Week 2: Apply 1
Jackson Curtis
2022-08-30
Import stock prices
stocks <- tq_get(c("AMZN", "TSLA", "TM"),
get = "stock.prices",
from = "2016-01-01")
stocks## # A tibble: 5,910 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2016-01-04 32.8 32.9 31.4 31.8 186290000 31.8
## 2 AMZN 2016-01-05 32.3 32.3 31.4 31.7 116452000 31.7
## 3 AMZN 2016-01-06 31.1 32.0 31.0 31.6 106584000 31.6
## 4 AMZN 2016-01-07 31.1 31.5 30.3 30.4 141498000 30.4
## 5 AMZN 2016-01-08 31.0 31.2 30.3 30.4 110258000 30.4
## 6 AMZN 2016-01-11 30.6 31.0 29.9 30.9 97832000 30.9
## 7 AMZN 2016-01-12 31.3 31.3 30.6 30.9 94482000 30.9
## 8 AMZN 2016-01-13 31.0 31.0 29.0 29.1 153104000 29.1
## 9 AMZN 2016-01-14 29.0 30.1 28.5 29.6 144760000 29.6
## 10 AMZN 2016-01-15 28.6 29.2 28.3 28.5 155690000 28.5
## # ℹ 5,900 more rowsPlot stock prices
stocks %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line()
Filter rows
stocks %>% filter(close > 70) ## # A tibble: 4,251 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2018-01-26 69.6 70.1 69.0 70.1 97146000 70.1
## 2 AMZN 2018-01-29 70.5 71.6 70.0 70.9 114038000 70.9
## 3 AMZN 2018-01-30 70.2 72.0 69.6 71.9 117438000 71.9
## 4 AMZN 2018-01-31 72.6 73.6 72.5 72.5 128494000 72.5
## 5 AMZN 2018-02-02 73.9 74.9 70.7 71.5 222514000 71.5
## 6 AMZN 2018-02-06 68.1 72.2 67.6 72.1 221336000 72.1
## 7 AMZN 2018-02-07 72.4 73.0 70.8 70.8 143254000 70.8
## 8 AMZN 2018-02-13 69.3 71.0 69.2 70.7 118358000 70.7
## 9 AMZN 2018-02-14 70.3 72.6 70.2 72.6 119184000 72.6
## 10 AMZN 2018-02-15 73.3 73.4 71.8 73.1 113054000 73.1
## # ℹ 4,241 more rowsstocks %>% filter(close > 70 & low > 65)## # A tibble: 4,251 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2018-01-26 69.6 70.1 69.0 70.1 97146000 70.1
## 2 AMZN 2018-01-29 70.5 71.6 70.0 70.9 114038000 70.9
## 3 AMZN 2018-01-30 70.2 72.0 69.6 71.9 117438000 71.9
## 4 AMZN 2018-01-31 72.6 73.6 72.5 72.5 128494000 72.5
## 5 AMZN 2018-02-02 73.9 74.9 70.7 71.5 222514000 71.5
## 6 AMZN 2018-02-06 68.1 72.2 67.6 72.1 221336000 72.1
## 7 AMZN 2018-02-07 72.4 73.0 70.8 70.8 143254000 70.8
## 8 AMZN 2018-02-13 69.3 71.0 69.2 70.7 118358000 70.7
## 9 AMZN 2018-02-14 70.3 72.6 70.2 72.6 119184000 72.6
## 10 AMZN 2018-02-15 73.3 73.4 71.8 73.1 113054000 73.1
## # ℹ 4,241 more rowsArrange rows
stocks %>% arrange(stocks, desc(close), desc(adjusted))## # A tibble: 5,910 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2016-01-04 32.8 32.9 31.4 31.8 186290000 31.8
## 2 AMZN 2016-01-05 32.3 32.3 31.4 31.7 116452000 31.7
## 3 AMZN 2016-01-06 31.1 32.0 31.0 31.6 106584000 31.6
## 4 AMZN 2016-01-07 31.1 31.5 30.3 30.4 141498000 30.4
## 5 AMZN 2016-01-08 31.0 31.2 30.3 30.4 110258000 30.4
## 6 AMZN 2016-01-11 30.6 31.0 29.9 30.9 97832000 30.9
## 7 AMZN 2016-01-12 31.3 31.3 30.6 30.9 94482000 30.9
## 8 AMZN 2016-01-13 31.0 31.0 29.0 29.1 153104000 29.1
## 9 AMZN 2016-01-14 29.0 30.1 28.5 29.6 144760000 29.6
## 10 AMZN 2016-01-15 28.6 29.2 28.3 28.5 155690000 28.5
## # ℹ 5,900 more rows#select columns
stocks %>% select(symbol, date, high:close)## # A tibble: 5,910 × 5
## symbol date high low close
## <chr> <date> <dbl> <dbl> <dbl>
## 1 AMZN 2016-01-04 32.9 31.4 31.8
## 2 AMZN 2016-01-05 32.3 31.4 31.7
## 3 AMZN 2016-01-06 32.0 31.0 31.6
## 4 AMZN 2016-01-07 31.5 30.3 30.4
## 5 AMZN 2016-01-08 31.2 30.3 30.4
## 6 AMZN 2016-01-11 31.0 29.9 30.9
## 7 AMZN 2016-01-12 31.3 30.6 30.9
## 8 AMZN 2016-01-13 31.0 29.0 29.1
## 9 AMZN 2016-01-14 30.1 28.5 29.6
## 10 AMZN 2016-01-15 29.2 28.3 28.5
## # ℹ 5,900 more rowsstocks %>% select(symbol, date, contains("a"))## # A tibble: 5,910 × 3
## symbol date adjusted
## <chr> <date> <dbl>
## 1 AMZN 2016-01-04 31.8
## 2 AMZN 2016-01-05 31.7
## 3 AMZN 2016-01-06 31.6
## 4 AMZN 2016-01-07 30.4
## 5 AMZN 2016-01-08 30.4
## 6 AMZN 2016-01-11 30.9
## 7 AMZN 2016-01-12 30.9
## 8 AMZN 2016-01-13 29.1
## 9 AMZN 2016-01-14 29.6
## 10 AMZN 2016-01-15 28.5
## # ℹ 5,900 more rowsAdd columns
mutate(stocks,
