From Lab 1: it is important to keep track of where files are located in your computer.
Your hard drive is organized in folders or directories.
In Mac os, ~/Desktop is your desktop.
If you have a directory called lab 1 in your desktop, ~/Desktop/lab1 is the location of that folder.
Run setwd("~/Desktop/lab1") to make that the working directory.
list.files() prints the contents of the working directory in your console.
R code is organized in functions.
Functions take arguments and return values.
Data is stored in objects.
Assignment (<-) makes variables point to objects.
x <- c(1, 2, 3)mean(x)## [1] 2str(mtcars)## 'data.frame': 32 obs. of 11 variables:## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...## $ disp: num 160 160 108 258 360 ...## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...## $ qsec: num 16.5 17 18.6 19.4 17 ...## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...## $ am : num 1 1 1 0 0 0 0 0 0 0 ...## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...# NBA championship 2017/2018 82 regular season gamesdf <- readRDS('./nba.rds')df## Team Playoff GP MIN PTS W L P2M P2A P2p P3M## 1 Atlanta Hawks N 82 3941 8475 24 58 2213 4471 49.49676 917## 2 Boston Celtics Y 82 3961 8529 55 27 2202 4483 49.11889 939## 3 Brooklyn Nets N 82 3971 8741 28 54 2095 4190 50.00000 1041## 4 Charlotte Hornets N 82 3956 8874 36 46 2373 4873 48.69690 824## 5 Chicago Bulls N 82 3971 8440 27 55 2264 4736 47.80405 906## 6 Cleveland Cavaliers Y 82 3946 9091 50 32 2330 4314 54.01020 981## 7 Dallas Mavericks N 82 3961 8390 24 58 2161 4354 49.63252 967## 8 Denver Nuggets N 82 3976 9020 46 36 2398 4566 52.51862 940## 9 Detroit Pistons N 82 3961 8509 39 43 2322 4756 48.82254 886## 10 Golden State Warriors Y 82 3946 9304 58 24 2583 4611 56.01822 926## 11 Houston Rockets Y 82 3951 9213 65 17 1918 3436 55.82072 1256## 12 Indiana Pacers Y 82 3951 8656 48 34 2604 5073 51.33057 741## 13 LA Clippers N 82 3941 8937 42 40 2525 4808 52.51664 777## 14 Los Angeles Lakers N 82 3981 8862 35 47 2516 4864 51.72697 822## 15 Memphis Grizzlies N 82 3941 8145 22 60 2255 4636 48.64107 758## 16 Miami Heat Y 82 3986 8480 44 38 2281 4491 50.79047 903## 17 Milwaukee Bucks Y 82 3966 8731 44 38 2539 4783 53.08384 718## 18 Minnesota Timberwolves Y 82 3961 8980 47 35 2707 5218 51.87811 658## 19 New Orleans Pelicans Y 82 3991 9161 48 34 2663 4929 54.02719 837## 20 New York Knicks N 82 3966 8566 29 53 2661 5279 50.40727 673## 21 Oklahoma City Thunder Y 82 3966 8844 48 34 2390 4730 50.52854 881## 22 Orlando Magic N 82 3946 8479 25 57 2338 4637 50.42053 844## 23 Philadelphia 76ers Y 82 3956 9004 52 30 2448 4653 52.61122 901## 24 Phoenix Suns N 82 3941 8522 21 61 2390 4855 49.22760 763## 25 Portland Trail Blazers Y 82 3951 8661 49 33 2377 4824 49.27446 845## 26 Sacramento Kings N 82 3951 8104 27 55 2441 5096 47.90031 738## 27 San Antonio Spurs Y 82 3946 8424 47 35 2506 5022 49.90044 696## 28 Toronto Raptors Y 82 3966 9156 59 23 2415 4464 54.09946 968## 29 Utah Jazz Y 82 3951 8540 48 34 2252 4372 51.50961 887## 30 Washington Wizards Y 82 3971 8742 43 39 2461 4845 50.79463 814## P3A P3p