A factor is a special character vector where the elements have pre-defined groups or ‘levels’. You can think of these as qualitative or categorical variables:
x <- c("yellow", "red", "red", "blue", "yellow", "blue")
class(x)
## [1] "character"
x_fact <- factor(x) # factor() is a function class(x_fact)
## [1] "factor"
Factors have levels (character types do not).
x
## [1] "yellow" "red" "red" "blue" "yellow" "blue"
x_fact
## [1] yellow red red blue yellow blue ## Levels: blue red yellow
Note that levels are, by default, in alphanumerical order.
Extract the levels of a factor vector using levels():
levels(x_fact)
## [1] "blue" "red" "yellow"
ufo <-
read_csv("https://raw.githubusercontent.com/SISBID/Module1/gh-pages/data/ufo/ufo_data_complete.csv")
## Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.: ## dat <- vroom(...) ## problems(dat)
## Rows: 88875 Columns: 11 ## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ## Delimiter: "," ## chr (10): datetime, city, state, country, shape, duration (hours/min), comme... ## dbl (1): duration (seconds) ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Using janitor.
ufo <- clean_names(ufo) glimpse(ufo)
## Rows: 88,875 ## Columns: 11 ## $ datetime <chr> "10/10/1949 20:30", "10/10/1949 21:00", "10/10/1955… ## $ city <chr> "san marcos", "lackland afb", "chester (uk/england)… ## $ state <chr> "tx", "tx", NA, "tx", "hi", "tn", NA, "ct", "al", "… ## $ country <chr> "us", NA, "gb", "us", "us", "us", "gb", "us", "us",… ## $ shape <chr> "cylinder", "light", "circle", "circle", "light", "… ## $ duration_seconds <dbl> 2700, 7200, 20, 20, 900, 300, 180, 1200, 180, 120, … ## $ duration_hours_min <chr> "45 minutes", "1-2 hrs", "20 seconds", "1/2 hour", … ## $ comments <chr> "This event took place in early fall around 1949-50… ## $ date_posted <chr> "4/27/2004", "12/16/2005", "1/21/2008", "1/17/2004"… ## $ latitude <chr> "29.8830556", "29.38421", "53.2", "28.9783333", "21… ## $ longitude <chr> "-97.9411111", "-98.581082", "-2.916667", "-96.6458…
ufo <- ufo |>separate(datetime,into = c("date", "time"), sep = " ")
glimpse(ufo)
## Rows: 88,875 ## Columns: 12 ## $ date <chr> "10/10/1949", "10/10/1949", "10/10/1955", "10/10/19… ## $ time <chr> "20:30", "21:00", "17:00", "21:00", "20:00", "19:00… ## $ city <chr> "san marcos", "lackland afb", "chester (uk/england)… ## $ state <chr> "tx", "tx", NA, "tx", "hi", "tn", NA, "ct", "al", "… ## $ country <chr> "us", NA, "gb", "us", "us", "us", "gb", "us", "us",… ## $ shape <chr> "cylinder", "light", "circle", "circle", "light", "… ## $ duration_seconds <dbl> 2700, 7200, 20, 20, 900, 300, 180, 1200, 180, 120, … ## $ duration_hours_min <chr> "45 minutes", "1-2 hrs", "20 seconds", "1/2 hour", … ## $ comments <chr> "This event took place in early fall around 1949-50… ## $ date_posted <chr> "4/27/2004", "12/16/2005", "1/21/2008", "1/17/2004"… ## $ latitude <chr> "29.8830556", "29.38421", "53.2", "28.9783333", "21… ## $ longitude <chr> "-97.9411111", "-98.581082", "-2.916667", "-96.6458…
ufo <- ufo |> separate(time, into= c("hour", "min"))
glimpse(ufo)
