An R package is a collection of functions and data that pertain to a certain topic. Install.packages downloads packages to your computer while library reads in the package so you have access to it in your R session.
We use the str() function on R objects to determine what type of object we are working with.
A named list in a one-dimensional object where each element in the list has a name.
A delimiter is a character that separates data values in a raw data file.
library(readxl)
#reads the readxl package into the R session
car <- read_excel("C:/Users/lukeb/Downloads/CarDepreciation.xlsx")
#reads the excel file and stores it as and R object car
car[, c(1,4)]## # A tibble: 20 × 2
## Car Depreciation
## <chr> <dbl>
## 1 Mazda3 2630
## 2 Buick Encore 2135
## 3 Toyota Corolla 1330
## 4 Chrevolet Tahoe 2026
## 5 Chrevolet Equinox 2447
## 6 Ford Fiesta 2026
## 7 BMW 528i 1645
## 8 Mitsubishi Mirage 2410
## 9 GMC Yukon 1660
## 10 Dodge Dart 2259
## 11 Honda Accord Hybrid 2116
## 12 Audi Q5 1942
## 13 Hyundai Elantra 1931
## 14 Kia Sedona 3532
## 15 Dodge Grand Caravan 3947
## 16 Lexus CT 3561
## 17 Lincoln MKZ Hybrid 2630
## 18 Mercedez-Benz E-Class 4222
## 19 Scion tC 1051
## 20 MINI Countryman 1617#subsets the data to only return the car and depreciation columns
car[order(car$Depreciation,decreasing = F), c(1,4)]## # A tibble: 20 × 2
## Car Depreciation
## <chr> <dbl>
## 1 Scion tC 1051
## 2 Toyota Corolla 1330
## 3 MINI Countryman 1617
## 4 BMW 528i 1645
## 5 GMC Yukon 1660
## 6 Hyundai Elantra 1931
## 7 Audi Q5 1942
## 8 Chrevolet Tahoe 2026
## 9 Ford Fiesta 2026
## 10 Honda Accord Hybrid 2116
## 11 Buick Encore 2135
## 12 Dodge Dart 2259
## 13 Mitsubishi Mirage 2410
## 14 Chrevolet Equinox 2447
## 15 Mazda3 2630
## 16 Lincoln MKZ Hybrid 2630
## 17 Kia Sedona 3532
## 18 Lexus CT 3561
## 19 Dodge Grand Caravan 3947
## 20 Mercedez-Benz E-Class 4222#subsets the data to only return the car and depreciation columns as well as indexing the rows by depreciation value from smallest to largestx <- "https://www4.stat.ncsu.edu/~online/datasets/Cereal.csv"
# saves the url as the variable x
cols <- c("Name", "Company", "Serving", "Calories", "Fat", "Sodium", "Carbs", "Fiber", "Sugars", "Protein")
#creates a character vector with the names
cereal <- read.csv(x, col.names = cols)
#saves the file with the new names as an R object cereal
cereal## Name Company Serving Calories Fat Sodium Carbs Fiber Sugars
## 1 Boo Berry G 1.00 118 0.8 211 27 0.1 14.0
## 2 Cap'n Crunch Q 0.75 144 2.1 269 31 1.1 16.0
## 3 Cinnamon Toast Crunch G 0.75 169 4.4 408 32 1.7 13.3
## 4 Cocoa Blasts Q 1.00 130 1.2 135 29 0.8 16.0
## 5 Cocoa Puffs G 1.00 117 1.0 171 26 0.8 14.0
## 6 Cookie Crisp G 1.00 117 0.9 178 26 0.5 13.0
## 7 Corn Flakes K 1.00 101 0.1 202 24 0.8 3.0
## 8 Corn Pops K 1.00 117 0.2 120 28 0.3 15.0
## 9 Crispix K 1.00 113 0.3 229 26 0.1 3.0
## 10 Crunchy Bran Q 0.75 120 1.3 309 31 6.4 8.0
## 11 Froot Loops K 