High_Low_Difference = high - low) %>%
# Selecting which columns to show
select(symbol, date, High_Low_Difference)## # A tibble: 5,910 × 3
## symbol date High_Low_Difference
## <chr> <date> <dbl>
## 1 AMZN 2016-01-04 1.51
## 2 AMZN 2016-01-05 0.963
## 3 AMZN 2016-01-06 0.974
## 4 AMZN 2016-01-07 1.24
## 5 AMZN 2016-01-08 0.907
## 6 AMZN 2016-01-11 1.06
## 7 AMZN 2016-01-12 0.688
## 8 AMZN 2016-01-13 2.09
## 9 AMZN 2016-01-14 1.62
## 10 AMZN 2016-01-15 0.966
## # ℹ 5,900 more rows# Using the transmute()
transmute(stocks,
HL_Difference_Transmute = high - low) %>%
# Selecting just new HL after using the transmute function
select(HL_Difference_Transmute)## # A tibble: 5,910 × 1
## HL_Difference_Transmute
## <dbl>
## 1 1.51
## 2 0.963
## 3 0.974
## 4 1.24
## 5 0.907
## 6 1.06
## 7 0.688
## 8 2.09
## 9 1.62
## 10 0.966
## # ℹ 5,900 more rowssummarize by groups
stocks## # A tibble: 5,910 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2016-01-04 32.8 32.9 31.4 31.8 186290000 31.8
## 2 AMZN 2016-01-05 32.3 32.3 31.4 31.7 116452000 31.7
## 3 AMZN 2016-01-06 31.1 32.0 31.0 31.6 106584000 31.6
## 4 AMZN 2016-01-07 31.1 31.5 30.3 30.4 141498000 30.4
## 5 AMZN 2016-01-08 31.0 31.2 30.3 30.4 110258000 30.4
## 6 AMZN 2016-01-11 30.6 31.0 29.9 30.9 97832000 30.9
## 7 AMZN 2016-01-12 31.3 31.3 30.6 30.9 94482000 30.9
## 8 AMZN 2016-01-13 31.0 31.0 29.0 29.1 153104000 29.1
## 9 AMZN 2016-01-14 29.0 30.1 28.5 29.6 144760000 29.6
## 10 AMZN 2016-01-15 28.6 29.2 28.3 28.5 155690000 28.5
## # ℹ 5,900 more rows# average closing price
summarise(stocks, close = mean(close, na.rm = TRUE))## # A tibble: 1 × 1
## close
## <dbl>
## 1 116.summarize by group
stocks %>%
#Group by Symbol
group_by(symbol) %>%
# Calculate average adjusted price
summarise(adjusted = mean(adjusted, na.rm = TRUE)) %>%
#Sort it
arrange(adjusted)## # A tibble: 3 × 2
## symbol adjusted
## <chr> <dbl>
## 1 AMZN 99.5
## 2 TSLA 111.
## 3 TM 137.stocks %>%
group_by(symbol) %>%
summarise(count = n(),
adjusted = mean(adjusted, na.rm = TRUE),
close = mean(close, na.rm = TRUE)) %>%
# Plot
ggplot(mapping = aes(x = symbol, y = adjusted)) +
geom_point(aes(size = count), alpha = 0.3) +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Missing Value
stocks %>%
#remove missing values
filter(!is.na(adjusted))## # A tibble: 5,910 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2016-01-04 32.8 32.9 31.4 31.8 186290000 31.8
## 2 AMZN 2016-01-05 32.3 32.3 31.4 31.7 116452000 31.7
## 3 AMZN 2016-01-06 31.1 32.0 31.0 31.6 106584000 31.6
## 4 AMZN 2016-01-07 31.1 31.5 30.3 30.4 141498000 30.4
## 5 AMZN 2016-01-08 31.0 31.2 30.3 30.4 110258000 30.4
## 6 AMZN 2016-01-11 30.6 31.0 29.9 30.9 97832000 30.9
## 7 AMZN 2016-01-12 31.3 31.3 30.6 30.9 94482000 30.9
## 8 AMZN 2016-01-13 31.0 31.0 29.0 29.1 153104000 29.1
## 9 AMZN 2016-01-14 29.0 30.1 28.5 29.6 144760000 29.6
## 10 AMZN 2016-01-15 28.6 29.2 28.3 28.5 155690000 28.5
## # ℹ 5,900 more rowsGrouping Multiple Variable
stocks %>%
group_by(symbol, open, adjusted) %>%
summarise(count = n()) %>%
ungroup()## `summarise()` has grouped output by 'symbol', 'open'. You can override using
## the `.groups` argument.## # A tibble: 5,907 × 4
## symbol open adjusted count
## <chr> <dbl> <dbl> <int>
## 1 AMZN 23.9 24.1 1
## 2 AMZN 24.3 24.4 1
## 3 AMZN 24.6 25.2 1
## 4 AMZN 24.6 24.5 1
## 5 AMZN 25.5 25.4 1
## 6 AMZN 26.0 26.1 1
## 7 AMZN 26.0 26.7 1
## 8 AMZN 26.2 26.8 1
## 9 AMZN 26.4 26.7 1
## 10 AMZN 26.5 25.1 1
## # ℹ 5,897 more rows