Data Wrangling with tidyverse
1/29/2024
What we’ll cover
Please load some pakages
library(tidyverse)
library(AER)
data('Affairs')
affairs <- Affairs
summary(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. :5.000
Basic wrangling verbs for today
arrange
filter
mutate
summarise
group_by
select
rename
Sorting by rating and inserting into a new object
sorted_by_rating <- arrange(affairs , rating) head(sorted_by_rating)
## affairs gender age yearsmarried children religiousness education ## 224 0 female 27 4 yes 2 18 ## 277 0 female 37 15 yes 4 14 ## 491 0 male 57 15 yes 3 16 ## 751 0 female 52 15 yes 5 17 ## 1160 0 female 37 15 yes 2 14 ## 1207 0 female 27 7 yes 2 12 ## occupation rating ## 224 6 1 ## 277 3 1 ## 491 6 1 ## 751 1 1 ## 1160 1 1 ## 1207 5 1
Sorting by rating in descending order and inserting into a new object
sorted_by_desc_rating <- arrange(affairs , desc(rating)) head(sorted_by_rating)
## affairs gender age yearsmarried children religiousness education ## 224 0 female 27 4 yes 2 18 ## 277 0 female 37 15 yes 4 14 ## 491 0 male 57 15 yes 3 16 ## 751 0 female 52 15 yes 5 17 ## 1160 0 female 37 15 yes 2 14 ## 1207 0 female 27 7 yes 2 12 ## occupation rating ## 224 6 1 ## 277 3 1 ## 491 6 1 ## 751 1 1 ## 1160 1 1 ## 1207 5 1
Sorting by rating in descending order and introducing the word then %>%
sorted_by_desc_rating <- affairs %>% arrange(desc(rating)) head(sorted_by_rating)
## affairs gender age yearsmarried children religiousness education ## 224 0 female 27 4 yes 2 18 ## 277 0 female 37 15 yes 4 14 ## 491 0 male 57 15 yes 3 16 ## 751 0 female 52 15 yes 5 17 ## 1160 0 female 37 15 yes 2 14 ## 1207 0 female 27 7 yes 2 12 ## occupation rating ## 224 6 1 ## 277 3 1 ## 491 6 1 ## 751 1 1 ## 1160 1 1 ## 1207 5 1
Time for quick exercises using the affairs data
sorted_by_desc_rating <- affairs %>% arrange(desc(rating))
- Create a new object sorted by affairs in descending order
- Create a new object with affairs in descending order and religiousness
Filtering data - condition operators
== reads equal
!= not equal
>= greater than or equal
<= less than or equal
> greater than < less than
Filtering where rating = 5
filtered_rating_5 <- affairs %>% filter(rating == 5) nrow(filtered_rating_5)
## [1] 232
head(filtered_rating_5)
## affairs gender age yearsmarried children religiousness education occupation ## 16 0 male 57 15.0 yes 5 18 6 ## 29 0 female 32 1.5 no 2 17 5 ## 49 0 male 22 1.5 no 4 14 4 ## 93 0 female 37 15.0 yes 1 17 5 ## 115 0 female 22 1.5 no 2 16 5 ## 116 0 female 27 10.0 yes 2 14 1 ## rating ## 16 5 ## 29 5 ## 49 5 ## 93 5 ## 115 5 ## 116 5
Filtering where rating = 5
filtered_rating_5 <- affairs %>% filter(rating == 5) nrow(filtered_rating_5)
## [1] 232
head(filtered_rating_5)
## affairs gender age yearsmarried children religiousness education occupation ## 16 0 male 57 15.0 yes 5 18 6 ## 29 0 female 32 1.5 no 2 17 5 ## 49 0 male 22 1.5 no 4 14 4 ## 93 0 female 37 15.0 yes 1 17 5 ## 115 0 female 22 1.5 no 2 16 5 ## 116 0 female 27 10.0 yes 2 14 1 ## rating ## 16 5 ## 29 5 ## 49 5 ## 93 5 ## 115 5 ## 116 5
Filtering where rating = 3 with between
filtered_rating_3 <- affairs %>% filter(rating > 2 , rating < 4) nrow(filtered_rating_3)
## [1] 93
head(filtered_rating_3)
## affairs gender age yearsmarried children religiousness education occupation ## 23 0 male 22 0.75 no 2 17 6 ## 44 0 female 22 0.75 no 2 12 1 ## 108 0 female 37 15.00 yes 2 18 4 ## 162 0 male 42 15.00 yes 4 20 6 ## 172 0 female 22 1.50 no 4 16 5 ## 217 0 male 52 15.00 yes 5 18 6 ## rating ## 23 3 ## 44 3 ## 108 3 ## 162 3 ## 172 3 ## 217 3
Filtering exercise using the affairs data
filtered_rating_3 <- affairs %>% filter(rating > 2 , rating < 4)
- Create a new object with the data filtered where children = ‘Yes’
- Create a new object with the data filtered where
- affairs > 0
- religiousness == 5
- rating == 5
Creating a new variable: how do you define a cheater?
