2.2 funciones y paquetes de R para análisis de datos
2.2.1 arrange ()
df <- df %>%
mutate(Order.Date=mdy(Order.Date)) %>%
mutate(Ship.Date=mdy(Ship.Date)) %>%
arrange(Order.Date)
head(df)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
2.2.2 as.character()
df <- df %>%
mutate(Postal.Code=as.character(Postal.Code))
head(df)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
2.2.3 as.factor()
#La creación de factores facilita la división
#de la base de datos en grupos.
#Acelera el cómputo
df <- df %>%
mutate(Ship.Mode=as.factor(Ship.Mode)) %>%
mutate(Segment=as.factor(Segment)) %>%
mutate(Country=as.factor(Country)) %>%
mutate(City=as.factor(City)) %>%
mutate(State=as.factor(State)) %>%
mutate(Region=as.factor(Region)) %>%
mutate(Category=as.factor(Category)) %>%
mutate(Sub.Category=as.factor(Sub.Category))
head(df)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
2.2.4 as.numeric()
df %>%
select(Sales, Quantity, Discount, Profit) %>%
mutate_all(as.numeric) %>%
head(.)
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
#aplica función head a lo queda de lo anterior
2.2.5 class()
#Permite evaluar el tipo de clase de objetos y el tipo de datos de columnas
class(df)
## [1] "data.frame"
#permite conocer el tipo de dato de una columna
class(df$Order.Date)
## [1] "Date"
2.2.6 day ()
#Sirve para recuperar el dia de una columna guardada como fecha
df %>%
select(Order.Date) %>%
mutate(Day.Order=day(Order.Date)) %>%
mutate(Week.Order=week(Order.Date)) %>%
mutate(Month.Order=month(Order.Date)) %>%
mutate(Year.Order=year(Order.Date)) %>%
head(.)
## Order.Date Day.Order Week.Order Month.Order Year.Order
## 1 2014-01-03 3 1 1 2014
## 2 2014-01-04 4 1 1 2014
## 3 2014-01-04 4 1 1 2014
## 4 2014-01-04 4 1 1 2014
## 5 2014-01-05 5 1 1 2014
## 6 2014-01-06 6 1 1 2014
2.2.7 dym()
# Me permite convertir texto tipo date
dmy("30/08/2024")
## [1] "2024-08-30"
# Tener cuidado de orden en el que está almacenada la fecha
dmy("30/08/2024")
## [1] "2024-08-30"
mdy("30/08/2024") # NA aparece porque no hay mes 30
## Warning: All formats failed to parse. No formats found.
## [1] NA
2.2.8 filter()
# Me permite obtenerrangos de datos por fechas
df %>%
filter(year(Order.Date) < 2015) %>% #filtrar usando función
head(.)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
# Filtrar a partir de una fecha especifica 15 sep del 2017
df %>%
filter(Order.Date < ymd("2017/09/15")) %>%
head(.)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
# Quiero ventas estado de Michigan
df %>%
filter(State == "Michigan") %>%
head(.)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 6327 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## 2 6328 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## 3 6329 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## 4 6330 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## 5 6331 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## 6 6332 CA-2014-167927 2014-01-20 2014-01-26 Standard Class XP-21865
## Customer.Name Segment Country City State Postal.Code Region
## 1 Xylona Preis Consumer United States Westland Michigan 48185 Central
## 2 Xylona Preis Consumer United States Westland Michigan 48185 Central
## 3 Xylona Preis Consumer United States Westland Michigan 48185 Central
## 4 Xylona Preis Consumer United States Westland Michigan 48185 Central
## 5 Xylona Preis Consumer United States Westland Michigan 48185 Central
## 6 Xylona Preis Consumer United States Westland Michigan 48185 Central
## Product.ID Category Sub.Category
## 1 OFF-ST-10000760 Office Supplies Storage
## 2 FUR-FU-10002918 Furniture Furnishings
## 3 OFF-BI-10000605 Office Supplies Binders
## 4 OFF-AP-10002311 Office Supplies Appliances
## 5 OFF-ST-10003123 Office Supplies Storage
## 6 OFF-AR-10004456 Office Supplies Art
## Product.Name
## 1 Eldon Fold 'N Roll Cart System
## 2 Eldon ClusterMat Chair Mat with Cordless Antistatic Protection
## 3 Acco Pressboard Covers with Storage Hooks, 9 1/2" x 11", Executive Red
## 4 Holmes Replacement Filter for HEPA Air Cleaner, Very Large Room, HEPA Filter
## 5 Fellowes Bases and Tops For Staxonsteel/High-Stak Systems
## 6 Panasonic KP-4ABK Battery-Operated Pencil Sharpener
## Sales Quantity Discount Profit
## 1 13.980 1 0.0 4.0542
## 2 272.940 3 0.0 30.0234
## 3 19.050 5 0.0 8.9535
## 4 247.716 4 0.1 93.5816
## 5 66.580 2 0.0 15.9792
## 6 43.920 3 0.0 12.7368
# Quiero ventas excepto estado de Michigan
df %>%
filter(State != "Michigan") %>%
head(.)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 7981 CA-2014-103800 2014-01-03 2014-01-07 Standard Class DP-13000
## 2 740 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 3 741 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 4 742 CA-2014-112326 2014-01-04 2014-01-08 Standard Class PO-19195
## 5 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 6 5328 CA-2014-130813 2014-01-06 2014-01-08 Second Class LS-17230
## Customer.Name Segment Country City State
## 1 Darren Powers Consumer United States Houston Texas
## 2 Phillina Ober Home Office United States Naperville Illinois
## 3 Phillina Ober Home Office United States Naperville Illinois
## 4 Phillina Ober Home Office United States Naperville Illinois
## 5 Mick Brown Consumer United States Philadelphia Pennsylvania
## 6 Lycoris Saunders Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 77095 Central OFF-PA-10000174 Office Supplies Paper
## 2 60540 Central OFF-LA-10003223 Office Supplies Labels
## 3 60540 Central OFF-ST-10002743 Office Supplies Storage
## 4 60540 Central OFF-BI-10004094 Office Supplies Binders
## 5 19143 East OFF-AR-10003478 Office Supplies Art
## 6 90049 West OFF-PA-10002005 Office Supplies Paper
## Product.Name
## 1 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
## 2 Avery 508
## 3 SAFCO Boltless Steel Shelving
## 4 GBC Standard Plastic Binding Systems Combs
## 5 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 6 Xerox 225
## Sales Quantity Discount Profit
## 1 16.448 2 0.2 5.5512
## 2 11.784 3 0.2 4.2717
## 3 272.736 3 0.2 -64.7748
## 4 3.540 2 0.8 -5.4870
## 5 19.536 3 0.2 4.8840
## 6 19.440 3 0.0 9.3312
# Filtrar los clientes de apellido Brown
df %>%
filter(grepl("Brown", Customer.Name))
