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Suppose you had the closing prices for four large technology stocks, and it was given to you in this form:
stock_close
## # A tibble: 1,258 x 5
## Date AAPL AMZN FB GOOG
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2014-01-02 79.0 398. 54.7 553.
## 2 2014-01-03 77.3 396. 54.6 549.
## 3 2014-01-06 77.7 394. 57.2 555.
## 4 2014-01-07 77.1 398. 57.9 566.
## 5 2014-01-08 77.6 402. 58.2 567.
## 6 2014-01-09 76.6 401. 57.2 561.
## 7 2014-01-10 76.1 398. 57.9 561.
## 8 2014-01-13 76.5 391. 55.9 558.
## 9 2014-01-14 78.1 398. 57.7 571.
## 10 2014-01-15 79.6 396. 57.6 571.
## # i 1,248 more rows
Using the tidyr package, complete the code to convert this into a tidy format shown below:
library(tidyr)
stock_close |>
(cols = !Date, names_to = , values_to = )
## # A tibble: 5,032 x 3
## Date Symbol Close
## <date> <chr> <dbl>
## 1 2014-01-02 AAPL 79.0
## 2 2014-01-02 AMZN 398.
## 3 2014-01-02 FB 54.7
## 4 2014-01-02 GOOG 553.
## 5 2014-01-03 AAPL 77.3
## 6 2014-01-03 AMZN 396.
## 7 2014-01-03 FB 54.6
## 8 2014-01-03 GOOG 549.
## 9 2014-01-06 AAPL 77.7
## 10 2014-01-06 AMZN 394.
## # i 5,022 more rows
You’re writing an article on various diets for young chicks, and have computed this summary of the data:
ChickWeight |>
group_by(Time, Diet) |>
summarise(AverageWeight = mean(weight), .groups = "drop")
## # A tibble: 48 x 3
## Time Diet AverageWeight
## <dbl> <fct> <dbl>
## 1 0 1 41.4
## 2 0 2 40.7
## 3 0 3 40.8
## 4 0 4 41
## 5 2 1 47.2
## 6 2 2 49.4
## 7 2 3 50.4
## 8 2 4 51.8
## 9 4 1 56.5
## 10 4 2 59.8
## # i 38 more rows
Finish the code to convert this summary into a nice table for your report that looks like this:
ChickWeight |>
group_by(Time, Diet) |>
summarise(AverageWeight = mean(weight), .groups = "drop") |>
(names_from = , names_prefix = "Diet ", values_from = )
Time | Diet 1 | Diet 2 | Diet 3 | Diet 4 |
---|---|---|---|---|
0 | 41.4 | 40.7 | 40.8 | 41.0 |
2 | 47.2 | 49.4 | 50.4 | 51.8 |
4 | 56.5 | 59.8 | 62.2 | 64.5 |
6 | 66.8 | 75.4 | 77.9 | 83.9 |
8 | 79.7 | 91.7 | 98.4 | 105.6 |
10 | 93.1 | 108.5 | 117.1 | 126.0 |
12 | 108.5 | 131.3 | 144.4 | 151.4 |
14 | 123.4 | 141.9 | 164.5 | 161.8 |
16 | 144.6 | 164.7 | 197.4 | 182.0 |
18 | 158.9 | 187.7 | 233.1 | 202.9 |
20 | 170.4 | 205.6 | 258.9 | 233.9 |
21 | 177.8 | 214.7 | 270.3 | 238.6 |
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