the function to be applied: see ‘Details’. The articles on the left provide an introduction to R for people who are … If I see this file in R, I have: V1 V2 V3 V4 V5 V6 V7 1 14 25 83 64 987 45 78 2 15 65 789 32 14 NA NA 3 14 67 89 14 NA NA NA If I want the maximum value in each column, I use this: apply(df,2,max) and this is the result: V1 V2 V3 V4 V5 V6 V7 15 67 789 64 NA NA NA Why? You can use the help section to get a description of this function. Of course, not all the variants can be discussed, but when possible, you will be introduced to the use of these functions in cooperation, via a couple of slightly more beefy examples. In R, you can use the apply () function to apply a function over every row or column of a matrix or data frame. Value. through …. They want a cover letter. In this example, I created a function that returns a vector ofboth the mean and standard deviation. In this example, 1:9 is specifying the value to repeat, and 9:1 is specifying how many times to repeat. lapply is probably a better choice than apply here, as apply first coerces your data.frame to an array which means all the columns must have the same type. practice to name the first three arguments if … is passed (e.g., a data frame) or via as.array. I have a function f(var1, var2) in R. Suppose we set var2 = 1 and now I want to apply the function f() to the list L. Basically I want to get a new list L* with the outputs [f(L[1],1),f(L[2],1),.... Stack Overflow. If n is 0, the result has length 0 but not necessarily the ‘correct’ dimension.. of the basic vector types before the dimensions are set, so that (for or FUN and ensures that a sensible error message is given if The apply command or rather family of commands, pertains to the R base package. Returns a vector or array or list of values obtained by applying a In this case, you split a vector into groups, apply a function to each group, and then combine the result into a vector. You can create a function like this for any apply function, not just tapply. In this example, the apply function is used to transform the values in each cell. If n is 0, the result has length 0 but not necessarily Taking a sample is easy with R because a sample is really nothing more than a subset of data. … What if instead, I wanted to find n-1 for each column? Slam the brakes! First, let’s create data with an factor for indexing. lapply returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.. sapply is a user-friendly version and wrapper of lapply by default returning a vector, matrix or, if simplify = "array", an array if appropriate, by applying simplify2array(). There isn’t a function in R to do this automatically, so I can create my own function. In a previous post, you covered part of the R language control flow, the cycles or loop structures.In a subsequent one, you learned more about how to avoid looping by using the apply() family of functions, which act on compound data in repetitive ways. Apply operates on arrays: apply(X, MARGIN, FUN, …). This is especially useful where there is a need to use functionality available only in R or R packages that is not available in Apache Spark nor Spark Packages. Dear. FUN.VALUE is where you specify the type of data you are expecting. Practical advice for writing a cover letter. the apply function looks like this: apply(X, MARGIN, FUN). In all cases the result is coerced by as.vector to one dim set to MARGIN if this has length greater than one. is either a function or a symbol (e.g., a backquoted name) or a The Apply Functions As Alternatives To Loops. mapply is a multivariate version of sapply. See how these two examples gave the same answers, but returned a vector instead? I am expecting each item in the list to return a single numeric value, so FUN.VALUE = numeric(1). To call a function for each row in an R data frame, we shall use R apply function. This time, the lapply function seemed to work better. my.matrx is a matrix with 1-10 in column 1, 11-20 in column 2, and 21-30 in column 3. my.matrx will be used to show some of the basic uses for the apply function. This function didn’t add up the values like we may have expected it to. Apply a Function to Multiple List or Vector Arguments. : http://stackoverflow.com/questions/12339650/why-is-vapply-safer-than-sapply, ---
title: 'Chapter 4: apply Functions'
author: "Erin Sovansky Winter"
output:
  html_document:
    theme: cerulean
    highlight: textmate
    fontsize: 8pt
    toc: true
    number_sections: true
    code_download: true
    toc_float:
      collapsed: false
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

#  What are apply functions?
Apply functions are a family of functions in base R which allow you to repetitively perform an
action on multiple chunks of data. An apply function is essentially a loop, but run faster than 
loops and often require less code. 

The apply functions that this chapter will address are apply, lapply, sapply, vapply, tapply, and
mapply. There are so many different apply functions because they are meant to operate on different
types of data. 

#  The apply function
First, let's go over the basic apply function. You can use the help section to get a description
of this function.
```{r, eval=FALSE}
?apply
```
the apply function looks like this: apply(X, MARGIN, FUN). 

* X is an array or matrix (this is the data that you will be performing the function on)
* Margin specifies whether you want to apply the function across rows (1) or columns (2)
* FUN is the function you want to use

## apply examples
my.matrx is a matrix with 1-10 in column 1, 11-20 in column 2, and 21-30 in column 3. 
my.matrx will be used to show some of the basic uses for the apply function.
```{r}
my.matrx <- matrix(c(1:10, 11:20, 21:30), nrow = 10, ncol = 3)
my.matrx
```

### Example 1: Using apply to find row sums
What if I wanted to summarize the data in matrix m by finding the sum of each row? The arguments 
are X = m, MARGIN = 1 (for row), and FUN = sum

```{r}
apply(my.matrx, 1, sum)
```
The apply function returned a vector containing the sums for each row.

