We are using the dataset LungCap which is a dataset of 725 rows with 6 variables.

Read in data to R

We start by reading in data. In this case, I read in from Excel with the read_excel() function and I will need to change three of the variables to factors.

head()

The head command is practical for viewing the first rows of a dataset and thus to get an overall idea of rows and columns in this case.

<script src="https://gist.github.com/DataGrube/4507ed0541e5b45998704e22bcbc75ba.js"></script>

dim() and length()

# Dimensions of the dataset
dim(LungCapData)

## [1] 725   6

# length in a vector or a variable
length(Age)

## [1] 725

Subsetting with brackets []

# Subsetting row 11 to 14
Age[11:14]

## [1] 19 17 12 10

# Subsetting with brackets on matrix or data frame
# Blank space after comma to include all columns
# Row 11 to 14 including all columns
LungCapData[11:14, ]

## # A tibble: 4 x 6
##   LungCap   Age Height Smoke Gender Caesarean
##     <dbl> <dbl>  <dbl> <fct> <fct>  <fct>
## 1   11.5     19   76.4 no    male   yes
## 2   10.9     17   71.7 no    male   no
## 3    6.52    12   57.5 no    male   no
## 4    6       10   61.1 no    female no

Subsetting a step further…

# double equal sign (==) is used to represent the meaning of equality in a mathematical sense
# mean age for females
mean(Age[Gender==“female”])

## [1] 12.44972

# mean age for males
mean(Age[Gender==“male”])

## [1] 12.20708

Subset as per gender

# Save gender data into objects
FemData <- LungCapData[Gender==“female”, ]
MaleData <- LungCapData[Gender==“female”, ]

# Checking
dim(FemData)

## [1] 358   6

dim(MaleData)

## [1] 358   6

summary(Gender)

## female   male
##    358    367

FemData[1:4, ]

## # A tibble: 4 x 6
##   LungCap   Age Height Smoke Gender Caesarean
##     <dbl> <dbl>  <dbl> <fct> <fct>  <fct>
## 1   10.1     18   74.7 yes   female no
## 2    9.55    16   69.7 no    female yes
## 3    6.22    11   58.7 no    female no
## 4    6       10   61.1 no    female no

# Subset for males over 15
MaleOver15 <- LungCapData[Gender==“male” & Age>15, ]

# Checking
dim(MaleOver15)

## [1] 89  6

MaleOver15[1:4,]

## # A tibble: 4 x 6
##   LungCap   Age Height Smoke Gender Caesarean
##     <dbl> <dbl>  <dbl> <fct> <fct>  <fct>
## 1    11.5    19   76.4 no    male   yes
## 2    10.9    17   71.7 no    male   no
## 3    10.0    16   72.4 no    male   no
## 4    11.3    17   77.7 no    male   no

This page

This page is inspired by Mike Marons Statlectures video ‘Subsetting data in R…’. View my Rpubs for this page here.

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