FTM FTA FTp OREB DREB AST TOV STL BLK PF PM team## 1 2544 36.04560 1298 1654 78.47642 743 2693 1946 1276 638 348 1606 -447 ATL## 2 2492 37.68058 1308 1697 77.07720 767 2878 1842 1149 604 373 1618 294 BOS## 3 2924 35.60192 1428 1850 77.18919 792 2852 1941 1245 512 390 1688 -307 BKN## 4 2233 36.90103 1656 2216 74.72924 827 2901 1770 1041 559 373 1409 21 CHA## 5 2549 35.54335 1194 1574 75.85769 790 2873 1923 1147 626 289 1571 -577 CHI## 6 2636 37.21548 1488 1909 77.94657 694 2761 1916 1126 582 312 1524 77 CLE## 7 2688 35.97470 1167 1530 76.27451 666 2717 1858 1007 578 310 1578 -249 DAL## 8 2536 37.06625 1404 1830 76.72131 902 2748 2059 1227 627 404 1533 121 DEN## 9 2373 37.33670 1207 1621 74.46021 830 2756 1868 1103 628 317 1508 -12 DET## 10 2369 39.08822 1360 1668 81.53477 691 2877 2402 1265 655 612 1607 490 GSW## 11 3470 36.19597 1609 2061 78.06890 739 2825 1767 1135 699 392 1597 695 HOU## 12 2010 36.86567 1225 1573 77.87667 788 2684 1819 1088 721 340 1544 113 IND## 13 2196 35.38251 1556 2095 74.27208 832 2767 1832 1204 628 373 1638 3 LAC## 14 2384 34.47987 1364 1910 71.41361 876 2927 1949 1295 633 388 1736 -127 LAL## 15 2152 35.22305 1361 1732 78.57968 779 2544 1767 1227 612 396 1900 -509 MEM## 16 2506 36.03352 1209 1601 75.51530 763 2801 1862 1178 620 437 1648 39 MIA## 17 2024 35.47431 1499 1915 78.27676 688 2579 1905 1135 722 443 1752 -25 MIL## 18 1845 35.66396 1592 1980 80.40404 848 2593 1861 1021 689 345 1495 183 MIN## 19 2312 36.20242 1324 1716 77.15618 712 2924 2195 1223 657 485 1570 107 NOP## 20 1914 35.16196 1225 1557 78.67694 859 2752 1912 1207 552 421 1682 -292 NYK## 21 2491 35.36732 1421 1985 71.58690 1024 2671 1750 1147 743 412 1653 280 OKC## 22 2405 35.09356 1271 1678 75.74493 722 2692 1921 1192 622 400 1579 -395 ORL## 23 2445 36.85072 1405 1868 75.21413 893 2996 2221 1353 682 420 1811 369 PHI## 24 2286 33.37708 1453 1962 74.05708 842 2776 1743 1289 569 370 1807 -768 PHX## 25 2308 36.61179 1372 1715 80.00000 835 2893 1599 1109 573 423 1599 213 POR## 26 1967 37.51906 1008 1371 73.52298 777 2578 1768 1125 643 340 1639 -573 SAC## 27 1977 35.20486 1324 1715 77.20117 849 2777 1868 1078 628 460 1408 237 SAS## 28 2705 35.78558 1422 1790 79.44134 800 2807 1995 1095 626 500 1783 638 TOR## 29 2425 36.57732 1375 1766 77.85957 740 2807 1839 1205 708 420 1608 353 UTA## 30 2173 37.45973 1378 1786 77.15566 823 2713 2065 1196 645 353 1746 48 WAS## Conference Division Rank## 1 E Southeast 15## 2 E Atlantic 2## 3 E Atlantic 12## 4 E Southeast 10## 5 E Central 13## 6 E Central 4## 7 W Southwest 13## 8 W Northwest 9## 9 E Central 9## 10 W Pacific 2## 11 W Southwest 1## 12 E Central 5## 13 W Pacific 10## 14 W Pacific 11## 15 W Southwest 14## 16 E Southeast 6## 17 E Central 7## 18 W Northwest 8## 19 W Southwest 6## 20 E Atlantic 11## 21 W Northwest 4## 22 E Southeast 14## 23 E Atlantic 3## 24 W Pacific 15## 25 W Northwest 3## 26 W Pacific 12## 27 W Southwest 7## 28 E Atlantic 1## 29 W Northwest 5## 30 E Southeast 8library(dplyr)glimpse(df)## 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, …Key functions. Take a data.frame as input and return a data.frame.
