Load packages

If you are using the package for the first time, you will first have to install it.  

# install.packages("survival") 
# install.packages("lattice")
# install.packages("ggplot2")
# install.packages("emojifont")
# install.packages("gtrendsR")

If you have already downloaded this package in the current version of R, you will only have to load the package.

library(survival)
library(lattice)
library(ggplot2)
library(emojifont)
library(gtrendsR)

Get the data

Load a data set from a package.
You can use the double colon symbol (:), to return the pbc and pbcseq objects from the package survival. We store these data sets to new objects with the names pbc and pbcseq.

pbc <- survival::pbc
pbcseq <- survival::pbcseq

Basic plots

Basic plot with 1 continuous variable using the function plot(). For example, investigate the variable bili of the pbc data set.

plot(x = pbc$bili)

Basic plot with 2 continuous variables. For example, Check the correlation between age and bili of the pbc data set.

plot(x = pbc$age, y = pbc$bili)

Basic plot with 2 continuous variables. Now, insert labels for the x and y-axis (use the argument xlab).

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age")

Basic plot with 2 continuous variables. Now, insert labels for the x and y-axis and change the size of the axis and labels (use the arguments cex.axis and cex.lab).

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age",
     cex.axis = 1.2, cex.lab = 1.4)

Basic plot with 2 continuous variables. Insert axis labels and change the size and type of points. Change also the size and the type of the points (use the arguments cex and pch). If you are not sure which arguments to use, check the help page.

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age",
     cex.axis = 1.2, cex.lab = 1.4,
     cex = 2, pch = 16)

Basic plot with 2 continuous variables. Insert labels for the x and y-axis and change the size of the axis and labels. Change also the colour of the points.
Note that we can set the colours in different ways:
* using numbers that correspond to a colour
* using the name of the colour
* using the RGB colour specification (Red Green Blue) ?rgb
* using the HEX colour code

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age", 
     cex.axis = 1.2, cex.lab = 1.4,
     col = 2)

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age", 
     cex.axis = 1.2, cex.lab = 1.4,
     col = "red")

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age", 
     cex.axis = 1.2, cex.lab = 1.4,
     col = rgb(1,0,0))

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age", 
     cex.axis = 1.2, cex.lab = 1.4,
     col = "#FF0000")

Basic plot with 3 variables (2 continuous and 1 categorical). X-axis represents age, y-axis represents serum bilirubin and colours represent sex.

plot(x = pbc$age, y = pbc$bili, ylab = "Serum bilirubin", xlab = "Age", 
     cex.axis = 1.5, cex.lab = 1.4, col = pbc$sex, pch = 16)
legend(30, 25, legend = c("male", "female"), col = c(1,2), pch = 16)

Histogram for continuous variables. Check the distribution of bili and investigate the argument breaks and length.

hist(x = pbc$bili, breaks = 50)

hist(x = pbc$bili, breaks = seq(min(pbc$bili), max(pbc$bili), length = 20))

Multiple panels (using the par() function).

par(mfrow=c(2,2))
hist(x = pbc$bili, freq = TRUE)
hist(x = pbc$chol, freq = TRUE)
hist(x = pbc$albumin, freq = TRUE)
hist(x = pbc$alk.phos, freq = TRUE)

Check what the argument freq does.
Tip: Note that sometimes you will have to clear all plots in order to get 1 panel again (brush icon in Plots tab). Barchart for categorical variables using the function plot(). Check the frequency of males and females.

plot(x = pbc$sex)

Piechart for categorical variables using the functions pie() and table(). Check the frequency of males and females.

pie(x = table(pbc$sex))

Boxplot for investigating the distribution of a continuous variable per group using the function boxplot(). Check the distribution of age per sex group.

boxplot(formula = pbc$age ~ pbc$sex, ylab = "Age", xlab = "Gender")

Multivariate plot of the variables bili, chol and albumin. We first need to create a matrix/data.frame.

pairs(x = data.frame(pbc$bili, pbc$chol, pbc$albumin))

pairs(x = cbind(pbc$bili, pbc$chol, pbc$albumin))

pairs(formula = ~ bili + chol + albumin, data = pbc)

In the last case we set the data set to pbc. That means that we do not have to specify pbc every time we select a variable. The function knows that it has to look in the pbc data set for these names. Density plots of bili per sex group to investigate the distribution.
Several ways exist to obtain this plot.

