Session 6

2023-11-22

Types

R has different types of objects.

class(mtcars)
[1] "data.frame"
class(c(1, 2, 3))
[1] "numeric"
class(c("Hello", "Goodbye"))
[1] "character"

Data Frames

  • Data frames are special type of object composed of multiple objects.
glimpse(mpg)
Rows: 234
Columns: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
$ class        <chr> "compact", "compact", "compact", "compact", "compact", "c…

The Manufacturer column

mpg %>% count(manufacturer)
# A tibble: 15 × 2
   manufacturer     n
   <chr>        <int>
 1 audi            18
 2 chevrolet       19
 3 dodge           37
 4 ford            25
 5 honda            9
 6 hyundai         14
 7 jeep             8
 8 land rover       4
 9 lincoln          3
10 mercury          4
11 nissan          13
12 pontiac          5
13 subaru          14
14 toyota          34
15 volkswagen      27

A new type

  • Factors are used to represent categorical variables
mpg_2 <- mpg %>% 
  mutate(manufacturer=as.factor(manufacturer))

glimpse(mpg_2)
Rows: 234
Columns: 11
$ manufacturer <fct> audi, audi, audi, audi, audi, audi, audi, audi, audi, aud…
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
$ class        <chr> "compact", "compact", "compact", "compact", "compact", "c…

Class of the vector

class(mpg_2$manufacturer)
[1] "factor"

Factors

  • Behind the scenes, each category of a factor is assigned to a numeric value.
  • The levels
levels(mpg_2$manufacturer)
 [1] "audi"       "chevrolet"  "dodge"      "ford"       "honda"     
 [6] "hyundai"    "jeep"       "land rover" "lincoln"    "mercury"   
[11] "nissan"     "pontiac"    "subaru"     "toyota"     "volkswagen"
  • In this case, audi is 1, chevrolet 2, etc.
  • The order of the levels is this codification.
  • It is separate from the order in which the data frame is arranged.

Forcats

  • This package has functions to change the order of a factor. Think carefully about what you want.

  • fct_inorder(): by the order in which they first appear.

  • In this case, the alphabetical order is the same as the order in which they appear in the data. This might not be true.

fct_infreq

  • fct_infreq(): by number of observations with each level (largest first)
mpg_3 <- mpg %>% 
  mutate(manufacturer=fct_infreq(manufacturer))
levels(mpg_3$manufacturer)
 [1] "dodge"      "toyota"     "volkswagen" "ford"       "chevrolet" 
 [6] "audi"       "hyundai"    "subaru"     "nissan"     "honda"     
[11] "jeep"       "pontiac"    "land rover" "mercury"    "lincoln"

