Welcome
Illya Mowerman
2/5/2018
Data Wrangling with tidyverse
library(tidyverse)
library(AER)
data('Affairs')
affairs <- Affairssummary(affairs)## affairs gender age yearsmarried children
## Min. : 0.000 female:315 Min. :17.50 Min. : 0.125 no :171
## 1st Qu.: 0.000 male :286 1st Qu.:27.00 1st Qu.: 4.000 yes:430
## Median : 0.000 Median :32.00 Median : 7.000
## Mean : 1.456 Mean :32.49 Mean : 8.178
## 3rd Qu.: 0.000 3rd Qu.:37.00 3rd Qu.:15.000
## Max. :12.000 Max. :57.00 Max. :15.000
## religiousness education occupation rating
## Min. :1.000 Min. : 9.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:14.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :16.00 Median :5.000 Median :4.000
## Mean :3.116 Mean :16.17 Mean :4.195 Mean :3.932
## 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :5.000 Max. :20.00 Max. :7.000 Max. :5.000arrange
filter
mutate
summarise
group_by
select
rename
inner_join
left_join
semi_join
anti_join
gather
spread
sorted_by_rating <- arrange(affairs , rating)
head(sorted_by_rating)## affairs gender age yearsmarried children religiousness education
## 1 0 female 27 4 yes 2 18
## 2 0 female 37 15 yes 4 14
## 3 0 male 57 15 yes 3 16
## 4 0 female 52 15 yes 5 17
## 5 0 female 37 15 yes 2 14
## 6 0 female 27 7 yes 2 12
## occupation rating
## 1 6 1
## 2 3 1
## 3 6 1
## 4 1 1
## 5 1 1
## 6 5 1sorted_by_desc_rating <- arrange(affairs , desc(rating))
head(sorted_by_rating)## affairs gender age yearsmarried children religiousness education
## 1 0 female 27 4 yes 2 18
## 2 0 female 37 15 yes 4 14
## 3 0 male 57 15 yes 3 16
## 4 0 female 52 15 yes 5 17
## 5 0 female 37 15 yes 2 14
## 6 0 female 27 7 yes 2 12
## occupation rating
## 1 6 1
## 2 3 1
## 3 6 1
## 4 1 1
## 5 1 1
## 6 5 1sorted_by_desc_rating <- affairs %>% arrange(desc(rating))
head(sorted_by_rating)## affairs gender age yearsmarried children religiousness education
## 1 0 female 27 4 yes 2 18
## 2 0 female 37 15 yes 4 14
## 3 0 male 57 15 yes 3 16
## 4 0 female 52 15 yes 5 17
## 5 0 female 37 15 yes 2 14
## 6 0 female 27 7 yes 2 12
## occupation rating
## 1 6 1
## 2 3 1
## 3 6 1
## 4 1 1
## 5 1 1
## 6 5 1sorted_by_desc_rating <- affairs %>% arrange(desc(rating))== reads equal
!= not equal
>= greater than or equal
<= less than or equal
> greater than < less than
filtered_rating_5 <- affairs %>%
filter(rating == 5)
nrow(filtered_rating_5)## [1] 232head(filtered_rating_5)## affairs gender age yearsmarried children religiousness education
## 1 0 male 57 15.0 yes 5 18
## 2 0 female 32 1.5 no 2 17
## 3 0 male 22 1.5 no 4 14
## 4 0 female 37 15.0 yes 1 17
## 5 0 female 22 1.5 no 2 16
## 6 0 female 27 10.0 yes 2 14
## occupation rating
## 1 6 5
## 2 5 5
## 3 4 5
## 4 5 5
## 5 5 5
## 6 1 5filtered_rating_5 <- affairs %>%
filter(rating == 5)
nrow(filtered_rating_5)## [1] 232head(filtered_rating_5)## affairs gender age yearsmarried children religiousness education
