tApply Function in R | R Tutorial 1.16 | MarinStatsLectures

MarinStatsLectures-R Programming & Statistics
4 Jun 201804:54
EducationalLearning
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TLDRIn this educational video, Mike Marin introduces the 'tapply' command in R, a powerful tool for applying functions to subsets of data. Using the 'lungcapdata', Marin demonstrates how to calculate mean ages for smokers and non-smokers, emphasizing the command's efficiency and flexibility. He also discusses the 'simplify' argument, shows how to use custom functions, and compares 'tapply' with the 'by' function. The video is a concise guide for R users looking to streamline data manipulation tasks.

Takeaways
  • πŸ˜€ The video discusses the T apply function in R, which is used to apply a function to subsets of a variable or vector.
  • πŸ“š The lungcapdata is used as an example dataset throughout the video, which was also used in earlier videos in the series.
  • πŸ” To access help in R, you can use a question mark before the command name or search in the help search window.
  • πŸ“‹ The T apply function has several arguments, including X (the variable/vector), FUN (the function to apply), INDEX (grouping variable), and additional arguments (passed to the function).
  • βœ… The 'simplify = TRUE' argument in T apply is used to simplify the results if possible, which is the default setting.
  • πŸ‘΄πŸ‘΅ The video demonstrates calculating the mean age of smokers and non-smokers separately using T apply.
  • πŸ“Š The output of T apply can be saved in an object for later use, as shown with the 'em' object.
  • πŸ”„ When 'simplify = FALSE', the output is returned in a list format instead of a simplified vector.
  • πŸ› οΈ T apply can apply various functions, not just the mean, such as summary and quantile functions.
  • πŸ€– Custom functions can be written and applied to subsets using T apply, though this is a topic for a separate video.
  • πŸ‘₯ T apply can also apply a function to subsets created by multiple factors, such as calculating mean age based on smoking status and gender.
Q & A
  • What is the purpose of the 'tapply' function in R?

    -The 'tapply' function in R is used to apply a specific function to subsets of a variable or vector, allowing for efficient data manipulation and analysis.

  • What data set does Mike Marin use to demonstrate the 'tapply' function in the video?

    -Mike Marin uses the 'lungcapdata' set in the video, which was also used in earlier videos of the series.

  • How can one access the help menu for the 'tapply' function in R?

    -To access the help menu for 'tapply', you can place a question mark in front of the command name or search it in the help search window in R.

  • What are the main arguments required by the 'tapply' function?

    -The main arguments for 'tapply' are X (the variable or vector), FUN (the function to be applied), and INDEX (a grouping variable used to create subsets of the data). Additional arguments can be passed using the dot-dot-dot (...) syntax.

  • What does the 'simplify' argument in 'tapply' do?

    -The 'simplify' argument, when set to true (default), instructs R to simplify the results if possible, returning a simplified output rather than a list format.

  • How does the 'tapply' function handle missing values when calculating statistics like mean?

    -The 'tapply' function, when used with the 'mean' function and the 'na.rm' argument set to true, will remove any missing values before calculating the mean.

  • Can the 'tapply' function be used to apply functions other than 'mean'?

    -Yes, 'tapply' can be used to apply a variety of functions, including 'summary', 'quantile', and even custom functions defined by the user.

  • What is the difference between using 'tapply' and using square brackets for subsetting data?

    -While both methods can achieve similar results, 'tapply' is more efficient and compact, requiring less code to perform the same operations.

  • How can 'tapply' be used to create subsets based on multiple factors?

    -By passing a list of both factors to the INDEX argument of 'tapply', the function can create subsets based on multiple criteria, such as smoking status and gender.

  • Is there another function in R that performs a similar operation to 'tapply'?

    -Yes, the 'by' function in R can perform similar operations to 'tapply', but it returns the output in a vector format.

Outlines
00:00
πŸ“Š Introduction to the T apply Command in R

In this introductory segment, Mike Marin presents the T apply command in R, a function used to apply a specific function to subsets of a variable or vector. The video will utilize the lungcapdata set, previously introduced in the series. The script and data can be accessed through the video description. The T apply command's syntax and arguments are explained, including the variable or vector 'X', the function 'FUN', the grouping variable 'INDEX', and additional arguments. The 'SIMPLIFY' argument is also discussed, which by default is set to TRUE to simplify results where possible. The video provides a practical example of calculating the mean age of smokers and non-smokers using T apply, highlighting the command's efficiency and simplicity.

