Line charts
In this post, we will work towards creating the line plot below.
The post will take you from a basic line plot and explain all the customization we add to the code step-by-step.
In this chapter, we will learn to:
** build simple line chart
** modify line properties, including type, color, and size
** create grouped line chart
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.5 v dplyr 1.0.3
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(ggtext)## Warning: package 'ggtext' was built under R version 4.0.5library(cowplot)## Warning: package 'cowplot' was built under R version 4.0.5Example: Average sale price of detached home in Toronto
Loading data
data <- read_csv("gta_realestate.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## city = col_character(),
## year = col_double(),
## type = col_character(),
## price = col_double()
## )head(data,10)## # A tibble: 10 x 4
## city year type price
## <chr> <dbl> <chr> <dbl>
## 1 Toronto 2011 detached home 701846
## 2 Toronto 2012 detached home 722393
## 3 Toronto 2013 detached home 864351
## 4 Toronto 2014 detached home 934039
## 5 Toronto 2015 detached home 1039638
## 6 Toronto 2016 detached home 1286605
## 7 Toronto 2017 detached home 1250235
## 8 Toronto 2018 detached home 1145892
## 9 Toronto 2019 detached home 1363357
## 10 Toronto 2020 detached home 1475758df <- data %>%
filter(type == "detached home" & city == "Toronto") %>%
mutate(year = as.factor(year))
df## # A tibble: 10 x 4
## city year type price
## <chr> <fct> <chr> <dbl>
## 1 Toronto 2011 detached home 701846
## 2 Toronto 2012 detached home 722393
## 3 Toronto 2013 detached home 864351
## 4 Toronto 2014 detached home 934039
## 5 Toronto 2015 detached home 1039638
## 6 Toronto 2016 detached home 1286605
## 7 Toronto 2017 detached home 1250235
## 8 Toronto 2018 detached home 1145892
## 9 Toronto 2019 detached home 1363357
## 10 Toronto 2020 detached home 14757581. Creating a basic line plot
The main function here is geom_line()
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
theme_minimal()
At this stage, I would like to show you how to modify graph using ggplot2. There will be other posts will focus on improve the aesthetic of the image.
ggsave("Figure 1.Basic line graph using geom_line.png", height = 3, width = 5,units = "in",dpi = 300)Alternatively, we can also use geom_path()
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_path() +
theme_minimal()
ggsave("Figure 2.Basic line graph using geom_path.png", height = 3, width = 5,units = "in",dpi = 300)2. Change line properties
2.1 size
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line(size = 1) +
theme_minimal()
ggsave("Figure 3.Customize line size.png", height = 3, width = 5,units = "in",dpi = 300)2.2. Type
The line type can be modified using the linetype argument. It can take 7 different values.
We can specify the line type either using numbers or words as shown below:
0 : blank
1 : solid
2 : dashed
3 : dotted
4 : dotdash
5 : longdash
6 : twodash
2.2.1. using words
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line(linetype = "dashed", size = 1) +
theme_minimal()
ggsave("Figure 4.Customize line type using words.png", height = 3, width = 5,units = "in",dpi = 300)2.2.2. using number
Here the number 4 corresponds to “dotdash”.
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line(linetype = 4, size = 1) +
theme_minimal()
ggsave("Figure 5.Customize line type using number.png", height = 3, width = 5,units = "in",dpi = 300)2.3 Changing colors
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line(color = "blue", size =1, linetype ="longdash") +
theme_minimal()
ggsave("Figure 6.Customize color.png", height = 3, width = 5,units = "in",dpi = 300)2.4 Adding points to the line
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
geom_point() + # changing size and color of the point
theme_minimal()
ggsave("Figure 7.Adding points.png", height = 3, width = 5,units = "in",dpi = 300)2.4.1. changing size and color of the point
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
geom_point(size = 5, color = "purple") +
theme_minimal()
ggsave("Figure 8.Customize color of points.png", height = 3, width = 5,units = "in",dpi = 300)2.4.2. changing the shape
ggplot(data= df,
aes(x = year, y = price, group= 1)) +
geom_line() +
geom_point(size = 3, color = "blue", shape=23) +
theme_minimal()
ggsave("Figure 9.Customize shape of points.png", height = 3, width = 5,units = "in",dpi = 300)2.4.3. changing filing color
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
geom_point(size = 3, color = "blue", shape=23, fill= "purple") +
theme_minimal()
ggsave("Figure 10.Customize color of points.png", height = 3, width = 5,units = "in",dpi = 300)2.4.4.Adding labels
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
geom_point(size = 2, color = "blue",fill= "blue", shape=21) +
geom_text(data= df, aes(x = year, y = price, group = 1,
label= paste("$",round(price/1000000,2),"M")),
color = "blue",
vjust = 2.5)+
scale_y_continuous(expand = c(0,0),
limits = c(500000,1500000),
n.breaks = 5) +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 11A.Adding labels.png", height = 5, width = 8,units = "in",dpi = 300)Note: We can customize labels. For example, we want to compares only the data of 2011 and 2020.