## 1 0 male 57 15.0 yes 5 18
## 2 0 female 32 1.5 no 2 17
## 3 0 male 22 1.5 no 4 14
## 4 0 female 37 15.0 yes 1 17
## 5 0 female 22 1.5 no 2 16
## 6 0 female 27 10.0 yes 2 14
## occupation rating
## 1 6 5
## 2 5 5
## 3 4 5
## 4 5 5
## 5 5 5
## 6 1 5filtered_rating_3 <- affairs %>%
filter(rating > 2 , rating < 4)
nrow(filtered_rating_3)## [1] 93head(filtered_rating_3)## affairs gender age yearsmarried children religiousness education
## 1 0 male 22 0.75 no 2 17
## 2 0 female 22 0.75 no 2 12
## 3 0 female 37 15.00 yes 2 18
## 4 0 male 42 15.00 yes 4 20
## 5 0 female 22 1.50 no 4 16
## 6 0 male 52 15.00 yes 5 18
## occupation rating
## 1 6 3
## 2 1 3
## 3 4 3
## 4 6 3
## 5 5 3
## 6 6 3filtered_rating_3 <- affairs %>%
filter(rating > 2 , rating < 4)cheater <- affairs %>%
mutate(cheater = ifelse(affairs > 0 , 1 , 0))
head(arrange(cheater , affairs))## affairs gender age yearsmarried children religiousness education
## 1 0 male 37 10.00 no 3 18
## 2 0 female 27 4.00 no 4 14
## 3 0 female 32 15.00 yes 1 12
## 4 0 male 57 15.00 yes 5 18
## 5 0 male 22 0.75 no 2 17
## 6 0 female 32 1.50 no 2 17
## occupation rating cheater
## 1 7 4 0
## 2 6 4 0
## 3 1 4 0
## 4 6 5 0
## 5 6 3 0
## 6 5 5 0head(arrange(cheater , desc(affairs)))## affairs gender age yearsmarried children religiousness education
## 1 12 female 32 10.0 yes 3 17
## 2 12 male 37 15.0 yes 4 14
## 3 12 female 42 15.0 yes 5 9
## 4 12 male 37 10.0 yes 2 20
## 5 12 female 32 15.0 yes 3 14
## 6 12 male 27 1.5 yes 3 17
## occupation rating cheater
## 1 5 2 1
## 2 5 2 1
## 3 4 1 1
## 4 6 2 1
## 5 1 2 1
## 6 5 4 1cheater <- affairs %>%
mutate(cheater = ifelse(affairs > 0 , 1 , 0) ,
age_when_married = age - yearsmarried)
head(arrange(cheater , affairs))## affairs gender age yearsmarried children religiousness education
## 1 0 male 37 10.00 no 3 18
## 2 0 female 27 4.00 no 4 14
## 3 0 female 32 15.00 yes 1 12
## 4 0 male 57 15.00 yes 5 18
## 5 0 male 22 0.75 no 2 17
## 6 0 female 32 1.50 no 2 17
## occupation rating cheater age_when_married
## 1 7 4 0 27.00
## 2 6 4 0 23.00
## 3 1 4 0 17.00
## 4 6 5 0 42.00
## 5 6 3 0 21.25
## 6 5 5 0 30.50head(arrange(cheater , desc(affairs)))## affairs gender age yearsmarried children religiousness education
## 1 12 female 32 10.0 yes 3 17
## 2 12 male 37 15.0 yes 4 14
## 3 12 female 42 15.0 yes 5 9
## 4 12 male 37 10.0 yes 2 20
## 5 12 female 32 15.0 yes 3 14
## 6 12 male 27 1.5 yes 3 17
## occupation rating cheater age_when_married
## 1 5 2 1 22.0
## 2 5 2 1 22.0
## 3 4 1 1 27.0
## 4 6 2 1 27.0
## 5 1 2 1 17.0
## 6 5 4 1 25.5summary(cheater$cheater)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2496 0.0000 1.0000summary(cheater$age_when_married)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.50 20.50 22.00 24.31 27.00 45.00Likely the variable age is the age when married rather than the current age.