cheater <- affairs %>% mutate(cheater = ifelse(affairs > 0 , 1 , 0)) head(arrange(cheater , affairs))
## affairs gender age yearsmarried children religiousness education occupation ## 4 0 male 37 10.00 no 3 18 7 ## 5 0 female 27 4.00 no 4 14 6 ## 11 0 female 32 15.00 yes 1 12 1 ## 16 0 male 57 15.00 yes 5 18 6 ## 23 0 male 22 0.75 no 2 17 6 ## 29 0 female 32 1.50 no 2 17 5 ## rating cheater ## 4 4 0 ## 5 4 0 ## 11 4 0 ## 16 5 0 ## 23 3 0 ## 29 5 0
head(arrange(cheater , desc(affairs)))
## affairs gender age yearsmarried children religiousness education occupation ## 53 12 female 32 10.0 yes 3 17 5 ## 122 12 male 37 15.0 yes 4 14 5 ## 174 12 female 42 15.0 yes 5 9 4 ## 176 12 male 37 10.0 yes 2 20 6 ## 181 12 female 32 15.0 yes 3 14 1 ## 252 12 male 27 1.5 yes 3 17 5 ## rating cheater ## 53 2 1 ## 122 2 1 ## 174 1 1 ## 176 2 1 ## 181 2 1 ## 252 4 1
Creating multiple new variables
cheater <- affairs %>%
mutate(cheater = ifelse(affairs > 0 , 1 , 0) ,
age_when_married = age - yearsmarried)
head(arrange(cheater , affairs))
## affairs gender age yearsmarried children religiousness education occupation ## 4 0 male 37 10.00 no 3 18 7 ## 5 0 female 27 4.00 no 4 14 6 ## 11 0 female 32 15.00 yes 1 12 1 ## 16 0 male 57 15.00 yes 5 18 6 ## 23 0 male 22 0.75 no 2 17 6 ## 29 0 female 32 1.50 no 2 17 5 ## rating cheater age_when_married ## 4 4 0 27.00 ## 5 4 0 23.00 ## 11 4 0 17.00 ## 16 5 0 42.00 ## 23 3 0 21.25 ## 29 5 0 30.50
head(arrange(cheater , desc(affairs)))
## affairs gender age yearsmarried children religiousness education occupation ## 53 12 female 32 10.0 yes 3 17 5 ## 122 12 male 37 15.0 yes 4 14 5 ## 174 12 female 42 15.0 yes 5 9 4 ## 176 12 male 37 10.0 yes 2 20 6 ## 181 12 female 32 15.0 yes 3 14 1 ## 252 12 male 27 1.5 yes 3 17 5 ## rating cheater age_when_married ## 53 2 1 22.0 ## 122 2 1 22.0 ## 174 1 1 27.0 ## 176 2 1 27.0 ## 181 2 1 17.0 ## 252 4 1 25.5
summary(cheater$cheater)
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.0000 0.0000 0.0000 0.2496 0.0000 1.0000
By the way, I think the age when married is not correct
summary(cheater$age_when_married)
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 7.50 20.50 22.00 24.31 27.00 45.00
Likely the variable age is the age when married rather than the current age.
