Factors

Jeff Stevens

2023-03-20

Introduction

The problem

What is needed to create data2 from data1?

data1
# A tibble: 8 × 2
  species      n
  <chr>    <int>
1 Droid        6
2 Gungan       3
3 Human       35
4 Kaminoan     2
5 Mirialan     2
6 Twi'lek      2
7 Wookiee      2
8 Zabrak       2
data2
# A tibble: 4 × 2
  species     n
  <fct>   <int>
1 Human      35
2 Other      10
3 Android     6
4 Gungan      3

Factors

Categorical variables represented by augmented integers

(x1 <- c("Apr", "Jun", "Oct", "Jan"))
[1] "Apr" "Jun" "Oct" "Jan"
month.abb
 [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
(y1 <- factor(x1))
[1] Apr Jun Oct Jan
Levels: Apr Jan Jun Oct
class(y1)
[1] "factor"
typeof(y1)
[1] "integer"

Mental model

View/set factor levels

levels()

y1
[1] Apr Jun Oct Jan
Levels: Apr Jan Jun Oct
levels(y1)
[1] "Apr" "Jan" "Jun" "Oct"
(y2 <- factor(x1, levels = month.abb))
[1] Apr Jun Oct Jan
Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
levels(y2)
 [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

Label factors

labels argument

(y3 <- factor(y1, labels = c("April", "January", "June", "October")))
[1] April   June    October January
Levels: April January June October

Note

Note you have to label them in the order they appear as levels.

Factors with {forcats}

Data set

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

Reordering factor levels

Check level order

levels(y1)
[1] "Apr" "Jan" "Jun" "Oct"

Note

By default, level elements are sorted alphabetically.

Check level order with pipes

marital <- gss_cat |>
  pull(marital)
marital |> levels()
[1] "No answer"     "Never married" "Separated"     "Divorced"     
[5] "Widowed"       "Married"

Note

Here, order was determined already.

Reordering levels

In the order present in the data with fct_inorder()

head(marital, 20)
 [1] Never married Divorced      Widowed       Never married Divorced     
 [6] Married       Never married Divorced      Married       Married      
[11] Married       Married       Married       Married       Divorced     
[16] Married       Widowed       Never married Married       Married      
Levels: No answer Never married Separated Divorced Widowed Married
marital |> fct_inorder() |>
  levels()
[1] "Never married" "Divorced"      "Widowed"       "Married"      
[5] "Separated"     "No answer"

Reordering levels

In the order based on number of observations of each level with fct_infreq()

gss_cat |>
  count(marital, sort = TRUE)
# A tibble: 6 × 2
  marital           n
  <fct>         <int>
1 Married       10117
2 Never married  5416
3 Divorced       3383
4 Widowed        1807
5 Separated       743
6 No answer        17
marital |> fct_infreq() |>
  levels()
[1] "Married"       "Never married" "Divorced"      "Widowed"      
[5] "Separated"     "No answer"

Reordering levels

In reverse order of factor levels with fct_rev()

levels(marital)
[1] "No answer"     "Never married" "Separated"     "Divorced"     
[5] "Widowed"       "Married"      
marital |> fct_rev() |>
  levels()
[1] "Married"       "Widowed"       "Divorced"      "Separated"    
[5] "Never married" "No answer"

Manually reorder factor levels

fct_relevel()

(relig_summary <- gss_cat |>
    group_by(relig) |>
    summarise(
      tvhours = mean(tvhours, na.rm = TRUE),
      n = n()))
# A tibble: 15 × 3
   relig                   tvhours     n
   <fct>                     <dbl> <int>
 1 No answer                  2.72    93
 2 Don't know                 4.62    15
 3 Inter-nondenominational    2.87   109
 4 Native american            3.46    23
 5 Christian                  2.79   689
 6 Orthodox-christian         2.42    95
 7 Moslem/islam               2.44   104
 8 Other eastern              1.67    32
 9 Hinduism                   1.89    71
10 Buddhism                   2.38   147
11 Other                      2.73   224
12 None                       2.71  3523
13 Jewish                     2.52   388
14 Catholic                   2.96  5124
15 Protestant                 3.15 10846

Manually reorder factor levels

fct_relevel()

relig_summary |> ggplot(aes(x = tvhours, y = relig)) +
  geom_point()

Manually reorder factor levels

fct_relevel()

relig_summary_releveled <- relig_summary |>
  mutate(relig = fct_relevel(relig, c("None", "Other"), after = 2))
levels(relig_summary_releveled$relig)
 [1] "No answer"               "Don't know"             
 [3] "None"                    "Other"                  
 [5] "Inter-nondenominational" "Native american"        
 [7] "Christian"               "Orthodox-christian"     
 [9] "Moslem/islam"            "Other eastern"          
[11] "Hinduism"                "Buddhism"               
[13] "Jewish"                  "Catholic"               
[15] "Protestant"              "Not applicable"

