data <- data.frame(
waymet = c("Met Online", "Met Offline"),
percent = c(18.9, 81.1)
)
data waymet percent
1 Met Online 18.9
2 Met Offline 81.1Waymet
Make bar graphs
Data in R
mtcars mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2library(palmerpenguins)
penguins# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>library(gapminder)
gapminder# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# ℹ 1,694 more rowslibrary(Lahman)
LahmanData file class nobs nvar title
1 AllstarFull data.frame 5454 8 AllstarFull table
2 Appearances data.frame 110423 21 Appearances table
3 AwardsManagers data.frame 179 6 AwardsManagers table
4 AwardsPlayers data.frame 6531 6 AwardsPlayers table
5 AwardsShareManagers data.frame 425 7 AwardsShareManagers table
6 AwardsSharePlayers data.frame 6879 7 AwardsSharePlayers table
7 Batting data.frame 110495 22 Batting table
8 BattingPost data.frame 15879 22 BattingPost table
9 CollegePlaying data.frame 17350 3 CollegePlaying table
10 Fielding data.frame 147080 18 Fielding table
11 FieldingOF data.frame 12028 6 FieldingOF table
12 FieldingOFsplit data.frame 34563 18 FieldingOFsplit table
13 FieldingPost data.frame 15063 17 FieldingPost data
14 HallOfFame data.frame 4191 9 Hall of Fame Voting Data
15 HomeGames data.frame 3195 9 HomeGames table
16 Managers data.frame 3684 10 Managers table
17 ManagersHalf data.frame 93 10 ManagersHalf table
18 Parks data.frame 255 6 Parks table
19 People data.frame 20370 26 People table
20 Pitching data.frame 49430 30 Pitching table
21 PitchingPost data.frame 6308 30 PitchingPost table
22 Salaries data.frame 26428 5 Salaries table
23 Schools data.frame 1207 5 Schools table
24 SeriesPost data.frame 367 9 SeriesPost table
25 Teams data.frame 2985 48 Teams table
26 TeamsFranchises data.frame 120 4 TeamFranchises table
27 TeamsHalf data.frame 52 10 TeamsHalf tablelibrary(fueleconomy)
common# A tibble: 347 × 4
make model n years
<chr> <chr> <int> <int>
1 Acura Integra 42 16
2 Acura Legend 28 10
3 Acura MDX 4WD 12 12
4 Acura NSX 28 14
5 Acura TSX 27 11
6 Audi A4 49 19
7 Audi A4 Avant quattro 49 15
8 Audi A4 quattro 66 19
9 Audi A6 20 19
10 Audi A6 Avant quattro 12 12
# ℹ 337 more rows