Chapter 15 Factors

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
## # … with 21,473 more rows

Modifying factor orders

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)
    )

# 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")
## Warning: 1 unknown level in `f`: Don't Know
## 1 unknown level in `f`: Don't Know

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
## # … with 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
## # … with 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/Time

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"

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] "2022-11-02 UTC"
#From date-time to date
now() %>% as_date()
## [1] "2022-11-02"

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 = ceiling_date(dep_time, "month")) %>%
    select(dep_time, week) %>%
    sample_n(10)
## # A tibble: 10 × 2
##    dep_time            week               
##    <dttm>              <dttm>             
##  1 2013-01-29 07:25:00 2013-02-01 00:00:00
##  2 2013-11-08 17:30:00 2013-12-01 00:00:00
##  3 2013-02-14 14:30:00 2013-03-01 00:00:00
##  4 2013-10-07 10:16:00 2013-11-01 00:00:00
##  5 2013-06-26 08:40:00 2013-07-01 00:00:00
##  6 2013-03-20 07:58:00 2013-04-01 00:00:00
##  7 2013-10-15 13:57:00 2013-11-01 00:00:00
##  8 2013-03-09 13:11:00 2013-04-01 00:00:00
##  9 2013-12-31 13:54:00 2014-01-01 00:00:00
## 10 2013-10-17 08:23:00 2013-11-01 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 2013-03-01 22:32:00 2013-01-01 22:32:00
##  2 2013-11-22 07:26:00 2013-01-01 07:26:00
##  3 2013-01-02 13:39:00 2013-01-01 13:39:00
##  4 2013-08-29 18:16:00 2013-01-01 18:16:00
##  5 2013-07-13 19:59:00 2013-01-01 19:59:00
##  6 2013-11-11 18:38:00 2013-01-01 18:38:00
##  7 2013-10-19 19:20:00 2013-01-01 19:20:00
##  8 2013-09-03 10:04:00 2013-01-01 10:04:00
##  9 2013-06-07 14:48:00 2013-01-01 14:48:00
## 10 2013-06-21 18:50:00 2013-01-01 18:50:00