Task 2
Wax On, Wax off
Kyle Tolliver
March 16, 2020
Background
This task is intended to get all of us on the same page in R. You will copy code from your readings and then improve the Research and Creative Works conference visulization from this Excel file. Even if you have used R before, this will be an excellent refresher. If you do not get the title of this task, watch this.
Readings
- Chapter 4: R for Data Scientists - Workflow Basics
- Modern Drive: Chapter 1 Getting Started with Data in R
- Using the geom_col function
- Using the geom_line function
Tasks
[X] Make an R script that has the code examples from Chapter 4 of R for data science and Chapter 1 of Modern Drive
- Write a short comment in your R script that explains 1-2 new concepts you learned about R
Chapter 4 – R for data science
#Examples
1 / 200 * 30## [1] 0.15(59 + 73 + 2) / 3## [1] 44.66667sin(pi / 2)## [1] 1x <- 3 * 4
x## [1] 12x <- "hello world" #This is how to output
x #things in variables## [1] "hello world"x <- "hello" #Assignments are different then other programming languages
x## [1] "hello"y <- seq(1, 10, length.out = 5)
y## [1] 1.00 3.25 5.50 7.75 10.00(y <- seq(1, 10, length.out = 5))## [1] 1.00 3.25 5.50 7.75 10.00#Practice
my_variable <- 10
my_varıable## [1] 10#Fix This Code
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
filter(mpg, cyl == 8)## # A tibble: 70 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a6 quatt… 4.2 2008 8 auto(… 4 16 23 p mids…
## 2 chevrolet c1500 su… 5.3 2008 8 auto(… r 14 20 r suv
## 3 chevrolet c1500 su… 5.3 2008 8 auto(… r 11 15 e suv
## 4 chevrolet c1500 su… 5.3 2008 8 auto(… r 14 20 r suv
## 5 chevrolet c1500 su… 5.7 1999 8 auto(… r 13 17 r suv
## 6 chevrolet c1500 su… 6 2008 8 auto(… r 12 17 r suv
## 7 chevrolet corvette 5.7 1999 8 manua… r 16 26 p 2sea…
## 8 chevrolet corvette 5.7 1999 8 auto(… r 15 23 p 2sea…
## 9 chevrolet corvette 6.2 2008 8 manua… r 16 26 p 2sea…
## 10 chevrolet corvette 6.2 2008 8 auto(… r 15 25 p 2sea…
## # … with 60 more rows filter(diamonds, carat > 3)## # A tibble: 32 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 3.01 Premium I I1 62.7 58 8040 9.1 8.97 5.67
## 2 3.11 Fair J I1 65.9 57 9823 9.15 9.02 5.98
## 3 3.01 Premium F I1 62.2 56 9925 9.24 9.13 5.73
## 4 3.05 Premium E I1 60.9 58 10453 9.26 9.25 5.66
## 5 3.02 Fair I I1 65.2 56 10577 9.11 9.02 5.91
## 6 3.01 Fair H I1 56.1 62 10761 9.54 9.38 5.31
## 7 3.65 Fair H I1 67.1 53 11668 9.53 9.48 6.38
## 8 3.24 Premium H I1 62.1 58 12300 9.44 9.4 5.85
## 9 3.22 Ideal I I1 62.6 55 12545 9.49 9.42 5.92
## 10 3.5 Ideal H I1 62.8 57 12587 9.65 9.59 6.03
## # … with 22 more rowsChapter 1 – Modern Drive
#Examples
2 + 1 == 3 #Comparator operations## [1] TRUE4 + 2 >= 3## [1] TRUE3 + 5 <= 1## [1] FALSE(2 + 1 == 3) & (2 + 1 == 4)## [1] FALSE(2 + 1 == 3) | (2 + 1 == 4)## [1] TRUEseq(from = 1, to = 1)## [1] 1seq(from = 2, to = 5)## [1] 2 3 4 5flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>View(flights)
glimpse(flights)## Observations: 336,776
## Variables: 19
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558,…
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600,…
## $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -…
## $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851…
## $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -…
## $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", …
## $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, …
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N39…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA"…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD"…
## $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, …
## $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733,…
## $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, …
## $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, …
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 …airlines## # A tibble: 16 x 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.kable(airlines)| carrier | name |
|---|---|
| 9E | Endeavor Air Inc. |
| AA | American Airlines Inc. |
| AS | Alaska Airlines Inc. |
| B6 | JetBlue Airways |
| DL | Delta Air Lines Inc. |
| EV | ExpressJet Airlines Inc. |
| F9 | Frontier Airlines Inc. |
| FL | AirTran Airways Corporation |
| HA | Hawaiian Airlines Inc. |
| MQ | Envoy Air |
| OO | SkyWest Airlines Inc. |
| UA | United Air Lines Inc. |
| US | US Airways Inc. |
| VX | Virgin America |
| WN | Southwest Airlines Co. |
| YV | Mesa Airlines Inc. |
airlines$name## [1] "Endeavor Air Inc." "American Airlines Inc."
