choose an effective visual
Minh-Trung Dang
01/07/2021
NOTE: no single right approach for visualizing data
meals <- c(40139,127020,168193,153115,202102,232897,277912,205350,233389,232797)
year <- c(2010:2019)
df <- data.frame(year, meals)library(ggplot2)ggplot(data=df,
aes(x=year,
y=meals)) +
geom_bar(stat="identity", fill="steelblue", width = 0.75) +
scale_x_continuous(name = "Campaign year",breaks=seq(2010, 2019,1)) +
scale_y_continuous(breaks=seq(0, 300000, 50000),
labels = function(x) format(x, scientific = FALSE)) +
ggtitle("Meals served over time") +
ylab("# of meals served") +
theme(plot.title = element_text(size=16),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## 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
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggrepel)## Warning: package 'ggrepel' was built under R version 4.0.5highlight_df <- df %>%
filter(year %in% c(2010, 2013, 2016, 2019))
ggplot(data=df, aes(x=year,
y=meals)) +
geom_line(color="steelblue", size = 1.5) +
geom_point(data = highlight_df,
aes(x = year,y = meals),
color = "steelblue",
size=5) +
geom_text(data = highlight_df,
aes(x = year,y = meals,label = meals, size = 12),
color = "blue",
nudge_x = 0.7,
nudge_y = 0,
show.legend =F) +
scale_x_continuous(name = "Campaign year",breaks=seq(2010, 2019,1)) +
scale_y_continuous(breaks=seq(0, 310000, 50000),
labels = function(x) format(x, scientific = FALSE)) +
ggtitle("Meals served over time") +
ylab("# of meals served") +
theme(plot.title = element_text(size=16),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
capacity and demand measured in number of project hours over time. It is currently graphed as a horizontal bar chart. But is this the only way to show this data? Certainly not!
project <- read_csv("project.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## DATE = col_character(),
## CAPACITY = col_number(),
## DEMAND = col_number()
## )library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionproject$DATE2 <-ym(project$DATE)Now we are ready to tidy our data and convert the table to long format. To do this we will use the gather function from the tidyr package.
df <- gather(project, category, project, DEMAND:CAPACITY)
df$category = factor(df$category, levels = c("DEMAND", "CAPACITY"), ordered = TRUE)
df## # A tibble: 18 x 4
## DATE DATE2 category project
## <chr> <date> <ord> <dbl>
## 1 19-Apr 2019-04-01 DEMAND 46193
## 2 19-May 2019-05-01 DEMAND 49131
## 3 19-Jun 2019-06-01 DEMAND 50124
## 4 19-Jul 2019-07-01 DEMAND 48850
## 5 19-Aug 2019-08-01 DEMAND 47602
## 6 19-Sep 2019-09-01 DEMAND 43697
## 7 19-Oct 2019-10-01 DEMAND 41058
## 8 19-Nov 2019-11-01 DEMAND 37364
## 9 19-Dec 2019-12-01 DEMAND 34364
## 10 19-Apr 2019-04-01 CAPACITY 29263
## 11 19-May 2019-05-01 CAPACITY 28037
## 12 19-Jun 2019-06-01 CAPACITY 21596
## 13 19-Jul 2019-07-01 CAPACITY 25895
## 14 19-Aug 2019-08-01 CAPACITY 25813
## 15 19-Sep 2019-09-01 CAPACITY 22427
## 16 19-Oct 2019-10-01 CAPACITY 23605
## 17 19-Nov 2019-11-01 CAPACITY 24263
## 18 19-Dec 2019-12-01 CAPACITY 24243Basic bars
ggplot(df, aes(x = DATE2,
y = project,
fill = category)) +
geom_bar(stat = "identity",
position = 'dodge',
color="#1974D2",
size =1.2) +
scale_fill_manual(values = c("white", "#1974D2")) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2020-03-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
labs(subtitle = bquote("DAMANDE|"~ bold("CAPACITY") )) +
ylab("Number of project hours") +
theme(plot.title = element_text(size=16),
plot.subtitle = element_text(color = "#1974D2"),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.ticks = element_blank(),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggplot(df, aes(x = DATE2,
y = project)) +
geom_line(data = filter(df, category == "DEMAND"), size = 0.5, color = "#1974D2") + # bright navy blue
geom_line(data = filter(df, category == "CAPACITY"), size = 1.5, color = "#1974D2") +
annotate("text", x = as.Date("2019-12-10"), y = 34360, label = "34K", color = "#1974D2") + # as.Date is required
annotate("text", x = as.Date("2020-01-10"), y = 34360, label = "DEMAND", color = "#1974D2") + # as.Date is required
annotate("text", x = as.Date("2019-12-10"), y = 24360, label = "24K", color = "#1974D2", fontface = "bold") +
annotate("text", x = as.Date("2020-01-10"), y = 24360, label = "CAPACITY", color = "#1974D2", fontface = "bold") +
expand_limits(x = as.Date(c("2019-04-01", "2020-01-15"))) +
scale_y_continuous(limits = c(0, 60000), breaks = seq(0, 60000, by = 10000)) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
labs(subtitle = bquote("DAMAND |"~ bold("CAPACITY") )) +
ylab("Number of project hours") +
theme(plot.title = element_text(size=16),
plot.subtitle = element_text(color = "#1974D2"),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