Hope so…
cheater <- affairs %>%
mutate(cheater = ifelse(affairs > 0 , 1 , 0))Create a new object with two new variables:
1. cheater defined as having had at least 6 affairs
2. religiousness into rating (religiousness/rating)
Using the data affairs
THEN
create a new variable cheater
THEN
filtering for religiousness = 5 and cheater = 1
cheater_rel5 <- affairs %>%
mutate(cheater = ifelse(affairs > 0 , 1 , 0)) %>%
filter(religiousness == 5 ,
cheater != 0)
nrow(cheater_rel5)## [1] 13summary(cheater_rel5)## affairs gender age yearsmarried children
## Min. : 1.000 female:7 Min. :17.50 Min. : 0.75 no : 1
## 1st Qu.: 1.000 male :6 1st Qu.:27.00 1st Qu.:10.00 yes:12
## Median : 3.000 Median :37.00 Median :15.00
## Mean : 4.769 Mean :36.65 Mean :11.87
## 3rd Qu.: 7.000 3rd Qu.:47.00 3rd Qu.:15.00
## Max. :12.000 Max. :57.00 Max. :15.00
## religiousness education occupation rating cheater
## Min. :5 Min. : 9.00 Min. :1.000 Min. :1.000 Min. :1
## 1st Qu.:5 1st Qu.:14.00 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:1
## Median :5 Median :16.00 Median :5.000 Median :4.000 Median :1
## Mean :5 Mean :16.15 Mean :4.308 Mean :3.692 Mean :1
## 3rd Qu.:5 3rd Qu.:18.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:1
## Max. :5 Max. :20.00 Max. :6.000 Max. :5.000 Max. :1You can create a new object with the mean cheater using native R
mean_cheater <- mean(cheater$cheater)
mean_cheater## [1] 0.249584str(mean_cheater)## num 0.25In dplyr it would be:
mean_cheater <- cheater %>% summarise(mean_cheater = mean(cheater))
mean_cheater## mean_cheater
## 1 0.249584str(mean_cheater)## 'data.frame': 1 obs. of 1 variable:
## $ mean_cheater: num 0.25summarise_cheater <- cheater %>%
summarise(mean_cheater = mean(cheater) ,
mean_rel = mean(religiousness))
summarise_cheater## mean_cheater mean_rel
## 1 0.249584 3.116473str(summarise_cheater)## 'data.frame': 1 obs. of 2 variables:
## $ mean_cheater: num 0.25
## $ mean_rel : num 3.12summarise_cheater <- cheater %>%
summarise(mean_cheater = mean(cheater) ,
mean_rel = mean(religiousness))Create a data frame using the cheater data that calculates:
1. The sum of cheater
2. The mean of rating
Cheater proportion by religiousness
cheater_by_rel <- cheater %>%
group_by(religiousness) %>%
summarise(proportion_cheater = mean(cheater))
cheater_by_rel## # A tibble: 5 x 2
## religiousness proportion_cheater
## <int> <dbl>
## 1 1 0.417
## 2 2 0.250
## 3 3 0.333
## 4 4 0.174
## 5 5 0.186Cheater proportion by religiousness
cheater_by_rel <- cheater %>%
group_by(religiousness) %>%
mutate(proportion_cheater = mean(cheater)) %>%
select(1:3 , religiousness, cheater , proportion_cheater) %>%
ungroup()
head(cheater_by_rel)## # A tibble: 6 x 6
## affairs gender age religiousness cheater proportion_cheater
## <dbl> <fct> <dbl> <int> <dbl> <dbl>
## 1 0 male 37.0 3 0 0.333
## 2 0 female 27.0 4 0 0.174
## 3 0 female 32.0 1 0 0.417
## 4 0 male 57.0 5 0 0.186
## 5 0 male 22.0 2 0 0.250
## 6 0 female 32.0 2 0 0.250This has many uses
Check out the documentation to see the multiple ways you can select variables
?dplyr::select
cheater %>%
select(starts_with('r'))## religiousness rating
