Karim Hommod

My analysis includs analysing the sales of video games all around the world, I used more than one Tidyverse packges, and collected the data from Kaggle.

====================================================================================================================================================================== Vignette on Tidyverse packages by Alexis Mekueko

library(tidyverse) 
library(knitr)
library(readr)
library(dplyr)
library(stringr)

Github Link: https://github.com/asmozo24/DATA607_Tidyverse-CREATE-Assignmen Web link: https://rpubs.com/amekueko/682620 data source: https://www.kaggle.com/omarhanyy/500-greatest-songs-of-all-time

Description

This assignment is about getting familiar with two or more Tidyverse packages. So, I am going to write a vignette using readr, dplyr , and stringr which are part of the core tidyverse packages used for data analysis.

Data

For this assignment, I found a dataset from kaggle.com about the 500 greatest songs of all time. I am going to use it to practice tidyverse packages as states in the description part. The data is large for display and might cause some issue in attemptin to display all output.

Below is sample of code...for full view, please use web link or Github link.

# load the csv file which has all the variable.

Top_500Songs <- read.csv("https://raw.githubusercontent.com/asmozo24/DATA607_Tidyverse-CREATE-Assignmen/main/Top%20500%20Songs.csv", stringsAsFactors=FALSE)

# file to big, cleaning/removing the column I don't need
Top_500Songs <- Top_500Songs [, -2]
# saving the new csv file 
write.csv(Top_500Songs,'Top_500Songs.csv')

#view the details of top_500songs
glimpse(Top_500Songs)

# let's check if there is a missing value in a specific column
# return 06 rows with empty values...tempted to delete data but will not do it now....no need
Top_500Songs %>% 
  filter( is.na(streak) | streak == "") 
# another way
filter(Top_500Songs, is.na(streak) | streak == "")
filter(Top_500Songs, !grepl("weeks", streak))
 
# Being in the top 500 greatest songs of all time, I will assum the song hits the hit parade of billboard for few months...lets check that
Top_500Songs %>% 
  select(streak)%>% 
  filter(grepl("weeks", streak))

======================================================================================================================================================================

my analysis includs analysing the sales of video games all around the world, I used more than one Tidyverse packges, and collected the data from Kaggle.

Any additional analysis is welcome.

Rachel Greenlee extended Karim's work by displaying only PC games since 2010 by genre in an animated bar chart by year. This is an exaple of how to extend ggplot's functionality using the gganimate package, which can be used for animated bar charts or bubble charts.

=======

TidyVerse Recipe CREATE

Name: Arushi Arora

Introduction:

The core tidyverse package includes "readr", "tidyr" and "dplyr"

• readr provides a fast and friendly way to read rectangular data
• tidyr provides a set of functions that help you get to tidy data. Tidy data is data with a consistent form: in brief, every variable goes in a column, and every column is a variable
• dplyr provides a grammar of data manipulation, providing a consistent set of verbs that solve the most common data manipulation

Approach:

Read the csv for the article https://fivethirtyeight.com/features/how-americans-like-their-steak/ published on Five Thirty Eight from Github using readr. Rename all the variables using dplyr package and remove the first row with no real reponses. Mutate the variable type to factor using the function mutate for further analysis

Conclusion:

The dataset was imported and cleaned using packages in TidyVerse

---

SYNTAX

• Import CSV from Github

  urlfile="https://raw.githubusercontent.com/fivethirtyeight/data/master/steak-survey/steak-risk-survey.csv"
  
  steakdata<- readr::read_csv(url(urlfile))
  

• Rename variables

  steakdata1 = dplyr::rename(steakdata, 
  "lottery" = "Consider the following hypothetical situations: <br>In Lottery A, you have a 50% chance of success, with a payout of $100. <br>In Lottery B, you have a 90% chance of success, with a payout of $20. <br><br>Assuming you have $10 to bet, would you play Lottery A or Lottery B?", 
  "smoke_cigs" = "Do you ever smoke cigarettes?" ,
  "drink_alcohol" = "Do you ever drink alcohol?", 
  "gamble" = "Do you ever gamble?",
  "skydiving" = "Have you ever been skydiving?",
  "overspeeding" = "Do you ever drive above the speed limit?",
  "cheat_patner" = "Have you ever cheated on your significant other?",
  "eat_steak" = "Do you eat steak?",
  "steak_prep" = "How do you like your steak prepared?",
  "hh_income" = "Household Income",
  "location" = "Location (Census Region)")
  