ggplot(data= df,
aes(x = year, y = price, group = 1)) +
geom_line() +
geom_point(size = 3, color = "blue",fill= "blue", shape=21) +
geom_text(data= df %>%
filter(year == 2011 | year == 2020), # to label only the data point at 2011 and 2020 only
aes(x = year, y = price,
group = 1,
label= paste("$",round(price/1000000,2),"M")),
color = "blue",
vjust =2.5)+
scale_y_continuous(expand = c(0,0),
limits = c(500000,1500000),
n.breaks = 5) +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
(There will be a post dedicated to labels)
ggsave("Figure 11B.Adding labels.png", height = 5, width = 8,units = "in",dpi = 300)3. Multiple lines
Example: We would like to see the change of detached homes in three cities: Toronto, Brampton, and Scarborough.
Data preparation
df <- data %>%
filter(type == "detached home") %>%
mutate(year = as.factor(year),
city = as.factor(city))To display multiple lines, you can use the group attribute in the data aesthetics layer.
3.1. Basic line chart
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city), # to draw multiple lines, we need to use group here
size = 1.5) 
ggsave("Figure 12.Create multiple lines.png", height = 3, width = 5,units = "in",dpi = 300)In order to distinguish these three lines, we have to map them with color.
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city, #
color = city), # three line by three default color
size = 1.5) +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))# increase the size of the line
The three colors here are default colors.
When applying color for three line, the legend will automatically created
ggsave("Figure 13.Adding color to lines.png", height = 5, width = 8,units = "in",dpi = 300)geom_path() connects the observations in the order in which they appear in the data.
geom_line() connects them in order of the variable on the x axis.
3.2. Customize color of the line
we can change manually line colors using the functions :
scale_color_manual() : to use custom colors
scale_color_brewer() : to use color palettes from RColorBrewer package
scale_color_grey() : to use grey color palettes
3.2.1. scale_color_manual()
3.2.1.1. using color codes
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city, #
color = city), # three line by three default color
size = 1.5) + # increase the size of the line +
scale_colour_manual(values=c("#6495ED", "#808000", "#000080")) +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 14A.Customize colors using scale_colour_manual().png", height = 5, width = 8,units = "in",dpi = 300)3.2.1.2. using words
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city, #
color = city), # three line by three default color
size = 1.5) + # increase the size of the line +
scale_colour_manual(values=c("red", "darkgreen", "orange")) +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 14B.Customize colors using scale_colour_manual().png", height = 5, width = 8,units = "in",dpi = 300)3.2.2. scale_color_brewer()
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city, #
color = city), # three line by three default color
size = 1.5) + # increase the size of the line +
scale_color_brewer(palette = "Set1") +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 15.Customize colors using scale_color_brewer().png", height = 5, width = 8,units = "in",dpi = 300)See https://ggplot2.tidyverse.org/reference/scale_brewer.html for more information about scale_color_brewer
3.2.3. scale_color_grey()
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city, #
color = city), # three line by three default color
size = 1.5) + # increase the size of the line +
scale_color_grey() +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 16.Customize colors using scale_color_grey().png", height = 5, width = 8,units = "in",dpi = 300)3.3. Set line types manually
You must map a column in your data to the linetype aesthetic and then assign linetypes to the values of that column.
