GGPLOT
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.4
cars <- mtcars
str(cars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
head(cars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
p <- ggplot(data= cars,
aes(x= as.factor(am), y= mpg, col= as.factor(am)))
p
p + geom_boxplot()
p <- ggplot(data= cars,
aes(x= as.factor(am), y= mpg))
p + geom_boxplot()
p <- ggplot(data= cars,
aes(x= as.factor(am), y= drat))
p + geom_boxplot()
p <- ggplot(data= cars,
aes(x= as.factor(am), y= hp))
p + geom_boxplot()
ggplot(data= cars, aes(x= as.factor(am), y= hp)) + geom_boxplot()
ggplot(data= cars, aes(x= as.factor(am), y= mpg)) + geom_boxplot()
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point(position = position_jitter(width= 0.1))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point()
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point(position = position_jitter(width = 0.3))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point(position = position_jitter(width = 0.5))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point(position = position_jitter(width = 0.2))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_boxplot() + geom_point(position = position_jitter(width = 0.15))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_point(position = position_jitter(width = 0.15))
ggplot(data=cars, aes(x= as.factor(am), y=mpg)) + geom_point(position = position_jitter(width = 0.1))
p <- ggplot(data=cars, aes(x= as.factor(am), y=mpg))
p + geom_point(position = position_jitter(width = 0.15))
p + geom_point(position = position_jitter(width = 0.15)) + geom_boxplot()
p + geom_boxplot() + geom_point(position = position_jitter(width = 0.15))
cars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
rownames(cars)
## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"
### Bars
cars$name <- rownames(cars)
head(cars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## name
## Mazda RX4 Mazda RX4
## Mazda RX4 Wag Mazda RX4 Wag
## Datsun 710 Datsun 710
## Hornet 4 Drive Hornet 4 Drive
## Hornet Sportabout Hornet Sportabout
## Valiant Valiant
str(cars)
## 'data.frame': 32 obs. of 12 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
## $ name: chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
cars$cyl.F <- as.factor(cars$cyl)
str(cars)
## 'data.frame': 32 obs. of 13 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp : num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat : num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec : num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear : num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb : num 4 4 1 1 2 1 4 2 2 4 ...
## $ name : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ cyl.F: Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
p <- ggplot(data=cars,
aes(x= as.factor(am)))
p
p + geom_bar()
p <- ggplot(data= cars,
aes(x=name))
p
p + geom_bar()
p + geom_bar() + guides(x= guide_axis(angle= 80))
p + geom_bar() + guides(x= guide_axis(angle= 90))
p + geom_bar() + guides(x= guide_axis(angle= 70))
p + geom_bar() + guides(x= guide_axis(angle= 90))
p + geom_bar() + guides(x= guide_axis(angle= 90))
p + geom_bar() + guides(x= guide_axis(angle= 0))
ggplot(data=cars, aes(x=cyl.F)) + geom_bar() + guides(x= guide_axis(angle= 90))
ggplot(data=cars, aes(x=cyl.F)) + geom_bar() + guides(x= guide_axis(angle= 0))
p <- ggplot(data= cars,
aes(x=cyl.F, y= hp, col= as.factor(am)))
p
p + geom_point()
p + geom_point() + geom_boxplot()
p + geom_point(position = position_jitter(width = 0.1)) + geom_boxplot()
p + geom_boxplot() + geom_point(position = position_jitter(width = 0.1))
p <- ggplot(data= cars,
aes(x=wt, y= drat, col= as.factor(am)))
p + geom_point()
p <- ggplot(data= cars,
aes(x=wt, y= drat, col= cyl.F) )
p + geom_point()
p <- ggplot(data= cars,
aes(x=wt, y= drat) )
p + geom_point()
p + geom_point() + geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
p + geom_point() + geom_smooth(method= "lm")
## `geom_smooth()` using formula 'y ~ x'
p <- ggplot(data= cars,
aes(x=wt, y= drat, col= as.factor(am)) )
p + geom_point()
p + geom_point() + geom_smooth(method= "lm")
## `geom_smooth()` using formula 'y ~ x'
p <- ggplot(data= cars,
aes(x=wt, y= drat, col= cyl.F )
)
p + geom_point() + geom_smooth(method= "lm")
## `geom_smooth()` using formula 'y ~ x'
p <- ggplot(data= cars,
aes(x=wt, y= drat, col= as.factor(am) ))
p + geom_point() + geom_smooth(method= "lm")
## `geom_smooth()` using formula 'y ~ x'
### plots con base de datos de 'cancer'
#1. y= remission ~ x=White blood cells
### white blood cells ~ Remission ??
library(googlesheets4)
ss = "https://docs.google.com/spreadsheets/d/1KjHM67cVFQ9iMXwJpNvkDuJqEc1Sw5u8CCCZUl5RV1E/edit?usp=sharing"
hoja="cancer"
rango = "A2:AA8527"
cancer <- read_sheet(ss,
sheet= hoja,
range= rango,
col_names = TRUE,
col_types = NULL,
na= "NA")
