Take-home Exercise 4

1.0 The Task

In this take-home exercise, we are required to uncover the impact of COVID-19 as well as the global economic and political dynamic in 2022 on Singapore bi-lateral trade (i.e. Import, Export and Trade Balance) by using appropriate analytical visualisation techniques learned in Lesson 6: It’s About Time. Students are encouraged to apply appropriate interactive techniques to enhance user and data discovery experiences.

2.0 The Data

For the purpose of this take-home exercise, Merchandise Trade provided by Department of Statistics, Singapore (DOS) will be used. The data are available under the sub-section of Merchandise Trade by Region/Market. We should download the data by clicking on the link Download all in Excel on the same webpage. The study period should be between January 2020 to December 2022.

3.0 The Designing Tool

For the purpose of this take-home exercise, ggplot2 and its extension should be used to design the analytical visualisation. tidyverse family of packages should be used to prepare the data.

3.1 Packages used

Before we get started, it is important for us to install the necessary R packages into R and launch these R packages into R environment.

The R packages needed for this exercise are as follows:

Show the code 
pacman::p_load(plotly, ggplot2, timetk, scales, viridis, lubridate, ggthemes, gridExtra, readxl, knitr, data.table, CGPfunctions, ggHoriPlot, tidyverse,funModeling)

4.0 Data preparation

4.1 Importing Data

Show the code 
Import <- read_excel("data/outputFile.xlsx", sheet = "T1", skip = 9)
Export <- read_excel("data/outputFile.xlsx", sheet = "T2", skip = 9)

4.2 Data wrangling

select data from 2020 Jan to 2022 Dec.

Show the code 
Import_1 <- Import %>%
  select(1,3:38)%>%
  filter(row_number()<92 & row_number()>1)
Show the code 
Export_1 <- Export %>%
  select(1,3:38.)%>%
  filter(row_number()<120 & row_number()>1)

