Add a header to begin generating the table of contents
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Combining multiple plots in R
#the library
library(patchwork)
#combine the plots
p1 <- ggplot(iris) + geom_point(aes(Sepal.Width, Sepal.Length))
p2 <- ggplot(iris) + geom_boxplot(aes(Sepal.Width, Sepal.Length, group = Species))
p3 <- ggplot(iris) + geom_point(aes(Petal.Width, Petal.Length))
p4 <- ggplot(iris) + geom_boxplot(aes(Petal.Width, Petal.Length, group = Species))
p1 + p2 + p3 + p4
Correlation plot in R
#using the mtcars dataset
#Plottinng coefs
library(corrplot)
cp = cor(mtcars) corrplot(cp, method = 'number', type = 'lower', diag = FALSE)
#Plottinng both coefs and symbols
corrplot.mixed(cp)
Clustered bar chart in R
#the library
library(ggplot2)
#making the plot
ggplot(data=diamonds) + geom_bar(mapping = aes(x=cut, fill = color),
position="dodge")+ coord_flip()+
theme_light() 
Correlation plot in Stata
#load a sample dataset
sysuse auto, clear
#create the correlation matrix:
correlate price mpg trunk weight length turn foreign
matrix m = r(C)
#plot
heatplot C, values(format(%9.3f)) lower nodiagonal legend(off) sch(burd4)
Changing to polar shape
Coord polar chart in R
#making the plot
ggplot(data=diamonds) + geom_bar(mapping = aes(x=cut, fill = color),
position="dodge")+ coord_flip()+
theme_light() 
Changing to polar shape
p + coord_polar()
Colouring indivudual bars in R
Step 1: making the plot
packages <- c( "tidyverse", "WDI", "forcats",'scales' ) pacman::p_load(packages, character.only = TRUE, install = FALSE) df <- WDI(indicator = "SP.POP.TOTL", start = 2020, end = 2020) country_code <- as_tibble(WDI_data$country) df <- df %>% rename(Population = SP.POP.TOTL) %>% inner_join(country_code, by = "iso2c") %>% filter(region != "Aggregates") %>% top_n(20, Population) %>% mutate(country = fct_reorder(country.x, Population)) p <- ggplot(df, aes(x = country, y = Population, fill = region)) + geom_bar(stat = "identity", alpha = .6, width = .4) p
Step 2: Add additional features to the chart
p+ coord_flip(ylim = c(10000, 1500000000)) +
geom_hline(yintercept = 0, alpha = .5) +
xlab("") +
ylab("20 most populous countries (2020)") +
theme_classic() +
scale_fill_brewer(palette = "Set2", name = "Region") +
theme(
axis.line.y = element_blank(),
axis.title.x = element_text(size = 12),
axis.text.x = element_text(size = 10),
axis.ticks.y = element_blank(),
legend.text = element_text(size = 10) +
scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
) 