The dataset: immigrants count to Canada from 1980 to 2013.
Source: United Nations - Population Division - Department of Economic and Social Affairs.
Consists of: Immigrants record from 150+ countries to Canada between 1980 to 2013.
Import Necessary Libraries
For simplicity purposes, there are two dataframes after cleaning,
as differnt visualizations require different rows and columns etc.
However, both dataframes are 99% similar with minor differences only.
df = read.csv("/Users/salahkaf/Desktop/canadian_immigration_data.csv")
DF = read.csv("/Users/salahkaf/Desktop/UpdatedDF.csv")
Renaming Years columns as "charecters" as R does not accept column name to be started with an integer.
names(df)[4:37] <- c("1980", "1981", "1982", "1983", "1984",
"1985", "1986", "1987", "1988", "1989",
"1990", "1991", "1992", "1993", "1994",
"1995", "1996", "1997", "1998", "1999",
"2000", "2001", "2002", "2003", "2004",
"2005", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013")
Descriptive Analysis Questions
Q1 - How many total immigrants to Canada from 1980 to 2013.
Visualized by line chart. Displayed.
Q1Aplot <- df %>%
filter(Country=="Total") %>%
select(1,4:37) %>%
gather(,key = "year", value = "number", 2:35) %>%
ggplot(aes(x=year, y=number))+
geom_line(aes(group=Country, color=Country))+
geom_point(aes(color=Country))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="Total immigrants",title="Total immigrants to Canada from 1980 to 2013")+
scale_x_discrete(breaks = seq(1980, 2013, by = 2))+ #A jump of 2 years
scale_y_continuous(breaks = seq(10000, 300000, by = 10000)) #Scale y axis
Q1Aplot
Visualized by bar chart. Displayed.
Q1Bplot <- df %>%
filter(Country=="Total") %>%
select(1,4:37) %>%
gather(,key = "year",value = "number", 2:35) %>%
ggplot(aes(x=year,y=number))+
geom_col(fill="lightblue")+
# scale_y_continuous(expand = c(0,0))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="Total immigrants",title="Total immigrants to Canada from 1980 to 2013")+
scale_x_discrete(breaks = seq(1980, 2013, by = 2))+ #A jump of 2 years
scale_y_continuous(breaks = seq(10000, 300000, by = 10000)) #Scale y axis
Q1Bplot
Combine two plots.
A <- ggarrange(Q1Aplot, Q1Bplot,
labels = c("A", "B"),
ncol = 2, nrow = 1)
A
Q2 - How many total immigrants to Canada by continent from 1980 to 2013.
Visualized by bar chart. Displayed.
Q2Aplot <- ggplot(data = df[196:201,], aes(x=Continent,y=Total, fill=Continent))+
geom_col()+
scale_y_continuous(expand = c(0,0))+
geom_text(aes(label=Total),
position = position_stack(vjust = 0.5))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=30, hjust=1))+
labs(x="Continent",y="Total immigration",title="Total immigrants to Canada by continent from 1980 to 2013")
Q2Aplot
Visualized by pie chart. Displayed.
#Creating a sub dataset
Q2DF <- DF[-c(196,197),] #Remove last two rows as they do not have a continent
Q2DF <- tapply(Q2DF$Total,Q2DF$Continent,sum) #Sum total immigrants per continent.
Q2DF <- as.data.frame.table(Q2DF) #Convert it to a data frame
names <- c("Africa", "Asia", "Europe", "Latin America and the Caribbean","Northern America","Oceania")
percentage <- round(Q2DF$Freq/sum(Q2DF$Freq)*100,2) #Count the percentage of each Continent
lebals <- paste(names, percentage) # add percents to labels
lebals <- paste(lebals,"%",sep="") # add % to labels
Q2DF$Continent <- lebals #Add the labels as a new column "Continent"
Q2DF<- Q2DF[,-1] #Remove the old column of names
names(Q2DF)[1] <- "Total" #Rename Freq to Total
Q2DF #Final DF for continent
Total Continent
1 618948 Africa 9.66%
2 3317794 Asia 51.77%
3 1410947 Europe 22.01%
4 765148 Latin America and the Caribbean 11.94%
5 241142 Northern America 3.76%
6 55174 Oceania 0.86%
# Visualization.
Q2Bplot <- ggplot(Q2DF, aes(x="", y=Total, fill=Continent)) +
geom_bar(stat="identity", width=1, color="White") +
coord_polar("y", start=0) +
ggtitle("Percentage of Immigrants per Continent [1980-2013]") +
theme(plot.title = element_text(hjust = 0.5))+
theme_void() # remove background, grid, numeric labels
Q2Bplot
Q3 - How many immigrants to Canada by country from 1980-2013.