## Suitable tokens found in the cache, associated with these emails:
## * fvillatorop@miumg.edu.gt
## * msc.fvilla@digi.usac.edu.gt
## The first will be used.
## Using an auto-discovered, cached token.
## To suppress this message, modify your code or options to clearly consent to the use of a cached token.
## See gargle's "Non-interactive auth" vignette for more details:
## https://gargle.r-lib.org/articles/non-interactive-auth.html
## The googlesheets4 package is using a cached token for fvillatorop@miumg.edu.gt.
## Reading from "Remission"
## Range "'cancer'!A2:AA8527"
str(cancer)
## tibble [8,525 x 27] (S3: tbl_df/tbl/data.frame)
## $ remission : num [1:8525] 0 0 0 0 0 0 0 0 0 0 ...
## $ ntumors : num [1:8525] 0 0 0 0 0 0 0 0 2 0 ...
## $ tumorsize : num [1:8525] 68 64.7 51.6 86.4 53.4 ...
## $ co2 : num [1:8525] 1.53 1.68 1.53 1.45 1.57 ...
## $ pain : num [1:8525] 4 2 6 3 3 4 3 3 4 5 ...
## $ wound : num [1:8525] 4 3 3 3 4 5 4 3 4 4 ...
## $ mobility : num [1:8525] 2 2 2 2 2 2 2 3 3 3 ...
## $ nmorphine : num [1:8525] 0 0 0 0 0 0 0 0 0 0 ...
## $ lungcapacity: num [1:8525] 0.801 0.326 0.565 0.848 0.886 ...
## $ Age : num [1:8525] 65 53.9 53.3 41.4 46.8 ...
## $ Married : num [1:8525] 0 0 1 0 0 1 1 0 1 0 ...
## $ FamilyHx : chr [1:8525] "no" "no" "no" "no" ...
## $ SmokingHx : chr [1:8525] "a.former" "a.former" "never" "a.former" ...
## $ Sex : chr [1:8525] "male" "female" "female" "male" ...
## $ CancerStage : chr [1:8525] "II" "II" "II" "I" ...
## $ LengthofStay: num [1:8525] 6 6 5 5 6 5 4 5 6 7 ...
## $ WBC : num [1:8525] 6088 6700 6043 7163 6443 ...
## $ RBC : num [1:8525] 4.87 4.68 5.01 5.27 4.98 ...
## $ BMI : num [1:8525] 24.1 29.4 29.5 21.6 29.8 ...
## $ IL6 : num [1:8525] 3.7 2.63 13.9 3.01 3.89 ...
## $ CRP : num [1:8525] 8.086 0.803 4.034 2.126 1.349 ...
## $ DID : num [1:8525] 1 1 1 1 1 1 1 1 1 1 ...
## $ Experience : num [1:8525] 25 25 25 25 25 25 25 25 25 25 ...
## $ School : chr [1:8525] "average" "average" "average" "average" ...
## $ Lawsuits : num [1:8525] 3 3 3 3 3 3 3 3 3 3 ...
## $ HID : num [1:8525] 1 1 1 1 1 1 1 1 1 1 ...
## $ Medicaid : num [1:8525] 0.606 0.606 0.606 0.606 0.606 ...
p <- ggplot(data=cancer,
aes(x=as.factor(remission),
y=WBC,
col=as.factor(remission)))
p
p + geom_boxplot()
p + geom_point(position = position_jitter(width = 0.3))
p + geom_point(position = position_jitter(width = 0.6))
p + geom_point(position = position_jitter(width = 0.4))
p + geom_violin() + geom_point(position = position_jitter(width = 0.4))
p + geom_point(position = position_jitter(width = 0.4))+ geom_violin()
p + geom_point(position = position_jitter(width = 0.4))
p + geom_point(position = position_jitter(width = 0.4)) + geom_violin()
p + geom_point(position = position_jitter(width = 0.3)) + geom_violin()
p <- ggplot(data=cancer,
aes(x= as.factor(CancerStage),
y= WBC,
col=as.factor(remission)))
p
p + geom_point(position = position_jitter(width = 0.3)) + geom_violin()
p + geom_point(position = position_jitter(width = 0.1)) + geom_violin()
p <- ggplot(data=cancer,
aes(x= as.factor(CancerStage),
y= WBC))
p + geom_point(position = position_jitter(width = 0.1)) + geom_violin()
p + geom_point(position = position_jitter(width = 0.1)) + geom_boxplot()
p + geom_point(position = position_jitter(width = 0.1)) + geom_violin()
p <- ggplot(data=cancer,
aes( x= BMI,
y= WBC))
p + geom_point()
p + geom_point() + geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
p <- ggplot(data=cancer,
aes( x= BMI,
y= remission))
p + geom_point()
p <- ggplot(data=cancer,
aes( x= as.factor(remission),
y= BMI))
p + geom_boxplot()
p <- ggplot(data=cancer,
aes( x= as.factor(remission),
y= BMI))
p + geom_boxplot()
p + geom_boxplot() + geom_point(position = position_jitter(width= 0.2))
p + geom_point(position = position_jitter(width= 0.2))
p + geom_point(position = position_jitter(width= 0.2)) + geom_violin()