Check if any missing values

Show the code 
skimr::skim(Import_1)
Data summary
Name Import_1
Number of rows 90
Number of columns 37
_______________________
Column type frequency:
character 1
numeric 36
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Data Series 0 1 22 52 0 90 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
2022 Dec 0 1 586243.8 1368046.9 0 6475.38 48296.50 307285.0 7403998 ▇▁▁▁▁
2022 Nov 0 1 571033.4 1301748.3 0 6575.65 47146.00 323984.0 6773005 ▇▁▁▁▁
2022 Oct 0 1 603702.2 1400359.3 0 7424.25 63806.50 360812.2 7810503 ▇▁▁▁▁
2022 Sep 0 1 667836.1 1489183.4 0 10033.75 57056.00 384880.0 7405741 ▇▁▁▁▁
2022 Aug 0 1 671755.9 1505473.5 0 9132.50 51208.50 455167.2 7548001 ▇▁▁▁▁
2022 Jul 0 1 686508.2 1515344.2 0 9656.75 58018.00 445801.0 7792074 ▇▁▁▁▁
2022 Jun 0 1 682164.9 1581835.2 0 8749.75 69630.00 408044.5 7717915 ▇▁▁▁▁
2022 May 0 1 639902.7 1478007.1 0 6531.58 56812.00 447629.0 7486002 ▇▁▁▁▁
2022 Apr 0 1 642572.5 1466588.8 0 9290.00 70217.50 444242.5 7481265 ▇▁▁▁▁
2022 Mar 0 1 680540.6 1609795.4 0 7703.25 64959.50 404368.2 8912350 ▇▁▁▁▁
2022 Feb 0 1 546062.8 1277520.9 0 6197.60 41282.00 355951.0 6929035 ▇▁▁▁▁
2022 Jan 0 1 590077.8 1357593.5 0 9586.50 52396.00 318862.2 7163479 ▇▁▁▁▁
2021 Dec 0 1 635704.1 1553241.9 0 10139.25 56858.00 320817.0 9215862 ▇▁▁▁▁
2021 Nov 0 1 608560.5 1474650.5 0 8109.50 55961.50 336858.2 8281345 ▇▁▁▁▁
2021 Oct 0 1 578280.1 1393269.2 0 7423.75 50327.50 372287.2 8461516 ▇▁▁▁▁
2021 Sep 0 1 561719.2 1422245.6 0 7359.50 48116.50 241825.8 8785105 ▇▁▁▁▁
2021 Aug 0 1 562337.3 1391492.2 0 7536.00 54621.50 251813.0 8134968 ▇▁▁▁▁
2021 Jul 0 1 536820.2 1305877.2 0 8512.75 40744.90 346546.8 7991895 ▇▁▁▁▁
2021 Jun 0 1 535805.9 1282636.4 0 7159.00 45186.00 356035.8 7300460 ▇▁▁▁▁
2021 May 0 1 510561.5 1277424.5 0 4913.75 41337.00 230461.0 7161940 ▇▁▁▁▁
2021 Apr 0 1 543848.6 1330817.3 0 6076.80 43846.00 287915.0 7838848 ▇▁▁▁▁
2021 Mar 0 1 595467.7 1412024.8 0 6628.00 41011.70 359609.2 7470910 ▇▁▁▁▁
2021 Feb 0 1 460230.5 1061792.0 0 5578.25 35105.50 284150.5 5518278 ▇▁▁▁▁
2021 Jan 0 1 487403.0 1122842.9 0 5970.75 42248.00 337218.8 5693673 ▇▁▁▁▁
2020 Dec 0 1 500637.4 1244183.6 0 6825.50 35537.90 219175.2 7090834 ▇▁▁▁▁
2020 Nov 0 1 468219.2 1169362.9 0 6485.75 36915.00 244342.5 6852887 ▇▁▁▁▁
2020 Oct 0 1 476473.3 1169379.8 0 5673.25 35167.45 255064.2 6992713 ▇▁▁▁▁
2020 Sep 0 1 473427.6 1141417.2 0 5206.77 34522.15 249263.5 6561515 ▇▁▁▁▁
2020 Aug 0 1 475320.8 1194167.0 0 5240.23 31650.25 243462.5 7970167 ▇▁▁▁▁
2020 Jul 0 1 464059.9 1141729.3 0 4713.25 32678.00 242386.5 5935191 ▇▁▁▁▁
2020 Jun 0 1 439898.8 1041629.8 0 4884.17 31531.00 215720.0 5603145 ▇▁▁▁▁
2020 May 0 1 394076.4 986818.5 0 4120.50 22439.40 180320.5 5080433 ▇▁▁▁▁
2020 Apr 0 1 429729.4 1068014.7 0 4385.75 26420.35 202348.5 5439082 ▇▁▁▁▁
2020 Mar 0 1 488955.1 1134882.1 0 5154.25 35816.00 330576.5 5823657 ▇▁▁▁▁
2020 Feb 0 1 462422.8 1002846.1 0 5214.97 42334.50 321633.8 5116584 ▇▁▁▁▁