filterselectmutategroup_bysummarizearrangeFilter rows from the data.frame.
playoff_teams <- filter(df, Playoff=='Y')glimpse(playoff_teams)## Rows: 16## Columns: 28## $ Team <chr> "Boston Celtics", "Cleveland Cavaliers", "Golden State Warr…## $ Playoff <fct> Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y## $ GP <int> 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82,…## $ MIN <int> 3961, 3946, 3946, 3951, 3951, 3986, 3966, 3961, 3991, 3966,…## $ PTS <int> 8529, 9091, 9304, 9213, 8656, 8480, 8731, 8980, 9161, 8844,…## $ W <int> 55, 50, 58, 65, 48, 44, 44, 47, 48, 48, 52, 49, 47, 59, 48,…## $ L <int> 27, 32, 24, 17, 34, 38, 38, 35, 34, 34, 30, 33, 35, 23, 34,…## $ P2M <int> 2202, 2330, 2583, 1918, 2604, 2281, 2539, 2707, 2663, 2390,…## $ P2A <int> 4483, 4314, 4611, 3436, 5073, 4491, 4783, 5218, 4929, 4730,…## $ P2p <dbl> 49.11889, 54.01020, 56.01822, 55.82072, 51.33057, 50.79047,…## $ P3M <int> 939, 981, 926, 1256, 741, 903, 718, 658, 837, 881, 901, 845…## $ P3A <int> 2492, 2636, 2369, 3470, 2010, 2506, 2024, 1845, 2312, 2491,…## $ P3p <dbl> 37.68058, 37.21548, 39.08822, 36.19597, 36.86567, 36.03352,…## $ FTM <int> 1308, 1488, 1360, 1609, 1225, 1209, 1499, 1592, 1324, 1421,…## $ FTA <int> 1697, 1909, 1668, 2061, 1573, 1601, 1915, 1980, 1716, 1985,…## $ FTp <dbl> 77.07720, 77.94657, 81.53477, 78.06890, 77.87667, 75.51530,…## $ OREB <int> 767, 694, 691, 739, 788, 763, 688, 848, 712, 1024, 893, 835…## $ DREB <int> 2878, 2761, 2877, 2825, 2684, 2801, 2579, 2593, 2924, 2671,…## $ AST <int> 1842, 1916, 2402, 1767, 1819, 1862, 1905, 1861, 2195, 1750,…## $ TOV <int> 1149, 1126, 1265, 1135, 1088, 1178, 1135, 1021, 1223, 1147,…## $ STL <int> 604, 582, 655, 699, 721, 620, 722, 689, 657, 743, 682, 573,…## $ BLK <int> 373, 312, 612, 392, 340, 437, 443, 345, 485, 412, 420, 423,…## $ PF <int> 1618, 1524, 1607, 1597, 1544, 1648, 1752, 1495, 1570, 1653,…## $ PM <int> 294, 77, 490, 695, 113, 39, -25, 183, 107, 280, 369, 213, 2…## $ team <fct> BOS, CLE, GSW, HOU, IND, MIA, MIL, MIN, NOP, OKC, PHI, POR,…## $ Conference <fct> E, E, W, W, E, E, E, W, W, W, E, W, W, E, W, E## $ Division <fct> Atlantic, Central, Pacific, Southwest, Central, Southeast, …## $ Rank <int> 2, 4, 2, 1, 5, 6, 7, 8, 6, 4, 3, 3, 7, 1, 5, 8Return another data frame with the rows where the second argument is TRUE.