# Here we start by assigning the `bili` values for `males` and `females` to a new object.
pbc_male_bili <- pbc$bili[pbc$sex == "m"]
pbc_female_bili <- pbc$bili[pbc$sex == "f"]
# We first plot the `bili` values for `males`.
plot(density(pbc_male_bili), col = rgb(0,0,1,0.5), ylim = c(0,0.40),
     main = "Density plots", xlab = "bili", ylab = "")
# Then we fill in the area under the curve using the function `polygon()`.
polygon(density(pbc_male_bili), col = rgb(0,0,1,0.5), border = "blue")
# Then we add the `bili` values for `females`. Since a plot has been already specified we can use the function `lines()` to add a line.
lines(density(pbc_female_bili), col = rgb(1,0,0,0.5))
# Then we fill in the area under the curve using the function `polygon()`.
polygon(density(pbc_female_bili), col = rgb(1,0,0,0.5), border = "red")
# Finally, we add a legend using the `legend()` function.
legend(5,0.3, legend = c("male", "female"), 
       col = c(rgb(0,0,1,0.5), rgb(1,0,0,0.5)), lty = 1)

Lattice family

Correlation between bili and age. Investigate the arguments type and lwd.

xyplot(x = bili ~ age, data = pbc, type = "p", lwd = 2)

Smooth evolution of bili with age. To change the type of plot use the argument type.

xyplot(x = bili ~ age, data = pbc, type = c("p", "smooth"), lwd = 2)

Smooth evolution of bili with age per sex. Assume different colours for each sex category using the group argument.

xyplot(x = bili ~ age, group = sex, data = pbc, type = "smooth", 
       lwd = 2, col = c("red", "blue"))

Smooth evolution with points of bili with age per sex. Assume different colours for each sex category.

xyplot(x = bili ~ age, group = sex, data = pbc, type = c("p", "smooth"), 
       lwd = 2, col = c("red", "blue"))

Smooth evolution with points of bili with age per sex (as separate panel).

xyplot(x = bili ~ age | sex, data = pbc, type = c("p", "smooth"), 
       lwd = 2, col = c("red"))

Smooth evolution with points of bili with age per status (as separate panel).

xyplot(x = bili ~ age | status, data = pbc, type = c("p", "smooth"), 
       lwd = 2, col = c("red"))

Smooth evolution with points of bili with age per status (as separate panel - change layout).

xyplot(x = bili ~ age | status, data = pbc, type = c("p", "smooth"), 
       lwd = 2, col = c("red"), layout = c(2,2))

Smooth evolution with points of bili with age per status (as separate panel - change layout).
Transform status into a factor with labels and run the plot again.

pbc$status <- factor(x = pbc$status, levels = c(0, 1, 2), 
                     labels = c("censored", "transplant", "dead"))
xyplot(x = bili ~ age | status, data = pbc, type = c("p", "smooth"), 
       lwd = 2, col = c("red"), layout = c(3,1))

Individual patient plot.

xyplot(x = bili ~ day, group = id, data = pbcseq, type = "l", col = "black")

Individual patient plot per status.

pbcseq$status <- factor(x = pbcseq$status, levels = c(0, 1, 2), 
                        labels = c("censored", "transplant", "dead"))
xyplot(x = bili ~ day | status, group = id, data = pbcseq, type = "l", 
       col = "black", layout = c(3,1),
       grid = TRUE, xlab = "Days", ylab = "Serum bilirubin")

