knitr::opts_chunk$set(echo = TRUE)

# install.packages("tidyverse")
library(tidyverse)

my_string <- "comment me with ctrl-shift-C"
print(my_string)


7 # 执行单行代码的快捷键 ctrl-enter

x <- 1
2 -> y 
z = x + y
m = z/y
n = m * y
# 运行代码块的快捷键 ctrl-shift-enter

# Logical	TRUE, FALSE	

v <- TRUE 
print(class(v))

# Numeric	12.3, 5, 999	

v <- 23.5
print(class(v))


# Integer	2L, 34L, 0L	

v <- 2L
print(class(v))


# Complex	3 + 2i	

v <- 2+5i
print(class(v))


# Character	'a' , '"good", "TRUE", '23.4'	

v <- "TRUE"
print(class(v))


# Raw	"Hello" is stored as 48 65 6c 6c 6f	

v <- charToRaw("Hello")
print(class(v))
v

##################################################

# Create a vector.
apple <- c('red','green',"yellow")
print(apple)

# Get the class of the vector.
print(class(apple))

# Create a list.
list1 <- list(c(2,5,3),21.3,sin, TRUE)

# Print the list.
print(list1)

# Create a matrix.
M = matrix( c('a','a','b','c','b','a'), nrow = 2, ncol = 3, byrow = TRUE)
print(M)

# Create an array.
a <- array(c('green','yellow'),dim = c(3,3,2))
print(a)

# Create a vector.
apple_colors <- c('green','green','yellow','red','red','red','green')

# Create a factor object.
factor_apple <- factor(apple_colors)

# Print the factor.
print(factor_apple)
print(nlevels(factor_apple))

# Create the data frame.
BMI <- 	data.frame(
   gender = factor(c("Male", "Male","Female")), 
   height = c(152, 171.5, 165), 
   weight = c(81,93, 78),
   Age = c(42,38,26)
)
print(BMI)
BMI %>% 
dplyr::filter(gender == 'Male')

install.packages("tidyverse")
library(tidyverse)

mpg %>% 
dplyr::filter(model == 'a4') %>%  # return all rows that satisfy conditions

dplyr::arrange(displ, cyl) %>%  # reorder your columns

dplyr::mutate(old = year < 2000,
              full_Wheel_drive = drv == 'f') %>%   # add new preserve old

# dplyr::transmute(full_wheel_drive = drv == 'f') # add new drop old

dplyr::select(-drv) # keep or remove columns

mpg %>% 
dplyr::count(model,sort = T) %>% 
  dplyr::filter(n > 8)# create aggregate stats

# ggplot(data = <DATA>) + 
#   <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

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

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

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

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

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

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

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

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

smaller <- diamonds %>% 
  filter(carat < 3)
  
ggplot(data = smaller, mapping = aes(x = carat)) +
  geom_histogram(binwidth = 0.3)

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy))

diamonds %>% 
  count(color, cut, sort = T) %>%  
  ggplot(mapping = aes(x = color, y = cut)) +
  
    geom_tile(mapping = aes(fill = n))

# read in files

data


# paste tribbles



preg <- tribble(
  ~pregnant, ~male, ~female,
  "yes",     NA,    10,
  "no",      20,    12
)

preg

# or use the data pasta package

library(datapasta)










# Ease of making ggplots


library(esquisse)




library(ggplot2)

ggplot(data) +
 aes(x = carat, y = price) +
 geom_point(size = 1L, colour = "#26828e") +
 geom_smooth(span = 0.75) +
 labs(x = "carat", y = "price", title = "heres my title", subtitle = "a view of diamonds", caption = "hello caption") +
 theme_minimal()

data %>%
 filter(depth >= 60L & depth <= 79L) %>%
 ggplot() +
 aes(x = carat, y = price) +
 geom_point(size = 0.78, colour = "#000000") +
 labs(x = "carat", y = "price", title = "heres my title", subtitle = "a view of diamonds", caption = "hello caption") +
 theme_minimal()

data %>%
 filter(depth >= 60L & depth <= 79L) %>%
 ggplot() +
 aes(x = carat, y = price) +
 geom_point(size = 0.78, colour = "#000000") +
 labs(x = "carat", y = "price", title = "heres my title", subtitle = "a view of diamonds", caption = "hello caption") +
 theme_minimal()




# Ray shaded graphics

library(rayshader)

#Here, I load a map with the raster package.
loadzip = tempfile() 
download.file("https://tylermw.com/data/dem_01.tif.zip", loadzip)
localtif = raster::raster(unzip(loadzip, "dem_01.tif"))
unlink(loadzip)

#And convert it to a matrix:
elmat = raster_to_matrix(localtif)

#We use another one of rayshader's built-in textures:
elmat %>%
  sphere_shade(texture = "desert") %>%
  plot_map()

############################################################################

a = data.frame(x = rnorm(20000, 10, 1.9), y = rnorm(20000, 10, 1.2))
b = data.frame(x = rnorm(20000, 14.5, 1.9), y = rnorm(20000, 14.5, 1.9))
c = data.frame(x = rnorm(20000, 9.5, 1.9), y = rnorm(20000, 15.5, 1.9))
data = rbind(a, b, c)

#Lines
pp = ggplot(data, aes(x = x, y = y)) +
  geom_hex(bins = 20, size = 0.5, color = "black") +
  scale_fill_viridis_c(option = "C")

par(mfrow = c(1, 2))
plot_gg(pp, width = 5, height = 4, scale = 300, raytrace = FALSE, preview = TRUE)
plot_gg(pp, width = 5, height = 4, scale = 300, multicore = TRUE, windowsize = c(1000, 800))
render_camera(fov = 70, zoom = 0.5, theta = 130, phi = 35)
Sys.sleep(0.2)
render_snapshot(clear = TRUE)

par(mfrow = c(1, 1))
plot_gg(pp, width = 5, height = 4, scale = 300, raytrace = FALSE, windowsize = c(1200, 960),
        fov = 70, zoom = 0.4, theta = 330, phi = 20,  max_error = 0.01, verbose = TRUE)

install.packages('gapminder')
library(gapminder)

df=gapminder
# explore data

summary(df)
x = mean(df$lifeExp)
x

attach(df)
median(pop)
hist(lifeExp)
hist(log(pop))
boxplot(lifeExp ~ continent)
plot(lifeExp ~ gdpPercap)

# data operation
library(dplyr)
df %>%
  select(country, lifeExp) %>%
  filter(country=="South Africa" | country=="Ireland") %>%
  group_by(country) %>%
  summarise(aveLife=mean(lifeExp))


#T 检验
df1 = filter(df, country=="South Africa" | country=="Ireland")
df1$country = factor(df1$country)
t.test(data=df1, lifeExp ~ country)

boxplot(data=df1, lifeExp ~ country)

library(ggplot2)
ggplot(df1, aes(x=country, y=lifeExp)) +
  geom_boxplot()

# regression


ggplot(df, aes(x=log(gdpPercap), y=lifeExp, color=continent, size=pop)) +
  geom_point(alpha=0.3) + 
  geom_smooth()

summary(lm(lifeExp ~ gdpPercap, data=df))


res = lm(lifeExp ~ gdpPercap, data=df)

# lifeExp = 5.396 + 7.649e-04 gdpPercap