• Remove first row

    steakdata2 <- steakdata1[-c(1), ]
  

• Code for populating the tables from .csv file stored locally

  steakdata3 <- steakdata2 %>% as_tibble() 
  
  steakdata4 <- steakdata3 %>%
  mutate(lottery = as.factor(lottery)) %>%
  mutate(smoke_cigs = as.factor(smoke_cigs)) %>%
  mutate(drink_alcohol = as.factor(drink_alcohol)) %>%
  mutate(gamble = as.factor(gamble)) %>%
  mutate(skydiving = as.factor(skydiving)) %>%
  mutate(overspeeding = as.factor(overspeeding)) %>%
  mutate(cheat_patner = as.factor(cheat_patner)) %>%
  mutate(eat_steak = as.factor(eat_steak)) %>%
  mutate(steak_prep = as.factor(steak_prep)) %>%
  mutate(Gender = as.factor(Gender)) %>%
  mutate(Age = as.factor(Age)) %>%
  mutate(hh_income = as.factor(hh_income)) %>%
  mutate(Education = as.factor(Education)) %>%
  mutate(location = as.factor(location))
  
  

=======

FALL2020TIDYVERSE

Ian Costello Tidyverse Create

Tidyverse Create

I decided to pick a data set regarding the senate race fundamentals. Using dplyr and stringr, I created a new column "state_ID for just the two character initials of states. I also filtered based on what I believe are the most competitive states this cycle.

CUNY DATA 607 TIDYVERSE Collaborative project

The accompanying vignette is a breif introduct to the GGplot2 package.

GGplot2 is a grammatically layered approch towards visualizations. The combinations of the layers, features, and options are nearly endless, providing a fully customizable visual to cater any data set. For this example, we will be using categorical information based on a Holloween Candy survey.

Below is a very basic snapshot of the GGplot2 package and its potential. Lets tackle the below concepts. -Set-up -Objects -Aesthetics -Facets -Coordinates

M_Skonberg Tidyverse CREATE - function(ggplot2), data(Happiness & Alcohol)

Stefano Biguzzi - vignette on the dplyr library group_by(), tally(), and summarise() functions.

BDavidoff Tidyverse CREATE - packages: [ggplot2, dpylr] data source: [COVID and presidental approval ratings (https://raw.githubusercontent.com/fivethirtyeight/covid-19-polls/master/covid_approval_polls.csv)]

Using ggExtra for Exploratory Plotting by Rachel Greenlee -adding boxplots and histograms to axis of a standard scatterplot -super quick frequency histograms

Magnus Skonberg extended Rachel's work by exploring the frequency UFO sightings within the US and applying plotCount(), removeGrid(), ggMarginal(), and rotateTextX() functions.

=======

CREATE Vignette Tidyverse Project describes how to use tidyverse functions see palmorezm_tidyverse_vignette for importing data from comma separated values

Orli Khaimova

fct_rev relevels the levels of a factor in reverse order. In this case, I factored the regions and then used the function in order to put them in reverse alphabetical order. By doing so, I was able to print the regions alphabetically in the graph.

geom_pointrange graphs the interval for the average price of avocados for each region. I had to define a ymin and ymax as well. It is useful for drawing confidence intervals and in this case the range of prices.

avocados <- read.csv("https://raw.githubusercontent.com/okhaimova/DATA-607/master/avocado.csv")

avocados$Date <- as.Date(avocados$Date)

avocados$year <- as.character(avocados$year)

#factors the regions and then using forcats, we reverse the order to make it z-a
avocados$region <- avocados$region %>%
  as.factor() %>%
  fct_rev()

avocados %>% 
  ggplot(aes(y = AveragePrice, x = region, 
             ymin = AveragePrice-sd(AveragePrice), ymax = AveragePrice+sd(AveragePrice))) +
  geom_pointrange(aes(color = as.factor(region)), size =.01) +
  ylab("Average Price") +
  xlab("Region") +
  ggtitle("Average Price Range") +
  coord_flip() +
  theme(legend.position = "none")

======= Douglas Barley added KivaLoans.Rmd vignette demonstrating group_by() and ggplot() functions from the Tidyverse.