## Warning: There were 94 warnings in `filter()`.
## The first warning was:
## ℹ In argument: `grepl("Brown", Customer.Name)`.
## Caused by warning in `grepl()`:
## ! unable to translate 'Barry Franz<f6>sisch' to a wide string
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 93 remaining warnings.
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 1760 CA-2014-141817 2014-01-05 2014-01-12 Standard Class MB-18085
## 2 2996 US-2014-150532 2014-07-14 2014-07-21 Standard Class PB-19150
## 3 8544 CA-2014-137575 2014-08-06 2014-08-11 Standard Class TB-21625
## 4 8867 CA-2014-120411 2014-09-20 2014-09-23 First Class SB-20185
## 5 8868 CA-2014-120411 2014-09-20 2014-09-23 First Class SB-20185
## 6 4309 CA-2014-125829 2014-11-04 2014-11-11 Standard Class WB-21850
## 7 4310 CA-2014-125829 2014-11-04 2014-11-11 Standard Class WB-21850
## 8 4311 CA-2014-125829 2014-11-04 2014-11-11 Standard Class WB-21850
## 9 4312 CA-2014-125829 2014-11-04 2014-11-11 Standard Class WB-21850
## 10 4313 CA-2014-125829 2014-11-04 2014-11-11 Standard Class WB-21850
## 11 7786 US-2014-111353 2014-11-29 2014-12-06 Standard Class PB-19150
## 12 9195 CA-2014-146843 2014-11-30 2014-12-06 Standard Class PB-19150
## 13 9196 CA-2014-146843 2014-11-30 2014-12-06 Standard Class PB-19150
## 14 2149 CA-2014-141607 2014-12-12 2014-12-17 Standard Class WB-21850
## 15 5486 CA-2014-107573 2014-12-12 2014-12-17 Standard Class PB-19150
## 16 6107 CA-2014-120852 2014-12-20 2014-12-25 Standard Class WB-21850
## 17 6108 CA-2014-120852 2014-12-20 2014-12-25 Standard Class WB-21850
## 18 4857 CA-2015-137526 2015-01-13 2015-01-17 Standard Class PB-19150
## 19 4858 CA-2015-137526 2015-01-13 2015-01-17 Standard Class PB-19150
## 20 4859 CA-2015-137526 2015-01-13 2015-01-17 Standard Class PB-19150
## 21 6201 CA-2015-146675 2015-04-16 2015-04-20 Standard Class SB-20185
## 22 6202 CA-2015-146675 2015-04-16 2015-04-20 Standard Class SB-20185
## 23 8102 CA-2015-149846 2015-05-22 2015-05-26 Standard Class SB-20185
## 24 8103 CA-2015-149846 2015-05-22 2015-05-26 Standard Class SB-20185
## 25 8104 CA-2015-149846 2015-05-22 2015-05-26 Standard Class SB-20185
## 26 8105 CA-2015-149846 2015-05-22 2015-05-26 Standard Class SB-20185
## 27 8387 CA-2015-149601 2015-05-28 2015-06-03 Standard Class TB-21625
## 28 8388 CA-2015-149601 2015-05-28 2015-06-03 Standard Class TB-21625
## 29 1024 CA-2015-167269 2015-06-16 2015-06-20 Standard Class PB-19150
## 30 6644 US-2015-156496 2015-08-10 2015-08-16 Standard Class WB-21850
## 31 6645 US-2015-156496 2015-08-10 2015-08-16 Standard Class WB-21850
## 32 6646 US-2015-156496 2015-08-10 2015-08-16 Standard Class WB-21850
## 33 5597 CA-2015-159779 2015-09-25 2015-09-29 Standard Class SB-20185
## 34 4381 CA-2015-152891 2015-10-25 2015-10-30 Standard Class TB-21625
## 35 4382 CA-2015-152891 2015-10-25 2015-10-30 Standard Class TB-21625
## 36 2134 CA-2015-122210 2015-11-30 2015-12-04 Standard Class WB-21850
## 37 2135 CA-2015-122210 2015-11-30 2015-12-04 Standard Class WB-21850
## 38 2136 CA-2015-122210 2015-11-30 2015-12-04 Standard Class WB-21850
## 39 2137 CA-2015-156377 2015-12-31 2016-01-05 Standard Class TB-21625
## 40 2138 CA-2015-156377 2015-12-31 2016-01-05 Standard Class TB-21625
## 41 3742 CA-2016-137848 2016-01-15 2016-01-21 Standard Class WB-21850
## 42 3743 CA-2016-137848 2016-01-15 2016-01-21 Standard Class WB-21850
## 43 3744 CA-2016-137848 2016-01-15 2016-01-21 Standard Class WB-21850
## 44 1462 US-2016-128902 2016-03-11 2016-03-15 Standard Class MB-18085
## 45 418 CA-2016-148796 2016-04-14 2016-04-18 Standard Class PB-19150
## 46 4815 CA-2016-111696 2016-05-08 2016-05-10 First Class TB-21625
## 47 7833 CA-2016-112382 2016-05-09 2016-05-13 Standard Class MB-18085
## 48 9073 CA-2016-142524 2016-09-04 2016-09-08 Standard Class MB-18085
## 49 9074 CA-2016-142524 2016-09-04 2016-09-08 Standard Class MB-18085
## 50 5414 CA-2016-153157 2016-09-11 2016-09-14 First Class TB-21625
## 51 9835 CA-2016-126627 2016-10-10 2016-10-12 First Class WB-21850
## 52 9836 CA-2016-126627 2016-10-10 2016-10-12 First Class WB-21850
## 53 7051 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 54 7052 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 55 7053 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 56 7054 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 57 7055 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 58 7056 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 59 7057 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 60 7058 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 61 7059 