A Quick Review

---

# Prerequisites

### Packages

```r
library(dplyr) # or library(tidyverse)
```

]

### Data

```r
transactions <- data.table::fread("data/transactions.csv", data.table = FALSE) %>% as.tibble()
transactions
## # A tibble: 1,857,567 x 9
## basket_num hshd_num purchase_ product_num spend units store_r week_num
## <int> <int> <chr> <int> <dbl> <int> <chr> <int>
## 1 100369 3708 09-DEC-17 93466 3.18 2 SOUTH 101
## 2 891779 719 20-SEP-17 85201 3.49 1 CENTRAL 90
## 3 609562 4995 07-MAR-17 2507006 0.89 1 CENTRAL 62
## 4 760220 44 19-JUN-17 4819172 8.99 1 SOUTH 77
## 5 869525 3937 04-SEP-17 1055355 1 1 SOUTH 88
## 6 922989 2356 13-OCT-17 4285485 2.87 1 WEST 93
## 7 732544 694 31-MAY-17 8511 0.64 1 CENTRAL 74
## 8 536185 3728 14-JAN-17 85939 1 1 SOUTH 54
## 9 967914 887 15-NOV-17 3775301 4.69 1 CENTRAL 98
## 10 102995 574 27-DEC-17 72765 1.29 1 EAST 104
## # ... with 1,857,557 more rows, and 1 more variable: year <int>
```

]

---

# Leverage the cheat sheet

---

# Question 1

### Challenge

How much total spend did household 3937 have in week 54?

```r
transactions %>%
  filter(_____, _____) %>%
  summarize(_____)
```

]

### Solution

```r
transactions %>%
 filter(hshd_num == 3937, week_num == 54) %>%
 summarize(spend = sum(spend, na.rm = TRUE))
## # A tibble: 1 x 1
## spend
## <dbl>
## 1 86.2
```

]

---

# Question 2

### Challenge

Compute the total spend for baskets that household 3937 had in week 54.

```r
transactions %>%
  filter(_____, _____) %>%
  group_by(_____) %>%
  summarize(_____)
```

]

### Solution

```r
transactions %>%
 filter(hshd_num == 3937, week_num == 54) %>%
 group_by(basket_num) %>%
 summarize(spend = sum(spend, na.rm = TRUE))
## # A tibble: 6 x 2
## basket_num spend
## <int> <dbl>
## 1 529106 5.59
## 2 530413 3.99
## 3 530495 10.3 
## 4 530607 17.8 
## 5 533329 29.8 
## 6 536201 18.8
```

]

---

# Question 3

### Challenge

Compute the total units per basket for the central region.  Which basket contained the most units?

```r
transactions %>%
  filter(_____) %>%
  group_by(_____) %>%
  summarize(_____) %>%
  arrange(_____)
```

]

### Solution

```r
transactions %>%
 filter(store_r == "CENTRAL") %>%
 group_by(basket_num) %>%
 summarize(units = sum(units, na.rm = TRUE)) %>%
 arrange(desc(units))
## # A tibble: 90,410 x 2
## basket_num units
## <int> <int>
## 1 100268 96
## 2 101497 96
## 3 577720 89
## 4 100466 84
## 5 101895 84
## 6 101994 84
## 7 102184 84
## 8 101476 83
## 9 100292 79
## 10 705629 79
## # ... with 90,400 more rows
```

]

---

# Question 4

### Challenge

In the east region, compute each household's spend-to-units ratio for each week.  Which household has the largest spend-to-units ratio?

```r
transactions %>%
  filter(_____) %>%
  group_by(_____, _____) %>%
  summarize(
    _____,
    _____
    ) %>%
  mutate(_____) %>%
  arrange(_____)
```

]

### Solution

```r
transactions %>%
 filter(store_r == "EAST") %>%
 group_by(hshd_num, week_num) %>%
 summarize(
 spend = sum(spend, na.rm = TRUE),
 units = sum(units, na.rm = TRUE)
 ) %>%
 mutate(ratio = spend / units) %>%
 arrange(desc(ratio))
## # A tibble: 45,865 x 5
## # Groups: hshd_num [1,251]
## hshd_num week_num spend units ratio
## <int> <int> <dbl> <int> <dbl>
## 1 1324 74 40.3 1 40.3
## 2 1492 70 40.0 1 40.0
## 3 144 80 37.0 1 37.0
## 4 144 104 37.0 1 37.0
## 5 4912 77 37.0 1 37.0
## 6 4397 67 35.0 1 35.0
## 7 4039 88 34.0 1 34.0
## 8 4429 100 97.6 3 32.5
## 9 14 93 30.0 1 30.0
## 10 1050 58 59.9 2 29.9
## # ... with 45,855 more rows
```

]

---

# Question 5

### Challenge

Compute total spend by basket for each purchase date.  Can you find the date that has the largest average (mean) total spend?

```r
transactions %>% 
  ______ %>%
  ______ %>%
  ______ %>%
  ______ %>%
```

]

### Solution

```r
transactions %>%
 group_by(purchase_, basket_num) %>%
 summarize(spend = sum(spend, na.rm = TRUE)) %>%
 summarize(avg_spend = mean(spend)) %>%
 arrange(desc(avg_spend))
## # A tibble: 363 x 2
## purchase_ avg_spend
## <chr> <dbl>
## 1 23-DEC-17 165.
## 2 17-DEC-17 164.
## 3 22-DEC-17 161.
## 4 30-DEC-17 158.
## 5 10-DEC-17 158.
## 6 16-DEC-17 146.
## 7 09-DEC-17 132.
## 8 15-DEC-17 132.
## 9 24-DEC-17 127.
## 10 29-DEC-17 127.
## # ... with 353 more rows
```

]

---

background-image: url(https://raw.githubusercontent.com/bradleyboehmke/Dayton-Weather-2018/master/Dayton_Weather.png)
background-size: cover

.font200.bold[Next up...[Visualizing Data](https://uc-r.github.io/Intro-R/day-1e-visualization.html)]