Sofia Gardiner

Sofia Gardiner


Golang Tutorial - Functions in Golang

Functions in Golang are very clean. You will surely avoid a lot of common errors while defining functions in golang.

A function is an independent section of code that maps zero or more input parameters to zero or more output parameters. Functions (also known as procedures or subroutines) are often represented as a black box: (the black box represents the function)

Until now the programs we have written in Go have used only one function:

func main() {}

We will now begin writing programs that use more than one function.

Your Second Function

func main() {
  xs := []float64{98,93,77,82,83}

  total := 0.0
  for _, v := range xs {
    total += v
  fmt.Println(total / float64(len(xs)))

This program computes the average of a series of numbers. Finding the average like this is a very general problem, so it’s an ideal candidate for definition as a function.

The average function will need to take in a slice of float64s and return one float64. Insert this before the main function:

func average(xs []float64) float64 {
  panic("Not Implemented")

Functions start with the keyword func, followed by the function’s name. The parameters (inputs) of the function are defined like this: name type, name type, …. Our function has one parameter (the list of scores) that we named xs. After the parameters we put the return type. Collectively the parameters and the return type are known as the function’s signature.

Finally we have the function body which is a series of statements between curly braces. In this body we invoke a built-in function called panic which causes a run time error. (We’ll see more about panic later in this chapter) Writing functions can be difficult so it’s a good idea to break the process into manageable chunks, rather than trying to implement the entire thing in one large step.

Now let’s take the code from our main function and move it into our average function:

func average(xs []float64) float64 {
  total := 0.0
  for _, v := range xs {
    total += v
  return total / float64(len(xs))

Notice that we changed the fmt.Println to be a return instead. The return statement causes the function to immediately stop and return the value after it to the function that called this one. Modify main to look like this:

func main() {
  xs := []float64{98,93,77,82,83}

Running this program should give you exactly the same result as the original. A few things to keep in mind:

  • The names of the parameters don’t have to match in the calling function. For example we could have done this:
func main() {
  someOtherName := []float64{98,93,77,82,83}

And our program would still work.

  • Functions don’t have access to anything in the calling function. This won’t work:
func f() {
func main() {
  x := 5

We need to either do this:

func f(x int) {
func main() {
  x := 5

Or this:

var x int = 5
func f() {
  func main() {
  • Functions are built up in a “stack”. Suppose we had this program:
func main() {
func f1() int {
  return f2()
func f2() int {
  return 1
  • We could visualize it like this:

Each time we call a function we push it onto the call stack and each time we return from a function we pop the last function off of the stack.

  • We can also name the return type:
func f2() (r int) {
  r = 1

Returning Multiple Values

Go is also capable of returning multiple values from a function:

func f() (int, int) {
  return 5, 6

func main() {
  x, y := f()

Three changes are necessary: change the return type to contain multiple types separated by ,, change the expression after the return so that it contains multiple expressions separated by , and finally change the assignment statement so that multiple values are on the left side of the := or =.

Multiple values are often used to return an error value along with the result (x, err := f()), or a boolean to indicate success (x, ok := f()).

Variadic Functions

There is a special form available for the last parameter in a Go function:

func add(args int {
  total := 0
  for _, v := range args {
    total += v
  return total
func main() {

By using ... before the type name of the last parameter you can indicate that it takes zero or more of those parameters. In this case we take zero or more ints. We invoke the function like any other function except we can pass as many ints as we want.

This is precisely how the fmt.Println function is implemented:

func Println(a ...interface{}) (n int, err error)

The Println function takes any number of values of any type. (The special interface{} type will be discussed in more detail in chapter 9)

We can also pass a slice of ints by following the slice with ...:

func main() {
  xs := []int{1,2,3}


It is possible to create functions inside of functions:

func main() {
  add := func(x, y int) int {
    return x + y

add is a local variable that has the type func(int, int) int (a function that takes two ints and returns an int). When you create a local function like this it also has access to other local variables:

func main() {
  x := 0
  increment := func() int {
    return x

increment adds 1 to the variable x which is defined in the main function’s scope. This x variable can be accessed and modified by the increment function. This is why the first time we call increment we see 1 displayed, but the second time we call it we see 2 displayed.

A function like this together with the non-local variables it references is known as a closure. In this case increment and the variable x form the closure.