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 62 7060 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 63 7061 CA-2016-165330 2016-12-11 2016-12-11 Same Day WB-21850
## 64 5075 CA-2017-151071 2017-04-25 2017-04-29 Second Class MB-18085
## 65 6635 CA-2017-144498 2017-05-06 2017-05-06 Same Day MB-18085
## 66 6636 CA-2017-144498 2017-05-06 2017-05-06 Same Day MB-18085
## 67 6637 CA-2017-144498 2017-05-06 2017-05-06 Same Day MB-18085
## 68 8013 CA-2017-120168 2017-05-25 2017-05-25 Same Day TB-21625
## 69 8014 CA-2017-120168 2017-05-25 2017-05-25 Same Day TB-21625
## 70 8015 CA-2017-120168 2017-05-25 2017-05-25 Same Day TB-21625
## 71 8016 CA-2017-120168 2017-05-25 2017-05-25 Same Day TB-21625
## 72 9175 CA-2017-119424 2017-06-12 2017-06-14 Second Class SB-20185
## 73 9176 CA-2017-119424 2017-06-12 2017-06-14 Second Class SB-20185
## 74 9584 CA-2017-116127 2017-06-25 2017-06-27 Second Class SB-20185
## 75 4708 CA-2017-138149 2017-06-29 2017-06-30 First Class WB-21850
## 76 4709 CA-2017-138149 2017-06-29 2017-06-30 First Class WB-21850
## 77 4710 CA-2017-138149 2017-06-29 2017-06-30 First Class WB-21850
## 78 4711 CA-2017-138149 2017-06-29 2017-06-30 First Class WB-21850
## 79 8485 CA-2017-142391 2017-09-24 2017-09-24 Same Day PB-19150
## 80 4227 CA-2017-120327 2017-11-11 2017-11-16 Standard Class WB-21850
## 81 7589 CA-2017-122945 2017-11-16 2017-11-22 Standard Class MB-18085
## 82 9294 US-2017-116505 2017-11-17 2017-11-21 Second Class TB-21625
## 83 9289 US-2017-165456 2017-11-30 2017-12-03 First Class TB-21625
## 84 9410 US-2017-110149 2017-12-10 2017-12-13 First Class WB-21850
## 85 9411 US-2017-110149 2017-12-10 2017-12-13 First Class WB-21850
## Customer.Name Segment Country City State
## 1 Mick Brown Consumer United States Philadelphia Pennsylvania
## 2 Philip Brown Consumer United States Phoenix Arizona
## 3 Trudy Brown Consumer United States New York City New York
## 4 Sarah Brown Consumer United States Chicago Illinois
## 5 Sarah Brown Consumer United States Chicago Illinois
## 6 William Brown Consumer United States Los Angeles California
## 7 William Brown Consumer United States Los Angeles California
## 8 William Brown Consumer United States Los Angeles California
## 9 William Brown Consumer United States Los Angeles California
## 10 William Brown Consumer United States Los Angeles California
## 11 Philip Brown Consumer United States New York City New York
## 12 Philip Brown Consumer United States Avondale Arizona
## 13 Philip Brown Consumer United States Avondale Arizona
## 14 William Brown Consumer United States Concord California
## 15 Philip Brown Consumer United States Miami Florida
## 16 William Brown Consumer United States Grand Prairie Texas
## 17 William Brown Consumer United States Grand Prairie Texas
## 18 Philip Brown Consumer United States Los Angeles California
## 19 Philip Brown Consumer United States Los Angeles California
## 20 Philip Brown Consumer United States Los Angeles California
## 21 Sarah Brown Consumer United States Evanston Illinois
## 22 Sarah Brown Consumer United States Evanston Illinois
## 23 Sarah Brown Consumer United States Los Angeles California
## 24 Sarah Brown Consumer United States Los Angeles California
## 25 Sarah Brown Consumer United States Los Angeles California
## 26 Sarah Brown Consumer United States Los Angeles California
## 27 Trudy Brown Consumer United States Manchester Connecticut
## 28 Trudy Brown Consumer United States Manchester Connecticut
## 29 Philip Brown Consumer United States Philadelphia Pennsylvania
## 30 William Brown Consumer United States Redmond Oregon
## 31 William Brown Consumer United States Redmond Oregon
## 32 William Brown Consumer United States Redmond Oregon
## 33 Sarah Brown Consumer United States Concord New Hampshire
## 34 Trudy Brown Consumer United States San Francisco California
## 35 Trudy Brown Consumer United States San Francisco California
## 36 William Brown Consumer United States Philadelphia Pennsylvania
## 37 William Brown Consumer United States Philadelphia Pennsylvania
## 38 William Brown Consumer United States Philadelphia Pennsylvania
## 39 Trudy Brown Consumer United States Grand Prairie Texas
## 40 Trudy Brown Consumer United States Grand Prairie Texas
## 41 William Brown Consumer United States New York City New York
## 42 William Brown Consumer United States New York City New York
## 43 William Brown Consumer United States New York City New York
## 44 Mick Brown Consumer United States Vineland New Jersey
## 45 Philip Brown Consumer United States Los Angeles California
## 46 Trudy Brown Consumer