Visualized by line chart. AUTOMATED.
Q3Aplot <- df %>%
filter(Country=="China") %>% #Here the country needs to be automated.
select(1,4:37) %>%
gather(,key = "year", value = "number", 2:35) %>%
ggplot(aes(x=year, y=number))+
geom_line(aes(group=Country, color=Country))+
geom_point(aes(color=Country))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="The number of immigrants",title="immigration to Canada from the chosen country during 1980-2013") +
scale_x_discrete(breaks = seq(1980, 2013, by = 2)) #A jump of 2 years
Q3Aplot
Visualized by bar chart. AUTOMATED.
Q3Bplot <- df %>%
filter(Country=="China") %>%
select(1,4:37) %>%
gather(,key = "year",value = "number", 2:35) %>%
ggplot(aes(x=year,y=number))+
geom_col(fill="lightblue")+
scale_y_continuous(expand = c(0,0))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="The number of immigrants",title="immigration to the chosen country during 1980-2013")+
scale_x_discrete(breaks = seq(1980, 2013, by = 2)) #A jump of 2 years
Q3Bplot
Visualized by box plot. AUTOMATED.
# Creating a sub data set
Q3DF <- DF[DF$Country == "Chad"] #This must be automated (User input)
Q3DF <- Q3DF[-nrow(Q3DF),]
Q3DF <- as.data.frame(Q3DF)
# Visualization
Q3Cplot_box <- ggplot(data = Q3DF, aes(x = "", y = Q3DF)) +
geom_boxplot(fill="white") +
coord_cartesian(ylim = c(0,10000)) + # I set the y axis scale so the plot looks better.
ggtitle("Number of Immigrants distributed by a box plot") +
theme(plot.title = element_text(hjust = 0.5))+
xlab("chosen country") + ylab("Number of Immigrants")
Q3Cplot_box
Q4 - What are the immigrants counts for the top 5 immigrants countries from 1980 to 2013.
Visualized by line charts. Displayed.
# Visualization
df_top <- df[1:195,] %>%
arrange(desc(Total)) %>%
select(1,4:37)
Q4plot <- df_top[1:5,] %>%
gather(,key = "year", value = "number", 2:35) %>%
ggplot(aes(x=year, y=number))+
geom_line(aes(group=Country, color=Country))+
geom_point(aes(color=Country))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="The number of immigrants",title="immigration counts for the top 5 immigrants countries from 1980 to 2013")
Q4plot
Q5 - What are the minimum, median, maximum, inter quartile range, and outlier values
for immigrants to Canada from 1980 to 2013 per decade.
Visualized by box plot. Displayed.
# creating empty lists to append the values for each year using for loop
total_decade80s = vector(mode = "list")
total_decade90s = vector(mode = "list")
total_decade2000s = vector(mode = "list")
Years20s <- for (country in DF$Country) {
a <- select(filter(DF, Country == country), X2000:X2009 )
total <- apply(a, 1, sum)
total_decade2000s <- append(total_decade2000s, total)
}
Years90s <- for (country in DF$Country) {
a <- select(filter(DF, Country == country), X1990:X1999 )
total <- apply(a, 1, sum)
total_decade90s <- append(total_decade90s, total)
}
Years80s <- for (country in DF$Country) {
a <- select(filter(DF, Country == country), X1980:X1989 )
total <- apply(a, 1, sum)
total_decade80s <- append(total_decade80s, total)
}
# making a data frame using the appended lists
total_decade <- as.data.frame(cbind(DF$Country, total_decade80s,total_decade90s,total_decade2000s))
total_decade <- total_decade[-nrow(total_decade),] #deleting the total row
names(total_decade) <- c("Country", "1980s", "1990s", "2000s")
total_decade$`1980s` <- as.integer(total_decade$`1980s`) #changing the values of each column to integer
total_decade$`1990s` <- as.integer(total_decade$`1990s`)
total_decade$`2000s` <- as.integer(total_decade$`2000s`)
#filtering the data to avoid outliers
total_decade <- filter(total_decade , `1980s`<1000 & `1990s`<1000 & `2000s`<2500 )
#reshaping the data to a from suitable for box plotting
total_decade <- gather(total_decade, Decades, value, 2:4)
#Visualization.