Hope so…
Exercises using the affairs data
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)
Let’s do multiple verbs, taking advantage of the word %>%
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] 13
summary(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. :1
Summarizing data
You can create a new object with the mean cheater using native R
mean_cheater <- mean(cheater$cheater) mean_cheater
## [1] 0.249584
str(mean_cheater)
## num 0.25
In dplyr it would be:
mean_cheater <- cheater %>% summarise(mean_cheater = mean(cheater)) mean_cheater
## mean_cheater ## 1 0.249584
str(mean_cheater)
## 'data.frame': 1 obs. of 1 variable: ## $ mean_cheater: num 0.25
We can summarise multiple variables in one statement
summarise_cheater <- cheater %>%
summarise(mean_cheater = mean(cheater) ,
mean_rel = mean(religiousness))
summarise_cheater
## mean_cheater mean_rel ## 1 0.249584 3.116473
str(summarise_cheater)
## 'data.frame': 1 obs. of 2 variables: ## $ mean_cheater: num 0.25 ## $ mean_rel : num 3.12
Exercise: summarise
summarise_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
Now let’s summarise by group
Cheater proportion by religiousness
cheater_by_rel <- cheater %>% group_by(religiousness) %>% summarise(proportion_cheater = mean(cheater)) cheater_by_rel
## # A tibble: 5 × 2 ## religiousness proportion_cheater ## <int> <dbl> ## 1 1 0.417 ## 2 2 0.25 ## 3 3 0.333 ## 4 4 0.174 ## 5 5 0.186
group_by + mutate = ?
Cheater 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 × 6 ## affairs gender age religiousness cheater proportion_cheater ## <dbl> <fct> <dbl> <int> <dbl> <dbl> ## 1 0 male 37 3 0 0.333 ## 2 0 female 27 4 0 0.174 ## 3 0 female 32 1 0 0.417 ## 4 0 male 57 5 0 0.186 ## 5 0 male 22 2 0 0.25 ## 6 0 female 32 2 0 0.25
This has many uses
The verb select is extremely powerful
Check out the documentation to see the multiple ways you can select variables
?dplyr::select
cheater %>%
select(starts_with('r'))