## [3] "Alaska Airlines Inc." "JetBlue Airways"
## [5] "Delta Air Lines Inc." "ExpressJet Airlines Inc."
## [7] "Frontier Airlines Inc." "AirTran Airways Corporation"
## [9] "Hawaiian Airlines Inc." "Envoy Air"
## [11] "SkyWest Airlines Inc." "United Air Lines Inc."
## [13] "US Airways Inc." "Virgin America"
## [15] "Southwest Airlines Co." "Mesa Airlines Inc."glimpse(airports)## Observations: 1,458
## Variables: 8
## $ faa <chr> "04G", "06A", "06C", "06N", "09J", "0A9", "0G6", "0G7", "0P2", …
## $ name <chr> "Lansdowne Airport", "Moton Field Municipal Airport", "Schaumbu…
## $ lat <dbl> 41.13047, 32.46057, 41.98934, 41.43191, 31.07447, 36.37122, 41.…
## $ lon <dbl> -80.61958, -85.68003, -88.10124, -74.39156, -81.42778, -82.1734…
## $ alt <dbl> 1044, 264, 801, 523, 11, 1593, 730, 492, 1000, 108, 409, 875, 1…
## $ tz <dbl> -5, -6, -6, -5, -5, -5, -5, -5, -5, -8, -5, -6, -5, -5, -5, -5,…
## $ dst <chr> "A", "A", "A", "A", "A", "A", "A", "A", "U", "A", "A", "U", "A"…
## $ tzone <chr> "America/New_York", "America/Chicago", "America/Chicago", "Amer…[X] Improve the 3D barchart visuallization using R (code below) to provide a graphic that answers the question, What is the trend by department of RC&W attendance? (data and 3D visualization) — library(tidyverse) dat <- read_csv(“https://byuistats.github.io/M335/data/rcw.csv”)
theme_update(plot.title = element_text(hjust = 0.5))
dat <- read_csv("https://byuistats.github.io/M335/data/rcw.csv")## Parsed with column specification:
## cols(
## Year = col_double(),
## Semester = col_character(),
## Semester_Date = col_character(),
## Count = col_double(),
## Department = col_character()
## )dat %>%
mutate(Year2 = Year %>% as.character(Year)) %>%
unite("Semester_Year", Semester, Year2, remove = FALSE) %>%
mutate(Semester_Year = Semester_Year %>%
factor(level = c("Spring_2016", "Fall_2016", "Winter_2017", "Spring_2017", "Fall_2017", "Winter_2018"))) %>%
ggplot() +
geom_point(mapping = aes(x = Department, y = Count, color = Semester_Year)) +
labs(color = "Semester",
title = "Research and Creative Works Conference")
[X] Create a GitHub account — https://github.com/kctolli
- Post your github username on the google docs sheet — kctolli
- Post a professional picture to your Github, LinkedIn, and Slack icons. Go to the BYU-I LinkedIn Photobooth if you don’t already have one.
[X] Attend Data Science Society on the second Wednesday of the semester.
- 6pm in STC 385
Class Notes
Effectively Communicating Numbers
- Message
- Graphics
- Data Munging
- Fine the Message
- Clarify the point
Asking the Right Questions
- Harness the power of questions and Clarify Terms
- A truly wise person may not have the answers, but know which questions to ask
Three Traits of a Data Scientist
- Ask questions that matter?
- What does that mean?
- Determine the question has been answered.
- Identify and satisfy assumptions
- A commitment to intellectual honesty
- What does that mean?
- Creative thinking
- Vigorous attempts to fail their own answers?
An easy way to assess job candidates on the three traits is to ask them to briefly describe one or more of their past projects, and then ask them many follow-up questions about whether and how they applied the three traits.
HotKeys
- %>% – ctrl shift m
- comments – ctrl shift c
- hotkeys – shift alt k