Week 11: Code Along 10
R for Data Science: Chapter 15
Ava Clark
2025-04-08
Chapter 15
Introduction
Creating Factors
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 rowsGeneral Social Survey
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 = "MeanDaily Hours Watching TV")
Moving single level to the front
tvhours_by_relig %>%
ggplot(aes(x = avg_tvhours,
y = fct_reorder(.f = relig, .x = avg_tvhours) %>%
fct_relevel("Don't know"))) +
geom_point() +
# Labeling
labs(y = NULL, x = "MeanDaily Hours Watching TV")
Modifying Factor Order
gss_cat %>% distinct(race)## # A tibble: 3 × 1
## race
## <fct>
## 1 White
## 2 Black
## 3 Other# Recode
gss_cat %>%
# Rename levels
mutate(race_rev = fct_recode(race, "African American" = "Black")) %>%
select(race, race_rev) %>%
filter(race == "Black")## # A tibble: 3,129 × 2
## race race_rev
## <fct> <fct>
## 1 Black African American
## 2 Black African American
## 3 Black African American
## 4 Black African American
## 5 Black African American
## 6 Black African American
## 7 Black African American
## 8 Black African American
## 9 Black African American
## 10 Black African American
## # ℹ 3,119 more rows# Collapse multiple levels into one
gss_cat %>%
mutate(race_col = fct_collapse(race, "Minority" = c("Black","Other"))) %>%
select(race, race_col) %>%
filter(race != "White")## # A tibble: 5,088 × 2
## race race_col
## <fct> <fct>
## 1 Black Minority
## 2 Black Minority
## 3 Black Minority
## 4 Other Minority
## 5 Black Minority
## 6 Other Minority
## 7 Black Minority
## 8 Other Minority
## 9 Black Minority
## 10 Black Minority
## # ℹ 5,078 more rows# Lump small levels into other levels
gss_cat %>% count(race)## # A tibble: 3 × 2
## race n
## <fct> <int>
## 1 Other 1959
## 2 Black 3129
## 3 White 16395gss_cat %>% mutate(race_lump = fct_lump(race)) %>% distinct(race_lump)## # A tibble: 2 × 1
## race_lump
## <fct>
## 1 White
## 2 OtherChapter 16
Introduction
##Creating Dates and Times
From Stringd
# From Strings
"2022/10/28" %>% ymd()## [1] "2022-10-28"# From Numbers
20221028 %>% ymd()## [1] "2022-10-28""2022-1028 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 rowsFrom Other Types
# From date to date-time
today() %>% as_datetime()## [1] "2025-04-08 UTC"# From date-time to date
now() %>% as_date()## [1] "2025-04-08"Date-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] 2022month(date_time, label = TRUE, abbr = FALSE)## [1] October
## 12 Levels: January < February < March < April < May < June < ... < Decemberyday(date_time)## [1] 301mday(date_time)## [1] 28wday(date_time, label = TRUE, abbr = FALSE)## [1] Friday
## 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday# Create flight dt
make_datetime_100 <- function(year, month, day, time) {
make_datetime(year, month, day, time %/% 100, time %% 100)
}
flights_dt <- flights %>%
filter(!is.na(dep_time), !is.na(arr_time)) %>%
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 2013-09-15 15:45:00 2013-09-01 00:00:00
## 2 2013-11-19 06:08:00 2013-11-01 00:00:00
## 3 2013-08-18 20:43:00 2013-08-01 00:00:00
## 4 2013-07-05 15:53:00 2013-07-01 00:00:00
## 5 2013-08-02 13:33:00 2013-08-01 00:00:00
## 6 2013-05-24 07:28:00 2013-05-01 00:00:00
## 7 2013-01-25 11:31:00 2013-01-01 00:00:00
## 8 2013-11-28 18:42:00 2013-11-01 00:00:00
## 9 2013-03-17 08:17:00 2013-03-01 00:00:00
## 10 2013-06-01 05:39:00 2013-06-01 00:00:00# ceiling_date for rounding down
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 2013-06-11 13:29:00 2013-07-01 00:00:00
## 2 2013-02-26 11:29:00 2013-03-01 00:00:00
## 3 2013-07-23 13:13:00 2013-08-01 00:00:00
## 4 2013-07-16 19:55:00 2013-08-01 00:00:00
## 5 2013-08-08 17:48:00 2013-09-01 00:00:00
## 6 2013-05-18 13:50:00 2013-06-01 00:00:00
## 7 2013-11-22 18:56:00 2013-12-01 00:00:00
## 8 2013-07-25 07:04:00 2013-08-01 00:00:00
## 9 2013-01-04 08:43:00 2013-02-01 00:00:00
## 10 2013-12-01 12:35:00 2014-01-01 00:00:00Setting 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 2013-07-06 19:25:00 2013-01-01 19:25:00
## 2 2013-03-19 14:56:00 2013-01-01 14:56:00
## 3 2013-05-07 12:37:00 2013-01-01 12:37:00
## 4 2013-11-28 07:23:00 2013-01-01 07:23:00
## 5 2013-12-19 21:05:00 2013-01-01 21:05:00
## 6 2013-11-01 17:14:00 2013-01-01 17:14:00
## 7 2013-02-25 13:22:00 2013-01-01 13:22:00
## 8 2013-03-07 16:46:00 2013-01-01 16:46:00
## 9 2013-04-22 15:22:00 2013-01-01 15:22:00
## 10 2013-01-11 17:34:00 2013-01-01 17:34:00