### Example 2: Creating a function in the arguments
What if I wanted to be able to find how many datapoints (n) are in each column of m? I can use 
the length function to do this. Because we are using columns, MARGIN = 2.
```{r}
apply(my.matrx, 2, length)
```
What if instead, I wanted to find n-1 for each column? There isn't a function in R to do this
automatically, so I can create my own function. If the function is simple, you can create it
right inside the arguments for apply. In the arguments I created a function that returns
length - 1.
```{r}
apply(my.matrx, 2, function (x) length(x)-1)
```
As you can see, the function correctly returned a vector of n-1 for each column.
 
### Example 3: Using a function defined outside of apply
If you don't want to write a function inside of the arguments, you can define the function 
outside of apply, and then use that function in apply later. This may be useful if you want to 
have the function available to use later. In this example, a function to find standard error was
created, then passed into an apply function.
```{r}
st.err <- function(x){
  sd(x)/sqrt(length(x))
}
apply(my.matrx,2, st.err)
```

### Example 4: Transforming data
Now for something a little different. In the previous examples, apply was used to summarize
over a row or column. It can also be used to repeat a function on cells within a matrix. In this
example, the apply function is used to transform the values in each cell. Pay attention to the
MARGIN argument. If you set the MARGIN to 1:2 it will have the function operate on each cell.
```{r}
my.matrx2 <- apply(my.matrx,1:2, function(x) x+3)
my.matrx2
```

### Example 5: Vectors?
The previous examples showed several ways to use the apply function on a matrix. But what if I 
wanted to loop through a vector instead? Will the apply function work?

```{r, }
vec <- c(1:10)
vec
```
```{r, eval=FALSE}
apply(vec, 1, sum)
```
If you run this function it will return the error: Error in apply(v, 1, sum) : dim(X) must have a positive length. 
As you can see, this didn't work because apply was expecting the data to have at least two dimensions. If your data is a vector you need to use lapply, sapply, or vapply instead.

# lapply, sapply, and vapply
lapply, sapply, and vapply are all functions that will loop a function through data in a list or
vector. First, try looking up lapply in the help section to see a description of all three 
function.

```{r, eval=FALSE}
?lapply
```

Here are the agruments for the three functions:

* lapply(X, FUN, ...)
* sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
* vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)

In this case, X is a vector or list, and FUN is the function you want to use. sapply and vapply have extra arguments, but most of them have default values, so you don't need to worry about
them. However, vapply requires another agrument called FUN.VALUE, which we will look at later.

### Example 1: Getting started with lapply
Earlier, we created the vector v. Let's use that vector to test out the lapply function.
```{r}
lapply(vec, sum)
```
This function didn't add up the values like we may have expected it to. This is because lapply
applies treats the vector like a list, and applies the function to each point in the vector.

Let's try using a list instead
```{r}
A<-c(1:9)
B<-c(1:12)
C<-c(1:15)
my.lst<-list(A,B,C)
lapply(my.lst, sum)
```
This time, the lapply function seemed to work better. The function summed each vector in the list
and returned a list of the 3 sums. 

### Example 2: sapply
sapply works just like lapply, but will simplify the output if possible. This means that instead
of returning a list like lapply, it will return a vector instead if the data is simplifiable.

```{r}
sapply(vec, sum)
```

```{r}
sapply(my.lst, sum)
```
See how these two examples gave the same answers, but returned a vector instead?

### Example 3: vapply
vapply is similar to sapply, but it requires you to specify what type of data you are expecting
the arguments for vapply are vapply(X, FUN, FUN.VALUE).
FUN.VALUE is where you specify the type of data you are expecting.
I am expecting each item in the list to return a single numeric value, so FUN.VALUE = numeric(1).

```{r}
vapply(vec, sum, numeric(1))
```

```{r}
vapply(my.lst, sum, numeric(1))
```

If your function were to return more than one numeric value, FUN.VALUE = numeric(1) will cause the function to return an error. This could be useful if you are expecting only one result per subject. 
```{r}
#vapply(my.lst, function(x) x+2, numeric(1))
```

### Example 4: Transforming data with sapply
Like apply, these functions can also be used for transforming data inside the list
```{r}
my.lst2 <- sapply(my.lst, function(x) x*2)
my.lst2
```

### Which function should I use, lapply, sapply, or vapply?

If you are trying to decide which of these three functions to use, because it is the simplest, I would suggest to use sapply if possible. If you do not want your results to be simplified to a vector, lapply should be used. If you want to specify the type of result you are expecting, use vapply.


