Chapter 15 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
## # 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()

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 date/times

# 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] "2024-11-04 UTC"
# From date-time to date
now() %>% as_date()
## [1] "2024-11-04"

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
## # A tibble: 328,063 × 9
##    origin dest  dep_delay arr_delay dep_time            sched_dep_time     
##    <chr>  <chr>     <dbl>     <dbl> <dttm>              <dttm>             
##  1 EWR    IAH           2        11 2013-01-01 05:17:00 2013-01-01 05:15:00
##  2 LGA    IAH           4        20 2013-01-01 05:33:00 2013-01-01 05:29:00
##  3 JFK    MIA           2        33 2013-01-01 05:42:00 2013-01-01 05:40:00
##  4 JFK    BQN          -1       -18 2013-01-01 05:44:00 2013-01-01 05:45:00
##  5 LGA    ATL          -6       -25 2013-01-01 05:54:00 2013-01-01 06:00:00
##  6 EWR    ORD          -4        12 2013-01-01 05:54:00 2013-01-01 05:58:00
##  7 EWR    FLL          -5        19 2013-01-01 05:55:00 2013-01-01 06:00:00
##  8 LGA    IAD          -3       -14 2013-01-01 05:57:00 2013-01-01 06:00:00
##  9 JFK    MCO          -3        -8 2013-01-01 05:57:00 2013-01-01 06:00:00
## 10 LGA    ORD          -2         8 2013-01-01 05:58:00 2013-01-01 06:00:00
## # ℹ 328,053 more rows
## # ℹ 3 more variables: arr_time <dttm>, sched_arr_time <dttm>, air_time <dbl>
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 2013-08-04 20:26:00 2013-08-01 00:00:00
##  2 2013-08-11 06:57:00 2013-08-01 00:00:00
##  3 2013-05-31 08:00:00 2013-05-01 00:00:00
##  4 2013-08-17 08:55:00 2013-08-01 00:00:00
##  5 2013-11-07 13:57:00 2013-11-01 00:00:00
##  6 2013-06-26 15:59:00 2013-06-01 00:00:00
##  7 2013-10-22 07:17:00 2013-10-01 00:00:00
##  8 2013-11-24 06:54:00 2013-11-01 00:00:00
##  9 2013-09-25 06:14:00 2013-09-01 00:00:00
## 10 2013-09-02 18:29:00 2013-09-01 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 2013-07-05 13:54:00 2013-08-01 00:00:00
##  2 2013-02-04 12:45:00 2013-03-01 00:00:00
##  3 2013-03-25 11:25:00 2013-04-01 00:00:00
##  4 2013-04-21 10:08:00 2013-05-01 00:00:00
##  5 2013-09-05 22:50:00 2013-10-01 00:00:00
##  6 2013-06-10 17:04:00 2013-07-01 00:00:00
##  7 2013-05-03 10:43:00 2013-06-01 00:00:00
##  8 2013-10-01 17:54:00 2013-11-01 00:00:00
##  9 2013-02-01 09:56:00 2013-03-01 00:00:00
## 10 2013-03-31 17:59:00 2013-04-01 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 2013-01-01 15:27:00 2013-01-01 15:27:00
##  2 2013-10-26 07:37:00 2013-01-01 07:37:00
##  3 2013-04-10 14:55:00 2013-01-01 14:55:00
##  4 2013-12-17 21:45:00 2013-01-01 21:45:00
##  5 2013-03-17 07:42:00 2013-01-01 07:42:00
##  6 2013-03-06 21:54:00 2013-01-01 21:54:00
##  7 2013-08-12 18:11:00 2013-01-01 18:11:00
##  8 2013-12-26 21:53:00 2013-01-01 21:53:00
##  9 2013-05-20 17:59:00 2013-01-01 17:59:00
## 10 2013-03-29 09:52:00 2013-01-01 09:52:00

Time Spans