ggplot() +
geom_line(data = df,
aes(x= year, y = price,
group = city,
color = city,
linetype = city),
size = 1.5) + # increase the size of the line +
scale_colour_manual(values=c("#6495ED", "#808000", "#000080")) +
scale_linetype_manual(values=c("dotted","dotted","solid")) +
theme_minimal() +
theme(axis.title = element_text(size =12),
axis.text = element_text(size =12))
ggsave("Figure 17.Customize line types using scale_linetype_manual().png", height = 5, width = 8,units = "in",dpi = 300)see https://ggplot2.tidyverse.org/reference/scale_linetype.html for more information
4. The final graphe
Applying all the steps above, we can obtain the following graph.
ggplot() +
geom_line(data = df,
aes(x= year, y = price, group = city, color = city),
size = 1.5) +
scale_y_continuous(expand = c(0,0),
limits = c(0,1500000),
n.breaks = 6) +
scale_colour_manual(values=c("#6495ED", "#808000", "#1F497D")) +
geom_text(data = df %>% filter(year == 2020),
aes(x = year,y = price, color = city, label = city),
nudge_x = 0.3,
hjust =0,
size = 4) +
labs(title = "Average sale price for detached homes (Dec.2011-Dec.2020)",
# subtitle = "The cost of owning a home has more than doubled across GTA since the end of 2011",
caption = "Data source: Toronto Regional Real Estate Board ",
x = "Year",
y = "Average price sale (CAD)") +
coord_cartesian(clip = 'off') +
theme(plot.title = element_markdown(size=14, margin=margin(0,0,30,0),face="bold"),
plot.subtitle = element_markdown(size=12, margin=margin(0,0,15,0)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 11, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 11),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 11, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 11),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,2.5,0.5,0.5),"cm"))
ggsave("Figure 18.Final graph.png", height = 5, width = 8,units = "in",dpi = 300)There will be a post dedicated to Titles, Subtitles, Captions, Axis Labels.
5. Other examples of line graphs.
5.1. Life expectation for North Americas Countries
library(gapminder)
df_gapminder <- gapminderdf_gapminder%>%
filter(country %in% c("Canada","Mexico","United States")) %>%
ggplot() +
geom_line(aes(x = factor(year),y = lifeExp,
color = country, group = country), size = 1.75)+
geom_text(data = df_gapminder %>% filter(country %in% c("Canada","Mexico","United States"), year == 2007),
aes(x = factor(year),y = lifeExp, color = country, label = country),
nudge_x = 0.2,
hjust = 0,
size = 4) +
scale_y_continuous(expand = c(0,0),limits = c(50,85),n.breaks = 8) +
scale_colour_manual(values=c("Mexico" ="#483D8B", "United States" = "#006400","Canada" ="#0b1d78"))+
labs(title = "Life expectancy of three North Americas Countries",
caption = "Data source: gapminder.org",
x = "Year",y = "Life expectancy") +
coord_cartesian(clip = 'off') +
theme(plot.title = element_markdown(size=14, margin=margin(0,0,30,0),face="bold"),
plot.subtitle = element_markdown(size=12, margin=margin(0,0,15,0)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 10, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 10),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 10, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 10),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,2.75,0.5,0.5),"cm"))
ggsave("Figure 19.Life expectancy of three North Americas countries.png", height = 5, width = 8,units = "in",dpi = 300)5.2. Indexed chart
The line chart can also be used for indexed chart.
data <- read_csv("gta_realestate.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## city = col_character(),
## year = col_double(),
## type = col_character(),
## price = col_double()
## )df_modified <- data%>%
filter(year >= 2015) %>%
group_by(city, type) %>%
mutate(ind = 100*price/ first(price))