## Rows: 88,875 ## Columns: 13 ## $ date <chr> "10/10/1949", "10/10/1949", "10/10/1955", "10/10/19… ## $ hour <chr> "20", "21", "17", "21", "20", "19", "21", "23", "20… ## $ min <chr> "30", "00", "00", "00", "00", "00", "00", "45", "00… ## $ city <chr> "san marcos", "lackland afb", "chester (uk/england)… ## $ state <chr> "tx", "tx", NA, "tx", "hi", "tn", NA, "ct", "al", "… ## $ country <chr> "us", NA, "gb", "us", "us", "us", "gb", "us", "us",… ## $ shape <chr> "cylinder", "light", "circle", "circle", "light", "… ## $ duration_seconds <dbl> 2700, 7200, 20, 20, 900, 300, 180, 1200, 180, 120, … ## $ duration_hours_min <chr> "45 minutes", "1-2 hrs", "20 seconds", "1/2 hour", … ## $ comments <chr> "This event took place in early fall around 1949-50… ## $ date_posted <chr> "4/27/2004", "12/16/2005", "1/21/2008", "1/17/2004"… ## $ latitude <chr> "29.8830556", "29.38421", "53.2", "28.9783333", "21… ## $ longitude <chr> "-97.9411111", "-98.581082", "-2.916667", "-96.6458…
ufo <-ufo |> mutate(hour = as.numeric(hour), min = as.numeric(min)) glimpse(ufo)
## Rows: 88,875 ## Columns: 13 ## $ date <chr> "10/10/1949", "10/10/1949", "10/10/1955", "10/10/19… ## $ hour <dbl> 20, 21, 17, 21, 20, 19, 21, 23, 20, 21, 13, 19, 16,… ## $ min <dbl> 30, 0, 0, 0, 0, 0, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 3… ## $ city <chr> "san marcos", "lackland afb", "chester (uk/england)… ## $ state <chr> "tx", "tx", NA, "tx", "hi", "tn", NA, "ct", "al", "… ## $ country <chr> "us", NA, "gb", "us", "us", "us", "gb", "us", "us",… ## $ shape <chr> "cylinder", "light", "circle", "circle", "light", "… ## $ duration_seconds <dbl> 2700, 7200, 20, 20, 900, 300, 180, 1200, 180, 120, … ## $ duration_hours_min <chr> "45 minutes", "1-2 hrs", "20 seconds", "1/2 hour", … ## $ comments <chr> "This event took place in early fall around 1949-50… ## $ date_posted <chr> "4/27/2004", "12/16/2005", "1/21/2008", "1/17/2004"… ## $ latitude <chr> "29.8830556", "29.38421", "53.2", "28.9783333", "21… ## $ longitude <chr> "-97.9411111", "-98.581082", "-2.916667", "-96.6458…
ufo <- ufo |> mutate(timespan =
case_when(hour %in%c(18,19,20,21)~ "Evening",
hour >21 ~ "Night",
hour >=0 & hour <12 ~ "Morning",
hour >=12 & hour <18 ~ "Afternoon"))
ufo |> count(timespan)
## # A tibble: 4 × 2 ## timespan n ## <chr> <int> ## 1 Afternoon 10828 ## 2 Evening 32684 ## 3 Morning 23517 ## 4 Night 21846
You may have noticed that character data is arranged alphabetically.
ufo |> count(timespan)
## # A tibble: 4 × 2 ## timespan n ## <chr> <int> ## 1 Afternoon 10828 ## 2 Evening 32684 ## 3 Morning 23517 ## 4 Night 21846
Character data is arranged alphabetically.
ufo |> arrange(timespan) |> glimpse()
## Rows: 88,875 ## Columns: 14 ## $ date <chr> "10/10/1955", "10/10/1968", "10/10/1970", "10/10/19… ## $ hour <dbl> 17, 13, 16, 17, 17, 12, 12, 17, 15, 17, 16, 17, 13,… ## $ min <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 15, 30, 30, 40,… ## $ city <chr> "chester (uk/england)", "hawthorne", "bellmore", "w… ## $ state <chr> NA, "ca", "ny", "az", "sc", "tx", "mi", "fl", "tx",… ## $ country <chr> "gb", "us", "us", NA, "us", "us", "us", "us", NA, N… ## $ shape <chr> "circle", "circle", "disk", "light", "light", "othe… ## $ duration_seconds <dbl> 20, 300, 1800, 120, 360, 30, 120, 3600, 3600, 0, 14… ## $ duration_hours_min <chr> "20 seconds", "5 min.", "30 min.", "2 min", "5-6 mi… ## $ comments <chr> "Green/Orange circular disc over Chester, Englan… ## $ date_posted <chr> "1/21/2008", "10/31/2003", "5/11/2000", "2/18/2001"… ## $ latitude <chr> "53.2", "33.9163889", "40.6686111", "0", "32.854444… ## $ longitude <chr> "-2.916667", "-118.3516667", "-73.5275000", "0", "-… ## $ timespan <chr> "Afternoon", "Afternoon", "Afternoon", "Afternoon",…
count(ufo, timespan) |> ggplot(aes(x = timespan, y = n)) + geom_col()
Currently timespan is class character but let’s change that to class factor which allows us to specify the levels or order of the values.
