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DS4PS / cpp-526-sum-2020

Course shell for CPP 526 Foundations of Data Science I for Summer 2020.
http://ds4ps.org/cpp-526-sum-2020/
MIT License
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Lab 06 - Rearranging Values for Identical Tables #20

Open JayCastro opened 4 years ago

JayCastro commented 4 years ago

when the instructions say "Your table will look something like this (this table not sorted to show the moneyball teams)." Does that mean out table will look different? I ask because i feel like i did everything correctly but its not exactly the same as what the table shows.

jamisoncrawford commented 4 years ago

Hi @JayCastro - what are the major differences? I don't think we expect an exact replication so long as you're able to demonstrate understanding of the material.

JayCastro commented 4 years ago

The teams arent in the right order . I have Cal 1985 but then it goes tex 1986 which i know isnt the second one on the list you guys show.

JayCastro commented 4 years ago

nevermind figured it out and arranged by year and team id

jamisoncrawford commented 4 years ago

That's great, @JayCastro - I'll post what's needed here for others to view.

Arranging Data by More Than One Variable

Function arrange() can be used with more than one variable. Typically, if we want to arrange by a variable, we'd feed the data into this function via the %>% operator ("piping").

dat %>%
    arrange(variable_1)

Or in reverse order...

dat %>%
    arrange(desc(variable_1))

We can, however, arrange by two or more variables:

dat %>%
    arrange(variable_1, variable2)

We can even have one in reverse order and the other not:

dat %>%
    arrange(variable_1, desc(variable2))
jamisoncrawford commented 4 years ago

Reproducible Example with 'arrange()'

Use package dplyr and the mtcars dataset to see how useful it can be to arrange by two or more variables.

library(dplyr)
data(mtcars)

mtcars %>%
  arrange(hp)

    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1  30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
2  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
3  33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
4  32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
5  27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
6  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
7  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
8  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
9  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
10 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
11 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
12 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
13 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
14 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
15 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
16 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
17 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
18 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
19 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
20 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
21 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
22 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
23 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
24 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
25 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
26 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
27 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
28 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
29 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
30 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
31 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
32 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

Cylinders in cyl are still out of order - let's try including that in arrange():

library(dplyr)
data(mtcars)

mtcars %>%
  arrange(cyl, hp)

    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1  30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
2  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
3  33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
4  32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
5  27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
6  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
7  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
8  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
9  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
10 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
11 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
12 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
13 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
14 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
15 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
16 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
17 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
18 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
19 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
20 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
21 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
22 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
23 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
24 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
25 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
26 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
27 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
28 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
29 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
30 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
31 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
32 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8