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.

Name: Arushi Arora

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

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

The dataset was imported and cleaned using packages in TidyVerse

• 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))
  
  

=======

Ian Costello Tidyverse Create

CUNY DATA 607 TIDYVERSE Collaborative project

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")

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

"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()

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)

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)

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 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))

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

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())

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]"

subset function will let you extract strings that contain vowels.

str_subset(v, p)

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)

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

str_count(v, p)

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)

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)

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, ",")

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, "/")

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:

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

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)

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

head(drivers)

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))

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.

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