2020 Jan 0 1 473619.4 1067404.8 0 5695.60 49916.50 268527.0 5091217 ▇▁▁▁▁
Show the code 
skimr::skim(Export_1)
Data summary
Name Export_1
Number of rows 118
Number of columns 37
_______________________
Column type frequency:
character 1
numeric 36
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Data Series 0 1 22 53 0 118 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
2022 Dec 0 1 418097.0 1194617.4 0 147.75 8210.50 118369.00 7642587 ▇▁▁▁▁
2022 Nov 0 1 423407.0 1272510.2 0 258.75 8015.70 120419.50 8285837 ▇▁▁▁▁
2022 Oct 0 1 444276.7 1284320.0 0 171.25 8924.00 145353.50 7377473 ▇▁▁▁▁
2022 Sep 0 1 467633.1 1334807.8 0 259.00 9367.50 143109.00 7672003 ▇▁▁▁▁
2022 Aug 0 1 489468.8 1414702.8 0 203.25 9749.00 160920.00 8196629 ▇▁▁▁▁
2022 Jul 0 1 510922.4 1446098.1 0 427.75 8572.25 180595.00 8510236 ▇▁▁▁▁
2022 Jun 0 1 498639.8 1431575.1 0 246.50 11288.50 165379.00 7976196 ▇▁▁▁▁
2022 May 0 1 484051.5 1367418.0 0 160.00 8995.00 158037.25 7478079 ▇▁▁▁▁
2022 Apr 0 1 468073.3 1303380.5 0 128.75 8904.00 139441.00 6958619 ▇▁▁▁▁
2022 Mar 0 1 485301.3 1388471.4 0 140.25 7754.00 143188.00 7882249 ▇▁▁▁▁
2022 Feb 0 1 373312.6 1046659.0 0 100.25 6967.50 157869.00 6469836 ▇▁▁▁▁
2022 Jan 0 1 417040.6 1182252.6 0 106.75 9321.50 136106.75 6978727 ▇▁▁▁▁
2021 Dec 0 1 454829.1 1310774.4 0 193.25 8350.50 121241.00 7876015 ▇▁▁▁▁
2021 Nov 0 1 420205.3 1196997.8 0 161.75 7770.50 132750.00 6670871 ▇▁▁▁▁
2021 Oct 0 1 398616.9 1134627.2 0 162.75 6626.90 153802.75 6447589 ▇▁▁▁▁
2021 Sep 0 1 384251.1 1112802.3 0 104.75 7500.50 160929.50 6184737 ▇▁▁▁▁
2021 Aug 0 1 370593.3 1084077.8 0 87.50 7024.50 125688.25 6519894 ▇▁▁▁▁
2021 Jul 0 1 384216.1 1072829.6 0 170.00 7928.00 136928.75 6094544 ▇▁▁▁▁
2021 Jun 0 1 372826.9 1046044.3 0 55.75 6234.30 143808.75 6198485 ▇▁▁▁▁
2021 May 0 1 347202.6 1004935.6 0 134.50 6421.25 139041.75 5932710 ▇▁▁▁▁
2021 Apr 0 1 375263.6 1095326.9 0 125.75 7301.75 125678.50 6329544 ▇▁▁▁▁
2021 Mar 0 1 393168.7 1094654.3 0 119.50 8530.50 126112.75 6291717 ▇▁▁▁▁
2021 Feb 0 1 310132.2 900528.0 0 74.50 5349.50 86724.00 5402465 ▇▁▁▁▁
2021 Jan 0 1 325242.2 976314.0 0 87.00 7120.50 115283.00 6121009 ▇▁▁▁▁
2020 Dec 0 1 333250.8 974409.3 0 169.75 7174.00 130818.50 6202327 ▇▁▁▁▁
2020 Nov 0 1 319340.5 938043.2 0 116.75 5735.25 133326.25 6280686 ▇▁▁▁▁
2020 Oct 0 1 317425.3 925927.0 0 80.50 5345.50 124435.75 5249328 ▇▁▁▁▁
2020 Sep 0 1 324791.5 925020.3 0 122.50 7756.00 93170.50 5479579 ▇▁▁▁▁
2020 Aug 0 1 303788.9 885364.5 0 144.25 6057.50 95664.00 5574117 ▇▁▁▁▁
2020 Jul 0 1 316079.8 906421.6 0 171.00 8152.50 113675.25 5539168 ▇▁▁▁▁
2020 Jun 0 1 293978.1 887991.3 0 112.00 4823.45 93924.50 5288235 ▇▁▁▁▁
2020 May 0 1 262592.9 773859.1 0 39.50 4444.00 95021.25 4843684 ▇▁▁▁▁
2020 Apr 0 1 299137.5 864578.2 0 31.00 5167.05 95948.75 5583246 ▇▁▁▁▁
2020 Mar 0 1 336379.9 950555.8 0 112.25 6413.15 116936.25 5979894 ▇▁▁▁▁
2020 Feb 0 1 325768.8 860980.8 0 132.75 7261.50 147114.25 4985932 ▇▁▁▁▁
2020 Jan 0 1 339509.0 914691.7 0 100.25 6510.35 122115.50 5469816 ▇▁▁▁▁