Example

  • In the NBA data, Team, Playoff, Conference and Division are factors.
nba <- readRDS('./nba.rds')
glimpse(nba)
Rows: 30
Columns: 28
$ Team       <chr> "Atlanta Hawks", "Boston Celtics", "Brooklyn Nets", "Charlo…
$ Playoff    <fct> N, Y, N, N, N, Y, N, N, N, Y, Y, Y, N, N, N, Y, Y, Y, Y, N,…
$ GP         <int> 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82,…
$ MIN        <int> 3941, 3961, 3971, 3956, 3971, 3946, 3961, 3976, 3961, 3946,…
$ PTS        <int> 8475, 8529, 8741, 8874, 8440, 9091, 8390, 9020, 8509, 9304,…
$ W          <int> 24, 55, 28, 36, 27, 50, 24, 46, 39, 58, 65, 48, 42, 35, 22,…
$ L          <int> 58, 27, 54, 46, 55, 32, 58, 36, 43, 24, 17, 34, 40, 47, 60,…
$ P2M        <int> 2213, 2202, 2095, 2373, 2264, 2330, 2161, 2398, 2322, 2583,…
$ P2A        <int> 4471, 4483, 4190, 4873, 4736, 4314, 4354, 4566, 4756, 4611,…
$ P2p        <dbl> 49.49676, 49.11889, 50.00000, 48.69690, 47.80405, 54.01020,…
$ P3M        <int> 917, 939, 1041, 824, 906, 981, 967, 940, 886, 926, 1256, 74…
$ P3A        <int> 2544, 2492, 2924, 2233, 2549, 2636, 2688, 2536, 2373, 2369,…
$ P3p        <dbl> 36.04560, 37.68058, 35.60192, 36.90103, 35.54335, 37.21548,…
$ FTM        <int> 1298, 1308, 1428, 1656, 1194, 1488, 1167, 1404, 1207, 1360,…
$ FTA        <int> 1654, 1697, 1850, 2216, 1574, 1909, 1530, 1830, 1621, 1668,…
$ FTp        <dbl> 78.47642, 77.07720, 77.18919, 74.72924, 75.85769, 77.94657,…
$ OREB       <int> 743, 767, 792, 827, 790, 694, 666, 902, 830, 691, 739, 788,…
$ DREB       <int> 2693, 2878, 2852, 2901, 2873, 2761, 2717, 2748, 2756, 2877,…
$ AST        <int> 1946, 1842, 1941, 1770, 1923, 1916, 1858, 2059, 1868, 2402,…
$ TOV        <int> 1276, 1149, 1245, 1041, 1147, 1126, 1007, 1227, 1103, 1265,…
$ STL        <int> 638, 604, 512, 559, 626, 582, 578, 627, 628, 655, 699, 721,…
$ BLK        <int> 348, 373, 390, 373, 289, 312, 310, 404, 317, 612, 392, 340,…
$ PF         <int> 1606, 1618, 1688, 1409, 1571, 1524, 1578, 1533, 1508, 1607,…
$ PM         <int> -447, 294, -307, 21, -577, 77, -249, 121, -12, 490, 695, 11…
$ team       <fct> ATL, BOS, BKN, CHA, CHI, CLE, DAL, DEN, DET, GSW, HOU, IND,…
$ Conference <fct> E, E, E, E, E, E, W, W, E, W, W, E, W, W, W, E, E, W, W, E,…
$ Division   <fct> Southeast, Atlantic, Atlantic, Southeast, Central, Central,…
$ Rank       <int> 15, 2, 12, 10, 13, 4, 13, 9, 9, 2, 1, 5, 10, 11, 14, 6, 7, …

Visualization

  • Create a barchart of the number of 3 Pts attempts for the season. Add color to the bars to show if each team made the playoffs.

ggplot(data=nba, mapping=aes(Team, P3A)) +
  geom_col()

Example (2)

Flipping coordinates is useful to show the labels of the Teams.

ggplot(nba, aes(Team, P3A)) +
  geom_col() + 
  coord_flip()

Order the factor

  • fct_reorder Reorders the factor by another variable.
nba %>% 
  mutate(Team=fct_reorder(Team, P3A)) %>% 
  ggplot(aes(Team, P3A, fill=Playoff)) +
  geom_col() +
  coord_flip()

Function arguments and defaults

  • You can pass arguments with or without names to functions
nba_data <- nba %>% 
  mutate(Team=fct_reorder(Team, P3A)) 

ggplot(data=nba_data, mapping=aes(x=Team, y=P3A, fill=Playoff)) +
  geom_col() +
  coord_flip()

ggplot(nba, aes(P2A,P3A)) + geom_point()

Time series plots

df <- read_excel('./mpd2018.xlsx', sheet = 'Full data')

df_uy_usa <- df %>% 
  filter(country %in% c("Uruguay", "United States"))


plt <- df_uy_usa %>% 
  filter(year>1800) %>% 
  ggplot(aes(year, cgdppc)) + 
  geom_line(aes(group=country, color=country)) +
  scale_color_discrete("") +
  labs(title="USA leaves Uruguay behind",
       subtitle="Income gap widened last century",
       x="Year",
       y="Per Capita GDP") +
  theme_minimal() +
  theme(legend.position = "bottom")

plt

Facets

  • Facets allow us to see different subsets of our data in different panes.

Facets

Mitch Henderson

Facets in ggplot

  • If we add a facetting layer to our ggplot, we get one pane for each subset of our facetting variable.
xw_long <- readRDS('./xw_long.rds')
glimpse(xw_long)
Rows: 360
Columns: 4
$ team  <chr> "Dallas Mavericks", "Dallas Mavericks", "Dallas Mavericks", "Dal…
$ conf  <fct> W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W, W…
$ stat  <chr> "GP", "MIN", "PTS", "W", "L", "P2M", "P2A", "P2p", "P3M", "P3A",…
$ value <dbl> 82.00000, 3961.00000, 8390.00000, 24.00000, 58.00000, 2161.00000…

Facets

stats_plot <- xw_long %>% 
  filter(stat %in% c("STL","BLK","DREB")) %>% 
  ggplot(aes(x=team, y=value, fill=stat)) +
  geom_col() + 
  coord_flip() +
  facet_grid(~stat)

Scales