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

#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 %>% 
    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 level
    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

Introduction

Creating date/times

# From strings
"2024-04-09" %>% ymd()
## [1] "2024-04-09"
#From Numbers
20240409 %>% ymd()
## [1] "2024-04-09"
"2024-04-09 8-51-45" %>% ymd_hms()
## [1] "2024-04-09 08:51:45 UTC"

individual components

flights %>% 
    select(year:day, hour, minute) %>%
    mutate(departure = make_datetime(year = year, month = month,
                                     day = day, 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 00:15:00
##  2  2013     1     1     5     29 2013-01-01 00:29:00
##  3  2013     1     1     5     40 2013-01-01 00:40:00
##  4  2013     1     1     5     45 2013-01-01 00:45:00
##  5  2013     1     1     6      0 2013-01-01 00:00:00
##  6  2013     1     1     5     58 2013-01-01 00:58:00
##  7  2013     1     1     6      0 2013-01-01 00:00:00
##  8  2013     1     1     6      0 2013-01-01 00:00:00
##  9  2013     1     1     6      0 2013-01-01 00:00:00
## 10  2013     1     1     6      0 2013-01-01 00:00:00
## # ℹ 336,766 more rows

from other objects

# from date to date-time
today() %>% as_datetime()
## [1] "2024-04-09 UTC"
#from date-time to date
now() %>% as_date()
## [1] "2024-04-09"

Date Time component

date_time <- ymd_hms("2024-04-09 9-08-30")
datetime_scale
## function (aesthetics, trans, palette, breaks = pretty_breaks(), 
##     minor_breaks = waiver(), labels = waiver(), date_breaks = waiver(), 
##     date_labels = waiver(), date_minor_breaks = waiver(), timezone = NULL, 
##     guide = "legend", ...) 
## {
##     if (is.character(breaks)) 
##         breaks <- breaks_width(breaks)
##     if (is.character(minor_breaks)) 
##         minor_breaks <- breaks_width(minor_breaks)
##     if (!is.waive(date_breaks)) {
##         breaks <- breaks_width(date_breaks)
##     }
##     if (!is.waive(date_minor_breaks)) {
##         minor_breaks <- breaks_width(date_minor_breaks)
##     }
##     if (!is.waive(date_labels)) {
##         labels <- function(self, x) {
##             tz <- if (is.null(self$timezone)) 
##                 "UTC"
##             else self$timezone
##             date_format(date_labels, tz)(x)
##         }
##     }
##     name <- switch(trans, date = "date", time = "datetime")
##     if (all(aesthetics %in% c("x", "xmin", "xmax", "xend", "y", 
##         "ymin", "ymax", "yend"))) {
##         scale_class <- switch(trans, date = ScaleContinuousDate, 
##             time = ScaleContinuousDatetime)
##     }
##     else {
##         scale_class <- ScaleContinuous
##     }
##     trans <- switch(trans, date = date_trans(), time = time_trans(timezone))
##     sc <- continuous_scale(aesthetics, name, palette = palette, 
##         breaks = breaks, minor_breaks = minor_breaks, labels = labels, 
##         guide = guide, trans = trans, ..., super = scale_class)
##     sc$timezone <- timezone
##     sc
## }
## <bytecode: 0x110395a50>
## <environment: namespace:ggplot2>
year(date_time)
## [1] 2024
month(date_time, label = TRUE, abbr = FALSE)
## [1] April
## 12 Levels: January < February < March < April < May < June < ... < December
yday(date_time)
## [1] 100
mday(date_time)
## [1] 9
wday(date_time, label = TRUE, abbr = FALSE)
## [1] Tuesday
## 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday
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"))

getting components

flights_dt %>% 
  mutate(wday = wday(dep_time, label = TRUE)) %>% 
  ggplot(aes(x = 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 2013-04-03 07:00:00 2013-04-01 00:00:00
##  2 2013-09-01 21:23:00 2013-09-01 00:00:00
##  3 2013-12-08 14:34:00 2013-12-01 00:00:00
##  4 2013-01-15 18:06:00 2013-01-01 00:00:00
##  5 2013-05-21 17:08:00 2013-05-01 00:00:00
##  6 2013-01-27 16:20:00 2013-01-01 00:00:00
##  7 2013-08-15 07:45:00 2013-08-01 00:00:00
##  8 2013-11-01 07:49:00 2013-11-01 00:00:00
##  9 2013-06-07 14:08:00 2013-06-01 00:00:00
## 10 2013-01-30 13:29:00 2013-01-01 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 2013-01-02 16:09:00 2013-02-01 00:00:00
##  2 2013-01-08 07:26:00 2013-02-01 00:00:00
##  3 2013-01-16 08:26:00 2013-02-01 00:00:00
##  4 2013-06-13 12:12:00 2013-07-01 00:00:00
##  5 2013-01-07 09:18:00 2013-02-01 00:00:00
##  6 2013-07-11 19:43:00 2013-08-01 00:00:00
##  7 2013-08-24 16:29:00 2013-09-01 00:00:00
##  8 2013-10-12 20:09:00 2013-11-01 00:00:00
##  9 2013-01-12 06:05:00 2013-02-01 00:00:00
## 10 2013-02-12 17:04:00 2013-03-01 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 2013-02-12 06:24:00 2013-01-01 06:24:00
##  2 2013-06-13 12:43:00 2013-01-01 12:43:00
##  3 2013-01-01 18:15:00 2013-01-01 18:15:00
##  4 2013-09-09 21:31:00 2013-01-01 21:31:00
##  5 2013-05-13 12:02:00 2013-01-01 12:02:00
##  6 2013-05-12 20:55:00 2013-01-01 20:55:00
##  7 2013-08-03 07:21:00 2013-01-01 07:21:00
##  8 2013-12-08 09:53:00 2013-01-01 09:53:00
##  9 2013-12-21 11:05:00 2013-01-01 11:05:00
## 10 2013-07-11 17:13:00 2013-01-01 17:13:00

Times spand