R Markdown

Two basic scatter plots:

Plot a, shows total antidepressant usage in Scandinavia: 2007-2017, M&F, 5-19yrs old.

Plot b, shows the same as plot a, with the age groups displayed in different colours.

# Plot a - Basic plot
# Load packages
pacman::p_load(pacman,tidyverse,GGally,ggthemes,ggvis,httr,plotly,rio,rmarkdown,shiny,ggplot2)
 c1 <- read.csv("census.csv") # c1 variable becomes selected csv file
 head(c1) # Shows first 6 lines of selected csv file, to see column attribute names, for plot (x,y)
##   year sex   age    cnt country
## 1 2007   F   5-9 164148      DK
## 2 2007   F 10-14 171864      DK
## 3 2007   F 15-19 157037      DK
## 4 2007   M   5-9 172068      DK
## 5 2007   M 10-14 181190      DK
## 6 2007   M 15-19 165784      DK
 plot(c1$year, c1$cnt, # plot selected attributes with colour and labels 
           col="red", 
            pch=19, # Use solid circles for points
             main="Antidepressant use in Scandinavia: 5-19yrs old.", # Main title
              xlab="Year", # x axis label
               ylab="Number of users.") # y axis label

# Plot b - Scatter plot
# Load packages
 pacman::p_load(pacman,tidyverse,GGally,ggthemes,ggvis,httr,plotly,rio,rmarkdown,shiny,ggplot2) 
 c1 <- read.csv("census.csv") # c1 variable becomes selected csv file
 head(c1) # Shows first 6 lines of selected csv file, to see column attribute names,for plot (x,y) and colour selection
##   year sex   age    cnt country
## 1 2007   F   5-9 164148      DK
## 2 2007   F 10-14 171864      DK
## 3 2007   F 15-19 157037      DK
## 4 2007   M   5-9 172068      DK
## 5 2007   M 10-14 181190      DK
## 6 2007   M 15-19 165784      DK
 ggplot(data=c1,aes(x=year,y=cnt))+geom_point(aes(color=age))

# ggplot uses data from c1 to plot x and y attributes and plots the different y variables from chosen attribute in different colours