United States Los Angeles California
## 47 Mick Brown Consumer United States Houston Texas
## 48 Mick Brown Consumer United States Springfield Missouri
## 49 Mick Brown Consumer United States Springfield Missouri
## 50 Trudy Brown Consumer United States Wichita Kansas
## 51 William Brown Consumer United States La Porte Texas
## 52 William Brown Consumer United States La Porte Texas
## 53 William Brown Consumer United States Anaheim California
## 54 William Brown Consumer United States Anaheim California
## 55 William Brown Consumer United States Anaheim California
## 56 William Brown Consumer United States Anaheim California
## 57 William Brown Consumer United States Anaheim California
## 58 William Brown Consumer United States Anaheim California
## 59 William Brown Consumer United States Anaheim California
## 60 William Brown Consumer United States Anaheim California
## 61 William Brown Consumer United States Anaheim California
## 62 William Brown Consumer United States Anaheim California
## 63 William Brown Consumer United States Anaheim California
## 64 Mick Brown Consumer United States Los Angeles California
## 65 Mick Brown Consumer United States Charlotte North Carolina
## 66 Mick Brown Consumer United States Charlotte North Carolina
## 67 Mick Brown Consumer United States Charlotte North Carolina
## 68 Trudy Brown Consumer United States New York City New York
## 69 Trudy Brown Consumer United States New York City New York
## 70 Trudy Brown Consumer United States New York City New York
## 71 Trudy Brown Consumer United States New York City New York
## 72 Sarah Brown Consumer United States Kent Washington
## 73 Sarah Brown Consumer United States Kent Washington
## 74 Sarah Brown Consumer United States New York City New York
## 75 William Brown Consumer United States Los Angeles California
## 76 William Brown Consumer United States Los Angeles California
## 77 William Brown Consumer United States Los Angeles California
## 78 William Brown Consumer United States Los Angeles California
## 79 Philip Brown Consumer United States Seattle Washington
## 80 William Brown Consumer United States Urbandale Iowa
## 81 Mick Brown Consumer United States Roseville California
## 82 Trudy Brown Consumer United States Hagerstown Maryland
## 83 Trudy Brown Consumer United States Philadelphia Pennsylvania
## 84 William Brown Consumer United States Philadelphia Pennsylvania
## 85 William Brown Consumer United States Philadelphia Pennsylvania
## Postal.Code Region Product.ID Category Sub.Category
## 1 19143 East OFF-AR-10003478 Office Supplies Art
## 2 85023 West OFF-ST-10000760 Office Supplies Storage
## 3 10035 East TEC-AC-10004571 Technology Accessories
## 4 60653 Central FUR-BO-10004218 Furniture Bookcases
## 5 60653 Central TEC-PH-10002185 Technology Phones
## 6 90045 West TEC-PH-10001079 Technology Phones
## 7 90045 West FUR-TA-10002041 Furniture Tables
## 8 90045 West OFF-BI-10001036 Office Supplies Binders
## 9 90045 West OFF-PA-10000223 Office Supplies Paper
## 10 90045 West TEC-MA-10003246 Technology Machines
## 11 10009 East OFF-LA-10002762 Office Supplies Labels
## 12 85323 West OFF-SU-10001664 Office Supplies Supplies
## 13 85323 West TEC-AC-10002550 Technology Accessories
## 14 94521 West FUR-FU-10003975 Furniture Furnishings
## 15 33178 South OFF-EN-10001099 Office Supplies Envelopes
## 16 75051 Central OFF-AP-10001563 Office Supplies Appliances
## 17 75051 Central TEC-AC-10004510 Technology Accessories
## 18 90004 West OFF-BI-10003364 Office Supplies Binders
## 19 90004 West FUR-FU-10001861 Furniture Furnishings
## 20 90004 West FUR-FU-10004845 Furniture Furnishings
## 21 60201 Central TEC-CO-10001766 Technology Copiers
## 22 60201 Central TEC-AC-10004396 Technology Accessories
## 23 90045 West OFF-LA-10004484 Office Supplies Labels
## 24 90045 West TEC-MA-10004002 Technology Machines
## 25 90045 West OFF-ST-10002406 Office Supplies Storage
## 26 90045 West TEC-PH-10003645 Technology Phones
## 27 6040 East OFF-ST-10001558 Office Supplies Storage
## 28 6040 East OFF-ST-10001328 Office Supplies Storage
## 29 19134 East OFF-EN-10003072 Office Supplies Envelopes
## 30 97756 West TEC-PH-10000576 Technology Phones
## 31 97756 West TEC-PH-10004188 Technology Phones
## 32 97756 West OFF-AP-10001469 Office Supplies Appliances
## 33 3301 East OFF-BI-10002735 Office Supplies Binders
## 34 94110 West OFF-AR-10004648 Office Supplies Art
## 35 94110 West FUR-TA-10001857 Furniture Tables
## 36 19134 East OFF-BI-10003656 Office Supplies Binders
## 37 19134 East OFF-FA-10000053 Office Supplies Fasteners
## 38 19134 