## 1 3 4
## 2 4 4
## 3 1 4
## 4 5 5
## 5 2 3
## 6 2 5
## 7 2 3
## 8 2 4
## 9 4 2
## 10 4 5
## 11 2 2
## 12 4 4
## 13 5 4
## 14 2 4
## 15 4 4
## 16 1 5
## 17 2 3
## 18 3 4
## 19 2 5
## 20 2 5
## 21 2 5
## 22 2 5
## 23 4 4
## 24 3 5
## 25 2 4
## 26 2 5
## 27 4 5
## 28 5 4
## 29 3 5
## 30 3 4
## 31 4 3
## 32 3 5
## 33 4 4
## 34 5 4
## 35 1 4
## 36 4 3
## 37 3 4
## 38 4 5
## 39 1 5
## 40 3 5
## 41 5 5
## 42 5 3
## 43 5 4
## 44 2 1
## 45 5 3
## 46 3 5
## 47 4 5
## 48 2 4
## 49 4 5
## 50 2 4
## 51 5 5
## 52 2 4
## 53 5 5
## 54 4 4
## 55 1 5
## 56 4 1
## 57 5 4
## 58 4 5
## 59 4 4
## 60 4 4
## 61 5 2
## 62 3 4
## 63 2 5
## 64 2 5
## 65 4 4
## 66 2 5
## 67 4 4
## 68 3 5
## 69 5 5
## 70 2 4
## 71 3 5
## 72 5 5
## 73 2 5
## 74 4 3
## 75 1 5
## 76 4 5
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## 80 2 2
## 81 3 5
## 82 4 5
## 83 2 4
## 84 4 3
## 85 4 5
## 86 1 4
## 87 4 5
## 88 3 2
## 89 3 5
## 90 3 4
## 91 4 4
## 92 2 3
## 93 4 4
## 94 5 3
## 95 1 4
## 96 5 5
## 97 3 1
## 98 1 4
## 99 3 5
## 100 4 5
## 101 5 5
## 102 2 4
## 103 4 5
## 104 5 5
## 105 4 4
## 106 4 4
## 107 4 4
## 108 4 4
## 109 3 3
## 110 2 4
## 111 4 5
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## 114 5 4
## 115 3 4
## 116 4 5
## 117 4 2
## 118 5 4
## 119 4 5
## 120 4 4
## 121 2 5
## 122 5 4
## 123 4 3
## 124 2 2
## 125 3 5
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## 150 1 4
## 151 4 3
## 152 2 3
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## 157 5 1
## 158 4 2
## 159 4 3
## 160 2 5
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## 171 4 5
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## 385 2 2
## 386 4 5
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## 406 4 5
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## 411 2 3
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## 413 4 3
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## 415 3 5
## 416 2 3
## 417 4 5
## 418 4 3
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## 422 2 4
## 423 2 1
## 424 5 4
## 425 2 4
## 426 4 5
## 427 1 5
## 428 4 4
## 429 5 3
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## 431 2 4
## 432 3 5
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## 435 2 2
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## 439 4 3
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## 464 5 1
## 465 2 2
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## 468 2 3
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## 475 4 2
## 476 4 4
## 477 4 3
## 478 2 2
## 479 4 4
## 480 3 3
## 481 4 4
## 482 2 2
## 483 5 5
## 484 4 5
## 485 2 4
## 486 2 4
## 487 1 3
## 488 2 5
## 489 2 4
## 490 1 3
## 491 2 3
## 492 3 5
## 493 3 4
## 494 1 2
## 495 3 5
## 496 4 5
## 497 4 1
## 498 3 4
## 499 4 5
## 500 3 4
## 501 5 4
## 502 2 4
## 503 2 4
## 504 2 5
## 505 5 3
## 506 3 3
## 507 4 4
## 508 3 4
## 509 2 1
## 510 1 2
## 511 3 4
## 512 4 4
## 513 1 4
## 514 3 2
## 515 1 4
## 516 4 4
## 517 1 4
## 518 4 2
## 519 1 3
## 520 3 1
## 521 3 4
## 522 3 3
## 523 2 2
## 524 4 5
## 525 3 4
## 526 1 5
## 527 3 3
## 528 2 5
## 529 3 3
## 530 2 4
## 531 4 2
## 532 3 4
## 533 1 3
## 534 2 3
## 535 3 3
## 536 5 5
## 537 2 2
## 538 1 3
## 539 4 2
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## 541 2 3
