Explore Data using dplyr
0 likes310 views
The document outlines data manipulation techniques using R's dplyr and readr libraries, demonstrating methods to extract, rename, and sample data from a tibble containing website visitor metrics. It includes information about distinct traffic sources, device types, and offers examples of tabulating and counting observations related to referrers and purchases. Additionally, it provides instructions on data extraction, slicing, and conditional mutation for analysis.
![Extract Device Column
pull(ecom_mini, device)
## [1] tablet tablet laptop tablet laptop mobile laptop laptop mobile m
## Levels: laptop tablet mobile
16](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-16-320.jpg)
![Extract First Column
pull(ecom_mini, 1)
## [1] direct social direct bing bing social bing direct google s
## Levels: bing direct social yahoo google
17](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-17-320.jpg)
![Extract Last Column
pull(ecom_mini, -1)
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
18](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-18-320.jpg)
![Tabulate Referrers and Bouncers
ecom %>%
group_by(referrer, bouncers) %>%
tally()
## # A tibble: 10 x 3
## # Groups: referrer [5]
## referrer bouncers n
## <fct> <lgl> <int>
## 1 bing FALSE 104
## 2 bing TRUE 90
## 3 direct FALSE 98
## 4 direct TRUE 93
## 5 social FALSE 93
## 6 social TRUE 107
## 7 yahoo FALSE 110
## 8 yahoo TRUE 97
## 9 google FALSE 101
## 10 google TRUE 107
24](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-24-320.jpg)
![Tabulate Referrers and Purchasers
ecom %>%
group_by(referrer, purchase) %>%
tally()
## # A tibble: 10 x 3
## # Groups: referrer [5]
## referrer purchase n
## <fct> <lgl> <int>
## 1 bing FALSE 177
## 2 bing TRUE 17
## 3 direct FALSE 166
## 4 direct TRUE 25
## 5 social FALSE 180
## 6 social TRUE 20
## 7 yahoo FALSE 185
## 8 yahoo TRUE 22
## 9 google FALSE 189
## 10 google TRUE 19
25](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-25-320.jpg)
![Tabulate Referrers & Converts
ecom %>%
group_by(referrer, purchase) %>%
tally() %>%
filter(purchase)
## # A tibble: 5 x 3
## # Groups: referrer [5]
## referrer purchase n
## <fct> <lgl> <int>
## 1 bing TRUE 17
## 2 direct TRUE 25
## 3 social TRUE 20
## 4 yahoo TRUE 22
## 5 google TRUE 19
26](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-26-320.jpg)
![Between
ecom_sample <- sample_n(ecom, 30)
ecom_sample %>%
pull(n_pages) %>%
between(5, 15)
## [1] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRU
## [12] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALS
## [23] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
30](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-30-320.jpg)
![Select First Observation
ecom %>%
pull(referrer) %>%
nth(1)
## [1] google
## Levels: bing direct social yahoo google
ecom %>%
pull(referrer) %>%
first()
## [1] google
## Levels: bing direct social yahoo google
32](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-32-320.jpg)
![Select 1000th Observation
ecom %>%
pull(referrer) %>%
nth(1000)
## [1] google
## Levels: bing direct social yahoo google
33](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-33-320.jpg)
![Select Last Observation
ecom %>%
pull(referrer) %>%
last()
## [1] google
## Levels: bing direct social yahoo google
34](https://image.slidesharecdn.com/dplyr-part-3-190312104607/85/Explore-Data-using-dplyr-34-320.jpg)
Explore Data using dplyr
- 1. 1
- 2. Agenda extract unique rows rename columns sample data extract columns slice rows arrange rows compare tables extract/mutate data using predicate functions count observations for different levels of a variable • • • • • • • • • 2