Overlapping bars
ggplot(df, aes(x = DATE2,
y = project)) +
geom_bar(data = filter(df, category == "DEMAND"), stat = "identity", width = 14, color = "#1974D2", fill = "white") + # bright navy blue
geom_bar(data = filter(df, category == "CAPACITY"), stat = "identity", width = 20, color = "#1974D2", fill = "#1974D2") +
scale_y_continuous(limits = c(0, 60000), breaks = seq(0, 60000, by = 10000)) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
labs(subtitle = bquote("DEMAND |"~ bold("CAPACITY") )) +
ylab("Number of project hours") +
theme(plot.title = element_text(size=18),
plot.subtitle = element_text(color = "#1974D2", size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
ggplot(df, aes(x = DATE2,
y = project)) +
geom_bar(data = filter(df, category == "DEMAND"), stat = "identity", width = 12, color = "#1974D2", fill = "white", size= 1) + # bright navy blue
geom_bar(data = filter(df, category == "CAPACITY"), stat = "identity", width = 20, color = "#1974D2", fill = "#1974D2", alpha = 0.65) +
scale_y_continuous(limits = c(0, 60000), breaks = seq(0, 60000, by = 10000)) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
labs(subtitle = bquote("DEMAND |"~ bold("CAPACITY") )) +
ylab("Number of project hours") +
theme(plot.title = element_text(size=18),
plot.subtitle = element_text(color = "#1974D2", size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
Stacked bars
ggplot(df, aes(x = DATE2,
y = project)) +
geom_bar(data = filter(df, category == "DEMAND"), stat = "identity", width = 20, color = "#1974D2", fill = "#1974D2") + # bright navy blue
geom_bar(data = filter(df, category == "CAPACITY"), stat = "identity", width = 20, color = "#1974D2", fill = "lightgrey") +
scale_y_continuous(limits = c(0, 60000), breaks = seq(0, 60000, by = 10000)) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
labs(subtitle = bquote("CAPACITY |"~ bold("UNMET DEMAND") )) +
ylab("Number of project hours") +
theme(plot.title = element_text(size=18),
plot.subtitle = element_text(color = "#1974D2", size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
changer the color od subtitle CAPACITY
Dot plot
ggplot(df, aes(x = DATE2,
y = project)) +
geom_point(data = filter(df, category == "DEMAND"), size = 12 , color = "#1974D2",shape=21) + # bright navy blue
geom_point(data = filter(df, category == "CAPACITY"), size =12, color = "#1974D2") +
scale_y_continuous(limits = c(0, 60000), breaks = seq(0, 60000, by = 10000)) +
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
ylab("Number of project hours") +
theme(plot.title = element_text(size=18),
plot.subtitle = element_text(color = "#1974D2", size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(),
axis.line = element_line(color="grey", size = 1),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
ggplot(df, aes(own, method)) + geom_point(aes(colour=factor(label), fill = factor(label)), shape=21, size = 4) + scale_fill_manual(values=c(“blue”, “cyan4”)) + scale_colour_manual(values=c(“white”, “black”))
ggplot(df, aes(x = DATE2,
y = project)) +
geom_bar(data = filter(df, category == "DEMAND"), stat = "identity", width = 17.8, color = "white", fill = "#1974D2",alpha = 0.4) +
geom_bar(data = filter(df, category == "CAPACITY"), stat = "identity", width = 17, color = "white", fill = "white") +
geom_point(aes(color = factor(category),fill = factor(category)),
shape=21, size = 14, stroke = 2.2) +
scale_fill_manual(values=c("white", "#1974D2")) +
scale_colour_manual(values=c("steelblue", "#1974D2")) +
geom_text(data = filter(df, category == "DEMAND"),
aes(x = DATE2,y = project,label = round(project/1000,0), size = 10),
color = "blue",
show.legend =F) +
geom_text(data = filter(df, category == "CAPACITY"),
aes(x = DATE2,y = project,label = round(project/1000,0), size = 10),
color = "white",
show.legend =F) +
annotate("text", x = as.Date("2019-12-30"), y = 34360, label = "DEMAND", color = "steelblue") + # as.Date is required
annotate("text", x = as.Date("2019-12-30"), y = 24360, label = "CAPACITY", color = "#1974D2", fontface = "bold") +
expand_limits(x = as.Date(c("2019-04-01", "2020-01-15"))) +
scale_y_continuous(limits = c(0, 55000), breaks = seq(0, 55000, by = 10000)) + # si non, l echelle de y ne marche pas
scale_x_date(breaks = seq(as.Date("2019-04-01"),
as.Date("2019-12-01"),
by = "1 month"),
date_labels = "%b-%y") +
ggtitle("Demande vs Capacity over time") +
ylab("Number of project hours \n (thousands)") +
theme(plot.title = element_text(size=18),
plot.subtitle = element_text(color = "#1974D2", size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.line.x= element_line(color="grey", size = 1),
axis.title.x=element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1.25,1.25,1.2),"cm")) # to change the margin top, right. bottom. left
Graph the difference.
