CDS-102: Lab 5 Workbook¶

Name: Bassil Alomari¶

March 2, 2017¶

# Run this code block to load the Tidyverse package
.libPaths(new = "~/Rlibs")
library(tidyverse)

office_data <- read_csv('OfficeSupplies.csv')

Parsed with column specification:
cols(
  OrderDate = col_character(),
  Region = col_character(),
  Rep = col_character(),
  Item = col_character(),
  Units = col_integer(),
  `Unit Price` = col_double()
)

print(office_data)

# A tibble: 43 × 6
     OrderDate  Region     Rep    Item Units `Unit Price`
         <chr>   <chr>   <chr>   <chr> <int>        <dbl>
1   4-Jul-2014    East Richard Pen Set    62         4.99
2  12-Jul-2014    East    Nick  Binder    29         1.99
3  21-Jul-2014 Central  Morgan Pen Set    55        12.49
4  29-Jul-2014    East   Susan  Binder    81        19.99
5   7-Aug-2014 Central Matthew Pen Set    42        23.95
6  15-Aug-2014    East Richard  Pencil    35         4.99
7  24-Aug-2014    West   James    Desk     3       275.00
8   1-Sep-2014 Central   Smith    Desk     2       125.00
9  10-Sep-2014 Central    Bill  Pencil     7         1.29
10 18-Sep-2014    East Richard Pen Set    16        15.99
# ... with 33 more rows

Office_data <- rownames_to_column(as_tibble(office_data),var = "orderdate")
print(office_data)

# A tibble: 43 × 6
     OrderDate  Region     Rep    Item Units `Unit Price`
         <chr>   <chr>   <chr>   <chr> <int>        <dbl>
1   4-Jul-2014    East Richard Pen Set    62         4.99
2  12-Jul-2014    East    Nick  Binder    29         1.99
3  21-Jul-2014 Central  Morgan Pen Set    55        12.49
4  29-Jul-2014    East   Susan  Binder    81        19.99
5   7-Aug-2014 Central Matthew Pen Set    42        23.95
6  15-Aug-2014    East Richard  Pencil    35         4.99
7  24-Aug-2014    West   James    Desk     3       275.00
8   1-Sep-2014 Central   Smith    Desk     2       125.00
9  10-Sep-2014 Central    Bill  Pencil     7         1.29
10 18-Sep-2014    East Richard Pen Set    16        15.99
# ... with 33 more rows

print(select(office_data, Region, Rep, Item))

# A tibble: 43 × 3
    Region     Rep    Item
     <chr>   <chr>   <chr>
1     East Richard Pen Set
2     East    Nick  Binder
3  Central  Morgan Pen Set
4     East   Susan  Binder
5  Central Matthew Pen Set
6     East Richard  Pencil
7     West   James    Desk
8  Central   Smith    Desk
9  Central    Bill  Pencil
10    East Richard Pen Set
# ... with 33 more rows

(select(office_data, Region, Rep, Item))
print(distinct(Region, Rep, Item))

Error in distinct_(.data, .dots = lazyeval::lazy_dots(...), .keep_all = .keep_all): object 'Region' not found
Traceback:

1. print(distinct(Region, Rep, Item))
2. distinct(Region, Rep, Item)
3. distinct_(.data, .dots = lazyeval::lazy_dots(...), .keep_all = .keep_all)

office_data.mutated <- mutate(office_data, TotalCost = Units * `Unit Price`)
print(select(office_data.mutated,OrderDate, Units,`Unit Price`, TotalCost))

# A tibble: 43 × 4
     OrderDate Units `Unit Price` TotalCost
         <chr> <int>        <dbl>     <dbl>
1   4-Jul-2014    62         4.99    309.38
2  12-Jul-2014    29         1.99     57.71
3  21-Jul-2014    55        12.49    686.95
4  29-Jul-2014    81        19.99   1619.19
5   7-Aug-2014    42        23.95   1005.90
6  15-Aug-2014    35         4.99    174.65
7  24-Aug-2014     3       275.00    825.00
8   1-Sep-2014     2       125.00    250.00
9  10-Sep-2014     7         1.29      9.03
10 18-Sep-2014    16        15.99    255.84
# ... with 33 more rows

by_Region <- group_by(office_data, Region)
print(summarise(by_Region,sum(Units), sum(`Unit Price`)))

# A tibble: 3 × 3
   Region `sum(Units)` `sum(\\`Unit Price\\`)`
    <chr>        <int>                   <dbl>
1 Central         1199                  432.45
2    East          691                  118.87
3    West          231                  321.95

Office_data.filtered <- filter(by_Region, Region == "Central")
print(Office_data.filtered)

Source: local data frame [24 x 6]
Groups: Region [1]

     OrderDate  Region     Rep    Item Units `Unit Price`
         <chr>   <chr>   <chr>   <chr> <int>        <dbl>
1  21-Jul-2014 Central  Morgan Pen Set    55        12.49
2   7-Aug-2014 Central Matthew Pen Set    42        23.95
3   1-Sep-2014 Central   Smith    Desk     2       125.00
4  10-Sep-2014 Central    Bill  Pencil     7         1.29
5   5-Oct-2014 Central  Morgan  Binder    28         8.99
6  31-Oct-2014 Central  Rachel  Pencil    14         1.29
7  17-Nov-2014 Central    Alex  Binder    11         4.99
8  25-Nov-2014 Central Matthew Pen Set    96         4.99
9   4-Dec-2014 Central    Alex  Binder    94        19.99
10 12-Dec-2014 Central   Smith  Pencil    67         1.29
# ... with 14 more rows

by_Region <- group_by(office_data, Region, Rep, Units, `Unit Price` )
print(summarise(by_Region))

Source: local data frame [43 x 4]
Groups: Region, Rep, Units [?]

    Region   Rep Units `Unit Price`
     <chr> <chr> <int>        <dbl>
1  Central  Alex    11         4.99
2  Central  Alex    36         4.99
3  Central  Alex    50         4.99
4  Central  Alex    90         4.99
5  Central  Alex    94        19.99
6  Central  Bill     7         1.29
7  Central  Bill    27        19.99
8  Central  Bill    46         8.99
9  Central  Bill    53         1.29
10 Central  Bill    80         8.99
# ... with 33 more rows

Region	Rep	Item
East	Richard	Pen Set
East	Nick	Binder
Central	Morgan	Pen Set
East	Susan	Binder
Central	Matthew	Pen Set
East	Richard	Pencil
West	James	Desk
Central	Smith	Desk
Central	Bill	Pencil
East	Richard	Pen Set
West	James	Pen
Central	Morgan	Binder
West	Thomas	Binder
East	Richard	Pen
Central	Rachel	Pencil
East	Susan	Pen
Central	Alex	Binder
Central	Matthew	Pen Set
Central	Alex	Binder
Central	Smith	Pencil
Central	Rachel	Binder
East	Susan	Pen Set
East	Richard	Pencil
Central	Bill	Binder
Central	Matthew	Binder
Central	Smith	Binder
Central	Alex	Pencil
East	Richard	Binder
Central	Bill	Pen
West	James	Binder
West	James	Pencil
Central	Alex	Pen Set
East	Richard	Binder
Central	Rachel	Pencil
Central	Rachel	Pencil
East	Nick	Pen
Central	Alex	Pencil
Central	Bill	Pencil
West	Thomas	Pencil
Central	Bill	Binder
East	Richard	Binder
Central	Matthew	Desk
Central	Morgan	Pencil