Ch15 Factors

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
## # … with 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

Ch16 Dates and times

Creating date/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
## # … with 336,766 more rows

From other types

# From date to date-time
today() %>% as_datetime()
## [1] "2023-04-11 UTC"
# From date-time to date
now() %>% as_date()
## [1] "2023-04-11"

Date-time components

Getting compnents

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 2013-08-19 20:52:00 2013-08-01 00:00:00
##  2 2013-09-03 18:33:00 2013-09-01 00:00:00
##  3 2013-02-05 10:27:00 2013-02-01 00:00:00
##  4 2013-04-16 07:43:00 2013-04-01 00:00:00
##  5 2013-05-29 16:56:00 2013-05-01 00:00:00
##  6 2013-05-11 20:38:00 2013-05-01 00:00:00
##  7 2013-02-22 09:06:00 2013-02-01 00:00:00
##  8 2013-01-13 15:00:00 2013-01-01 00:00:00
##  9 2013-11-11 13:24:00 2013-11-01 00:00:00
## 10 2013-05-25 20:06:00 2013-05-01 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 2013-05-29 19:31:00 2013-06-01 00:00:00
##  2 2013-10-05 09:27:00 2013-11-01 00:00:00
##  3 2013-08-15 21:18:00 2013-09-01 00:00:00
##  4 2013-04-14 06:55:00 2013-05-01 00:00:00
##  5 2013-03-09 08:30:00 2013-04-01 00:00:00
##  6 2013-10-07 18:55:00 2013-11-01 00:00:00
##  7 2013-10-05 08:25:00 2013-11-01 00:00:00
##  8 2013-07-02 07:29:00 2013-08-01 00:00:00
##  9 2013-01-05 08:03:00 2013-02-01 00:00:00
## 10 2013-09-25 19:33:00 2013-10-01 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 2013-10-14 10:54:00 2013-01-01 10:54:00
##  2 2013-08-09 13:04:00 2013-01-01 13:04:00
##  3 2013-04-08 20:11:00 2013-01-01 20:11:00
##  4 2013-04-02 15:40:00 2013-01-01 15:40:00
##  5 2013-07-14 19:59:00 2013-01-01 19:59:00
##  6 2013-07-31 16:16:00 2013-01-01 16:16:00
##  7 2013-12-16 17:04:00 2013-01-01 17:04:00
##  8 2013-06-28 08:20:00 2013-01-01 08:20:00
##  9 2013-08-02 15:57:00 2013-01-01 15:57:00
## 10 2013-07-07 08:24:00 2013-01-01 08:24:00

Time spans