# tapply

Sometimes you may want to perform the apply function on some data, but have it separated by 
factor. In that case, you should use tapply. Let's take a look at the information for tapply.

```{r, eval=FALSE}
?tapply
```
The arguments for tapply are tapply(X, INDEX, FUN). The only new argument is INDEX, which is the 
factor you want to use to separate the data.

### Example 1: Means split by condition
First, let's create data with an factor for indexing. Dataset t will be created by adding a factor to matrix m and converting it to a dataframe. 

```{r}
tdata <- as.data.frame(cbind(c(1,1,1,1,1,2,2,2,2,2), my.matrx))
colnames(tdata)
```
Now let's use column 1 as the index and find the mean of column 2

```{r}
tapply(tdata$V2, tdata$V1, mean)
```

### Example 2: Combining functions
You can use tapply to do some quick summary statistics on a variable split by condition. In this 
example, I created a function that returns a vector ofboth the mean and standard deviation. You 
can create a function like this for any apply function, not just tapply.
```{r}
summary <- tapply(tdata$V2, tdata$V1, function(x) c(mean(x), sd(x)))
summary
```

# mapply
the last apply function I will cover is mapply.
```{r, eval=FALSE}
?mapply
```
the arguments for mapply are mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE).
First you list the function, followed by the vectors you are using
the rest of the arguments have default values so they don't need to be changed for now. 
When you have a function that takes 2 arguments, the first vector goes into the first argument
and the second vector goes into the second argument.

### Example 1: Understanding mapply
In this example, 1:9 is specifying the value to repeat, and 9:1 is specifying how many times
to repeat. This order is based on the order of arguments in the rep function itself.
```{r}
mapply(rep, 1:9, 9:1)
```

### Example 2: Creating a new variable
Another use for mapply would be to create a new variable. For example, using dataset t, I could
divide one column by another column to create a new value. This would be useful for creating a 
ratio of two variables as shown in the example below. 

```{r}
tdata$V5 <- mapply(function(x, y) x/y, tdata$V2, tdata$V4)
tdata$V5
```

### Example 3: Saving data into a premade vector
When using an apply family function to create a new variable, one option is to create a new vector ahead of time with the size of the vector pre-allocated. I created a numeric vector of length 10 using the vector function. The arguments for the vector function are vector(mode, length). Inside mapply I created a function to multiple two variables together. The results of the mapply function are then saved into the vector.

```{r}
new.vec <- vector(mode = "numeric", length = 10)
new.vec <- mapply(function(x, y) x*y, tdata$V3, tdata$V4)
new.vec
```

# Using apply functions on real datasets
This last section will be a few examples of using apply functions on real data.This section will
make use of the MASS package, which is a collection of publicly available datasets. Please
install MASS if you do not already have it. If you do not have MASS installed, you can uncomment
the code below.

```{r}
#install.packages("MASS")
library(MASS)
```

load the state dataset. It contains information about all 50 states
```{r}
data(state)
```
Let's look at the data we will be using. We will be using the state.x77 dataset
```{r}
head(state.x77)
str(state.x77)
```
All the data in the dataset happens to be numeric, which is necessary when the function inside the apply function requires numeric data.

### Example 1: using apply to get summary data
You can use apply to find measures of central tendency and dispersion
```{r}
apply(state.x77, 2, mean)
apply(state.x77, 2, median)
apply(state.x77, 2, sd)
```

### Example 2: Saving the results of apply

In this, I created one function that gives the mean and SD, and another that give min, median, and max. Then I saved them as objects that could be used later.
```{r}
state.summary<- apply(state.x77, 2, function(x) c(mean(x), sd(x))) 
state.summary
state.range <- apply(state.x77, 2, function(x) c(min(x), median(x), max(x)))
state.range
```

### Example 3: Using mapply to compute a new variable
In this example, I want to find the population density for each state. In order to do this, I 
want to divide population by area. state.area and state.x77 are not from the same dataset, but 
that is fine as long as the vectors are the same length and the data is in the same order. Both
vectors are alphabetically by state, so mapply can be used.
```{r}
population <- state.x77[1:50]
area <- state.area
pop.dens <- mapply(function(x, y) x/y, population, area)
pop.dens
```

### Example 4: Using tapply  to explore population by region
In this example, I want to find out some information about the population of states split by
region. state.region is a factor with four levels: Northeast, South, North Central, and West.
For each region, I want the minimum, median, and maximum populations.

```{r}
region.info <- tapply(population, state.region, function(x) c(min(x), median(x), max(x)))
region.info
```

# References
Here are some sources I used to help me create this chapter:

Datacamp tutorial on apply functions: https://www.datacamp.com/community/tutorials/r-tutorial-apply-family

r-bloggers: Using apply, sapply, and lapply in R: https://www.r-bloggers.com/using-apply-sapply-lapply-in-r/

stackoverflow: Why is vapply safer than sapply?: http://stackoverflow.com/questions/12339650/why-is-vapply-safer-than-sapply


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