Barchart for categorical variables using the function barchart(). Checking the frequency of males and females.

barchart(x = pbc$sex)

Boxplot of serum bilirubin per sex group using the function bwplot().

bwplot(x = pbc$bili ~ pbc$sex)

Ggplot family

Correlation between age with bili.
Each sex has a different colour.

ggplot(data = pbc, mapping = aes(age, bili, colour = sex)) + 
  geom_point()

ggplot(data = pbc, mapping = aes(age, bili, colour = sex)) + 
geom_point(alpha = 0.3) +
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Correlation between day with bili for patient 93.
A smoothed curve is added in blue.

ggplot(data = pbcseq[pbcseq$id == 93,], mapping = aes(day, bili)) +
geom_line() +
geom_smooth(colour = "blue", span = 0.4) +
labs(title = "Patient 93", subtitle = "Evolution over time", 
     y = "Serum bilirubin", x = "Days")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Correlation between serum bilirubin per stage.

ggplot(data = pbc, mapping = aes(stage, bili, group = stage)) +
geom_boxplot() +
labs(y = "Serum bilirubin", x = "Stage")
## Warning: Removed 6 rows containing missing values (stat_boxplot).

Density plot of serum bilirubin per sex to investigate the distribution.
Be aware that a plot is an object in R, so you can save it.

p <- ggplot(data = pbc, mapping = aes(bili, fill = sex)) +
geom_density(alpha = 0.25) 
p

p + scale_fill_manual(values = c("#999999", "#E69F00"))

Let’s have some fun

set.seed(123)
x1 <- rnorm(10)
y1 <- rnorm(10)
x2 <- rnorm(10)
y2 <- rnorm(10)

plot(x = x1, y = y1, cex = 0)
points(x = x1, y = y1, pch = 16)

plot(x = x1, y1, cex = 0)
text(x = x1, y = y1, cex = 1.5, col = "red")

plot(x = x1, y = y1, cex = 0)
text(x = x1, y = y1, labels = emoji("heartbeat"), cex = 1.5, col = "red", family = "EmojiOne")
text(x = x2, y = y2, labels = emoji("cow"), cex = 1.5, col = "steelblue", family = "EmojiOne")

search_emoji("face")
##  [1] "slightly_smiling_face"        "upside_down_face"             "money_mouth_face"            
##  [4] "nerd_face"                    "clown_face"                   "cowboy_hat_face"             
##  [7] "slightly_frowning_face"       "frowning_face"                "tired_face"                  
## [10] "neutral_face"                 "dizzy_face"                   "drooling_face"               
## [13] "lying_face"                   "zipper_mouth_face"            "nauseated_face"              
## [16] "sneezing_face"                "face_with_thermometer"        "face_with_head_bandage"      
## [19] "crying_cat_face"              "fist_oncoming"                "facepunch"                   
## [22] "punch"                        "woman_facepalming"            "man_facepalming"             
## [25] "pouting_woman"                "person_with_pouting_face"     "fox_face"                    
## [28] "panda_face"                   "monkey_face"                  "dragon_face"                 
## [31] "new_moon_with_face"           "full_moon_with_face"          "sun_with_face"               
## [34] "first_quarter_moon_with_face" "last_quarter_moon_with_face"  "wind_face"                   
## [37] "trollface"
plot(x = x1, y = y1, cex = 0)
text(x = x1, y = y1, labels = emoji("nerd_face"), cex = 1.5, col = "red", family = "EmojiOne")

plot(x = x1, y = y1, cex = 0)
text(x = x1, y = y1, labels = emoji("face_with_head_bandage"), 
     cex = 1.5, col = "blue", family = "EmojiOne")

Using google data

google.trends1 = gtrends(c("feyenoord"), gprop = "web", time = "all")[[1]]

ggplot(data = google.trends1, mapping = aes(x = date, y = hits)) +
  geom_line() +
  labs(y = "Feyenoord", x = "Time") +
  ggtitle("Hits on Google")