- Basic Structure and syntax in ggplot2
- Exploration in data structure (dimentsion and variables properties) ; Continous vs Discrete ?
- Aesthetics definition
- Primitive Plots (Horiontal line vs Vertical line)
- One Varible plot (geom_histogram,geom_bar)
- Two varible plot (text, point,
- Cases study on Covid19 Data #
library(ggplot2)
data <- mtcars
#Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391â411.
dim(data)## [1] 32 11
#32 11
str(data)## '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 ...
#[, 1] mpg Miles/(US) gallon
#[, 2] cyl Number of cylinders
#[, 3] disp Displacement (cu.in.)
#[, 4] hp Gross horsepower
#[, 5] drat Rear axle ratio
#[, 6] wt Weight (1000 lbs)
#[, 7] qsec 1/4 mile time
#[, 8] vs Engine (0 = V-shaped, 1 = straight)
#[, 9] am Transmission (0 = automatic, 1 = manual)
#[,10] gear Number of forward gears
#[,11] carb Number of carburetors
#How ggplot works
# 1. Define data
# 2. Define aesthetic aes()
# 3. Define variables, x= ; y =#Example
a <- ggplot(data, aes(x=gear,y=mpg))
a <- a + geom_blank() + ylim(0,35) + xlim(0,5)
a <- a + geom_hline(yintercept = 25)
a <- a + geom_vline(xintercept = 4)
a <- a + geom_abline(intercept=10,slope=5)
a 
#----------------------------------------------------------
# One Variable
#----------------------------------------------------------
mpg <- mpg
dim(mpg)## [1] 234 11
#234 11
str(mpg)## tibble [234 x 11] (S3: tbl_df/tbl/data.frame)
## $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
## $ model : chr [1:234] "a4" "a4" "a4" "a4" ...
## $ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr [1:234] "f" "f" "f" "f" ...
## $ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr [1:234] "p" "p" "p" "p" ...
## $ class : chr [1:234] "compact" "compact" "compact" "compact" ...
#http://archive.ics.uci.edu/ml/datasets/Auto+MPG
#tibble [234 x 11] (S3: tbl_df/tbl/data.frame)
#$ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
#$ model : chr [1:234] "a4" "a4" "a4" "a4" ...
#$ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
#$ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
#$ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
#$ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
#$ drv : chr [1:234] "f" "f" "f" "f" ...
#$ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
#$ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
#$ fl : chr [1:234] "p" "p" "p" "p" ...
#$ class : chr [1:234] "compact" "compact" "compact" "compact" ...
#One Discreate Parameters
b <- ggplot(mpg, aes(fl))
b <- b + geom_bar()
b
#One Continous Parameters
c <- ggplot(mpg, aes(cyl))
c <- c + geom_histogram()
c## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
d <- ggplot(mpg, aes(cty))
d <- d + geom_histogram()
d## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#----------------------------------------------------------
# Two Variables
#----------------------------------------------------------
#continuous x , continuous y
e <- ggplot(mpg, aes(cty, hwy))
e + geom_point()
e + geom_jitter() 
e + geom_label(aes(label = cty)) 
e + geom_label(aes(label = hwy)) 
e + geom_text(aes(label = cty))
#discrete x , continuous y
f <- ggplot(mpg, aes(class, hwy))
f + geom_boxplot()
f + geom_violin(scale = "area")
#discrete x , discrete y
g <- ggplot(mpg, aes(class, drv))
g + geom_count()
#----------------------------------------------------------
# Two Variables (x and y), more variebles as gropuing
#----------------------------------------------------------
e <- ggplot(mpg, aes(cty, hwy))
e + geom_text(aes(label = cty))
e + geom_text(aes(label = cty,colour = factor(manufacturer))) 
e + geom_point(aes(colour = factor(cyl)))
e + geom_point(aes(colour = factor(manufacturer)))
e + geom_point(aes(colour = factor(manufacturer),shape = factor(cyl)))
e + geom_jitter(aes(colour = factor(manufacturer), shape = factor(cyl), size = displ))
#———————————————————- # Practical Data Plotting - Covid 19 #———————————————————- #https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide
library(utils)
COVID_19 <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM")
#1. Comparison between different countries, using a line plot (x = time, y=number of cases in log10).
#2. Total new cases in the world in a stack column plot, with each country as its own color (x = MOnth, y = number of cases in log10)
dim(COVID_19)## [1] 9791 10
#11768 10
str(COVID_19)## 'data.frame': 9791 obs. of 10 variables:
## $ dateRep : chr "11/01/2021" "04/01/2021" "28/12/2020" "21/12/2020" ...
## $ year_week : chr "2021-01" "2020-53" "2020-52" "2020-51" ...
## $ cases_weekly : int 675 902 1994 740 1757 1672 1073 1368 1164 606 ...
## $ deaths_weekly : int 71 60 88 111 71 137 68 69 61 24 ...
## $ countriesAndTerritories : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ geoId : chr "AF" "AF" "AF" "AF" ...
## $ countryterritoryCode : chr "AFG" "AFG" "AFG" "AFG" ...
## $ popData2019 : int 38041757 38041757 38041757 38041757 38041757 38041757 38041757 38041757 38041757 38041757 ...
## $ continentExp : chr "Asia" "Asia" "Asia" "Asia" ...
## $ notification_rate_per_100000_population_14.days: num 4.15 7.61 7.19 6.56 9.01 7.22 6.42 6.66 4.65 3.7 ...
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Totalcases_in_All_Countries <- COVID_19 %>% group_by(countriesAndTerritories) %>% summarise(Cases = sum(cases_weekly))## `summarise()` ungrouping output (override with `.groups` argument)
#1. Comparison between different countries, using a line plot (x = time, y=number of cases in log10).
cv <- ggplot(COVID_19, aes(x = dateRep, y= cases_weekly))
cv + geom_jitter()
cv + geom_jitter() + scale_y_log10()## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 7 rows containing missing values (geom_point).
#2. Total cases in the world in a stack column plot, with each country as its own color (x = MOnth, y = number of cases in log10)
cv + geom_col(aes(color=countryterritoryCode)) 
cv + geom_col(aes(color=countryterritoryCode)) + theme(legend.position = "none")
cv + geom_col(aes(color=countryterritoryCode)) + scale_y_log10() + theme(legend.position = "none")## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 7 rows containing missing values (position_stack).
## Warning: Removed 1299 rows containing missing values (geom_col).
COVID_19s <- COVID_19 %>% group_by(countryterritoryCode) %>% filter(sum(cases_weekly)>300000)
cv2 <- ggplot(COVID_19s, aes(x = dateRep, y= cases_weekly))
cv2 + geom_col(aes(fill=countryterritoryCode)) 
cv2 + geom_col(aes(fill=countryterritoryCode)) + scale_y_log10()## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 160 rows containing missing values (geom_col).
