Chapter 15
Introduction
Creating Factors
General Social Survey
gss_cat
## # A tibble: 21,483 × 9
## year marital age race rincome partyid relig denom tvhours
## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int>
## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… 12
## 2 2000 Divorced 48 White $8000 to 9999 Not str r… Prot… Bapt… NA
## 3 2000 Widowed 67 White Not applicable Independe… Prot… No d… 2
## 4 2000 Never married 39 White Not applicable Ind,near … Orth… Not … 4
## 5 2000 Divorced 25 White Not applicable Not str d… None Not … 1
## 6 2000 Married 25 White $20000 - 24999 Strong de… Prot… Sout… NA
## 7 2000 Never married 36 White $25000 or more Not str r… Chri… Not … 3
## 8 2000 Divorced 44 White $7000 to 7999 Ind,near … Prot… Luth… NA
## 9 2000 Married 44 White $25000 or more Not str d… Prot… Other 0
## 10 2000 Married 47 White $25000 or more Strong re… Prot… Sout… 3
## # ℹ 21,473 more rows
Modifying factor order
# Transform data: calculate average tv
gss_cat %>%
group_by(relig) %>%
summarise(
avg_tvhours = mean(tvhours)
)
## # A tibble: 15 × 2
## relig avg_tvhours
## <fct> <dbl>
## 1 No answer NA
## 2 Don't know NA
## 3 Inter-nondenominational NA
## 4 Native american NA
## 5 Christian NA
## 6 Orthodox-christian NA
## 7 Moslem/islam NA
## 8 Other eastern NA
## 9 Hinduism NA
## 10 Buddhism NA
## 11 Other NA
## 12 None NA
## 13 Jewish NA
## 14 Catholic NA
## 15 Protestant NA
Modifying Factor levels
gss_cat %>% distinct(race)
## # A tibble: 3 × 1
## race
## <fct>
## 1 White
## 2 Black
## 3 Other
Chapter 16
Introduction
Creating data/times
flights %>%
select(year:day, hour, minute) %>%
mutate(departure = make_datetime(year = year, month = month,
day = day, hour = hour, min = minute))
## # A tibble: 336,776 × 6
## year month day hour minute departure
## <int> <int> <int> <dbl> <dbl> <dttm>
## 1 2013 1 1 5 15 2013-01-01 05:15:00
## 2 2013 1 1 5 29 2013-01-01 05:29:00
## 3 2013 1 1 5 40 2013-01-01 05:40:00
## 4 2013 1 1 5 45 2013-01-01 05:45:00
## 5 2013 1 1 6 0 2013-01-01 06:00:00
## 6 2013 1 1 5 58 2013-01-01 05:58:00
## 7 2013 1 1 6 0 2013-01-01 06:00:00
## 8 2013 1 1 6 0 2013-01-01 06:00:00
## 9 2013 1 1 6 0 2013-01-01 06:00:00
## 10 2013 1 1 6 0 2013-01-01 06:00:00
## # ℹ 336,766 more rows
Data-Time Components
Time spans