East TEC-PH-10002807 Technology Phones
## 39 75051 Central FUR-FU-10002364 Furniture Furnishings
## 40 75051 Central OFF-BI-10002954 Office Supplies Binders
## 41 10011 East OFF-EN-10001137 Office Supplies Envelopes
## 42 10011 East OFF-PA-10004285 Office Supplies Paper
## 43 10011 East OFF-BI-10002225 Office Supplies Binders
## 44 8360 East FUR-TA-10001095 Furniture Tables
## 45 90004 West FUR-CH-10004886 Furniture Chairs
## 46 90004 West OFF-PA-10002751 Office Supplies Paper
## 47 77036 Central TEC-PH-10001552 Technology Phones
## 48 65807 Central OFF-EN-10003286 Office Supplies Envelopes
## 49 65807 Central TEC-AC-10000109 Technology Accessories
## 50 67212 Central TEC-PH-10003171 Technology Phones
## 51 77571 Central FUR-FU-10004963 Furniture Furnishings
## 52 77571 Central OFF-BI-10001597 Office Supplies Binders
## 53 92804 West OFF-BI-10004593 Office Supplies Binders
## 54 92804 West OFF-AR-10000914 Office Supplies Art
## 55 92804 West OFF-PA-10000241 Office Supplies Paper
## 56 92804 West TEC-AC-10000057 Technology Accessories
## 57 92804 West FUR-CH-10003774 Furniture Chairs
## 58 92804 West OFF-BI-10002824 Office Supplies Binders
## 59 92804 West OFF-BI-10004001 Office Supplies Binders
## 60 92804 West FUR-TA-10004619 Furniture Tables
## 61 92804 West OFF-ST-10000636 Office Supplies Storage
## 62 92804 West OFF-AP-10001469 Office Supplies Appliances
## 63 92804 West OFF-BI-10002897 Office Supplies Binders
## 64 90049 West OFF-BI-10002103 Office Supplies Binders
## 65 28205 South OFF-BI-10003982 Office Supplies Binders
## 66 28205 South TEC-PH-10004977 Technology Phones
## 67 28205 South OFF-LA-10003510 Office Supplies Labels
## 68 10009 East OFF-BI-10004519 Office Supplies Binders
## 69 10009 East TEC-AC-10002167 Technology Accessories
## 70 10009 East OFF-FA-10000936 Office Supplies Fasteners
## 71 10009 East FUR-FU-10000732 Furniture Furnishings
## 72 98031 West TEC-PH-10002564 Technology Phones
## 73 98031 West OFF-PA-10001639 Office Supplies Paper
## 74 10024 East FUR-BO-10002213 Furniture Bookcases
## 75 90049 West OFF-BI-10003091 Office Supplies Binders
## 76 90049 West OFF-ST-10002974 Office Supplies Storage
## 77 90049 West OFF-AR-10000255 Office Supplies Art
## 78 90049 West TEC-AC-10001284 Technology Accessories
## 79 98115 West FUR-FU-10002759 Furniture Furnishings
## 80 50322 Central OFF-FA-10004854 Office Supplies Fasteners
## 81 95661 West FUR-FU-10001196 Furniture Furnishings
## 82 21740 East OFF-BI-10000050 Office Supplies Binders
## 83 19134 East FUR-CH-10003981 Furniture Chairs
## 84 19143 East OFF-BI-10000014 Office Supplies Binders
## 85 19143 East FUR-FU-10001475 Furniture Furnishings
## Product.Name
## 1 Avery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
## 2 Eldon Fold 'N Roll Cart System
## 3 Logitech G700s Rechargeable Gaming Mouse
## 4 Bush Heritage Pine Collection 5-Shelf Bookcase, Albany Pine Finish, *Special Order
## 5 QVS USB Car Charger 2-Port 2.1Amp for iPod/iPhone/iPad/iPad 2/iPad 3
## 6 Polycom SoundPoint Pro SE-225 Corded phone
## 7 Bevis Round Conference Table Top, X-Base
## 8 Cardinal EasyOpen D-Ring Binders
## 9 Xerox 2000
## 10 Hewlett-Packard Deskjet D4360 Printer
## 11 Avery 485
## 12 Acme Office Executive Series Stainless Steel Trimmers
## 13 Memorex 25GB 6X Branded Blu-Ray Recordable Disc, 30/Pack
## 14 Eldon Advantage Chair Mats for Low to Medium Pile Carpets
## 15 Staple envelope
## 16 Belkin Premiere Surge Master II 8-outlet surge protector
## 17 Logitech Desktop MK120 Mouse and keyboard Combo
## 18 Binding Machine Supplies
## 19 Floodlight Indoor Halogen Bulbs, 1 Bulb per Pack, 60 Watts
## 20 Deflect-o EconoMat Nonstudded, No Bevel Mat
## 21 Canon PC940 Copier
## 22 Logitech Keyboard K120
## 23 Avery 476
## 24 Zebra GX420t Direct Thermal/Thermal Transfer Printer
## 25 Pizazz Global Quick File
## 26 Aastra 57i VoIP phone
## 27 Acco Perma 4000 Stacking Storage Drawers
## 28 Personal Filing Tote with Lid, Black/Gray
## 29 Peel & Seel Envelopes
## 30 AT&T 1080 Corded phone
## 31 OtterBox Commuter Series Case - Samsung Galaxy S4
## 32 Fellowes 8 Outlet Superior Workstation Surge Protector
## 33 GBC Prestige Therm-A-Bind Covers
## 34 Boston 19500 Mighty Mite Electric Pencil Sharpener
## 35 Balt Solid Wood Rectangular Table
## 36 Fellowes PB200 Plastic Comb Binding Machine
## 37 Revere Boxed Rubber Bands by Revere
## 38 Motorla HX550 Universal Bluetooth Headset
## 39 Eldon Expressions Wood Desk Accessories, Oak
## 40 Newell 3-Hole Punched Plastic Slotted Magazine Holders for Binders
## 41 #10 Gummed Flap White Envelopes, 100/Box
## 42 Xerox 1959