---

title: "Getting to know dplyr & stringr" author: "Jered Ataky" date: "r Sys.Date()" output: html_vignette: toc: true vignette: > %\VignetteIndexEntry{Getting to know dplyr & stringr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

1. Introduction

"dplyr" is one of the tidyverse packages, and it is used for data manipulation. In other words, it is a grammar of data manipulation providing verbs that help to solve many problems faced in data manipulation.

"stringir" in the other is also one of a tidyverse package, and it is focused on string manipulation. stringir is a grammar of string manipulation. It provides set of functions designed to make working with strings easier.

Note that tidyverse is just a collection of R packages underlying same design philosophy, grammar, and data structure. For more info about dplyr and stringir, read here.

Here, we are going to explore five major verbs for dplyr: filter(), select(), arrange(), mutate(), summarize()

and seven functions for stringr: str_subset(), str_detect(), str_count(), str_locate(), str_extract(), str_split(), str_replace(), str_match()

2. dplyr

Throughout this part of the vignette, we make use of "student performance", a dataset containing a sample of 1000 observations of 8 variables from kaggle datasets.

# library
library(dplyr)

# load in the dataset

data <- read.csv("https://raw.githubusercontent.com/jnataky/DATA-607/master/A2_Various_dataset_transformation/students_performance.csv")

# take a look at its structure
glimpse(data)

filter(): picking cases by their values

The filter function picks cases by their values. Let say you want to pick students which math score is 100, you might write:

data %>%
  filter(math.score == 100)

Similarly, if you want to pick students which math, writing, and reading score are all 100, you might write:

data %>%
  filter(math.score == 100, writing.score == 100, reading.score == 100)

select(): selecting variables based on their names

This second verb lets you select variables based on their names. Let say you want to select only the variables gender, math.score, and writing.score, you might write:

data2 <- data %>%
  select(gender, writing.score, reading.score)

# Print five first students
head(data2, 6)

arrange(): Reordering the cases

Arrange function lets reordering the cases in the order that you want. Let say that you want to reorder the head of previous selected variables data frame (data2) in descending order of students math score, you might write:

# name the head of data2 as data3 

data3 <- head(data2, 6)

# Reorder in descending order of math score

data3 %>%
  arrange(desc(writing.score))

mutate(): creating new variables that are functions of existing variables

The mutate function creates new variables that are functions of the existing variables. Let say you want to create "english.score" which is the average of writing.score and reading.score, you might write:

data3 %>%
  mutate(english.score = (writing.score + reading.score) / 2)

If you are interesting in keeping only the new variable from the existing variables, let say you want to keep only english.score and not the two others, you might use another function called transmuse():

data4 <- data3 %>%
  transmute(gender, english.score = (writing.score + reading.score) / 2)

data4

summarize(): summarizing multiple values to a single value

Here we will introduce the function group_by which helps grouping by the variable you want to do your summary. Let say you are interested in the summary of the average score of different gender in math, you might write:

data %>%
  group_by(gender) %>%
  summarize( math_score = sum (math.score)/ n())

3. stringr

The strings functions take for arguments one vector of strings and a second argument being the pattern. For this entire part of the vignette, we will use one vector of strings v and same pattern p which will be the regular expression matching any single character that is a vowel.

# library

library(stringr)

# Define the vector of strings

v <- c("sonority", "meal", "try", "cocktail", "cinema", "maximum", "mass")

# Pattern matching any vowel

p <- "[aeiou]"

str_subset(): extracting the matching components

subset function will let you extract strings that contain vowels.