My final view is a simple line graph that plots the Unmet Demand (Demand minus Capacity). This is my least favorite of all (or maybe I’d rate it as tied for last with the basic bars), as it feels like too much context is omitted when we go from the two data series to plotting the difference. See Figure 2.4f.
library(ggplot2)year <- c(2010:2019)
rate <- c(9.7, 2.0, 1.0, 7.0, 15.1, 5.6, 12.3, 4.5, 8.2, 9.1)
attrition <- data.frame(year, rate)
attrition## year rate
## 1 2010 9.7
## 2 2011 2.0
## 3 2012 1.0
## 4 2013 7.0
## 5 2014 15.1
## 6 2015 5.6
## 7 2016 12.3
## 8 2017 4.5
## 9 2018 8.2
## 10 2019 9.1ggplot(data = attrition,
aes(x = year, y = rate))+
geom_point(color = "#1974D2", size = 5) +
# annotation
annotate("segment", x = 2010, xend = 2019,
y = mean(rate), yend = mean(rate),
color = "royalblue",linetype="dashed") + # to control the length of the line
annotate("text", x = 2011, y = 8.0, label = "AVERAGE: 7.5%", color = "#1974D2") +
scale_x_continuous( n.breaks = 9) + # this is a good w and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
# title
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x = element_blank(),
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
highlight_df <- attrition %>%
filter(year == 2019)
ggplot(data=attrition , aes(x=year, y=rate)) +
geom_line(color="royalblue", size = 1.2) +
geom_point(data = highlight_df,
aes(x = year,y = rate),
color = "royalblue",size=4) +
annotate("segment", x = 2010, xend = 2019, y = mean(rate), yend = mean(rate), color = "royalblue",linetype="dashed") +
annotate("text", x =2018.5, y = 6.25, label = "AVG: 7.5%", color = "royalblue") +
annotate("text", x = 2019, y = 10.5, label = "9.1%", color = "royalblue") +
scale_x_continuous(expand = c(0, 0),limits = c(2010, 2019.5), n.breaks = 9) + # this is a good w and simple way to generat breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(), # remove year
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
MY IDEA is to combine two graphs
ggplot(data = attrition,
aes(x = year, y = rate))+
geom_line(color="royalblue", size = 0.5) +
geom_point(color = "#1974D2", size = 5) +
# annotation
annotate("segment", x = 2010, xend = 2019,
y = mean(rate), yend = mean(rate),
color = "royalblue",linetype="dashed") + # to control the length of the line
annotate("text", x = 2011.5, y = 8.0, label = "AVERAGE: 7.5%", color = "#1974D2") +
scale_x_continuous( n.breaks = 9) + # this is a good w and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
# title
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x = element_blank(),
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
### shaded area for the Average +
highlight_df <- attrition %>%
filter(year == 2019)
ggplot(data=attrition , aes(x=year, y=rate)) +
geom_line(color="royalblue", size = 1.75) +
geom_rect(aes(xmin=2010, xmax=2019, ymin=0, ymax= 7.5), alpha= 0.01, fill = "royalblue") +
geom_point(data = highlight_df,
aes(x = year,y = rate),
color = "royalblue",size=4) +
annotate("segment", x = 2010, xend = 2019, y = mean(rate), yend = mean(rate), color = "royalblue",linetype="dashed") +
annotate("text", x =2018.25, y = 6.5, label = "AVG: 7.5%", color = "royalblue") +
annotate("text", x = 2018.75, y = 10.5, label = "9.1%", color = "royalblue") +
# shading
# Set Origin of ggplot2 Plot Axes to Zero
scale_x_continuous(expand = c(0, 0), limits = c(2010, 2019.2), n.breaks = 9) + # this is a good w and simple way to generat breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(), # remove year
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
Area graph
ggplot(attrition, aes(x = year, y = rate)) +
geom_area(fill='royalblue', colour = "royalblue", size = 1) +
# scale_fill_brewer(palette = "Blues") ne marche pas +
annotate("segment", x = 2010, xend = 2019, y = mean(rate), yend = mean(rate), color = "royalblue",linetype="dashed") +
annotate("text", x =2018.25, y = 6.5, label = "AVG: 7.5%", color = "white") +
# Set Origin of ggplot2 Plot Axes to Zero
scale_x_continuous(n.breaks = 9) +
# this is a good and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(), # remove year
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