Q5plot_box <- ggplot(total_decade, aes(x = Decades, y = value, fill = Decades)) +
geom_boxplot(aes(x=country, y=value)) +
coord_cartesian(ylim = c(0,1500)) +
ggtitle("Number of Immigrants for each decade") +
theme(plot.title = element_text(hjust = 0.5)) +
xlab("decades") + ylab("Number of Immigrants")
Q5plot_box
Exploratory Analysis Questions
Q6 - Which countries in the future will have more immigrants,
and which will have less?
Visualized by Scatter & regression plots to observe the immigration patterns. Automated.
Q6DF <- DF[DF$Country=="China",5:38] #Here "China" is the user input, the purpose is to automate this option
Q6DF <- gather(Q6DF,Year,Total,1:34) #Transposing the dataset
Q6DF$Year<-gsub("X","",as.character(Q6DF$Year)) #Removing X from years
Q6DF$Year <- as.integer(Q6DF$Year)
Q6Regplot <- ggplot(data = Q6DF, aes(x= Year, y=Total)) +
geom_point() +
geom_smooth(method="lm") +
scale_x_discrete(breaks = seq(1980, 2013, by = 2)) + #A jump of 2 years
scale_y_continuous(breaks = seq(10000, 50000, by = 5000)) +
ggtitle("Linear Regression plot") +
theme(plot.title = element_text(hjust = 0.5))+
xlab("Years") + ylab("Number of Immigrants")
Q6Regplot
Q7 - Which countries would have a dominant immigrants’ population?
Visualized by word cloud. Displayed
Q7DFA <- DF[c("Country", "Total")] #Choosing these two columns only
Q7DFA <- Q7DFA[-c(nrow(Q7DFA), nrow(Q7DFA)-1),] # removing the last two rows ["Unknown" "Total"]
Q7Word_plot <- wordcloud(words = Q7DFA$Country, freq = Q7DFA$Total, min.freq = 1,
max.words=2000, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
Visualized by waffle plot. Displayed
Q7DFB <-arrange(Q7DFA, desc(Total))
Q7DFB <- Q7DFB[1:8,] #Choosing the first 8 rows >> top 8
Q7DFB$Total <- Q7DFB$Total%/%10000 # scaling down the values by a factor of 10000 to be plottable
# used split() to make a list that contains countries names and their values which is the total number of immigrants
waffle_list <- split(Q7DFB$Total, Q7DFB$Country)
waffle_list <- unlist(waffle_list) # unlist the data because waffle() doesn't support lists
waffle(waffle_list)
Visualized by waffle plot. Automated
#Automated visualization
Q7DFC <- filter(Q7DFA, Total >100)
Q7DFC <- filter(Q7DFC, Country %in% c("India" ,"China", "Sudan")) # the chosen countries ## To be automated
Q7DFC$Total <- Q7DFC$Total%/%min(Q7DFC$Total)
#used split() to make a lsit that contains countries names and their values which is the total number of immigrants
waffle_list2 <- split(Q7DFC$Total, Q7DFC$Country )
waffle_list2 <- unlist(waffle_list2) # unlist the data because waffle() doesn't support lists
Q7waffle <- waffle(waffle_list2)
Q7waffle
Q8 - What are the immigration trends of the continents from 1980 to 2013?
Visualized by line chart. Displayed.
# Visualized by line chart
df[196:201,] %>%
select(2,4:37) %>%
gather(,key = "year", value = "number", 2:35) %>%
ggplot(aes(x=year, y=number))+
geom_line(aes(group=Continent, color=Continent))+
geom_point(aes(color=Continent))+
theme(panel.background = element_blank(),
axis.line = element_line(),
legend.position = "bottom",
axis.text.x = element_text(angle=60, hjust=1))+
labs(x="Year",y="The number of immigrants",title="Immigration trends of the continents from 1980 to 2013")
