• Factors are used for categorical variables that have a fixed and known set of values
• Useful when we want to display character vectors in non-alphabetical order
• E.g. let's suppose we have a variable that records month:

x1 <- c("Dec", "Apr", "Jan", "Mar")

Two issues:

• Only twelve possible months, so if we typed "Jam" instead of "Jan", would be hard to catch the error.
• Sorting by characters is not meaningful:

sort(x1)
#> [1] "Apr" "Dec" "Jan" "Mar"

• If you create a factor from a vector, every element of the vector must be a level of a factor, otherwise you get NA:

x2 <- c("Dec", "Apr", "Jam", "Mar")
(y2 <- factor(x2, levels = month_levels))
#> [1] Dec  Apr  <NA> Mar 
#> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

• If you want to throw an error for non-level vector info, you can use `forcats::fct()

y2 <- fct(x2, levels = month_levels)
#> Error in `fct()`:
#> ! All values of `x` must appear in `levels` or `na`
#> ℹ Missing level: "Jam"

gss_cat %>%
  count(race)

• Let's look at the distribution of rincome (reported income)

ggplot(gss_cat, aes(x = rincome)) + geom_bar()

• A little hard to see what's happening on the x-axis!

• Does income vary by religion much?

ggplot(gss_cat, aes(x = rincome)) + geom_bar()

• A little hard to see what's happening on the x-axis!

• What are the most common religions in the survey? What about party ID?

gss_cat %>% count(relig) %>% arrange(n) %>% print(n=Inf)
#> # A tibble: 15 × 2
#>    relig                       n
#>    <fct>                   <int>
#>  1 Don't know                 15
#>  2 Native american            23
#>  3 Other eastern              32
#>  4 Hinduism                   71
#>  5 No answer                  93
#>  6 Orthodox-christian         95
#>  7 Moslem/islam              104
#>  8 Inter-nondenominational   109
#>  9 Buddhism                  147
#> 10 Other                     224
#> 11 Jewish                    388
#> 12 Christian                 689
#> 13 None                     3523
#> 14 Catholic                 5124
#> 15 Protestant              10846

• Let's look at different TV watching habits by marital status. Maybe we can first try doing a geom_freqpoly() with color given by religion.

ggplot(gss_cat,
       aes(x = tvhours, y = after_stat(density), color = marital)) + 
  geom_freqpoly()

• To reduce amount of information, we should summarize the data, e.g. by taking the average per religion.

relig_summary <- gss_cat |>
  group_by(relig) |>
  summarize(
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n()
  )
ggplot(relig_summary, aes(x = tvhours, y = relig)) + 
  geom_point()

• Hard to read because there's no clear pattern in the plot.

• Often will want to change values of levels so that plots look better

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
#> # ℹ 4 more rows

• Not ideal for plotting - inconsistent (acronyms vs full spelling etc)
• Key function: 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
#> # ℹ 4 more rows

• Levels not explicitly mentioned will be kept as is.

• To combine groups, assign multiple old levels to same new level

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",
      "Other"                 = "No answer",
      "Other"                 = "Don't know",
      "Other"                 = "Other party"
    )
  )

• Let's try plotting the monthly temps at Newark (EWR).

weather2 %>% filter(origin == 'EWR') %>%
  ggplot(aes(x = month)) + 
  geom_line(aes(y = min_temp), color = 'blue') +
  geom_line(aes(y = max_temp), color = 'red') + 
  geom_line(aes(y = avg_temp), color = 'black')

• This is OK, but not ideal. No legend appears by default, also have to manually add three geom_line's.
• Better idea is to clean the tibble so that we have groups of data: for this we need data to be long

• Now we can do color by temperature type:

ggplot(weather3 %>% filter(origin == 'EWR'),
       aes(x = month, y = temperature, color = measurement)) +
  geom_line()

• Now we'd like better legend; blue for min, black for avg, and red for max; fix ticks

ggplot(weather3 %>% filter(origin == 'EWR'),
       aes(x = month, y = temperature, color = measurement)) +
  geom_line(linewidth = 2) + 
  scale_color_manual(
    values = c(min_temp = "blue", avg_temp = "black", max_temp = "red"),
    labels = c("min_temp" = "minimum", "avg_temp" = "average", "max_temp" = "maximum")) + 
  scale_x_continuous(breaks = 1:12, minor_breaks = 1:12) + 
  labs(title = "Temperature at EWR per month")

• Now let's do the same type of plot but with each airport's min/avg/max temp per month plotted side-by-side.

ggplot(weather3,
       aes(x = month, y = temperature, color = measurement)) +
  geom_line(linewidth = 2) + 
  scale_color_manual(
    values = c(min_temp = "blue", avg_temp = "black", max_temp = "red"),
    labels = c("min_temp" = "minimum", "avg_temp" = "average", "max_temp" = "maximum")) + 
  scale_x_continuous(breaks = 1:12, minor_breaks = 1:12) + 
  facet_grid(. ~ origin)

weather %>%
  filter(month == 5, temp < 25)
#> # A tibble: 1 × 15
#>   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
#>   <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
#> 1 JFK     2013     5     8    22  13.1  12.0  95.3       80       8.06
#> # ℹ 5 more variables: wind_gust <dbl>, precip <dbl>, pressure <dbl>,
#> #   visib <dbl>, time_hour <dttm>

• Appears real!