Chapter 15

Introduction

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")

Modifying Factor Levels

Chapter 16

Introduction

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"

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] "2025-04-25 UTC"
# From date-time to date
now() %>% as_date()
## [1] "2025-04-25"

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 2013-08-12 18:39:00 2013-08-01 00:00:00
##  2 2013-03-14 17:19:00 2013-03-01 00:00:00
##  3 2013-04-12 08:25:00 2013-04-01 00:00:00
##  4 2013-08-22 20:00:00 2013-08-01 00:00:00
##  5 2013-12-02 18:26:00 2013-12-01 00:00:00
##  6 2013-04-24 13:03:00 2013-04-01 00:00:00
##  7 2013-11-24 05:56:00 2013-11-01 00:00:00
##  8 2013-01-14 08:58:00 2013-01-01 00:00:00
##  9 2013-10-16 15:58:00 2013-10-01 00:00:00
## 10 2013-09-26 18:13:00 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 2013-03-13 20:53:00 2013-04-01 00:00:00
##  2 2013-11-01 08:23:00 2013-12-01 00:00:00
##  3 2013-07-10 21:28:00 2013-08-01 00:00:00
##  4 2013-07-02 17:23:00 2013-08-01 00:00:00
##  5 2013-11-23 16:45:00 2013-12-01 00:00:00
##  6 2013-02-07 17:28:00 2013-03-01 00:00:00
##  7 2013-09-05 16:43:00 2013-10-01 00:00:00
##  8 2013-03-12 07:50:00 2013-04-01 00:00:00
##  9 2013-09-11 17:58:00 2013-10-01 00:00:00
## 10 2013-07-09 14:51:00 2013-08-01 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 2013-11-10 18:24:00 2013-01-01 18:24:00
##  2 2013-03-05 14:41:00 2013-01-01 14:41:00
##  3 2013-02-12 07:54:00 2013-01-01 07:54:00
##  4 2013-09-11 20:56:00 2013-01-01 20:56:00
##  5 2013-02-28 21:46:00 2013-01-01 21:46:00
##  6 2013-04-17 17:05:00 2013-01-01 17:05:00
##  7 2013-07-16 20:36:00 2013-01-01 20:36:00
##  8 2013-09-28 15:44:00 2013-01-01 15:44:00
##  9 2013-12-14 13:11:00 2013-01-01 13:11:00
## 10 2013-06-19 13:10:00 2013-01-01 13:10:00

Time Spans