Chapter 15

Creating Factors

x1 <- c("Dec", "Apr", "Jan", "Mar")
x2 <- c("Dec", "Apr", "Jam", "Mar")

month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun",
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)

y1 <- factor(x1, levels = month_levels)
y1
## [1] Dec Apr Jan Mar
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
sort(y1)
## [1] Jan Mar Apr Dec
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
y2 <- factor(x2, levels = month_levels)
y2
## [1] Dec  Apr  <NA> Mar 
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
y2 <- parse_factor(x2, levels = month_levels)
## Warning: 1 parsing failure.
## row col           expected actual
##   3  -- value in level set    Jam
f1 <- factor(x1, levels = unique(x1))
f1
## [1] Dec Apr Jan Mar
## Levels: Dec Apr Jan Mar
f2 <- x1 %>% factor() %>% fct_inorder()
f2
## [1] Dec Apr Jan Mar
## Levels: Dec Apr Jan Mar
levels(f2)
## [1] "Dec" "Apr" "Jan" "Mar"

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

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")

Moving a 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 = "Mean Daily Hours Watching TV")

Modifying factor levels

## Modifying factor levels

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 16395
gss_cat %>% mutate(race_lump = fct_lump(race)) %>% distinct(race_lump)
## # A tibble: 2 × 1
##   race_lump
##   <fct>    
## 1 White    
## 2 Other

Chapter 16 Dates and times

creating Dates/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] "2026-05-07 UTC"
# From date-time to date
now() %>% as_date()
## [1] "2026-05-07"

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] 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 %>%
  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 2013-01-30 11:18:00 2013-01-01 00:00:00
##  2 2013-03-08 16:19:00 2013-03-01 00:00:00
##  3 2013-06-06 15:05:00 2013-06-01 00:00:00
##  4 2013-04-14 20:31:00 2013-04-01 00:00:00
##  5 2013-05-15 10:55:00 2013-05-01 00:00:00
##  6 2013-01-25 07:59:00 2013-01-01 00:00:00
##  7 2013-03-01 17:43:00 2013-03-01 00:00:00
##  8 2013-08-11 06:43:00 2013-08-01 00:00:00
##  9 2013-08-19 13:20:00 2013-08-01 00:00:00
## 10 2013-09-05 10:18:00 2013-09-01 00:00:00
# floor_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 2013-09-01 10:47:00 2013-10-01 00:00:00
##  2 2013-08-21 10:59:00 2013-09-01 00:00:00
##  3 2013-08-07 10:30:00 2013-09-01 00:00:00
##  4 2013-11-21 16:45:00 2013-12-01 00:00:00
##  5 2013-04-21 07:01:00 2013-05-01 00:00:00
##  6 2013-02-04 05:53:00 2013-03-01 00:00:00
##  7 2013-03-22 06:58:00 2013-04-01 00:00:00
##  8 2013-09-13 09:57:00 2013-10-01 00:00:00
##  9 2013-06-03 16:03:00 2013-07-01 00:00:00
## 10 2013-08-18 13:34:00 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 2013-04-30 19:55:00 2013-01-01 19:55:00
##  2 2013-09-19 10:10:00 2013-01-01 10:10:00
##  3 2013-10-09 18:12:00 2013-01-01 18:12:00
##  4 2013-06-21 17:49:00 2013-01-01 17:49:00
##  5 2013-06-16 10:53:00 2013-01-01 10:53:00
##  6 2013-04-25 09:23:00 2013-01-01 09:23:00
##  7 2013-10-31 16:57:00 2013-01-01 16:57:00
##  8 2013-08-09 20:05:00 2013-01-01 20:05:00
##  9 2013-06-20 10:02:00 2013-01-01 10:02:00
## 10 2013-06-13 21:49:00 2013-01-01 21:49:00

Time spans

Durations

h_age <- today() - ymd(19791014)
h_age
## Time difference of 17007 days
as.duration(h_age)
## [1] "1469404800s (~46.56 years)"
dseconds(15)
## [1] "15s"
dminutes(10)
## [1] "600s (~10 minutes)"
dhours(24)
## [1] "86400s (~1 days)"
ddays(1)
## [1] "86400s (~1 days)"
dweeks(3)
## [1] "1814400s (~3 weeks)"
dyears(1)
## [1] "31557600s (~1 years)"

Periods

one_pm <- ymd_hms("2016-03-12 13:00:00", tz = "America/New_York")
one_pm + ddays(1)
## [1] "2016-03-13 14:00:00 EDT"
one_pm + days(1)
## [1] "2016-03-13 13:00:00 EDT"
flights_dt <- flights_dt %>%
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight * 1),
    sched_arr_time = sched_arr_time + days(overnight * 1)
  )

Intervals

next_year <- today() + years(1)
(today() %--% next_year) / ddays(1)
## [1] 365
(today() %--% next_year) %/% days(1)
## [1] 365