ufo <- ufo |> mutate(timespan = factor(timespan, levels =
c("Morning", "Afternoon", "Evening", "Night")))
Factor data is arranged by level.
ufo |> count(timespan)
## # A tibble: 4 × 2 ## timespan n ## <fct> <int> ## 1 Morning 23517 ## 2 Afternoon 10828 ## 3 Evening 32684 ## 4 Night 21846
Nice this is better!
Factor data is arranged by level.
ufo |> arrange(timespan) |> glimpse()
## Rows: 88,875 ## Columns: 14 ## $ date <chr> "10/10/1978", "10/10/1979", "10/10/1982", "10/10/19… ## $ hour <dbl> 2, 0, 7, 5, 0, 3, 3, 2, 2, 3, 0, 3, 4, 6, 11, 3, 3,… ## $ min <dbl> 0, 0, 0, 0, 0, 0, 20, 0, 30, 30, 1, 0, 0, 0, 0, 0, … ## $ city <chr> "elmont", "poughkeepsie", "gisborne (new zealand)",… ## $ state <chr> "ny", "ny", NA, "tx", "ca", NA, "mo", NA, "ca", "az… ## $ country <chr> "us", "us", NA, "us", "us", NA, "us", NA, "us", "us… ## $ shape <chr> "rectangle", "chevron", "disk", "circle", "disk", "… ## $ duration_seconds <dbl> 300, 900, 120, 60, 300, 1200, 3, 15, 300, 15, 3600,… ## $ duration_hours_min <chr> "5min", "15 minutes", "2min", "1 minute", "approx 5… ## $ comments <chr> "A memory I will never forget that happened meny ye… ## $ date_posted <chr> "2/1/2007", "4/16/2005", "1/11/2002", "4/18/2012", … ## $ latitude <chr> "40.7008333", "41.7002778", "-38.662334", "29.76305… ## $ longitude <chr> "-73.7133333", "-73.9213889", "178.017649", "-95.36… ## $ timespan <fct> Morning, Morning, Morning, Morning, Morning, Mornin…
The factor data is automatically plotted in the order we would like.
count(ufo, timespan) |> ggplot(
aes(x = timespan, y = n)) +
geom_col() +
xlab("General Time of Sighting") + ylab("Frequency")+
theme_bw(base_size = 16)
forcats packageA package called forcats is really helpful for working with factors.
forcats for orderingWe can order a factor by another variable by using the fct_reorder() function of the forcats package.
fct_reorder({column getting changed}, {guiding column}, {summarizing function})forcats for orderingLet’s reorder by another variable using fct_reorder.
count(ufo, timespan) |> ggplot(
aes(x = fct_reorder(timespan, n), y = n)) +
geom_col() +
xlab("General Time of Sighting") + ylab("Frequency")+
theme_bw(base_size = 16)
This would be useful for identifying easily which timespan to focus on.
.desc = argumentcount(ufo, timespan) |> ggplot(
aes(x = fct_reorder(timespan, n, .desc = TRUE), y = n)) +
geom_col() +
xlab("General Time of Sighting") + ylab("Frequency")+
theme_bw(base_size = 16)
A. Meaningful ordering of text data
B. Automatic ordering or numeric data
C. More precise values
fct_count()The fct_count() function of the forcats package is helpful for checking that the proportions of each level for a factor are similar. Need the prop = TRUE argument otherwise just counts are reported.
ufo |> pull(timespan) |> fct_count(prop = TRUE)
## # A tibble: 4 × 3 ## f n p ## <fct> <int> <dbl> ## 1 Morning 23517 0.265 ## 2 Afternoon 10828 0.122 ## 3 Evening 32684 0.368 ## 4 Night 21846 0.246
mutate and a factor creating function like factor() (alphabetical order by default)factor() we can specify the levels with the levels argument if we want a specific orderfct_reorder({variable_to_reorder}, {variable_to_order_by}, {summary function}) helps us reorder a variable by the values of another variable🏠 Class Website
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