Chapter 5 R markdown
9월 24일 목요일, 202AIE17 송채은
What is R markdown documents?
R markdown document(.Rmd) allows us to create a report in a variety of formats (ex.html, pdf, word, slides, interactive documents) that includes your codes, results, and texts
- When you render an R markdown document using the render() function
- knitr : executes all code chunks in an R markdown document to create a new markdown document (.md)
- pandoc : converts a markdown document to the final format
An R markdown document has YAML header, text, code chunks
5.1 Problem 1
## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compact
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compactggplot(mpg, aes(hwy, fill = drv)) +
geom_histogram(alpha = 0.5) +
theme_minimal() +
labs(title = "Histogram",
subtitle = "Histogram of Highway Mile Per Gallon",
caption = "Source: mpg")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
5.2 Problem 2
ggplot(mpg, aes(hwy, fill = drv)) +
geom_histogram(alpha = 0.5) +
facet_grid(rows = vars(drv)) +
theme_minimal() +
labs(title = "Histogram using facet_grid()",
subtitle = "Histogram of Highway Mile Per Gallon",
caption = "Source: mpg")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
5.3 Problem 3
## # A tibble: 6 x 28
## PID county state area poptotal popdensity popwhite popblack popamerindian popasian popother percwhite percblack percamerindan
## <int> <chr> <chr> <dbl> <int> <dbl> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 561 ADAMS IL 0.052 66090 1271. 63917 1702 98 249 124 96.7 2.58 0.148
## 2 562 ALEXA~ IL 0.014 10626 759 7054 3496 19 48 9 66.4 32.9 0.179
## 3 563 BOND IL 0.022 14991 681. 14477 429 35 16 34 96.6 2.86 0.233
## 4 564 BOONE IL 0.017 30806 1812. 29344 127 46 150 1139 95.3 0.412 0.149
## 5 565 BROWN IL 0.018 5836 324. 5264 547 14 5 6 90.2 9.37 0.240
## 6 566 BUREAU IL 0.05 35688 714. 35157 50 65 195 221 98.5 0.140 0.182
## # ... with 14 more variables: percasian <dbl>, percother <dbl>, popadults <int>, perchsd <dbl>, percollege <dbl>, percprof <dbl>,
## # poppovertyknown <int>, percpovertyknown <dbl>, percbelowpoverty <dbl>, percchildbelowpovert <dbl>, percadultpoverty <dbl>,
## # percelderlypoverty <dbl>, inmetro <int>, category <chr>ggplot(midwest, aes(area, poptotal)) +
geom_point(aes(color = state, size = popdensity), alpha = 0.4) +
geom_smooth(method="loess", se=FALSE) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
theme_classic() +
labs(title = "Scatterplot",
subtitle="Area Vs Population",
caption = "Source: midwest",
x = "Area",
y = "Population")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
5.4 Problem 4
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaggplot(iris, aes(Sepal.Length, Sepal.Width)) +
geom_point(aes(color = Species, shape = Species), size = 6, alpha = 0.5) +
theme_minimal() +
labs(title = "Scatterplot",
subtitle="Sepal.Length Vs Sepal.Width",
caption = "Source: iris")
5.5 Problem 5
## sex ageYear ageMonth heightIn weightLb
## 1 f 11.92 143 56.3 85.0
## 2 f 12.92 155 62.3 105.0
## 3 f 12.75 153 63.3 108.0
## 4 f 13.42 161 59.0 92.0
## 5 f 15.92 191 62.5 112.5
## 6 f 14.25 171 62.5 112.0ggplot(heightweight, aes(heightIn, weightLb)) +
geom_point(aes(color = sex), size = 3, alpha = 0.5) +
geom_smooth(aes(group = sex, color = sex), method = "lm", se = FALSE) +
theme_classic() +
labs(title = "Scatterplot",
subtitle = "Weight Vs Height",
caption = "Source: heightweight")## `geom_smooth()` using formula 'y ~ x'
5.6 Problem 6
## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compact
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compactggplot(mpg, aes(manufacturer, fill = class)) + geom_bar(width = 0.5) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 65)) +
scale_fill_brewer(palette = "Spectral") +
labs(title = "Barplot",
subtitle = "Manufacturer across Vehicle Classes")
ggplot(mpg, aes(manufacturer, fill = class)) + geom_bar(width = 0.5) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 65, hjust = 1.0)) +
scale_fill_brewer(palette = "Spectral") +
labs(title = "Barplot",
subtitle = "Manufacturer across Vehicle Classes")