Including Plots

You can also embed plots, for example:

Figure 1: My first figure

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Blogdown

Generating a site with Hugo is very fast; however, Rmd-files can take som time because all the code is executed and all the graphs are saved to disk. But the page is only regenerated when the contents have changed. On a 2GHz server with SSD, this page takes 24 seconds to produce each time it is modified. Pages with more sophisticated analysis will take longer. For complex analysis, the ability to just run the chunk you are working on in RStudio is very useful.

Drawing graphs with ggplot2

A lot examples are from the book (R for Data Science: Import, Tidy, Transform, Visualize, and Model Data)[http://amzn.to/2ixHRe4] which presents a consistent and easy to use collection of modern R packages for data science which is bundled in tidyverse library. Get the book, it’s great!

suppressMessages(library(tidyverse))
library(ggthemes)


ggplot(data = mpg) + geom_point(mapping = aes(x= displ, y = hwy,  color=class)) +
   theme_gdocs()

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_wrap(~ class, nrow = 2) + 
  theme_minimal()

front-wheel drive, r = rear wheel drive, 4 = 4wd

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color=class)) +
  facet_grid(. ~ cyl) +
  theme_minimal()

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color=class)) +
  facet_grid(drv ~ .) +
   theme_gdocs()

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color=class)) +
  facet_grid(. ~ cyl) +
   theme_gdocs()

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy),method='loess') +
  geom_point(mapping = aes(x = displ, y = hwy, color=class)) +
   theme_gdocs()

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype= drv), method=loess ) +
  geom_point(mapping = aes(x = displ, y = hwy, color=class))

Mapping passed to the geoms:

ggplot(data = mpg,(mapping = aes(x = displ, y = hwy, linetype= drv) )) +
  geom_smooth( method=loess ) +
  geom_point( mapping = aes( color=class)) + # <-- extend/overwrite global aes.
  theme_gdocs()

ggplot( data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = class), position="jitter")+
  geom_smooth(
    data = filter(mpg, class == 'subcompact'),
    se = FALSE,
    method = loess
  ) + 
  theme_gdocs()

ggplot( data = diamonds) +
  geom_bar( mapping = aes(x = cut)) +
   theme_gdocs() 

d <- tribble (
  ~a, ~b,
  "v1", 10,
  "v2", 20
)
ggplot(data = d) +
  geom_bar( mapping = aes(x = a, y = b), stat = "identity") +
  coord_flip() +
   theme_gdocs()

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
  stat_summary(
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  ) +
   theme_gdocs()

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") +
   theme_gdocs()

ggplot(data = mpg) +
  geom_boxplot( mapping = aes( x = class,y = cty)) +
  theme_gdocs()

dplyr

library(nycflights13)
library(htmlTable)
flights_to_IAH_HOU <- filter(flights, 
                               dest %in% c("IAH","HOU") &
                               arr_delay > 120 &
                               carrier %in% c("UA","DL") &
                               month %in% c(7,8,9) &
                               between(dep_time,0000,0600))

flights_by_date <- arrange(flights, year, month, day)
flights_by_descending_date <- arrange(flights, desc(year), desc(month), desc(day))

htmlTable(select(flights_to_IAH_HOU, year:day, carrier),
          caption = "Flights to honolulo with a delay of more than two hours flwn by United or Delta in july through september departing between 00 - 06")

vars <- c('year','month','day','dep_delay','arr_delay')
htmlTable(select(flights_to_IAH_HOU, one_of(vars)))

flights_sel <- select(flights, year:day, ends_with('delay'), distance, air_time)

mutate(flights_sel, 
       gain = arr_delay - dep_delay,
       speed = distance / air_time * 60
       )

## # A tibble: 336,776 x 9
##     year month   day dep_delay arr_delay distance air_time  gain speed
##    <int> <int> <int>     <dbl>     <dbl>    <dbl>    <dbl> <dbl> <dbl>
##  1  2013     1     1         2        11     1400      227     9  370.
##  2  2013     1     1         4        20     1416      227    16  374.
##  3  2013     1     1         2        33     1089      160    31  408.
##  4  2013     1     1        -1       -18     1576      183   -17  517.
##  5  2013     1     1        -6       -25      762      116   -19  394.
##  6  2013     1     1        -4        12      719      150    16  288.
##  7  2013     1     1        -5        19     1065      158    24  404.
##  8  2013     1     1        -3       -14      229       53   -11  259.
##  9  2013     1     1        -3        -8      944      140    -5  405.
## 10  2013     1     1        -2         8      733      138    10  319.
## # ... with 336,766 more rows

transmute(flights_sel, 
       gain = arr_delay - dep_delay,
       speed = distance / air_time * 60
       )

## # A tibble: 336,776 x 2
##     gain speed
##    <dbl> <dbl>
##  1     9  370.
##  2    16  374.
##  3    31  408.
##  4   -17  517.
##  5   -19  394.
##  6    16  288.
##  7    24  404.
##  8   -11  259.
##  9    -5  405.
## 10    10  319.
## # ... with 336,766 more rows

delay_mean <- summarize(flights,delay = mean(dep_delay, na.rm = TRUE))
print(delay_mean)

## # A tibble: 1 x 1
##   delay
##   <dbl>
## 1  12.6

by_month <- group_by(flights, month)
delay_month <- summarize(by_month, delay = mean(dep_delay, na.rm = TRUE))
delay_month <- mutate(delay_month, small_delay = (delay < 15) )
ggplot(data = delay_month, mapping = aes(x = month,y = delay, fill=small_delay)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(limits = month.abb) +
  theme_gdocs()

delay <- flights %>%
  group_by(dest) %>%
  summarize(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>%
  filter(count > 20, dest != "HNL")
ggplot(data = delay, mapping = aes( x = dist, y = delay)) +
  geom_point( aes(size = count), alpha = 1/1) +
  geom_smooth( se = FALSE, method = loess) 

not_cancelled <- flights %>% filter(!is.na(dep_delay), !is.na(arr_delay))

delays <- not_cancelled %>% 
  group_by(tailnum) %>%
  summarize(
    delay = mean(arr_delay)
  )

ggplot(data = delays, mapping = aes(x = delay)) +
  geom_area(stat="bin",binwidth = 10) + 
  geom_freqpoly(binwidth = 10)