Remove columns from the data frame
df_2 <- select(df, Team, Playoff, W, L)glimpse(df_2)## Rows: 30## Columns: 4## $ Team <chr> "Atlanta Hawks", "Boston Celtics", "Brooklyn Nets", "Charlotte…## $ Playoff <fct> N, Y, N, N, N, Y, N, N, N, Y, Y, Y, N, N, N, Y, Y, Y, Y, N, Y,…## $ W <int> 24, 55, 28, 36, 27, 50, 24, 46, 39, 58, 65, 48, 42, 35, 22, 44…## $ L <int> 58, 27, 54, 46, 55, 32, 58, 36, 43, 24, 17, 34, 40, 47, 60, 38…Return a new data frame with a new column:
df_rebs <- mutate(df, REB=OREB+DREB)glimpse(df_rebs)## Rows: 30## Columns: 29## $ 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, …## $ REB <int> 3436, 3645, 3644, 3728, 3663, 3455, 3383, 3650, 3586, 3568,…The first argument is a data.frame. The rest of the arguments is one or more expressions. You can use formulas and mathematical operators (-, +, *, /) in those expressions.
summarize).df_grouped <- group_by(df, Playoff)Returns a data frame with a summary of the argument. It will have one row per group in the argument data frame.
tbl <- summarize(df_grouped, avg_pts=mean(PTS))Like mutate, you need to pass one or more expression, that will be applied to each group in the data.
data.framesorted_df <- arrange(df, Rank)head(sorted_df, 5)## Team Playoff GP MIN PTS W L P2M P2A P2p P3M P3A## 1 Houston Rockets Y 82 3951 9213 65 17 1918 3436 55.82072 1256 3470## 2 Toronto Raptors Y 82 3966 9156 59 23 2415 4464 54.09946 968 2705## 3 Boston Celtics Y 82 3961 8529 55 27 2202 4483 49.11889 939 2492## 4 Golden State Warriors Y 82 3946 9304 58 24 2583 4611 56.01822 926 2369## 5 Philadelphia 76ers Y 82 3956 9004 52 30 2448 4653 52.61122 901 2445## P3p FTM FTA FTp OREB DREB AST TOV STL BLK PF PM team## 1 36.19597 1609 2061 78.06890 739 2825 1767 1135 699 392 1597 695 HOU## 2 35.78558 1422 1790 79.44134 800 2807 1995 1095 626 500 1783 638 TOR## 3 37.68058 1308 1697 77.07720 767 2878 1842 1149 604 373 1618 294 BOS## 4 39.08822 1360 1668 81.53477 691 2877 2402 1265 655 612 1607 490 GSW## 5 36.85072 1405 1868 75.21413 893 2996 2221 1353 682 420 1811 369 PHI## Conference Division Rank## 1 W Southwest 1## 2 E Atlantic 1## 3 E Atlantic 2## 4 W Pacific 2## 5 E Atlantic 3count(df)## n## 1 30count(df, Division)## Division n## 1 Atlantic 5## 2 Central 5## 3 Northwest 5## 4 Pacific 5## 5 Southeast 5## 6 Southwest 5Different functions take different type of objects.
df is a data.frame
data.frame is a collection of vectorsglimpse(df)## 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, …We can access vectors inside a data frame in multiple ways. $ operator.
mean(df$PTS)## [1] 8719.333df_with_mean <- mutate(df, mean_pts=mean(PTS))From Lab 1: it is important to keep track of where files are located in your computer.
Your hard drive is organized in folders or directories.
In Mac os, ~/Desktop is your desktop.
If you have a directory called lab 1 in your desktop, ~/Desktop/lab1 is the location of that folder.
Run setwd("~/Desktop/lab1") to make that the working directory.
list.files() prints the contents of the working directory in your console.
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