## 43 Square Ring Data Binders, Rigid 75 Pt. Covers, 11" x 14-7/8"
## 44 Chromcraft Round Conference Tables
## 45 Bevis Steel Folding Chairs
## 46 Xerox 1920
## 47 I Need's 3d Hello Kitty Hybrid Silicone Case Cover for HTC One X 4g with 3d Hello Kitty Stylus Pen Green/pink
## 48 Staple envelope
## 49 Sony Micro Vault Click 16 GB USB 2.0 Flash Drive
## 50 Plantronics Encore H101 Dual Earpieces\xa0Headset
## 51 Eldon 400 Class Desk Accessories, Black Carbon
## 52 Wilson Jones Ledger-Size, Piano-Hinge Binder, 2", Blue
## 53 Ibico Laser Imprintable Binding System Covers
## 54 Boston 16765 Mini Stand Up Battery Pencil Sharpener
## 55 IBM Multi-Purpose Copy Paper, 8 1/2 x 11", Case
## 56 Microsoft Natural Ergonomic Keyboard 4000
## 57 Global Wood Trimmed Manager's Task Chair, Khaki
## 58 Recycled Easel Ring Binders
## 59 GBC Recycled VeloBinder Covers
## 60 Hon Non-Folding Utility Tables
## 61 Rogers Profile Extra Capacity Storage Tub
## 62 Fellowes 8 Outlet Superior Workstation Surge Protector
## 63 Black Avery Memo-Size 3-Ring Binder, 5 1/2" x 8 1/2"
## 64 Cardinal Slant-D Ring Binder, Heavy Gauge Vinyl
## 65 Wilson Jones Century Plastic Molded Ring Binders
## 66 GE 30524EE4
## 67 Avery 4027 File Folder Labels for Dot Matrix Printers, 5000 Labels per Box, White
## 68 GBC DocuBind P100 Manual Binding Machine
## 69 Imation\xa08gb Micro Traveldrive Usb 2.0\xa0Flash Drive
## 70 Acco Hot Clips Clips to Go
## 71 Eldon 200 Class Desk Accessories
## 72 OtterBox Defender Series Case - Samsung Galaxy S4
## 73 Xerox 203
## 74 DMI Eclipse Executive Suite Bookcases
## 75 GBC DocuBind TL200 Manual Binding Machine
## 76 Trav-L-File Heavy-Duty Shuttle II, Black
## 77 Newell 328
## 78 Enermax Briskie RF Wireless Keyboard and Mouse Combo
## 79 12-1/2 Diameter Round Wall Clock
## 80 Vinyl Coated Wire Paper Clips in Organizer Box, 800/Box
## 81 DAX Cubicle Frames - 8x10
## 82 Angle-D Binders with Locking Rings, Label Holders
## 83 Global Commerce Series Low-Back Swivel/Tilt Chairs
## 84 Heavy-Duty E-Z-D Binders
## 85 Contract Clock, 14", Brown
## Sales Quantity Discount Profit
## 1 19.536 3 0.2 4.8840
## 2 55.920 5 0.2 6.2910
## 3 199.980 2 0.0 83.9916
## 4 493.430 5 0.3 -70.4900
## 5 11.120 2 0.2 3.4750
## 6 666.344 7 0.2 66.6344
## 7 573.728 4 0.2 -64.5444
## 8 21.936 3 0.2 8.2260
## 9 19.440 3 0.0 9.3312
## 10 447.968 4 0.2 139.9900
## 11 25.060 2 0.0 11.7782
## 12 47.992 7 0.2 3.5994
## 13 102.240 4 0.2 -16.6140
## 14 43.310 1 0.0 4.3310
## 15 23.472 3 0.2 7.6284
## 16 19.432 2 0.8 -49.5516
## 17 65.440 5 0.2 -8.1800
## 18 70.008 3 0.2 24.5028
## 19 77.600 4 0.0 38.0240
## 20 464.850 9 0.0 92.9700
## 21 1439.968 4 0.2 485.9892
## 22 43.560 3 0.2 -4.9005
## 23 8.260 2 0.0 3.7996
## 24 2973.320 7 0.2 334.4985
## 25 104.790 7 0.0 29.3412
## 26 775.728 6 0.2 58.1796
## 27 16.240 1 0.0 2.4360
## 28 77.550 5 0.0 21.7140
## 29 6.208 2 0.2 2.1728
## 30 438.368 4 0.2 38.3572
## 31 139.944 7 0.2 -31.4874
## 32 133.472 4 0.2 15.0156
## 33 68.620 2 0.0 32.2514
## 34 60.450 3 0.0 16.3215
## 35 253.176 3 0.2 -31.6470
## 36 152.991 3 0.7 -122.3928
## 37 10.584 7 0.2 -2.3814
## 38 94.920 4 0.4 15.8200
## 39 14.760 5 0.6 -11.4390
## 40 3.656 4 0.8 -5.8496
## 41 16.520 4 0.0 7.5992
## 42 60.120 9 0.0 28.8576
## 43 49.536 3 0.2 17.3376
## 44 244.006 2 0.3 -31.3722
## 45 383.800 5 0.2 38.3800
## 46 17.940 3 0.0 8.0730
## 47 19.136 2 0.2 1.9136
## 48 16.560 2 0.0 7.7832
## 49 279.950 5 0.0 67.1880
## 50 224.750 5 0.0 62.9300
## 51 14.000 4 0.6 -6.3000
## 52 16.392 2 0.8 -26.2272
## 53 209.600 5 0.2 68.1200
## 54 23.320 2 0.0 6.0632
## 55 30.980 1 0.0 13.9410
## 56 119.960 4 0.0 25.1916
## 57 363.920 5 0.2 -31.8430
## 58 35.808 3 0.2 11.1900
## 59 122.688 9 0.2 39.8736
## 60 892.136 7 0.2 111.5170
## 61 50.220 3 0.0 2.0088
## 62 83.420 2 0.0 24.1918
## 63 5.872 2 0.2 2.1286
## 64 13.904 2 0.2 4.5188
## 65 68.541 11 0.7 -52.5481
## 66 627.168 4 0.2 70.5564
## 67 122.120 5 0.2 39.6890
## 68 663.920 5 0.2 207.4750
## 69 120.000 8 0.0 13.2000
## 70 3.290 1 0.0 1.4805
## 71 18.840 3 0.0 6.0288
## 72 71.976 3 0.2 8.9970
## 73 19.440 3 0.0 9.3312
## 74 400.784 1 0.2 -5.0098
## 75 895.920 5 0.2 302.3730
## 76 130.710 3 0.0 39.2130
## 77 11.680 2 0.0 3.0368
## 78 62.310 3 0.0 22.4316
## 79 199.800 10 0.0 71.9280
## 80 45.920 4 0.0 21.5824
## 81 17.310 3 0.0 5.1930
## 82 43.800 6 0.0 20.5860
## 83 1079.316 6 0.3 -15.4188
## 84 3.273 1 0.7 -2.5093
## 85 87.920 5 0.2 15.3860
# Filtro por coincidencia
2.2.9 interval()
# Calcular las ventas del Guadalupe-Reyes
df %>%
filter(Order.Date %within% interval(ymd("2016/12/12"), ymd("2017/01/06"))) %>%
head(.)