## religiousness rating ## 4 3 4 ## 5 4 4 ## 11 1 4 ## 16 5 5 ## 23 2 3 ## 29 2 5 ## 44 2 3 ## 45 2 4 ## 47 4 2 ## 49 4 5 ## 50 2 2 ## 55 4 4 ## 64 5 4 ## 80 2 4 ## 86 4 4 ## 93 1 5 ## 108 2 3 ## 114 3 4 ## 115 2 5 ## 116 2 5 ## 123 2 5 ## 127 2 5 ## 129 4 4 ## 134 3 5 ## 137 2 4 ## 139 2 5 ## 147 4 5 ## 151 5 4 ## 153 3 5 ## 155 3 4 ## 162 4 3 ## 163 3 5 ## 165 4 4 ## 168 5 4 ## 170 1 4 ## 172 4 3 ## 184 3 4 ## 187 4 5 ## 192 1 5 ## 194 3 5 ## 210 5 5 ## 217 5 3 ## 220 5 4 ## 224 2 1 ## 227 5 3 ## 228 3 5 ## 239 4 5 ## 241 2 4 ## 245 4 5 ## 249 2 4 ## 262 5 5 ## 265 2 4 ## 267 5 5 ## 269 4 4 ## 271 1 5 ## 277 4 1 ## 290 5 4 ## 292 4 5 ## 293 4 4 ## 295 4 4 ## 299 5 2 ## 320 3 4 ## 321 2 5 ## 324 2 5 ## 334 4 4 ## 351 2 5 ## 355 4 4 ## 361 3 5 ## 362 5 5 ## 366 2 4 ## 370 3 5 ## 374 5 5 ## 378 2 5 ## 381 4 3 ## 382 1 5 ## 383 4 5 ## 384 3 5 ## 400 3 5 ## 403 3 5 ## 409 2 2 ## 412 3 5 ## 413 4 5 ## 416 2 4 ## 418 4 3 ## 422 4 5 ## 435 1 4 ## 439 4 5 ## 445 3 2 ## 447 3 5 ## 448 3 4 ## 449 4 4 ## 478 2 3 ## 482 4 4 ## 486 5 3 ## 489 1 4 ## 490 5 5 ## 491 3 1 ## 492 1 4 ## 503 3 5 ## 508 4 5 ## 509 5 5 ## 512 2 4 ## 515 4 5 ## 517 5 5 ## 532 4 4 ## 533 4 4 ## 535 4 4 ## 537 4 4 ## 538 3 3 ## 543 2 4 ## 547 4 5 ## 550 4 5 ## 558 5 5 ## 571 5 4 ## 578 3 4 ## 583 4 5 ## 586 4 2 ## 594 5 4 ## 597 4 5 ## 602 4 4 ## 603 2 5 ## 604 5 4 ## 612 4 3 ## 613 2 2 ## 621 3 5 ## 627 5 5 ## 630 2 5 ## 631 4 5 ## 632 2 5 ## 639 2 5 ## 645 4 3 ## 647 4 5 ## 648 2 5 ## 651 2 3 ## 655 3 3 ## 667 3 4 ## 670 2 5 ## 671 2 4 ## 673 4 5 ## 701 1 4 ## 705 2 4 ## 706 5 4 ## 709 5 3 ## 717 2 4 ## 719 3 3 ## 723 1 4 ## 724 4 4 ## 726 5 3 ## 734 4 4 ## 735 1 4 ## 736 4 3 ## 737 2 3 ## 739 2 3 ## 743 4 3 ## 745 2 3 ## 747 4 3 ## 751 5 1 ## 752 4 2 ## 754 4 3 ## 760 2 5 ## 763 2 5 ## 774 2 5 ## 776 5 5 ## 779 5 4 ## 784 3 4 ## 788 4 2 ## 794 2 2 ## 795 4 4 ## 798 3 4 ## 800 4 2 ## 803 4 5 ## 807 4 5 ## 812 4 5 ## 820 3 4 ## 823 2 5 ## 830 2 2 ## 843 3 4 ## 848 2 4 ## 851 4 3 ## 854 4 4 ## 856 5 2 ## 857 4 5 ## 859 3 3 ## 863 4 5 ## 865 3 5 ## 867 4 5 ## 870 3 5 ## 873 2 5 ## 875 4 5 ## 876 1 5 ## 877 3 5 ## 880 4 4 ## 903 4 4 ## 904 4 4 ## 905 2 3 ## 908 1 4 ## 909 4 3 ## 910 2 5 ## 912 4 2 ## 914 4 5 ## 915 5 5 ## 916 2 5 ## 920 3 5 ## 921 2 3 ## 925 2 2 ## 926 2 3 ## 929 4 2 ## 931 1 5 ## 945 2 4 ## 947 4 3 ## 949 3 3 ## 950 2 5 ## 961 2 2 ## 965 2 5 ## 966 2 5 ## 967 5 5 ## 987 4 5 ## 990 4 3 ## 992 1 4 ## 995 5 5 ## 1009 3 3 ## 1021 4 5 ## 1026 4 5 ## 1027 4 4 ## 1030 3 4 ## 1031 1 5 ## 1034 4 4 ## 1037 2 4 ## 1038 4 5 ## 1039 3 5 ## 1045 5 4 ## 1046 4 5 ## 1054 4 5 ## 1059 4 4 ## 1063 4 5 ## 1068 4 3 ## 1070 5 5 ## 1072 