Manually reorder factor levels

fct_relevel()

relig_summary_releveled |> ggplot(aes(x = tvhours, y = relig)) +
  geom_point()

Order factors by another variable

fct_reorder()

relig_summary |> mutate(relig = fct_reorder(relig, tvhours)) |>
  ggplot(aes(x = tvhours, y = relig)) +
  geom_point()

Order factors by another variable

fct_reorder()

ggplot(relig_summary, aes(x = tvhours, y = fct_reorder(relig, tvhours))) +
  geom_point()

Recode factors

Recode factor levels

fct_recode()

gss_cat |>
  count(partyid)
# A tibble: 10 × 2
   partyid                n
   <fct>              <int>
 1 No answer            154
 2 Don't know             1
 3 Other party          393
 4 Strong republican   2314
 5 Not str republican  3032
 6 Ind,near rep        1791
 7 Independent         4119
 8 Ind,near dem        2499
 9 Not str democrat    3690
10 Strong democrat     3490

Recode factor levels

fct_recode()

gss_cat |>
  mutate(partyid = fct_recode(partyid,
                              "Republican, strong"    = "Strong republican",
                              "Republican, weak"      = "Not str republican",
                              "Independent, near rep" = "Ind,near rep",
                              "Independent, near dem" = "Ind,near dem",
                              "Democrat, weak"        = "Not str democrat",
                              "Democrat, strong"      = "Strong democrat"
  )) |>
  count(partyid)
# A tibble: 10 × 2
   partyid                   n
   <fct>                 <int>
 1 No answer               154
 2 Don't know                1
 3 Other party             393
 4 Republican, strong     2314
 5 Republican, weak       3032
 6 Independent, near rep  1791
 7 Independent            4119
 8 Independent, near dem  2499
 9 Democrat, weak         3690
10 Democrat, strong       3490

Recode factor levels

fct_recode()

gss_cat |>
  mutate(partyid = fct_recode(partyid,
                              "Independent, near rep" = "Ind,near rep",
                              "Independent, near dem" = "Ind,near dem",
  )) |>
  count(partyid)
# A tibble: 10 × 2
   partyid                   n
   <fct>                 <int>
 1 No answer               154
 2 Don't know                1
 3 Other party             393
 4 Strong republican      2314
 5 Not str republican     3032
 6 Independent, near rep  1791
 7 Independent            4119
 8 Independent, near dem  2499
 9 Not str democrat       3690
10 Strong democrat        3490

Collapse multiple levels

fct_collapse()

gss_cat |>
  mutate(partyid = fct_collapse(partyid,
                                "other" = c("No answer", "Don't know", "Other party"),
                                "rep" = c("Strong republican", "Not str republican"),
                                "ind" = c("Ind,near rep", "Independent", "Ind,near dem"),
                                "dem" = c("Not str democrat", "Strong democrat")
  )) |>
  count(partyid)
# A tibble: 4 × 2
  partyid     n
  <fct>   <int>
1 other     548
2 rep      5346
3 ind      8409
4 dem      7180

Combine factor levels

Based on number of groups: fct_lump_n()

gss_cat |>
  mutate(relig = fct_lump_n(relig, n = 5)) |>
  count(relig, sort = TRUE)
# A tibble: 6 × 2
  relig          n
  <fct>      <int>
1 Protestant 10846
2 Catholic    5124
3 None        3523
4 Other        913
5 Christian    689
6 Jewish       388

Combine factor levels

Based on proportion of total: fct_lump_prop()

gss_cat |>
  mutate(relig = fct_lump_prop(relig, prop = 0.1, 
                               other_level = "Something else")) |>
  count(relig, sort = TRUE)
# A tibble: 4 × 2
  relig              n
  <fct>          <int>
1 Protestant     10846
2 Catholic        5124
3 None            3523
4 Something else  1990

Combine factor levels

Based on minimum number: fct_lump_min()

gss_cat |>
  mutate(relig = fct_lump_min(relig, min = 200)) |>
  count(relig, sort = TRUE)
# A tibble: 6 × 2
  relig          n
  <fct>      <int>
1 Protestant 10846
2 Catholic    5124
3 None        3523
4 Other        913
5 Christian    689
6 Jewish       388

Solving the problem

What code generates data2 from data1?

data1
# A tibble: 8 × 2
  species      n
  <chr>    <int>
1 Droid        6
2 Gungan       3
3 Human       35
4 Kaminoan     2
5 Mirialan     2
6 Twi'lek      2
7 Wookiee      2
8 Zabrak       2
data2
# A tibble: 4 × 2
  species     n
  <fct>   <int>
1 Human      35
2 Other      10
3 Android     6
4 Gungan      3

Let’s code!

Factors [Rmd]

DPaViR 2023