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 4035 US-2016-108777 2016-12-12 2016-12-12 Same Day JM-15655
## 2 4036 US-2016-108777 2016-12-12 2016-12-12 Same Day JM-15655
## 3 4574 CA-2016-139395 2016-12-12 2016-12-18 Standard Class MG-17650
## 4 4575 CA-2016-139395 2016-12-12 2016-12-18 Standard Class MG-17650
## 5 4576 CA-2016-139395 2016-12-12 2016-12-18 Standard Class MG-17650
## 6 4577 CA-2016-139395 2016-12-12 2016-12-18 Standard Class MG-17650
## Customer.Name Segment Country City State Postal.Code
## 1 Jim Mitchum Corporate United States Lorain Ohio 44052
## 2 Jim Mitchum Corporate United States Lorain Ohio 44052
## 3 Matthew Grinstein Home Office United States Jackson Michigan 49201
## 4 Matthew Grinstein Home Office United States Jackson Michigan 49201
## 5 Matthew Grinstein Home Office United States Jackson Michigan 49201
## 6 Matthew Grinstein Home Office United States Jackson Michigan 49201
## Region Product.ID Category Sub.Category
## 1 East OFF-BI-10003982 Office Supplies Binders
## 2 East TEC-AC-10002567 Technology Accessories
## 3 Central TEC-PH-10002103 Technology Phones
## 4 Central FUR-FU-10003724 Furniture Furnishings
## 5 Central OFF-AR-10003732 Office Supplies Art
## 6 Central OFF-ST-10000885 Office Supplies Storage
## Product.Name Sales Quantity Discount
## 1 Wilson Jones Century Plastic Molded Ring Binders 18.693 3 0.7
## 2 Logitech G602 Wireless Gaming Mouse 383.952 6 0.2
## 3 Jabra SPEAK 410 657.930 7 0.0
## 4 Westinghouse Clip-On Gooseneck Lamps 33.480 4 0.0
## 5 Newell 333 13.900 5 0.0
## 6 Fellowes Desktop Hanging File Manager 26.860 2 0.0
## Profit
## 1 -14.3313
## 2 76.7904
## 3 184.2204
## 4 8.7048
## 5 3.6140
## 6 6.7150
2.2.10 is.na
sapply(df, function(x) sum(is.na(x)))
## Row.ID Order.ID Order.Date Ship.Date Ship.Mode
## 0 0 0 0 0
## Customer.ID Customer.Name Segment Country City
## 0 0 0 0 0
## State Postal.Code Region Product.ID Category
## 0 0 0 0 0
## Sub.Category Product.Name Sales Quantity Discount
## 0 0 0 0 0
## Profit
## 0
2.2.11 summarise
# Presenta una tabla de resumen
df %>%
group_by(day(Order.Date)) %>%
summarise(Q.Sales = sum(Quantity), # Suma de cantidades vendidas
Pct.Sales = sum(Quantity)/n(), # Porcentaje de ventas
Op.Sales = n()) # Conteo de ventas
## # A tibble: 31 × 4
## `day(Order.Date)` Q.Sales Pct.Sales Op.Sales
## <int> <int> <dbl> <int>
## 1 1 1329 3.94 337
## 2 2 1486 3.92 379
## 3 3 1355 3.71 365
## 4 4 1068 3.47 308
## 5 5 1386 3.79 366
## 6 6 1071 3.76 285
## 7 7 1102 3.70 298
## 8 8 1372 3.85 356
## 9 9 1272 3.70 344
## 10 10 1117 3.72 300
## # ℹ 21 more rows
2.2.12 weekday
df %>%
mutate(weekday = wday(Order.Date, label = TRUE)) %>% # Extrae el día de la semana y lo convierte a un factor con etiquetas
group_by(weekday) %>% # Agrupa por día de la semana
summarise(Q.Sales = sum(Quantity), # Suma total de cantidad vendida
Pct.Sales = sum(Quantity)/n(), #Porcentaje de ventas
Op.Sales = n()) # Número de operaciones/ventas
## # A tibble: 7 × 4
## weekday Q.Sales Pct.Sales Op.Sales
## <ord> <int> <dbl> <int>
## 1 "dom\\." 6446 3.77 1710
## 2 "lun\\." 7098 3.79 1871
## 3 "mar\\." 4234 3.83 1106
## 4 "mié\\." 1454 3.92 371
## 5 "jue\\." 5397 3.69 1463
## 6 "vie\\." 6895 3.79 1818
## 7 "sáb\\." 6349 3.84 1655
Por día del mes
df %>%
select(Order.Date, Quantity, Sales) %>%
mutate(Day.Order = day(Order.Date)) %>%
group_by(Day.Order) %>%