One way to use closure is by writing a function which returns another function which – when called – can generate a sequence of numbers. For example here’s how we might generate all the even numbers:

func makeEvenGenerator() func() uint {
  i := uint(0)
  return func() (ret uint) {
    ret = i
    i += 2
func main() {
  nextEven := makeEvenGenerator()
  fmt.Println(nextEven()) // 0
  fmt.Println(nextEven()) // 2
  fmt.Println(nextEven()) // 4

makeEvenGenerator returns a function which generates even numbers. Each time it’s called it adds 2 to the local i variable which – unlike normal local variables – persists between calls.


Finally a function is able to call itself. Here is one way to compute the factorial of a number:

func factorial(x uint) uint {
  if x == 0 {
    return 1
  return x * factorial(x-1)

factorial calls itself, which is what makes this function recursive. In order to better understand how this function works, lets walk through factorial(2):

  • Is x == 0? No. (x is 2)

  • Find the factorial of x – 1

    • Is x == 0? No. (x is 1)

    • Find the factorial of x – 1

      • Is x == 0? Yes, return 1.
    • return 1 * 1

  • return 2 * 1

Closure and recursion are powerful programming techniques which form the basis of a paradigm known as functional programming. Most people will find functional programming more difficult to understand than an approach based on for loops, if statements, variables and simple functions.

Defer, Panic & Recover

Go has a special statement called defer which schedules a function call to be run after the function completes. Consider the following example:

package main

import "fmt"

func first() {
func second() {
func main() {
  defer second()

This program prints 1st followed by 2nd. Basically defer moves the call to second to the end of the function:

func main() {

defer is often used when resources need to be freed in some way. For example when we open a file we need to make sure to close it later. With defer:

f, _ := os.Open(filename)
defer f.Close()

This has 3 advantages: (1) it keeps our Close call near our Open call so it’s easier to understand, (2) if our function had multiple return statements (perhaps one in an if and one in an else) Close will happen before both of them and (3) deferred functions are run even if a run-time panic occurs.

Panic & Recover

Earlier we created a function that called the panic function to cause a run time error. We can handle a run-time panic with the built-in recover function. recover stops the panic and returns the value that was passed to the call to panic. We might be tempted to use it like this:

package main

import "fmt"

func main() {
  str := recover()

But the call to recover will never happen in this case because the call to panic immediately stops execution of the function. Instead we have to pair it with defer:

package main

import "fmt"

func main() {
  defer func() {
    str := recover()

A panic generally indicates a programmer error (for example attempting to access an index of an array that’s out of bounds, forgetting to initialize a map, etc.) or an exceptional condition that there’s no easy way to recover from. (Hence the name “panic”)


  • sum is a function which takes a slice of numbers and adds them together. What would its function signature look like in Go?

  • Write a function which takes an integer and halves it and returns true if it was even or false if it was odd. For example half(1) should return (0, false) and half(2) should return (1, true).

  • Write a function with one variadic parameter that finds the greatest number in a list of numbers.

  • Using makeEvenGenerator as an example, write a makeOddGenerator function that generates odd numbers.

  • The Fibonacci sequence is defined as: fib(0) = 0, fib(1) = 1, fib(n) = fib(n-1) + fib(n-2). Write a recursive function which can find fib(n).

  • What are defer, panic and recover? How do you recover from a run-time panic?

#go #golang

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Golang Tutorial - Functions in Golang
Willie  Beier

Willie Beier


Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

Table of Contents

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

Tutorial: Loading and Cleaning Data with R and the tidyverse

1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

  • Free of duplicate rows/values
  • Error-free (e.g. free of misspellings)
  • Relevant (e.g. free of special characters)
  • The appropriate data type for analysis
  • Free of outliers (or only contain outliers have been identified/understood), and
  • Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

  • Special characters (e.g. commas in numeric values)
  • Numeric values stored as text/character data types
  • Duplicate rows
  • Misspellings
  • Inaccuracies
  • White space
  • Missing data
  • Zeros instead of null values

2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

  • rollingsales_bronx.xls
  • rollingsales_brooklyn.xls
  • rollingsales_manhattan.xls
  • rollingsales_queens.xls
  • rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

3. Load Data into R with readxl

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse readr package function read_csv() is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the readxlpackage. We specify the function argument skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)

Note we saved this dataset with the variable name brooklyn for future use.

4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:


Once the package is installed, load it to memory:


Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…

The glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials

Jeromy  Lowe

Jeromy Lowe


Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Autumn  Blick

Autumn Blick


R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplrlubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Vincent Lab

Vincent Lab


The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences = "Bob";

const person = {
name: “Jon”,

<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);


person.func1(); // Call the Regular function
// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}

person.func2(); // Call the Arrow function
// Output: {name:“Bob”}

The new keyword with an arrow function
const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions