Untidy unemployment rates data
John Kellogg
2019-10-06
Untidy unemployment rates data
Found by: Erinda Budo
Client Comments:
The dataset I chose was taken from the World Bank Global Economic Monitor. This dataset contains unemployment rates from 88 countries from year 1990 through 2017.It can be found: https://github.com/ErindaB/Other The data will be transformed from wide to long format.Many columns need to be renamed and some blank ones need to be removed.The analysis will investigate annual unemployment rates from 2011 to 2015 of 71 countries and it will asnwer questions:1)For the five year period from 2011 to 2015, what’s the average annual unemployment rate of each country?2)For the five year period from 2011 to 2015, what’s the distribution of the average annual unemployment rate?3)For the five year period from 2011 to 2015, what’s the overall trend of the world’s annual unemployment rate?
Analyst Actions:
The data came in an Excel XLSX file with two tabs. I wanted those imported seperately into R. Prior to the first chunk below, I saved each tab into their own CSV. No other modifications were made to the data file.
As a backup to the file provided by the client, I found the dataset on the World Bank website (https://datacatalog.worldbank.org/dataset/global-economic-monitor)
# dowload file from github, save it locally in your home directory
download <- download.file('https://raw.githubusercontent.com/kelloggjohnd/DATA607/master/Unemployment%20Rate-seas-adj.csv', destfile = "Rate_adjust_yearly.csv", method = "wininet")
download2 <- download.file('https://raw.githubusercontent.com/kelloggjohnd/DATA607/master/Unemployment%20Rate-Monthly.csv', destfile = "Rate_adjust_monthly.csv", method = "wininet")
# manipulate the data into a data frame
data_raw_year <- data.frame(read.csv(file = "Rate_adjust_yearly.csv", header = TRUE, sep = ","))
data_raw_month <- data.frame(read.csv(file = "Rate_adjust_monthly.csv", header = TRUE, sep = ","))
# Inital view of data
head(data_raw_year, 5)## X Advanced.Economies Argentina Australia Austria Belgium Bulgaria
## 1 NA NA NA NA NA NA NA
## 2 1990 5.800582 NA 6.943297 5.373002 6.550260 NA
## 3 1991 6.728688 NA 9.614137 5.823096 6.439812 NA
## 4 1992 7.511064 NA 10.750080 5.941711 7.088092 13.23500
## 5 1993 7.936175 NA 10.866170 6.811381 8.619130 15.85583
## Bahrain Belarus Brazil Canada Switzerland Chile China Colombia Cyprus
## 1 NA NA NA NA NA NA NA NA NA
## 2 NA NA NA 8.15000 0.501328 NA NA NA NA
## 3 NA NA NA 10.31667 1.090451 NA NA NA NA
## 4 NA NA NA 11.21667 2.563105 NA NA NA NA
## 5 NA NA NA 11.37500 4.516116 NA NA NA NA
## Czech.Republic Germany Denmark Dominican.Republic Algeria
## 1 NA NA NA NA NA
## 2 NA NA NA NA 25.0
## 3 NA 4.864885 NA NA 25.0
## 4 NA 5.764563 NA NA 27.0
## 5 4.333333 6.931370 NA NA 23.2
## EMDE.East.Asia...Pacific EMDE.Europe...Central.Asia Ecuador
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## Egypt..Arab.Rep. Emerging.Market.and.Developing.Economies..EMDEs.
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## Spain Estonia Finland France United.Kingdom Greece
## 1 NA NA NA NA NA NA
## 2 15.48333 0.650 3.103129 7.625 7.091667 NA
## 3 15.51667 1.475 6.666424 7.800 8.825000 NA
## 4 17.06667 3.725 11.796830 8.650 9.966667 NA
## 5 20.83333 6.550 16.384210 9.650 10.400000 NA
## High.Income.Countries Hong.Kong.SAR..China Croatia Hungary India
## 1 NA NA NA NA NA
## 2 5.619945 1.318868 NA NA NA
## 3 6.771918 1.750180 NA NA NA
## 4 7.693434 1.946343 NA NA NA
## 5 8.192391 1.979785 NA NA NA
## Ireland Iceland Israel Italy Jordan Japan Kazakhstan Korea..Rep.
## 1 NA NA NA NA NA NA NA NA
## 2 13.41667 NA NA NA NA 2.108117 NA NA
## 3 14.73333 NA NA NA NA 2.099018 NA NA
## 4 15.40000 NA NA NA NA 2.151389 NA NA
## 5 15.63333 NA NA NA 19.7 2.503291 NA NA
## EMDE.Latin.America...Caribbean Low.Income.Countries..LIC. Sri.Lanka
## 1 NA NA NA
## 2 NA NA 15.9
## 3 NA NA 14.7
## 4 NA NA 14.6
## 5 NA NA 13.8
## Lithuania Luxembourg Latvia Morocco Moldova..Rep. Mexico
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA
## 5 4.191667 NA 4.658333 NA NA NA
## Middle.Income.Countries..MIC. North.Macedonia Malta
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## EMDE.Middle.East...N..Africa Netherlands Norway New.Zealand Pakistan
## 1 NA NA NA NA NA
## 2 NA NA 5.783333 7.984591 3.13
## 3 NA NA 6.041667 10.611440 6.28
## 4 NA NA 6.550000 10.644730 5.85
## 5 NA NA 6.608333 9.800159 4.73
## Peru Philippines Poland Portugal Romania Russian.Federation
## 1 NA NA NA NA NA NA
## 2 NA NA 3.441667 NA NA NA
## 3 NA 10.475 9.008333 NA NA NA
## 4 NA 9.850 12.933330 NA 5.450000 NA
## 5 NA 9.350 15.033330 NA 9.208333 NA
## EMDE.South.Asia Saudi.Arabia Singapore EMDE.Sub.Saharan.Africa Slovakia
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA 1.750 NA 7.05000
## 4 NA NA 1.800 NA 11.31833
## 5 NA NA 1.675 NA 12.85500
## Slovenia Sweden Thailand Tunisia Turkey Taiwan..China Uruguay
## 1 NA NA NA NA NA NA NA
## 2 NA 2.239701 NA NA NA 1.658333 NA
## 3 NA 4.005607 NA NA NA 1.533333 NA
## 4 11.56667 7.110956 NA NA NA 1.500000 NA
## 5 14.57500 11.146890 NA NA NA 1.425000 NA
## United.States Venezuela..RB Vietnam World..WBG.members. South.Africa
## 1 NA NA NA NA NA
## 2 5.616667 NA NA NA NA
## 3 6.850000 NA NA NA NA
## 4 7.491667 NA NA NA NA
## 5 6.908333 NA NA NA NA## X Advanced.Economies Argentina Australia Austria Belgium
## 1 NA NA NA NA NA
## 2 1990M01 5.810216 NA 6.213296 5.306584 6.831543
## 3 1990M02 5.745122 NA 6.406219 5.145882 6.749932
## 4 1990M03 5.622835 NA 6.226480 4.967532 6.670389
## 5 1990M04 5.750094 NA 6.348445 5.055378 6.574611
## Bulgaria Bahrain Belarus Brazil Canada Switzerland Chile China Colombia
## 1 NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA 7.9 0.468379 NA NA NA
## 3 NA NA NA NA 7.7 0.456800 NA NA NA
## 4 NA NA NA NA 7.3 0.445170 NA NA NA
## 5 NA NA NA NA 7.6 0.438875 NA NA NA
## Cyprus Czech.Republic Germany Denmark Dominican.Republic Algeria
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA 25
## 3 NA NA NA NA NA 25
## 4 NA NA NA NA NA 25
## 5 NA NA NA NA NA 25
## EMDE.East.Asia...Pacific EMDE.Europe...Central.Asia Ecuador
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## Egypt..Arab.Rep. Emerging.Market.and.Developing.Economies..EMDEs. Spain
## 1 NA NA NA
## 2 NA NA 15.9
## 3 NA NA 15.8
## 4 NA NA 15.6
## 5 NA NA 15.6
## Estonia Finland France United.Kingdom Greece High.Income.Countries
## 1 NA NA NA NA NA NA
## 2 0.7 2.856077 7.751852 6.9 NA 5.482747
## 3 0.7 2.615326 7.696296 6.9 NA 5.452225
## 4 0.7 3.135250 7.651852 6.9 NA 5.383208
## 5 0.6 3.842323 7.618519 6.9 NA 5.539168
## Hong.Kong.SAR..China Croatia Hungary India Ireland Iceland Israel Italy
## 1 NA NA NA NA NA NA NA NA
## 2 1.241434 NA NA NA 13.8 NA NA NA
## 3 1.323013 NA NA NA 13.7 NA NA NA
## 4 1.346259 NA NA NA 13.4 NA NA NA
## 5 1.302749 NA NA NA 13.2 NA NA NA
## Jordan Japan Kazakhstan Korea..Rep. EMDE.Latin.America...Caribbean
## 1 NA NA NA NA NA
## 2 NA 2.228275 NA NA NA
## 3 NA 2.224679 NA NA NA
## 4 NA 1.991986 NA NA NA
## 5 NA 2.155126 NA NA NA
## Low.Income.Countries..LIC. Sri.Lanka Lithuania Luxembourg Latvia Morocco
## 1 NA NA NA NA NA NA
## 2 NA 15.9 NA NA NA NA
## 3 NA 15.9 NA NA NA NA
## 4 NA 15.9 NA NA NA NA
## 5 NA 15.9 NA NA NA NA
## Moldova..Rep. Mexico Middle.Income.Countries..MIC. North.Macedonia Malta
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## EMDE.Middle.East...N..Africa Netherlands Norway New.Zealand Pakistan
## 1 NA NA NA NA NA
## 2 NA NA 6.0 7.185925 3.13
## 3 NA NA 5.9 7.161031 3.13
## 4 NA NA 5.8 7.200287 3.13
## 5 NA NA 5.6 7.597992 3.13
## Peru Philippines Poland Portugal Romania Russian.Federation
## 1 NA NA NA NA NA NA
## 2 NA 9.6 0.1 NA NA NA
## 3 NA 9.6 0.7 NA NA NA
## 4 NA 9.6 1.6 NA NA NA
## 5 NA NA 2.3 NA NA NA
## EMDE.South.Asia Saudi.Arabia Singapore EMDE.Sub.Saharan.Africa Slovakia
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## Slovenia Sweden Thailand Tunisia Turkey Taiwan..China Uruguay
## 1 NA NA NA NA NA NA NA
## 2 NA 2.182138 NA NA NA 1.5 NA
## 3 NA 1.959847 NA NA NA 1.5 NA
## 4 NA 1.976685 NA NA NA 1.6 NA
## 5 NA 1.847875 NA NA NA 1.5 NA
## United.States Venezuela..RB Vietnam World..WBG.members. South.Africa
## 1 NA NA NA NA NA
## 2 5.4 NA NA NA NA
## 3 5.3 NA NA NA NA
## 4 5.2 NA NA NA NA
## 5 5.4 NA NA NA NATidy the data
Client Comments:
The data will be transformed from wide to long format. Many columns need to be renamed and some blank ones need to be removed.
Analyst Actions:
Both raw sets were processed through tidyr procedures switching them from wide to long formats. Blank column on row 2 of files were removed. The import of the data did not keep the names accurate. They will be renamed as they are the region or country names. We will need to define the Aggregates vs. Countries.
- Chunk for initial tidy of the Year data
# rename the column names
colnames(data_raw_year) <-c("Year","Advanced.Economies","Argentina","Australia","Austria","Belgium","Bulgaria","Bahrain","Belarus","Brazil","Canada","Switzerland","Chile","China","Colombia","Cyprus","Czech.Republic","Germany","Denmark","Dominican.Republic","Algeria","EMDE.East.Asia_Pacific","EMDE.Europe_Central.Asia","Ecuador","Egypt_ Arab.Rep.","Emerging.Market.Developing.Economies","Spain","Estonia","Finland","France","United.Kingdom","Greece","High.Income.Countries#HIC","Hong.Kong.SAR_ China","Croatia","Hungary","India","Ireland","Iceland","Israel","Italy","Jordan","Japan","Kazakhstan","Korea.Rep.","EMDE.Latin.America_Caribbean","Low.Income.Countries#LIC","Sri.Lanka","Lithuania","Luxembourg","Latvia","Morocco","Moldova.Rep.","Mexico","Middle.Income.Countries#MIC","North.Macedonia","Malta","EMDE.Middle East_N.Africa","Netherlands","Norway","New.Zealand","Pakistan","Peru","Philippines","Poland","Portugal","Romania","Russian.Federation","EMDE.South.Asia","Saudi.Arabia","Singapore","EMDE.Sub-Saharan.Africa","Slovakia","Slovenia","Sweden","Thailand","Tunisia","Turkey","Taiwan_China","Uruguay","United States","Venezuela_ RB","Vietnam","World^WBG.members","South Africa")
# gather the data into a tidy form
Year_raw <- gather(data_raw_year, "Country", "Value", 2:85)
#Filter out the blank values due to the blank top row on import
Year_Data <- Year_raw %>%
filter(!is.na(Value))
#Change the Year to numeric
Year_Data$Year<- as.numeric(Year_Data$Year)
# QA dataframe
head(Year_Data,10)## Year Country Value
## 1 1990 Advanced.Economies 5.800582
## 2 1991 Advanced.Economies 6.728688
## 3 1992 Advanced.Economies 7.511064
## 4 1993 Advanced.Economies 7.936175
## 5 1994 Advanced.Economies 7.715897
## 6 1995 Advanced.Economies 7.264255
## 7 1996 Advanced.Economies 7.233725
## 8 1997 Advanced.Economies 6.942783
## 9 1998 Advanced.Economies 6.575284
## 10 1999 Advanced.Economies 6.260355- Chunk for initial tidy of the Month data
# rename the column names
colnames(data_raw_month) <-c("Year_Month","Advanced.Economies","Argentina","Australia","Austria","Belgium","Bulgaria","Bahrain","Belarus","Brazil","Canada","Switzerland","Chile","China","Colombia","Cyprus","Czech.Republic","Germany","Denmark","Dominican.Republic","Algeria","EMDE.East.Asia_Pacific","EMDE.Europe_Central.Asia","Ecuador","Egypt_ Arab.Rep.","Emerging.Market.Developing.Economies","Spain","Estonia","Finland","France","United.Kingdom","Greece","High.Income.Countries#HIC","Hong.Kong.SAR_ China","Croatia","Hungary","India","Ireland","Iceland","Israel","Italy","Jordan","Japan","Kazakhstan","Korea.Rep.","EMDE.Latin.America_Caribbean","Low.Income.Countries#LIC","Sri.Lanka","Lithuania","Luxembourg","Latvia","Morocco","Moldova.Rep.","Mexico","Middle.Income.Countries#MIC","North.Macedonia","Malta","EMDE.Middle East_N.Africa","Netherlands","Norway","New.Zealand","Pakistan","Peru","Philippines","Poland","Portugal","Romania","Russian.Federation","EMDE.South.Asia","Saudi.Arabia","Singapore","EMDE.Sub-Saharan.Africa","Slovakia","Slovenia","Sweden","Thailand","Tunisia","Turkey","Taiwan_China","Uruguay","United States","Venezuela_ RB","Vietnam","World^WBG.members_","South Africa")
# Gather the data into tidy form
Month_raw <- gather(data_raw_month, "Country", "Value", 2:85)
# Rename the first column
colnames(Month_raw) [colnames(Month_raw)=='X'] <-"Year_Month"
# Seperate the first column into seperate and filter out blank values
Month_Data <- Month_raw %>%
filter(!is.na(Value))%>%
separate(Year_Month, c("Year", "Month"), sep = "M")
#Change the Year to numeric
Month_Data$Year<- as.numeric(Month_Data$Year)
# QA dataframe
head(Month_Data,10)## Year Month Country Value
## 1 1990 01 Advanced.Economies 5.810216
## 2 1990 02 Advanced.Economies 5.745122
## 3 1990 03 Advanced.Economies 5.622835
## 4 1990 04 Advanced.Economies 5.750094
## 5 1990 05 Advanced.Economies 5.764293
## 6 1990 06 Advanced.Economies 5.648854
## 7 1990 07 Advanced.Economies 5.803347
## 8 1990 08 Advanced.Economies 5.900060
## 9 1990 09 Advanced.Economies 6.037763
## 10 1990 10 Advanced.Economies 6.099891Analyst Actions:
For clarity and ability to analize accuratly, I think the datasets need to be broken into 4 seperate subsets (Economies, EMDE, Income, countries). These actions will need to be done on both the Year and Month data.
- Set 1
- Advanced Economies
- Emerging Market and Developing Economies (EMDEs)
- Set 2
- EMDE East Asia & Pacific
- EMDE Europe & Central Asia
- EMDE Latin America & Caribbean
- EMDE Middle East & N. Africa
- EMDE South Asia
- EMDE Sub-Saharan Africa
- World (WBG members)
- Set 3
- High Income Countries
- Low-Income Countries (LIC) (No values Present)
- Middle-Income Countries (MIC)
- Set 4
- All Countries
Economics_Year<-
Year_Data %>%
filter(Country %in% c("Advanced.Economies","Emerging.Market.Developing.Economies"))
EMDE_Year<-
Year_Data %>%
filter(str_detect(Country, "EMDE|WBG"))
Income_year<-
Year_Data %>%
filter(Country %in% c("High.Income.Countries#HIC","Middle.Income.Countries#MIC", "Low.Income.Countries#LIC"))
Countries_year<-
Year_Data %>%
filter(Country %in% c("Argentina","Australia","Austria","Belgium","Bulgaria","Bahrain","Belarus","Brazil","Canada","Switzerland","Chile","China","Colombia","Cyprus","Czech.Republic","Germany","Denmark","Dominican.Republic","Algeria","Ecuador","Egypt_Arab.Rep.","Spain","Estonia","Finland","France","United.Kingdom","Greece","Hong.Kong.SAR_China","Croatia","Hungary","India","Ireland","Iceland","Israel","Italy","Jordan","Japan","Kazakhstan","Korea.Rep.","Sri.Lanka","Lithuania","Luxembourg","Latvia","Morocco","Moldova.Rep.","Mexico","North.Macedonia","Malta","Netherlands","Norway","New.Zealand","Pakistan","Peru","Philippines","Poland","Portugal","Romania","Russian.Federation","Saudi.Arabia","Singapore","Slovakia","Slovenia","Sweden","Thailand","Tunisia","Turkey","Taiwan_China","Uruguay","United States","Venezuela_ RB","Vietnam","South Africa"))Economics_month<-
Month_Data %>%
filter(Country %in% c("Advanced.Economies","Emerging.Market.Developing.Economies"))
EMDE_month<-
Month_Data %>%
filter(str_detect(Country, "EMDE|WBG"))
Income_month<-
Month_Data %>%
filter(Country %in% c("High.Income.Countries#HIC","Middle.Income.Countries#MIC", "Low.Income.Countries#LIC"))
Countries_month<-
Month_Data %>%
filter(Country %in% c("Argentina","Australia","Austria","Belgium","Bulgaria","Bahrain","Belarus","Brazil","Canada","Switzerland","Chile","China","Colombia","Cyprus","Czech.Republic","Germany","Denmark","Dominican.Republic","Algeria","Ecuador","Egypt_Arab.Rep.","Spain","Estonia","Finland","France","United.Kingdom","Greece","Hong.Kong.SAR_China","Croatia","Hungary","India","Ireland","Iceland","Israel","Italy","Jordan","Japan","Kazakhstan","Korea.Rep.","Sri.Lanka","Lithuania","Luxembourg","Latvia","Morocco","Moldova.Rep.","Mexico","North.Macedonia","Malta","Netherlands","Norway","New.Zealand","Pakistan","Peru","Philippines","Poland","Portugal","Romania","Russian.Federation","Saudi.Arabia","Singapore","Slovakia","Slovenia","Sweden","Thailand","Tunisia","Turkey","Taiwan_China","Uruguay","United States","Venezuela_ RB","Vietnam","South Africa"))Client Comments:
The analysis will investigate annual unemployment rates from 2011 to 2015 of 71 countries and it will answer questions:
- For the five year period from 2011 to 2015, what’s the average annual unemployment rate of each country?
- For the five year period from 2011 to 2015, what’s the distribution of the average annual unemployment rate?
- For the five year period from 2011 to 2015, what’s the overall trend of the world’s annual unemployment rate? ##### Analyst Actions: Defining each question into a seperate chunk
Client Questions
1. For the five year period from 2011 to 2015, what’s the average annual unemployment rate of each country?
Analyst Actions and Comments:
I think in order to understand the average annual unemployment rate of each country, we need to know the averages by sector first. The numbers start to have more meaning.
- Median and Mean of Economic groups
Q1_Economics<-
Economics_Year%>%
group_by(Country)%>%
filter(Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))- Median and Mean of Emerging Market and Developing Economies (EMDEs) groups
Q1_EMDE<-
EMDE_Year%>%
group_by(Country)%>%
filter(Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))- Median and Mean of Countries
Q1_Countries<-
Countries_year%>%
group_by(Country)%>%
filter(Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))
Q1_Countries## # A tibble: 70 x 3
## Country mean median
## <chr> <dbl> <dbl>
## 1 Algeria 10.5 10.6
## 2 Argentina 7.07 7.15
## 3 Australia 5.62 5.67
## 4 Austria 7.76 7.62
## 5 Bahrain 3.86 3.8
## 6 Belarus 0.650 0.633
## 7 Belgium 8.05 8.46
## 8 Brazil 7.43 7.19
## 9 Bulgaria 10.6 11.1
## 10 Canada 7.15 7.1
## # ... with 60 more rows2. For the five year period from 2011 to 2015, what’s the distribution of the average annual unemployment rate?
