7 yes female no
## 3 9.55 16 69.7 no female yes
## 4 11.1 14 71 no male no
## 5 4.8 5 56.9 no male no
## 6 6.22 11 58.7 no female no
## 7 4.95 8 63.3 no male yes
## 8 7.32 11 70.4 no male no
## 9 8.88 15 70.5 no male no
## 10 6.8 11 59.2 no male no# Having Gender and Smoke returned as logic statements
FemSmoke <- Gender=="female" & Smoke=="yes"
FemSmoke[1:10]## [1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE# Adding column with cbind
# Add column with TRUE/FALSE
ExtraColumn <- cbind(LungCapData, FemSmoke)
ExtraColumn[1:10, ]## LungCap Age Height Smoke Gender Caesarean FemSmoke
## 1 6.475 6 62.1 no male no FALSE
## 2 10.125 18 74.7 yes female no TRUE
## 3 9.550 16 69.7 no female yes FALSE
## 4 11.125 14 71.0 no male no FALSE
## 5 4.800 5 56.9 no male no FALSE
## 6 6.225 11 58.7 no female no FALSE
## 7 4.950 8 63.3 no male yes FALSE
## 8 7.325 11 70.4 no male no FALSE
## 9 8.875 15 70.5 no male no FALSE
## 10 6.800 11 59.2 no male no FALSEThis page is a run through of Statslectures with Mick Marin on his video ‘Logic Statements…’. View this page in my site: https://dataz4s.com/r-statistical-programming/logic-statements-cbind-r/