- 4. Data ## # A tibble: 1,000 x 7 ## referrer device bouncers n_visit n_pages duration purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 google laptop TRUE 10 1 693 FALSE ## 2 yahoo tablet TRUE 9 1 459 FALSE ## 3 direct laptop TRUE 0 1 996 FALSE ## 4 bing tablet FALSE 3 18 468 TRUE ## 5 yahoo mobile TRUE 9 1 955 FALSE ## 6 yahoo laptop FALSE 5 5 135 FALSE ## 7 yahoo mobile TRUE 10 1 75 FALSE ## 8 direct mobile TRUE 10 1 908 FALSE ## 9 bing mobile FALSE 3 19 209 FALSE ## 10 google mobile TRUE 6 1 208 FALSE ## # ... with 990 more rows 4
- 5. Data Dictionary referrer: referrer website/search engine device: device used to visit the website bouncers: whether a visit bounced (exited from landing page) duration: time spent on the website (in seconds) purchase: whether visitor purchased n_visit: number of visits n_pages: number of pages visited/browsed • • • • • • • 5
- 6. 6
- 7. Traffic Sources distinct(ecom, referrer) ## # A tibble: 5 x 1 ## referrer ## <fct> ## 1 google ## 2 yahoo ## 3 direct ## 4 bing ## 5 social 7
- 8. Device Types distinct(ecom, device) ## # A tibble: 3 x 1 ## device ## <fct> ## 1 laptop ## 2 tablet ## 3 mobile 8
- 9. 9
- 10. Rename Columns rename(ecom, time_on_site = duration) ## # A tibble: 1,000 x 7 ## referrer device bouncers n_visit n_pages time_on_site purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 google laptop TRUE 10 1 693 FALSE ## 2 yahoo tablet TRUE 9 1 459 FALSE ## 3 direct laptop TRUE 0 1 996 FALSE ## 4 bing tablet FALSE 3 18 468 TRUE ## 5 yahoo mobile TRUE 9 1 955 FALSE ## 6 yahoo laptop FALSE 5 5 135 FALSE ## 7 yahoo mobile TRUE 10 1 75 FALSE ## 8 direct mobile TRUE 10 1 908 FALSE ## 9 bing mobile FALSE 3 19 209 FALSE ## 10 google mobile TRUE 6 1 208 FALSE ## # ... with 990 more rows 10
- 11. 11
- 12. Sampling Data sample_n(ecom, size = 700) ## # A tibble: 700 x 7 ## referrer device bouncers n_visit n_pages duration purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 bing laptop FALSE 0 1 21 FALSE ## 2 social tablet FALSE 10 13 247 FALSE ## 3 yahoo tablet FALSE 5 8 80 FALSE ## 4 social tablet TRUE 3 1 111 FALSE ## 5 google mobile FALSE 4 20 420 FALSE ## 6 google laptop TRUE 1 1 958 FALSE ## 7 yahoo tablet TRUE 9 1 459 FALSE ## 8 yahoo mobile FALSE 5 5 150 FALSE ## 9 google mobile FALSE 5 3 66 FALSE ## 10 google tablet TRUE 6 1 551 FALSE ## # ... with 690 more rows 12
- 13. Sampling Data sample_frac(ecom, size = 0.7) ## # A tibble: 700 x 7 ## referrer device bouncers n_visit n_pages duration purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 google laptop FALSE 4 9 117 FALSE ## 2 social mobile FALSE 1 16 336 TRUE ## 3 google tablet FALSE 10 8 200 FALSE ## 4 bing tablet FALSE 5 18 216 FALSE ## 5 social tablet FALSE 9 15 405 FALSE ## 6 direct mobile TRUE 1 1 180 FALSE ## 7 social mobile FALSE 10 14 350 TRUE ## 8 social tablet TRUE 4 1 392 FALSE ## 9 bing mobile FALSE 6 18 342 TRUE ## 10 yahoo tablet TRUE 5 1 10 FALSE ## # ... with 690 more rows 13
- 14. 14
- 15. Sample Data ecom_mini <- sample_n(ecom, size = 10) 15
- 16. Extract Device Column pull(ecom_mini, device) ## [1] tablet tablet laptop tablet laptop mobile laptop laptop mobile m ## Levels: laptop tablet mobile 16
- 17. Extract First Column pull(ecom_mini, 1) ## [1] direct social direct bing bing social bing direct google s ## Levels: bing direct social yahoo google 17
- 18. Extract Last Column pull(ecom_mini, -1) ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 18
- 19. 19