ggplot(attrition, aes(x = year, y = rate)) +
geom_bar(stat = "identity", fill='royalblue', colour = "royalblue", size = 1) +
# scale_fill_brewer(palette = "Blues") ne marche pas +
annotate("segment", x = 2010, xend = 2019, y = mean(rate), yend = mean(rate), color = "royalblue",linetype="dashed") +
annotate("text", x = 2011.5, y = 6.5, label = "AVG: 7.5%", color = "blue") +
# Set Origin of ggplot2 Plot Axes to Zero
scale_x_continuous(n.breaks = 9) +
# this is a good and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(0, 16), n.breaks = 9) +
ggtitle("Attrition rate over time") +
ylab("Attrition rate (%)") +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(), # remove year
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
year <- c(rep(2012:2019,2))
score <- c(795, 800, 804, 812, 830, 832, 824, 846, 774, 785, 805, 833, 839, 843, 827, 836)
category <- c(rep("Industry Average", 8), rep("Financial Saving",8))
data <- data.frame(year, category, score)
data## year category score
## 1 2012 Industry Average 795
## 2 2013 Industry Average 800
## 3 2014 Industry Average 804
## 4 2015 Industry Average 812
## 5 2016 Industry Average 830
## 6 2017 Industry Average 832
## 7 2018 Industry Average 824
## 8 2019 Industry Average 846
## 9 2012 Financial Saving 774
## 10 2013 Financial Saving 785
## 11 2014 Financial Saving 805
## 12 2015 Financial Saving 833
## 13 2016 Financial Saving 839
## 14 2017 Financial Saving 843
## 15 2018 Financial Saving 827
## 16 2019 Financial Saving 836ggplot(data = data,
aes(x= year, y = score,group=category, color = category))+
geom_line() +
scale_x_continuous(n.breaks = 7) +
# this is a good and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(700, 900), n.breaks = 10) +
theme(plot.title = element_text(size=18),
axis.text=element_text(size=12),
axis.title=element_text(size=12),
axis.title.x=element_blank(), # remove year
axis.line.x= element_line(color="grey", size = 1),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,1,1,1),"cm")) # to change the margin top, right. bottom. left
library(ggtext)## Warning: package 'ggtext' was built under R version 4.0.5ggplot(data = data,
aes(x= year, y = score))+
geom_line(data = filter(data, category == "Industry Average"), size = 1.5, color = "black") + # bright navy blue
geom_line(data = filter(data, category == "Financial Saving"), size = 1.5, color = "blue") +
geom_point(data = filter(data, category == "Industry Average", year == 2019), size = 4, color = "black") +
geom_point(data = filter(data, category == "Financial Saving", year == 2019), size = 4, color = "blue") +
annotate("text", x = 2020.2, y = 850, label = "Industry Average", color = "black") +
annotate("text", x = 2020.2, y = 837.5, label = "Financial Savings", color ="blue") +
scale_x_continuous(expand = c(0, 0),n.breaks = 10) +
coord_cartesian(xlim = c(2012, 2019), clip = 'off') +
# this is a good and simple way to generate breaks for scale_x_continuous
scale_y_continuous(expand = c(0, 0),limits = c(700, 900), n.breaks = 10) +
labs(title = "BRANCH SATISFACTION",
subtitle = "Financial Savings below industry for the first 5 years") +
ylab("SATISFACTION SCORE") +
xlab("SURVEY YEAR") +
theme(plot.title = element_text(size=16, vjust = 6, hjust = -0.25),
plot.subtitle = element_text(size = 20, vjust = 5 , hjust = 0.55),
# plot.title.position = "plot", # applied for subtitle
#plot.subtitle = element_markdown(size=16, hjust = 1), # when using ggtext package
# Adjust Space Between ggplot2 Axis Labels and Plot Area
axis.text.x = element_text(size=12, vjust = -2) ,
axis.text.y = element_text(size=12, hjust = -3), # ca marche pas cet commande
axis.title.x= element_text(size = 12, hjust = 0, vjust = -4, color ="black"), # change the position of label on y axis
axis.title.y = element_text(size = 12, hjust =1.05, vjust = 5 , color ="black"),# change the position of label on x axis
# axis.title.x=element_blank(),
axis.line.x= element_line(color="grey"),
axis.line.y= element_line(color="grey", size = 1),
# axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(1,4.5,1,1),"cm")) # to change the margin top, right. bottom. left # work with coord_cartesian()