Mindmap
Call to Action
Script Availability
Vector Format Output
by Function
Efficiency of T apply
Smoking Status and Gender
Custom Functions
Different Functions
List Format Output
Default Behavior
Output Retrieval
Storing Results
Handling Missing Values
Subset by Smoking Status
Simplify Argument
INDEX Argument
FUN Argument
X Argument
Data Attachment
Data Summary
Data Reading
Purpose of T apply
Presenter Introduction
Conclusion and Resources
Related R Functions
Multiple Factors Subsets
Versatility of T apply
Simplify Argument in Depth
Saving Output
Applying Mean Function
T apply Function Arguments
Lungcapdata Example
Introduction to T apply
R Programming with T apply Function
Alert
Keywords
πŸ’‘T apply
The 'T apply' function in R is a programming command used to apply a specific function to subsets of a variable or vector. It is central to the video's theme of demonstrating how to manipulate and analyze data subsets efficiently. In the script, 'T apply' is used to calculate the mean age of smokers and non-smokers from the 'lungcapdata', showcasing its utility in statistical analysis.
πŸ’‘R
R is a programming language and environment for statistical computing and graphics. It is the platform on which the 'T apply' function operates, and the video script provides an example of how to use R for data analysis. The script mentions reading data into R and using its functions to perform calculations, emphasizing R's role in data science.
πŸ’‘variable or vector
In the context of the video, a 'variable' or 'vector' refers to a dataset or a collection of data points that can be manipulated using functions in R. The script discusses applying the 'T apply' function to the 'age' variable, which is a vector of ages within the 'lungcapdata', to perform subset-based calculations.
πŸ’‘function
A 'function' in the video script refers to a specific operation or set of operations that can be applied to data using the 'T apply' command in R. The mean function is used as an example to calculate the average age of different subsets, demonstrating how functions process data within the R environment.
πŸ’‘index
The 'index' in the script is a grouping variable used in conjunction with the 'T apply' function to create subsets of the data. It is essential for organizing the data into groups that can be individually analyzed. For instance, the script uses smoking status as an index to calculate the mean age for smokers and non-smokers.
πŸ’‘subsets
Subsets are portions of the entire dataset that are selected based on certain criteria. In the video, subsets are created using the 'index' and are used to apply functions like calculating the mean age for specific groups, such as smokers or non-smokers, highlighting the ability to perform group-specific analyses.
πŸ’‘simplify
The 'simplify' argument in the 'T apply' function determines whether the results should be simplified into a more straightforward format if possible. The script explains that setting 'simplify' to true, which is the default, will return the results in a simplified form, while setting it to false returns a more complex list format.
πŸ’‘missing values
Missing values refer to data points that are absent or incomplete within a dataset. The script mentions using the 'T apply' function with the 'na.rm' argument set to true to exclude missing values when calculating the mean age, ensuring that the analysis is based on complete data only.
πŸ’‘summary
In the context of the video, 'summary' is a function applied to the 'age' variable using 'T apply' to provide a statistical summary for different subsets of the data. The script demonstrates applying the summary function to age grouped by smoking status, offering a quick overview of the data's distribution.
πŸ’‘quantile
A 'quantile' is a value that divides a dataset into equal intervals, and the 'quantile' function is used in the script to find specific percentiles of the age data for different subsets. The script shows how to apply the 'quantile' function to obtain the 20th and 80th percentiles, providing a detailed understanding of the age distribution within groups.
πŸ’‘multiple factors
The term 'multiple factors' in the script refers to using more than one variable to create subsets for analysis. The video demonstrates calculating the mean age based on both smoking status and gender, showing how 'T apply' can handle complex grouping criteria to perform more nuanced data analysis.
Highlights

Introduction to the 'tapply' command in R for applying a function to subsets of a variable or vector.

Use of 'tapply' with 'lungcapdata' from previous videos, with links provided in the video description.

Quick demonstration of reading data into R and summarizing it.

Explanation of 'tapply' arguments: X, FUN, index, ..., simplify.

Default setting of 'simplify' to true for result simplification.

Calculating mean age for smokers and non-smokers using 'tapply'.

Removal of missing values with 'na.rm = TRUE' during mean calculation.

Efficiency of 'tapply' without explicitly stating X and FUN in the command.

Saving the output of 'tapply' in an object for later use.

Discussion on the 'simplify' argument and its effect on output format.

Comparison of 'tapply' output with and without 'simplify' set to false.

Alternative method of subsetting data using square brackets.

Application of various functions with 'tapply', not limited to 'mean'.

Using 'tapply' to apply the 'summary' command to age by smoking status.

Applying the 'quantile' function to groups using 'tapply'.

Building custom functions for application to subsets with 'tapply'.

Applying a function to subsets created by multiple factors using 'tapply'.

Efficiency and compactness of 'tapply' compared to using square brackets for subsetting.

Mention of the 'by' function in R as an alternative to 'tapply'.

Encouragement to download the script for further exploration of 'tapply' and 'by'.

Closing remarks, thanking viewers and inviting them to engage with the channel.