Analyst Actions and Comments:
First, we need to see if the distrbution follows a normal distrbution.
ggplot(Q1_Countries, aes(x=mean))+ geom_density() +
geom_histogram(aes(x=mean, y= ..density..),
binwidth = 3, fill = "gray", color = "black")+
geom_density(alpha=.2, fill="Red")
- According to the graph, it does not follow a normal distrbution. Next, we need to determine the spread across the countries.
give.n <- function(x){
return(c(y = median(x)*1.05, label = length(x)))}
mean.n <- function(x){
return(c(y = median(x)*0.97, label = round(mean(x),2)))}
Q2_plot<- Q1_Countries
ggplot(data=Q2_plot,
aes(x = reorder(Country, +median), y=median, fill=Country))+
geom_bar(stat = "identity")+
scale_fill_hue(l=50)+
ggtitle(label = "Rate across Countries")+
theme_minimal()+
theme(legend.position = "none")+
theme(axis.text.x = element_text(angle = 75, hjust = 1, face = "bold"))+
xlab("Country")+ylab("Average Rate")
- North Macedonia, Greece, South Africa, Spain, Croatia make up the top 5 highest rates of unemployment. Lets compare the top5 with the world numbers of all members.
Q2_world<-
Year_Data %>%
group_by(Country)%>%
filter(Country == "World^WBG.members_" & Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))
Q2_t5<-Q1_Countries %>%
arrange(desc(median))%>%
top_n(median, n= 5)%>%
bind_rows(Q2_world)
ggplot(data=Q2_t5,
aes(x = reorder(Country, +median), y=median, fill=Country))+
geom_bar(stat = "identity")+
scale_fill_hue(l=50)+
ggtitle(label = "Rate across Countries")+
theme_minimal()+
theme(legend.position = "none")+
theme(axis.text.x = element_text(angle = 75, hjust = 1, face = "bold"))+
stat_summary(fun.data = mean.n, geom = "text", fun.y = mean, colour = "black")+
xlab("Country")+ylab("Average Rate")
3. For the five year period from 2011 to 2015, what’s the overall trend of the world’s annual unemployment rate?
Q3_world<-
Year_Data %>%
group_by(Year)%>%
filter(Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))
Q3_world$Year <- as.character(Q3_world$Year)
ggplot(data=Q3_world,
aes(x = Year, y=median, fill=Year))+
geom_bar(stat = "identity")+
ggtitle(label = "Rate across Years")+
scale_fill_brewer(palette="Dark2")+
theme_minimal()+
theme(legend.position = "none")+
theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+
stat_summary(fun.data = mean.n, geom = "text", fun.y = mean, colour = "black")+
xlab("Years")+ylab("Median")
- The Trend is going down (Right Skewed). Since we are looking at median numbers across 70 countries, a drop of .4% is pretty good over all. As a follow up question, what is the rate trend in the previously identified top 5 countries?
Q3_t5<-Year_Data %>%
group_by(Year)%>%
filter(Country %in% c("North.Macedonia", "Greece", "South Africa", "Spain", "Croatia") & Year %in% c(2011:2015))%>%
summarise(mean=mean(Value), median = median(Value))
Q3_t5$Year <- as.character(Q3_t5$Year)
ggplot(data=Q3_t5,
aes(x = Year, y=median, fill=Year))+
geom_bar(stat = "identity")+
ggtitle(label = "Rate across Years")+
scale_fill_brewer(palette="Dark2")+
theme_minimal()+
theme(legend.position = "none")+
theme(plot.title = element_text(hjust = 0.5, lineheight = 0.8, face = "bold"))+
stat_summary(fun.data = mean.n, geom = "text", fun.y = mean, colour = "black")+
xlab("Years")+ylab("Median")
- As we see the Top 5 does’t have the same shape. There is an upwards trend (left skewed) until 2013 than it starts to taper off.
Further analysis can happen on 2013 data from the Month data to pin point when and which country(s) may have caused the rise.