str_subset(v, p)

str_detect(): telling if there is any pattern matching

detect function detects if there is any match pattern. If you want to detect if there is any vowel in any components of v, you might write:

str_detect(v, p)

str_count(): counting the patterns

if you want to count the number of vowels in each components of the vector of strings, you might write:

str_count(v, p)

str_locate(): locating the position of the match

locate function helps you locate where there is the match. Let say that you want to know the position of vowels in each component of v, you might write:

str_locate(v, p)

str_extract(): extracting the text of the match

extract function lets you extracting the first match pattern in the string. The following will let you extracting the first vowel in each components of v:

str_extract(v, p)

str_split(): splitting up strings

Here we are going to split up strings separated by comma in different pieces

s <- c("dada, mum", "uncle, auntie, cousin", "men, women")
str_split(s, ",")

str_replace(): replacing the matches with new text

replace function let you replace the first match pattern by the replacement argument that you specify. If you want to replace the first vowel in components of v by "/", you might write:

str_replace(v, p, "/")

str_match(): extracting parts of the match defined by parentheses

If you want to extract the letter before the first vowel in components of vector of strings v, you might write:

str_match(v, "(.)[aeiou]")

For more about tidyverse packages:

R for Data Science

ggplot2_vignettes: By Jordan Tapke

Zhouxin Shi CREATE - function(read_csv,filter,select), data(Public Use Microdata Sample)

group_split() vignette by Jack Wright

-added an instructional vignette on group split

Vignettes

In the vignettes folder we will hold the different vignette files. I added one called readr-package-gc which serves as an introduction to readr

Data folder

The data folder holds several supporting files for the different examples.

Josef Waples - power plant data added as 19th pull request but am I first change to readme.md file?

Description

arrange() sorts the rows according to the values of the specified column, with the lowest values appearing near the top of the data frame.Place desc() around a column name to cause arrange() to sort by descending values of that column.

library(tidyverse)

senFunURL <- "https://projects.fivethirtyeight.com/2020-general-data/senate_fundamentals.csv"
senFun <- read.csv(file = senFunURL, header = TRUE, sep = ",")

senFun <- senFun %>%
  dplyr::mutate(state_ID = str_extract(district, "^[:alpha:]{2}")) %>%
  filter(state_ID == "ME" | state_ID == "MI" | state_ID == "AL" | state_ID == "CO" | state_ID == "IA" | state_ID == "GA" | state_ID == "AZ" | state_ID == "NC" | state_ID == "SC")

======= #read csv file file<- read.csv("https://raw.githubusercontent.com/hrensimin05/Data_607/master/2019.csv") #view(file)

list<-as.data.frame(file)%>% arrange(desc(Healthy.life.expectancy))

head(list)

=======
---
title: "TidyVerse Recipe"
author: "Dariusz Siergiejuk"
date: "10**22**2020"
output: html_document
---

## TidyVerse CREATE assignment

In this assignment, you’ll practice collaborating around a code project with GitHub.  You could consider our collective work as building out a book of examples on how to use TidyVerse functions.

Your task here is to Create an Example.  Using one or more TidyVerse packages, and any data set from fivethirtyeight.com or Kaggle, create a programming sample “vignette” that demonstrates how to use one or more of the capabilities of the selected TidyVerse package with your selected data set. (25 points)

### Loading TidyVerse.

```{r, echo = FALSE}
library(tidyverse)

Using readr to read data from a csv file.

drivers <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv")

head(drivers)

Utlizing ggplot2 to visualizae data; with pipe operation %>% from dplyr

drivers %>% ggplot(aes(x=reorder(State, -`Car Insurance Premiums ($)`), y=`Car Insurance Premiums ($)`, fill=State)) + 
  geom_bar(stat = "identity") + 
  guides(fill = FALSE) +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))

End of File and Text.

Tidyverse CREATE Assignment - Atina Karim

In this assignment I looked at Water Quality Data from the City of Austin's online data portal:https://data.austintexas.gov/Environment/Water-Quality-Sampling-Data/5tye-7ray. The dataset contains the results of about a 1000 water quality tests performed on water bodies in Austin, in 2020.

I used tidyverse packages such as tidyr and dplyr to clean the dataset, and then visualized the data using ggplot2.

======= John Mazon TidyVerse Create Assignment - Analysis of Diamond clarity and depth correlation/frequency as well as ratio [pertaining to price to depth] using multiple TidyVerse functionalities

• Data Analysis of Grocery items sold using Groceries_dataset.csv ======= Change Log: 26 October: Added vignette w/ examples for purrr and forcats, Cameron Smith