- 20. Extract 10 rows starting from 15 slice(ecom, 5:14) ## # A tibble: 10 x 7 ## referrer device bouncers n_visit n_pages duration purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 yahoo mobile TRUE 9 1 955 FALSE ## 2 yahoo laptop FALSE 5 5 135 FALSE ## 3 yahoo mobile TRUE 10 1 75 FALSE ## 4 direct mobile TRUE 10 1 908 FALSE ## 5 bing mobile FALSE 3 19 209 FALSE ## 6 google mobile TRUE 6 1 208 FALSE ## 7 direct laptop TRUE 9 1 738 FALSE ## 8 direct tablet FALSE 6 12 132 FALSE ## 9 direct mobile FALSE 9 14 406 TRUE ## 10 yahoo tablet FALSE 5 8 80 FALSE 20
- 21. Extract Last Row slice(ecom, n()) ## # A tibble: 1 x 7 ## referrer device bouncers n_visit n_pages duration purchase ## <fct> <fct> <lgl> <dbl> <dbl> <dbl> <lgl> ## 1 google mobile TRUE 9 1 269 FALSE 21
- 22. 22
- 23. Tabulate Referrers ecom %>% group_by(referrer) %>% tally() ## # A tibble: 5 x 2 ## referrer n ## <fct> <int> ## 1 bing 194 ## 2 direct 191 ## 3 social 200 ## 4 yahoo 207 ## 5 google 208 23
- 24. Tabulate Referrers and Bouncers ecom %>% group_by(referrer, bouncers) %>% tally() ## # A tibble: 10 x 3 ## # Groups: referrer [5] ## referrer bouncers n ## <fct> <lgl> <int> ## 1 bing FALSE 104 ## 2 bing TRUE 90 ## 3 direct FALSE 98 ## 4 direct TRUE 93 ## 5 social FALSE 93 ## 6 social TRUE 107 ## 7 yahoo FALSE 110 ## 8 yahoo TRUE 97 ## 9 google FALSE 101 ## 10 google TRUE 107 24
- 25. Tabulate Referrers and Purchasers ecom %>% group_by(referrer, purchase) %>% tally() ## # A tibble: 10 x 3 ## # Groups: referrer [5] ## referrer purchase n ## <fct> <lgl> <int> ## 1 bing FALSE 177 ## 2 bing TRUE 17 ## 3 direct FALSE 166 ## 4 direct TRUE 25 ## 5 social FALSE 180 ## 6 social TRUE 20 ## 7 yahoo FALSE 185 ## 8 yahoo TRUE 22 ## 9 google FALSE 189 ## 10 google TRUE 19 25
- 26. Tabulate Referrers & Converts ecom %>% group_by(referrer, purchase) %>% tally() %>% filter(purchase) ## # A tibble: 5 x 3 ## # Groups: referrer [5] ## referrer purchase n ## <fct> <lgl> <int> ## 1 bing TRUE 17 ## 2 direct TRUE 25 ## 3 social TRUE 20 ## 4 yahoo TRUE 22 ## 5 google TRUE 19 26
- 27. Count count(ecom, referrer, purchase) ## # A tibble: 10 x 3 ## referrer purchase n ## <fct> <lgl> <int> ## 1 bing FALSE 177 ## 2 bing TRUE 17 ## 3 direct FALSE 166 ## 4 direct TRUE 25 ## 5 social FALSE 180 ## 6 social TRUE 20 ## 7 yahoo FALSE 185 ## 8 yahoo TRUE 22 ## 9 google FALSE 189 ## 10 google TRUE 19 27
- 28. 28
- 29. Top 2 Referrers by Orders ecom %>% count(referrer, purchase) %>% filter(purchase) %>% arrange(desc(n)) %>% top_n(n = 2) ## Selecting by n ## # A tibble: 2 x 3 ## referrer purchase n ## <fct> <lgl> <int> ## 1 direct TRUE 25 ## 2 yahoo TRUE 22 29
- 30. Between ecom_sample <- sample_n(ecom, 30) ecom_sample %>% pull(n_pages) %>% between(5, 15) ## [1] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRU ## [12] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALS ## [23] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE 30
- 31. Case When ecom %>% mutate( repeat_visit = case_when( n_visit > 0 ~ TRUE, TRUE ~ FALSE ) ) %>% select(n_visit, repeat_visit) ## # A tibble: 1,000 x 2 ## n_visit repeat_visit ## <dbl> <lgl> ## 1 10 TRUE ## 2 9 TRUE ## 3 0 FALSE ## 4 3 TRUE ## 5 9 TRUE ## 6 5 TRUE ## 7 10 TRUE ## 8 10 TRUE ## 9 3 TRUE ## 10 6 TRUE ## # ... with 990 more rows 31
- 32. Select First Observation ecom %>% pull(referrer) %>% nth(1) ## [1] google ## Levels: bing direct social yahoo google ecom %>% pull(referrer) %>% first() ## [1] google ## Levels: bing direct social yahoo google 32
- 33. Select 1000th Observation ecom %>% pull(referrer) %>% nth(1000) ## [1] google ## Levels: bing direct social yahoo google 33
- 34. Select Last Observation ecom %>% pull(referrer) %>% last() ## [1] google ## Levels: bing direct social yahoo google 34
- 35. 35