tercera clase

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library(ggplot2)
library(dplyr)

Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union

library(skimr)
ggplot()

Algunos comandos utiles para explorar la data rectangular o dataframa o tibble

()=función

dplyr::glimpse(mpg)

Observations: 234
Variables: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "audi", "audi",...
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "a4 quattro", "...
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 3...
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 2008, 2008, 1999,...
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8,...
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto(l5)", "manual...
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4", "4", "4", "...
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 15, 15, 17, 16,...
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 25, 24, 25, 23,...
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "...
$ class        <chr> "compact", "compact", "compact", "compact", "compact", "compact", "comp...

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skim(mpg)

Skim summary statistics
 n obs: 234 
 n variables: 11 

-- Variable type:character -----------------------------------------------------
     variable missing complete   n min max empty n_unique
        class       0      234 234   3  10     0        7
          drv       0      234 234   1   1     0        3
           fl       0      234 234   1   1     0        5
 manufacturer       0      234 234   4  10     0       15
        model       0      234 234   2  22     0       38
        trans       0      234 234   8  10     0       10

-- Variable type:integer -------------------------------------------------------
 variable missing complete   n    mean   sd   p0  p25    p50  p75 p100     hist
      cty       0      234 234   16.86 4.26    9   14   17     19   35 <U+2585><U+2587><U+2587><U+2587><U+2581><U+2581><U+2581><U+2581>
      cyl       0      234 234    5.89 1.61    4    4    6      8    8 <U+2587><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2587>
      hwy       0      234 234   23.44 5.95   12   18   24     27   44 <U+2583><U+2587><U+2583><U+2587><U+2585><U+2581><U+2581><U+2581>
     year       0      234 234 2003.5  4.51 1999 1999 2003.5 2008 2008 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>

-- Variable type:numeric -------------------------------------------------------
 variable missing complete   n mean   sd  p0 p25 p50 p75 p100     hist
    displ       0      234 234 3.47 1.29 1.6 2.4 3.3 4.6    7 <U+2587><U+2587><U+2585><U+2585><U+2585><U+2583><U+2582><U+2581>

ggplot(mpg) +
geom_point(aes(x = displ, y=hwy, color=class))+
  facet_wrap (~drv)

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

ggplot(data=mpg)+
  geom_bar(aes(x=drv))

data_autos_resumida <- tribble(
~ tipo_traccion, ~ num_obs,
"4" , 104,
"f" , 102,
"r" , 25
)
data_autos_resumida

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

library(gapminder)
 gapminder

en vez de el bnombre del color se puede poner el numero de la tonalidad

paises_europa <- filter(gapminder, continent == "Europe")
paises_europa

glimpse (paises_europa)

Observations: 360
Variables: 6
$ country   <fct> Albania, Albania, Albania, Albania, Albania, Albania, Albania, Albania, A...
$ continent <fct> Europe, Europe, Europe, Europe, Europe, Europe, Europe, Europe, Europe, E...
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007, 1...
$ lifeExp   <dbl> 55.230, 59.280, 64.820, 66.220, 67.690, 68.930, 70.420, 72.000, 71.581, 7...
$ pop       <int> 1282697, 1476505, 1728137, 1984060, 2263554, 2509048, 2780097, 3075321, 3...
$ gdpPercap <dbl> 1601.056, 1942.284, 2312.889, 2760.197, 3313.422, 3533.004, 3630.881, 373...

ggplot()+
  geom_point(data = paises_europa,mapping = aes(x =gdpPercap , y=lifeExp, size=pop))