## 542 3 4
## 543 4 3
## 544 4 5
## 545 2 2
## 546 5 3
## 547 3 1
## 548 2 2
## 549 3 4
## 550 2 2
## 551 3 1
## 552 5 5
## 553 4 4
## 554 2 3
## 555 3 3
## 556 5 2
## 557 2 4
## 558 2 4
## 559 1 2
## 560 3 3
## 561 4 1
## 562 2 5
## 563 2 2
## 564 2 4
## 565 2 5
## 566 4 5
## 567 3 3
## 568 4 5
## 569 1 5
## 570 2 4
## 571 1 1
## 572 2 5
## 573 5 4
## 574 1 5
## 575 4 4
## 576 3 2
## 577 2 4
## 578 4 4
## 579 3 2
## 580 3 3
## 581 1 4
## 582 4 3
## 583 3 4
## 584 2 3
## 585 3 2
## 586 5 5
## 587 1 5
## 588 1 5
## 589 4 4
## 590 3 2
## 591 4 2
## 592 3 2
## 593 2 2
## 594 2 4
## 595 4 3
## 596 3 2
## 597 1 5
## 598 2 4
## 599 2 5
## 600 3 2
## 601 3 5Renaming religiousness to rel
cheater_renamed <- cheater %>%
rename(rel = religiousness)
summary(cheater_renamed)## affairs gender age yearsmarried children
## Min. : 0.000 female:315 Min. :17.50 Min. : 0.125 no :171
## 1st Qu.: 0.000 male :286 1st Qu.:27.00 1st Qu.: 4.000 yes:430
## Median : 0.000 Median :32.00 Median : 7.000
## Mean : 1.456 Mean :32.49 Mean : 8.178
## 3rd Qu.: 0.000 3rd Qu.:37.00 3rd Qu.:15.000
## Max. :12.000 Max. :57.00 Max. :15.000
## rel education occupation rating
## Min. :1.000 Min. : 9.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:14.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :16.00 Median :5.000 Median :4.000
## Mean :3.116 Mean :16.17 Mean :4.195 Mean :3.932
## 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :5.000 Max. :20.00 Max. :7.000 Max. :5.000
## cheater
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.2496
## 3rd Qu.:0.0000
## Max. :1.0000band_members## # A tibble: 3 x 2
## name band
## <chr> <chr>
## 1 Mick Stones
## 2 John Beatles
## 3 Paul Beatlesband_instruments## # A tibble: 3 x 2
## name plays
## <chr> <chr>
## 1 John guitar
## 2 Paul bass
## 3 Keith guitarinner <- inner_join(band_members , band_instruments)## Joining, by = "name"inner## # A tibble: 2 x 3
## name band plays
## <chr> <chr> <chr>
## 1 John Beatles guitar
## 2 Paul Beatles bassleft <- left_join(band_members , band_instruments)## Joining, by = "name"left## # A tibble: 3 x 3
## name band plays
## <chr> <chr> <chr>
## 1 Mick Stones <NA>
## 2 John Beatles guitar
## 3 Paul Beatles basssemi <- semi_join(band_members , band_instruments)## Joining, by = "name"semi## # A tibble: 2 x 2
## name band
## <chr> <chr>
## 1 John Beatles
## 2 Paul Beatlesanti <- anti_join(band_members , band_instruments)## Joining, by = "name"anti## # A tibble: 1 x 2
## name band
## <chr> <chr>
## 1 Mick Stonessummarise_cheater <- cheater %>%
group_by(children) %>%
summarise(mean_cheater = mean(cheater) ,
mean_rel = mean(religiousness))
summarise_cheater## # A tibble: 2 x 3
## children mean_cheater mean_rel
## <fct> <dbl> <dbl>
## 1 no 0.158 2.88
## 2 yes 0.286 3.21gathered <- summarise_cheater %>%
gather(metric , value , -children)
gathered## # A tibble: 4 x 3
## children metric value
## <fct> <chr> <dbl>
## 1 no mean_cheater 0.158
## 2 yes mean_cheater 0.286
## 3 no mean_rel 2.88
## 4 yes mean_rel 3.21gathered## # A tibble: 4 x 3
## children metric value
## <fct> <chr> <dbl>
## 1 no mean_cheater 0.158
## 2 yes mean_cheater 0.286
## 3 no mean_rel 2.88
## 4 yes mean_rel 3.21spreading <- gathered %>%
spread(metric , value)
spreading## # A tibble: 2 x 3
## children mean_cheater mean_rel
## * <fct> <dbl> <dbl>
## 1 no 0.158 2.88
## 2 yes 0.286 3.21