Transcripts
00:00

hi I'm Mike Marin and in this video

00:02

we'll discuss the use of the T apply

00:04

command in R T apply can be used to

00:08

apply a function to subsets of a

00:10

variable or vector in this video we'll

00:13

work with the lungcapdata

00:14

that we use in earlier videos in this

00:16

series you can find links to the data

00:18

and the script used in this video in the

00:20

video description below let's quickly

00:22

read the data into R and let's have a

00:25

quick look at a summary of it and let's

00:28

attach this data you know to access the

00:31

help menu you can place a question mark

00:33

in front of the command name or you can

00:35

search it in the help search window here

00:36

we can see that the t apply function has

00:39

a few arguments to be specified X is the

00:42

variable or vector we would like to

00:44

apply some function to a few n is the

00:47

function that will be applied the index

00:49

is a grouping variable that is the same

00:51

length as X and it is used to create the

00:54

subsets of the data the dot-dot-dot here

00:56

are any additional arguments that need

00:58

to be passed to the function we're going

01:00

to apply and this simplify equals true

01:03

is to let are to know to simplify the

01:05

results if possible we'll take a look at

01:07

the use of this later and we'll note

01:08

that the default is set to be true now

01:11

let's calculate the mean age of smokers

01:14

and non-smokers separately we'll use the

01:16

t apply command the variable that we're

01:18

going to apply to is age we will subset

01:21

the data based on smoking status the

01:23

function that we will apply is the mean

01:25

and we will pass the mean command the

01:28

argument of Na are M equals T or true

01:31

letting it know to remove any missing

01:33

values when calculating the mean we can

01:36

see here that were returned to the mean

01:38

age for both smokers and non-smokers

01:40

separately now a reminder that we don't

01:43

need to include the x index and fund in

01:46

the command as long as we enter them in

01:48

that order we also don't need to include

01:50

the n AR M equals true as there are no

01:53

missing values in this data set we can

01:56

see is that the results are the same in

01:58

this video I will not include the X

02:00

index and so on although I suggest that

02:03

you may want to until the ordering of

02:05

them becomes second nature to you and as

02:07

usual we can save the output in an

02:09

object for later use if we want to here

02:11

let's look at saving it in an

02:13

called em and then if we ask our to

02:15

return em we can see the output here

02:18

let's also quickly discuss the simplify

02:20

argument this is set to be true by

02:22

default which means that our will try to

02:24

simplify the results when possible let's

02:27

take a look at the output when this

02:28

argument is set to false here we can see

02:32

that the output is returned in a list

02:34

format rather than a simple vector most

02:37

often we would prefer the results to be

02:39

simplified and that's why it's the

02:40

default value but there are instances

02:42

where we might prefer the output to be

02:44

kept in a more complicated structure a

02:46

reminder that we can get the exact same

02:49

results using square brackets to subset

02:51

the data as we're going to look at here

02:54

the main thing with using the key apply

02:57

function is that it's just a more

02:59

efficient way to get the same results we

03:01

can apply all sorts of functions not

03:03

just the mean here we can take a look at

03:06

applying the summary command to age

03:08

separated by smoking status let's take a

03:10

look at that here or we could apply the

03:14

quantile function to the groups here

03:16

we're going to pass the quantile command

03:18

the probs argument to let it know that

03:20

we'd like the 20th and 80th percentile

03:22

returned you can also build your own

03:25

custom functions to be applied to

03:27

subsets writing your own functions is a

03:30

topic for a separate video we can also

03:32

apply a function to subsets created by

03:35

multiple factors let's take a look at

03:37

calculating the mean age for subsets

03:40

based on smoking status and gender to do

03:43

this for the index which is the

03:44

indicator of the groupings we will pass

03:47

our the list of both smoke and gender we

03:52

can then see that our returns the mean

03:54

for each of the four subsets and again a

03:57

quick reminder that we could do this

03:58

using square brackets as well T apply is

04:01

just a bit more efficient and compact in

04:03

terms of the amount of code required we

04:06

can take a look at using square brackets

04:07

here here we'll ask for the mean age

04:10

based on subsetting of smoking status

04:13

being no and gender being female and we

04:16

can do the same for all four groups now

04:20

while I won't discuss it in this video I

04:22

thought it was worth mentioning that the

04:24

by function in R does pretty much the

04:26

same thing

04:27

he apply accept it returns output in a

04:30

vector format I've included some extra

04:32

code here in this our script so you can

04:36

download and play around with this

04:38

script if you want to explore the by

04:39

command a little bit I hope this video

04:42

has helped you see the use of the T

04:44

apply command thanks for watching this

04:46

video and make sure to subscribe to our

04:48

channel like us on Facebook

04:49

visit our website and check out our

04:51

other instructional videos

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