df_modified ## # A tibble: 54 x 5
## # Groups: city, type [9]
## city year type price ind
## <chr> <dbl> <chr> <dbl> <dbl>
## 1 Toronto 2015 detached home 1039638 100
## 2 Toronto 2016 detached home 1286605 124.
## 3 Toronto 2017 detached home 1250235 120.
## 4 Toronto 2018 detached home 1145892 110.
## 5 Toronto 2019 detached home 1363357 131.
## 6 Toronto 2020 detached home 1475758 142.
## 7 Missisauga 2015 detached home 868380 100
## 8 Missisauga 2016 detached home 1114177 128.
## 9 Missisauga 2017 detached home 1140965 131.
## 10 Missisauga 2018 detached home 1033209 119.
## # ... with 44 more rowsggplot() +
geom_line(data =df_modified %>% filter(type == "detached home") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 142, label = "+142 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 180, label = "+181 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 162, label = "+163 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed average prices for detached homes (Dec 2011-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2011 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off') +
theme(plot.title = element_markdown(size=14, margin=margin(0,0,30,0),face="bold"),
plot.subtitle = element_markdown(size=12, margin=margin(0,0,15,0)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 10, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 10),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 10, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 10),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,3.5,0.5,0.5),"cm"))
ggsave("Figure 20.Indexed average prices for detached homes.png", height = 5, width = 8,units = "in",dpi = 300)ggplot() +
geom_line(data =df_modified %>% filter(type == "semi-detached home") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 156, label = "+156 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 178, label = "+178 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 162, label = "+160 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed average prices for semi-detached homes (Dec 2011-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2011 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off') +
theme(plot.title = element_markdown(size=14, margin=margin(0,0,30,0),face="bold"),
plot.subtitle = element_markdown(size=12, margin=margin(0,0,15,0)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 10, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 10),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 10, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 10),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,3.5,0.5,0.5),"cm"))
ggsave("Figure 21.Indexed average prices for semi-detached homes.png", height = 5, width = 8,units = "in",dpi = 300)df_modified ## # A tibble: 54 x 5
## # Groups: city, type [9]
## city year type price ind
## <chr> <dbl> <chr> <dbl> <dbl>
## 1 Toronto 2015 detached home 1039638 100
## 2 Toronto 2016 detached home 1286605 124.
## 3 Toronto 2017 detached home 1250235 120.
## 4 Toronto 2018 detached home 1145892 110.
## 5 Toronto 2019 detached home 1363357 131.
## 6 Toronto 2020 detached home 1475758 142.
## 7 Missisauga 2015 detached home 868380 100
## 8 Missisauga 2016 detached home 1114177 128.
## 9 Missisauga 2017 detached home 1140965 131.
## 10 Missisauga 2018 detached home 1033209 119.
## # ... with 44 more rowsggplot() +
geom_line(data =df_modified %>% filter(type == "condo") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 156, label = "+156 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 187, label = "+187 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 177, label = "+177 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed condo (Dec 2011-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2011 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off') +
theme(plot.title = element_markdown(size=14, margin=margin(0,0,30,0),face="bold"),
plot.subtitle = element_markdown(size=12, margin=margin(0,0,15,0)),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 10, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 10),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 10, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 10),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,3.5,0.5,0.5),"cm"))
ggsave("Figure 22.Indexed condo price.png", height = 5, width = 8,units = "in",dpi = 300)theme_ind <-theme(plot.title = element_markdown(size=12, margin=margin(0,0,15,0),face="bold"),
plot.subtitle = element_markdown(size=10, margin=margin(0,0,10,0)),
plot.title.position = "plot",
plot.caption = element_text(size=8,hjust = 0),
plot.caption.position = "plot",
axis.title.x= element_text(size = 10, hjust = 0, vjust = 0, color = "#6d6d6d"),
axis.text.x = element_text(size = 10),
axis.line.x = element_line(color = "#7F7F7F"),