5 5 ## 1073 3 5 ## 1077 4 4 ## 1081 2 4 ## 1083 3 4 ## 1084 1 5 ## 1086 3 5 ## 1087 4 4 ## 1089 3 4 ## 1096 3 3 ## 1102 4 5 ## 1103 3 4 ## 1107 5 4 ## 1109 4 3 ## 1115 2 4 ## 1119 4 5 ## 1124 4 4 ## 1126 1 5 ## 1128 3 4 ## 1129 2 5 ## 1130 2 4 ## 1133 4 4 ## 1140 2 5 ## 1143 3 5 ## 1146 4 3 ## 1153 1 4 ## 1156 2 5 ## 1157 3 3 ## 1158 2 5 ## 1160 2 1 ## 1161 2 5 ## 1166 4 5 ## 1177 3 5 ## 1178 4 5 ## 1180 3 5 ## 1187 2 3 ## 1191 2 3 ## 1195 4 4 ## 1207 2 1 ## 1208 5 3 ## 1209 2 5 ## 1211 4 4 ## 1215 1 5 ## 1221 4 4 ## 1226 4 4 ## 1229 4 4 ## 1231 3 3 ## 1234 3 4 ## 1235 3 2 ## 1242 3 5 ## 1245 3 2 ## 1260 3 2 ## 1266 2 4 ## 1271 3 4 ## 1273 2 5 ## 1276 5 4 ## 1280 4 3 ## 1282 4 5 ## 1285 2 3 ## 1295 4 5 ## 1298 2 5 ## 1299 4 3 ## 1304 5 4 ## 1305 2 2 ## 1311 2 5 ## 1314 2 4 ## 1319 3 5 ## 1322 4 4 ## 1324 2 4 ## 1327 4 5 ## 1328 5 3 ## 1330 4 3 ## 1332 3 5 ## 1333 2 2 ## 1336 4 5 ## 1341 5 5 ## 1344 1 4 ## 1352 5 3 ## 1358 4 5 ## 1359 2 5 ## 1361 2 4 ## 1364 2 4 ## 1368 4 4 ## 1384 3 5 ## 1390 2 5 ## 1393 4 4 ## 1394 4 5 ## 1402 2 5 ## 1407 1 5 ## 1408 3 4 ## 1412 2 5 ## 1413 5 5 ## 1416 4 3 ## 1417 2 4 ## 1418 4 4 ## 1419 4 4 ## 1420 5 5 ## 1423 3 5 ## 1424 4 2 ## 1432 4 5 ## 1433 4 5 ## 1437 3 5 ## 1438 2 5 ## 1439 2 5 ## 1446 2 3 ## 1450 2 4 ## 1451 2 5 ## 1452 4 4 ## 1453 4 4 ## 1456 4 5 ## 1464 3 3 ## 1469 4 3 ## 1473 5 2 ## 1481 2 5 ## 1482 3 5 ## 1496 5 4 ## 1497 4 5 ## 1504 4 5 ## 1513 2 5 ## 1515 3 3 ## 1534 2 5 ## 1535 4 4 ## 1536 3 5 ## 1540 3 5 ## 1551 5 5 ## 1555 5 4 ## 1557 3 5 ## 1566 4 5 ## 1567 3 5 ## 1576 4 5 ## 1584 4 5 ## 1585 1 4 ## 1590 3 4 ## 1594 4 2 ## 1595 5 2 ## 1603 2 5 ## 1608 4 3 ## 1609 4 5 ## 1615 4 4 ## 1616 3 5 ## 1617 3 4 ## 1620 3 5 ## 1621 2 5 ## 1637 1 5 ## 1638 4 5 ## 1650 4 5 ## 1654 4 4 ## 1665 2 2 ## 1670 4 5 ## 1671 2 4 ## 1675 2 4 ## 1688 2 5 ## 1691 3 4 ## 1695 5 5 ## 1698 4 4 ## 1704 3 2 ## 1705 3 2 ## 1711 2 2 ## 1719 5 5 ## 1723 1 5 ## 1726 1 5 ## 1749 4 5 ## 1752 2 3 ## 1754 5 5 ## 1758 4 5 ## 1761 5 4 ## 1773 2 3 ## 1775 4 1 ## 1786 4 5 ## 1793 2 4 ## 1799 4 5 ## 1803 2 4 ## 1806 4 5 ## 1807 2 3 ## 1808 2 5 ## 1814 4 3 ## 1815 4 4 ## 1818 3 5 ## 1827 2 3 ## 1834 4 5 ## 1835 4 3 ## 1843 3 5 ## 1846 4 5 ## 1850 4 5 ## 1851 2 4 ## 1854 2 1 ## 1859 5 4 ## 1861 2 4 ## 1866 4 5 ## 1873 1 5 ## 1875 4 4 ## 1885 5 