1.00 118 0.9 150 26 0.8 12.0
## 12 Frosted Mini-Wheats K 1.00 175 0.8 5 41 5.0 10.0
## 13 Golden Grahams G 0.75 149 1.3 359 33 1.3 14.7
## 14 Honey Nut Clusters G 1.00 214 2.7 249 46 2.8 17.0
## 15 Honey Nut Heaven Q 1.00 192 3.7 216 38 3.5 13.0
## 16 King Vitaman Q 1.50 80 0.7 173 17 0.9 4.0
## 17 Kix G 1.30 87 0.5 205 20 0.8 2.3
## 18 Life Q 0.75 160 1.9 219 33 2.7 8.0
## 19 Lucky Charms G 1.00 114 1.1 203 25 1.5 13.0
## 20 Multi-Grain Cheerios G 1.00 108 1.2 201 24 2.8 6.0
## 21 Product 19 K 1.00 100 0.4 207 25 1.0 4.0
## 22 Raisin Bran K 1.00 195 1.6 362 47 7.3 20.0
## 23 Reese's Puffs G 0.75 171 3.9 223 31 0.0 16.0
## 24 Rice Chex G 1.25 94 0.2 234 22 0.1 1.6
## 25 Rice Krispie Treats K 0.75 160 1.7 252 35 0.0 12.0
## 26 Smart Start K 1.00 182 0.7 275 43 2.8 14.0
## 27 Special K K 1.00 117 0.4 224 22 0.8 4.0
## 28 Total G 0.75 129 0.9 256 31 3.7 6.7
## 29 Wheaties G 1.00 107 1.0 218 24 3.0 4.0
## Protein
## 1 1.0
## 2 1.3
## 3 2.7
## 4 1.0
## 5 1.0
## 6 1.0
## 7 2.0
## 8 1.0
## 9 2.0
## 10 1.3
## 11 2.0
## 12 5.0
## 13 2.7
## 14 4.0
## 15 4.0
## 16 1.3
## 17 1.5
## 18 4.0
## 19 2.0
## 20 2.0
## 21 2.0
## 22 5.0
## 23 2.7
## 24 1.6
## 25 1.3
## 26 4.0
## 27 7.0
## 28 4.0
## 29 3.0I am majoring in business administration with a concentration in finance. In my future career, I will interact with data to determine revenues and sales of products in a product line. This information can then be communicated to decision makers.
url for where I found my data set: https://catalog.data.gov/dataset/fdic-failed-bank-list/resource/a8cfc40d-bf6d-4716-bba6-04fdbdf5f9c1 url for the data set: https://www.fdic.gov/bank/individual/failed/banklist.csv I chose this data set because I will be working in the finance industry. This data set contains bank failures in the United States. Working with banks is one possible option for me in a future career. This is why I found the data set Interesting.
banklist <- read_excel("C:/Users/lukeb/Downloads/banklist.xlsx")
#reads in the data set from excel and saves it as an R object banklist
banklist## # A tibble: 567 × 7
## `Bank Name ` `City ` `State ` `Cert ` Acquiring Institutio…¹
## <chr> <chr> <chr> <dbl> <chr>
## 1 Heartland Tri-State Bank Elkhart KS 25851 Dream First Bank, N.A.
## 2 First Republic Bank San Fr… CA 59017 JPMorgan Chase Bank, …
## 3 Signature Bank New Yo… NY 57053 Flagstar Bank, N.A.
## 4 Silicon Valley Bank Santa … CA 24735 First–Citizens Bank &…
## 5 Almena State Bank Almena KS 15426 Equity Bank
## 6 First City Bank of Florida Fort W… FL 16748 United Fidelity Bank,…
## 7 The First State Bank Barbou… WV 14361 MVB Bank, Inc.
## 8 Ericson State Bank Ericson NE 18265 Farmers and Merchants…
## 9 City National Bank of New Je… Newark NJ 21111 Industrial Bank
## 10 Resolute Bank Maumee OH 58317 Buckeye State Bank
## # ℹ 557 more rows
## # ℹ abbreviated name: ¹`Acquiring Institution `
## # ℹ 2 more variables: `Closing Date ` <dttm>, Fund <dbl>