axis.title.y = element_text(size = 10, hjust = 1 ,vjust = 2, color = "#6d6d6d"),
axis.text.y = element_text(size = 10),
axis.line.y = element_line(color = "#7F7F7F"),
axis.ticks = element_line(color="#a9a9a9") ,
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.5,2,0.5,0.5),"cm"))
p1 <- ggplot() +
geom_line(data =df_modified %>% filter(type == "condo") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 156, label = "+156 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 187, label = "+187 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 177, label = "+177 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed average price sale for condos (Dec 2015-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2015 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off') + theme_ind
p2 <- ggplot() +
geom_line(data =df_modified %>% filter(type == "semi-detached home") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 156, label = "+156 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 178, label = "+178 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 162, label = "+160 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed average price sale for semi-detached homes (Dec 2015-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2015 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off')+ theme_ind
p3 <- ggplot() +
geom_line(data =df_modified %>% filter(type == "detached home") ,
aes(x = year,
y = ind,
color = city, group = city), size = 1.75) +
scale_x_continuous(breaks=seq(2011, 2020,1)) +
scale_y_continuous(expand = c(0,0),limits = c(90,190),n.breaks =8) +
annotate("text", x = 2020, y = 142, label = "+142 %, Toronto", color = "#6495ED", hjust =-0.1) +
annotate("text", x = 2020, y = 180, label = "+181 %, Brampton", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 2020, y = 162, label = "+163 %, Missisauga", color = "#808000", hjust =-0.1 ) +
scale_colour_manual(values=c("Toronto" ="#6495ED", "Brampton" = "#1F497D","Missisauga" ="#808000")) +
labs(title = "Indexed average price sale for detached homes (Dec 2015-Dec 2020)",
caption = "Data source: Toronto Regional Real Estate Board",
subtitle = "Index, 2015 price = 100",
x = "Year", y= "") +
coord_cartesian(clip = 'off')+ theme_ind
plot_grid(p1,p2,p3, ncol = 1, nrow = 3,rel_heights = c(1, 1,1))+ theme_ind
ggsave("Figure 23.Indexed average price sale.png", height = 12, width = 7.5,units = "in",dpi = 300)5.3. Scaling effect
year <- c(1990:1999)
ABC <- c(1,2,3,4,5,6,5.3,9.2,9,10)
DEF <- c(10,20,30,40,50,60,53,92,90,100)
df_wide <- tibble(year, ABC, DEF)
df_wide## # A tibble: 10 x 3
## year ABC DEF
## <int> <dbl> <dbl>
## 1 1990 1 10
## 2 1991 2 20
## 3 1992 3 30
## 4 1993 4 40
## 5 1994 5 50
## 6 1995 6 60
## 7 1996 5.3 53
## 8 1997 9.2 92
## 9 1998 9 90
## 10 1999 10 100df_long <- df_wide %>%
pivot_longer(cols = c("ABC","DEF"),
names_to = "type",
values_to = "value")
head(df_long,10)## # A tibble: 10 x 3
## year type value
## <int> <chr> <dbl>
## 1 1990 ABC 1
## 2 1990 DEF 10
## 3 1991 ABC 2
## 4 1991 DEF 20
## 5 1992 ABC 3
## 6 1992 DEF 30
## 7 1993 ABC 4
## 8 1993 DEF 40
## 9 1994 ABC 5
## 10 1994 DEF 50ggplot() +
geom_line(data =df_long,aes(x = year,y = value,color = type, group = type), size = 1.75) +
geom_point(data =df_long,aes(x = year,y = value, color = type), size =3)+
scale_x_continuous(breaks=seq(1990, 1999,1)) +
scale_y_continuous(expand = c(0,0),limits = c(0,100),n.breaks =5) +
scale_colour_manual(values=c("ABC"="#1F497D", "DEF" = "#490206")) +
annotate("text", x = 1999.5, y = 10, label = "ABC", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 1999.5, y = 100, label = "DEF", color = "#490206", hjust =-0.1) +
geom_text(data= df_long, aes(x = year, y = value, group =type,label= value,color = type),vjust = -1, hjust =0.75)+
labs(title = "Stock Prices of two companies",
caption = "",
x = "Year", y= "") +
coord_cartesian(clip = 'off')+
theme_ind
Ref. Just Plain Data Analysis, Garry M. Klass, 2008, Rowman & Littlefield Inc.
ggsave("Figure 24.Linear scale.png", height = 5, width = 8,units = "in",dpi = 300)ggplot() +
geom_line(data =df_long,aes(x = year,y = value,color = type, group = type), size = 1.75) +
geom_point(data =df_long,aes(x = year,y = value, color = type), size =3)+
scale_x_continuous(breaks=seq(1990, 1999,1)) +
scale_y_log10() +
scale_colour_manual(values=c("ABC"="#1F497D", "DEF" = "#490206")) +
annotate("text", x = 1999.5, y = 10, label = "ABC", color = "#1F497D", hjust =-0.1) +
annotate("text", x = 1999.5, y = 100, label = "DEF", color = "#490206", hjust =-0.1) +
geom_text(data= df_long, aes(x = year, y = value, group =type,label= value,color = type),vjust = -1, hjust =0.75)+
labs(title = "Stock Prices of two companies",
caption = "",
x = "Year", y= "") +
coord_cartesian(clip = 'off')+
theme_ind
ggsave("Figure 25.Log scale.png", height = 5, width = 8,units = "in",dpi = 300)