3 ## 1892 4 4 ## 1895 2 4 ## 1896 3 5 ## 1897 4 4 ## 1899 5 5 ## 1904 2 2 ## 1905 3 4 ## 1908 2 4 ## 1916 4 4 ## 1918 4 3 ## 1920 2 2 ## 1930 3 3 ## 1940 3 5 ## 1947 3 4 ## 1949 2 5 ## 1951 4 4 ## 1952 4 5 ## 1960 2 5 ## 9001 4 4 ## 9012 2 2 ## 9023 3 4 ## 9029 2 4 ## 6 3 4 ## 12 3 5 ## 43 5 2 ## 53 3 2 ## 67 4 5 ## 79 2 5 ## 122 4 2 ## 126 2 4 ## 133 2 4 ## 138 4 2 ## 154 4 2 ## 159 3 4 ## 174 5 1 ## 176 2 2 ## 181 3 2 ## 182 1 5 ## 186 2 3 ## 189 3 5 ## 204 4 5 ## 215 5 5 ## 232 3 4 ## 233 5 4 ## 252 3 4 ## 253 4 2 ## 274 4 4 ## 275 4 3 ## 287 2 2 ## 288 4 4 ## 325 3 3 ## 328 4 4 ## 344 2 2 ## 353 5 5 ## 354 4 5 ## 367 2 4 ## 369 2 4 ## 390 1 3 ## 392 2 5 ## 423 2 4 ## 432 1 3 ## 436 2 3 ## 483 3 5 ## 513 3 4 ## 516 1 2 ## 518 3 5 ## 520 4 5 ## 526 4 1 ## 528 3 4 ## 553 4 5 ## 576 3 4 ## 611 5 4 ## 625 2 4 ## 635 2 4 ## 646 2 5 ## 657 5 3 ## 659 3 3 ## 666 4 4 ## 679 3 4 ## 729 2 1 ## 755 1 2 ## 758 3 4 ## 770 4 4 ## 786 1 4 ## 797 3 2 ## 811 1 4 ## 834 4 4 ## 858 1 4 ## 885 4 2 ## 893 1 3 ## 927 3 1 ## 928 3 4 ## 933 3 3 ## 951 2 2 ## 968 4 5 ## 972 3 4 ## 975 1 5 ## 977 3 3 ## 981 2 5 ## 986 3 3 ## 1002 2 4 ## 1007 4 2 ## 1011 3 4 ## 1035 1 3 ## 1050 2 3 ## 1056 3 3 ## 1057 5 5 ## 1075 2 2 ## 1080 1 3 ## 1125 4 2 ## 1131 2 4 ## 1138 2 3 ## 1150 3 4 ## 1163 4 3 ## 1169 4 5 ## 1198 2 2 ## 1204 5 3 ## 1218 3 1 ## 1230 2 2 ## 1236 3 4 ## 1247 2 2 ## 1259 3 1 ## 1294 5 5 ## 1353 4 4 ## 1370 2 3 ## 1427 3 3 ## 1445 5 2 ## 1460 2 4 ## 1480 2 4 ## 1505 1 2 ## 1543 3 3 ## 1548 4 1 ## 1550 2 5 ## 1561 2 2 ## 1564 2 4 ## 1573 2 5 ## 1575 4 5 ## 1599 3 3 ## 1622 4 5 ## 1629 1 5 ## 1664 2 4 ## 1669 1 1 ## 1674 2 5 ## 1682 5 4 ## 1685 1 5 ## 1697 4 4 ## 1716 3 2 ## 1730 2 4 ## 1731 4 4 ## 1732 3 2 ## 1743 3 3 ## 1751 1 4 ## 1757 4 3 ## 1763 3 4 ## 1766 2 3 ## 1772 3 2 ## 1776 5 5 ## 1782 1 5 ## 1784 1 5 ## 1791 4 4 ## 1831 3 2 ## 1840 4 2 ## 1844 3 2 ## 1856 2 2 ## 1876 2 4 ## 1929 4 3 ## 1935 3 2 ## 1938 1 5 ## 1941 2 4 ## 1954 2 5 ## 1959 3 2 ## 9010 3 5
Exercises: group_by
- Create a data frame grouped by children (Yes/No), summarizing:
- mean religiousness
- mean rating
- mean cheater
- Create the same table using the mutate statement
By now, renaming is all but covered
Renaming 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. :1.0000
Next time…
- Joins