summarise(Tot.Sales = sum(Sales),
Avg.Sales = mean(Sales),
Tot.Quantity = sum(Quantity),
Avg.Quantity = mean(Quantity),
Orders = n()) %>%
mutate(Pct.Quantity = Tot.Quantity*100/sum(Tot.Quantity))
## # A tibble: 31 × 7
## Day.Order Tot.Sales Avg.Sales Tot.Quantity Avg.Quantity Orders Pct.Quantity
## <int> <dbl> <dbl> <int> <dbl> <int> <dbl>
## 1 1 95525. 283. 1329 3.94 337 3.51
## 2 2 105139. 277. 1486 3.92 379 3.92
## 3 3 72320. 198. 1355 3.71 365 3.58
## 4 4 68162. 221. 1068 3.47 308 2.82
## 5 5 64521. 176. 1386 3.79 366 3.66
## 6 6 53307. 187. 1071 3.76 285 2.83
## 7 7 62611. 210. 1102 3.70 298 2.91
## 8 8 101578. 285. 1372 3.85 356 3.62
## 9 9 66582. 194. 1272 3.70 344 3.36
## 10 10 62994. 210. 1117 3.72 300 2.95
## # ℹ 21 more rows
Por semana del año
df %>%
select(Order.Date, Sales, Quantity) %>%
mutate(Week.Order = week(Order.Date)) %>%
group_by(Week.Order) %>%
summarise(Tot.Sales = sum(Sales),
Avg.Sales = mean(Sales),
Tot.Quantity = sum(Quantity),
Avg.Quantity = mean(Quantity),
Orders = n()) %>%
mutate(Pct.Quantity = Tot.Quantity*100/sum(Tot.Quantity))
## # A tibble: 53 × 7
## Week.Order Tot.Sales Avg.Sales Tot.Quantity Avg.Quantity Orders Pct.Quantity
## <dbl> <dbl> <dbl> <int> <dbl> <int> <dbl>
## 1 1 22081. 263. 339 4.04 84 0.895
## 2 2 17603. 229. 304 3.95 77 0.803
## 3 3 20955. 230. 338 3.71 91 0.892
## 4 4 22740. 292. 321 4.12 78 0.848
## 5 5 25041. 264. 324 3.41 95 0.855
## 6 6 17177. 204. 296 3.52 84 0.782
## 7 7 13319. 162. 283 3.45 82 0.747
## 8 8 10772. 163. 234 3.55 66 0.618
## 9 9 28880. 280. 402 3.90 103 1.06
## 10 10 30373. 212. 494 3.45 143 1.30
## # ℹ 43 more rows
Por mes
df%>%
select(Order.Date, Sales, Quantity) %>%
mutate(Month.Order = month(Order.Date, label=TRUE)) %>%
group_by(Month.Order) %>%
summarise(Tot.Sales = sum(Sales),
Avg.Sales = mean(Sales),
Tot.Quantity = sum(Quantity),
Avg.Quantity = mean(Quantity),
Orders = n()) %>%
mutate(Pct.Quantity = Tot.Quantity*100/sum(Tot.Quantity))
## # A tibble: 12 × 7
## Month.Order Tot.Sales Avg.Sales Tot.Quantity Avg.Quantity Orders Pct.Quantity
## <ord> <dbl> <dbl> <int> <dbl> <int> <dbl>
## 1 ene 94925. 249. 1475 3.87 381 3.89
## 2 feb 59751. 199. 1067 3.56 300 2.82
## 3 mar 205005. 295. 2564 3.68 696 6.77
## 4 abr 137762. 206. 2447 3.66 668 6.46
## 5 may 155029. 211. 2791 3.80 735 7.37
## 6 jun 152719. 213. 2680 3.74 717 7.08
## 7 jul 147238. 207. 2705 3.81 710 7.14
## 8 ago 159044. 225. 2784 3.94 706 7.35
## 9 sep 307650. 222. 5062 3.66 1383 13.4
## 10 oct 200323. 245. 3104 3.79 819 8.20
## 11 nov 352461. 240. 5775 3.93 1471 15.2
## 12 dic 325294. 231. 5419 3.85 1408 14.3
Por año
df%>%
select(Order.Date, Sales, Quantity) %>%
mutate(Year.Order = year(Order.Date)) %>%
group_by(Year.Order) %>%
summarise(Tot.Sales = sum(Sales),
Avg.Sales = mean(Sales),
Tot.Quantity = sum(Quantity),
Avg.Quantity = mean(Quantity),
Orders = n()) %>%
mutate(Pct.Quantity = Tot.Quantity*100/sum(Tot.Quantity)) %>%
arrange(Year.Order) %>%
mutate(Grate.Orders = (Orders - lag(Orders))*100/lag(Orders))
## # A tibble: 4 × 8
## Year.Order Tot.Sales Avg.Sales Tot.Quantity Avg.Quantity Orders Pct.Quantity
## <dbl> <dbl> <dbl> <int> <dbl> <int> <dbl>
## 1 2014 484247. 243. 7581 3.80 1993 20.0
## 2 2015 470533. 224. 7979 3.80 2102 21.1
## 3 2016 609206. 235. 9837 3.80 2587 26.0
## 4 2017 733215. 221. 12476 3.77 3312 32.9
## # ℹ 1 more variable: Grate.Orders <dbl>