Chapter 15 Factors
Creating factors
General Social Society
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
Unordered Factor Levels
# Transform data: calculate average tv hours by religion
tvhours_by_relig <- gss_cat %>%
group_by(relig) %>%
summarise(
avg_tvhours = mean(tvhours, na.rm = TRUE)
)
tvhours_by_relig
## # A tibble: 15 × 2
## relig avg_tvhours
## <fct> <dbl>
## 1 No answer 2.72
## 2 Don't know 4.62
## 3 Inter-nondenominational 2.87
## 4 Native american 3.46
## 5 Christian 2.79
## 6 Orthodox-christian 2.42
## 7 Moslem/islam 2.44
## 8 Other eastern 1.67
## 9 Hinduism 1.89
## 10 Buddhism 2.38
## 11 Other 2.73
## 12 None 2.71
## 13 Jewish 2.52
## 14 Catholic 2.96
## 15 Protestant 3.15
#Plot
tvhours_by_relig %>%
ggplot(aes(x = avg_tvhours, y = relig)) +
geom_point()
Ordered factor levels
tvhours_by_relig %>%
ggplot(aes(x = avg_tvhours, y = fct_reorder(.f = relig, .x = avg_tvhours))) +
geom_point() +
# Labeling
labs(y = NULL, x = "Mean Daily Hours Watching TV")
Modifying factor levels
Chapter 16
Creating data/ times
From strings
# From Strings
"2022/10/28" %>% ymd()
## [1] "2022-10-28"
# From Numbers
20221028 %>% ymd()
## [1] "2022-10-28"
"2022-10-28 4-41-30" %>% ymd_hms()
## [1] "2022-10-28 04:41:30 UTC"
From individual components
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
From other types
# From Date to Date-Time
today() %>% as_datetime()
## [1] "2025-11-04 UTC"
# From Date-Time to Date
now() %>% as_date()
## [1] "2025-11-04"
data time components
Getting components
date_time <- ymd_hms("2022-10-28 18-18-18")
date_time
## [1] "2022-10-28 18:18:18 UTC"
year(date_time)
## [1] 2022
month(date_time, label = TRUE, abbr = FALSE)
## [1] October
## 12 Levels: January < February < March < April < May < June < ... < December
yday(date_time)
## [1] 301
mday(date_time)
## [1] 28
wday(date_time, label = TRUE, abbr = FALSE)
## [1] Friday
## 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday
# Create flights_dt
make_datetime_100 <- function(year, month, day, time) {
make_datetime(year, month, day, time %/% 100, time %% 100)
}
flights_dt <- flights %>%
mutate(
dep_time = make_datetime_100(year, month, day, dep_time),
arr_time = make_datetime_100(year, month, day, arr_time),
sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
) %>%
select(origin, dest, ends_with("delay"), ends_with("time"))
flights_dt %>%
transmute(wday = wday(dep_time, label = TRUE)) %>%
ggplot(aes(wday)) +
geom_bar()
Rounding
# floor_date for rounding down
flights_dt %>%
mutate(week = floor_date(dep_time, "month")) %>%
select(dep_time, week) %>%
sample_n(10)
## # A tibble: 10 × 2
## dep_time week
## <dttm> <dttm>
## 1 2013-12-09 10:06:00 2013-12-01 00:00:00
## 2 2013-09-23 14:13:00 2013-09-01 00:00:00
## 3 2013-01-18 20:36:00 2013-01-01 00:00:00
## 4 2013-02-19 09:40:00 2013-02-01 00:00:00
## 5 2013-02-24 11:39:00 2013-02-01 00:00:00
## 6 2013-06-02 21:39:00 2013-06-01 00:00:00
## 7 2013-02-06 16:33:00 2013-02-01 00:00:00
## 8 2013-08-28 19:42:00 2013-08-01 00:00:00
## 9 2013-09-08 07:57:00 2013-09-01 00:00:00
## 10 2013-08-11 15:30:00 2013-08-01 00:00:00
# ceiling_date for rounding up
flights_dt %>%
mutate(week = ceiling_date(dep_time, "month")) %>%
select(dep_time, week) %>%
sample_n(10)
## # A tibble: 10 × 2
## dep_time week
## <dttm> <dttm>
## 1 2013-03-02 05:54:00 2013-04-01 00:00:00
## 2 2013-08-25 17:28:00 2013-09-01 00:00:00
## 3 2013-05-20 08:24:00 2013-06-01 00:00:00
## 4 2013-12-30 15:08:00 2014-01-01 00:00:00
## 5 2013-03-26 20:32:00 2013-04-01 00:00:00
## 6 2013-07-29 06:26:00 2013-08-01 00:00:00
## 7 2013-03-11 07:40:00 2013-04-01 00:00:00
## 8 2013-09-08 08:17:00 2013-10-01 00:00:00
## 9 2013-09-25 17:57:00 2013-10-01 00:00:00
## 10 2013-08-22 08:13:00 2013-09-01 00:00:00
Setting components
flights_dt %>%
mutate(dep_hour = update(dep_time, yday = 1)) %>%
select(dep_time, dep_hour) %>%
sample_n(10)
## # A tibble: 10 × 2
## dep_time dep_hour
## <dttm> <dttm>
## 1 2013-04-18 17:19:00 2013-01-01 17:19:00
## 2 2013-06-13 21:41:00 2013-01-01 21:41:00
## 3 2013-06-07 05:46:00 2013-01-01 05:46:00
## 4 2013-02-22 08:33:00 2013-01-01 08:33:00
## 5 2013-05-07 08:23:00 2013-01-01 08:23:00
## 6 2013-08-01 20:41:00 2013-01-01 20:41:00
## 7 2013-09-17 21:17:00 2013-01-01 21:17:00
## 8 2013-04-08 17:22:00 2013-01-01 17:22:00
## 9 2013-06-22 14:58:00 2013-01-01 14:58:00
## 10 2013-01-23 21:06:00 2013-01-01 21:06:00
Time spans