Separate the country and thousand/million

Show the code 
Import_2 <- Import_1 %>%
  separate(`Data Series`, c("country", "unit"),
           sep = ' \\(T|\\(M') %>%
  mutate(unit = str_remove(unit, '\\)'))
Show the code 
Export_2 <- Export_1 %>%
  separate(`Data Series`, c("country", "unit"),
           sep = ' \\(T|\\(M') %>%
  mutate(unit = str_remove(unit, '\\)'))

ImportK is Import data by thousand/countries.

Show the code 
ImportK <- Import_2 %>%
  filter(str_detect(unit, "housand")) %>%
  select(-(unit))

ImportM is Import data by million/regions.

Show the code 
ImportM <- Import_2 %>%
  filter(str_detect(unit, "illion")) %>%
  select(-(unit))

ExportK is Export data by thousand/countries.

Show the code 
ExportK<- Export_2 %>%
  filter(str_detect(unit, "housand")) %>%
  select(-(unit))

ExportM is Export data by million/regions.

Show the code 
ExportM<- Export_2 %>%
  filter(str_detect(unit, "illion")) %>%
  select(-(unit))

Create pivot table to list down the date period in the same column.

Show the code 
ImportK_pvL <- ImportK %>%
  pivot_longer(cols = !country,
               names_to = "temporary",
               values_to = "import") %>%
  mutate(period = (lubridate::ym(temporary))) %>%
  arrange(country, period)

ImportK_pvL <- ImportK_pvL %>%
  mutate(month = factor(month
                        (ImportK_pvL$`period`),
                        levels = 1:12, 
                        labels = month.abb, 
                        ordered = TRUE)) %>%
  mutate(year = as.character(year(ymd(ImportK_pvL$`period`)))) %>%     
  relocate(year, month) %>%
  select(-temporary)%>%
  arrange(desc(import))

Create pivot table to list down the date period in the same column.

Show the code 
ImportM_pvL <- ImportM %>%
  pivot_longer(cols = !country,
               names_to = "temporary",
               values_to = "import") %>%
  mutate(period = (lubridate::ym(temporary))) %>%
  arrange(country, period)

ImportM_pvL <- ImportM_pvL %>%
  mutate(month = factor(month
                        (ImportM_pvL$`period`),
                        levels = 1:12, 
                        labels = month.abb, 
                        ordered = TRUE)) %>%
  mutate(year = as.character(year(ymd(ImportM_pvL$`period`)))) %>%     
  relocate(year, month) %>%
  select(-temporary)%>%
  arrange(desc(import))

Create pivot table to list down the date period in the same column.

Show the code 
ExportK_pvL <- ExportK %>%
  pivot_longer(cols = !country,
               names_to = "temporary",
               values_to = "export") %>%
  mutate(period = (lubridate::ym(temporary))) %>%
  arrange(country, period)

ExportK_pvL<- ExportK_pvL %>%
  mutate(month = factor(month
                        (ExportK_pvL$`period`),
                        levels = 1:12, 
                        labels = month.abb, 
                        ordered = TRUE)) %>%
  mutate(year = as.character(year(ymd(ExportK_pvL$`period`)))) %>%     
  relocate(year, month) %>%
  select(-temporary)%>%
  arrange(desc(export))

Create pivot table to list down the date period in the same column.

Show the code 
ExportM_pvL <- ExportM %>%
  pivot_longer(cols = !country,
               names_to = "temporary",
               values_to = "export") %>%
  mutate(period = (lubridate::ym(temporary))) %>%
  arrange(country, period)

ExportM_pvL<- ExportM_pvL %>%
  mutate(month = factor(month
                        (ExportM_pvL$`period`),
                        levels = 1:12, 
                        labels = month.abb, 
                        ordered = TRUE)) %>%
  mutate(year = as.character(year(ymd(ExportM_pvL$`period`)))) %>%     
  relocate(year, month) %>%
  select(-temporary)%>%
  arrange(desc(export))

Merge import and export by countries.

Show the code 
TradeK<- ImportK_pvL %>% 
  full_join(ExportK_pvL)%>%
  mutate(trade_total = import + export)%>% 
  mutate(formatted_period = format(period, "%Y %B"))%>% 
  mutate(trade_balance = export - import)

Merge import and export by regions.

Show the code 
TradeM<- ImportM_pvL %>% 
  full_join(ExportM_pvL)%>%
  mutate(trade_total = import + export)%>% 
  mutate(formatted_period = format(period, "%Y %B"))%>% 
  mutate(trade_balance = export - import)

By countries: List “import” and “export” in the same column, name as “Type”, select the column that we need for further analysis, and filter the total trade >10000 million per country.

Show the code 
Trade_country<- TradeK %>% 
  pivot_longer(cols = c("import","export"),
               names_to = "Type",
               values_to = "Amount") %>% 
  na.omit("Amount")%>%
  group_by(country)%>%
  summarise(year,
            month,
            country,
            trade_country = sum(trade_total/1000),
            period,
            formatted_period,
            Type,
  
            Amount = round(Amount/1000)) %>%
  arrange(desc(trade_country))%>%
  filter(trade_country > 10000)

By regions: List “import” and “export” in the same column, name as “Type”, select the column that we need for further analysis

Show the code 
Trade_region<- TradeM %>% 
  pivot_longer(cols = c("import","export"),
               names_to = "Type",
               values_to = "Amount") %>% 
  na.omit("Amount")%>%
  group_by(country)%>%
  summarise(year,
            month,
            country,
            trade_country = sum(trade_total),
            period,
            formatted_period,
            Type,
            Amount) %>%
  arrange(desc(trade_country))

Examine the final data set.

Show the code 
summary(Trade_country)
   country              year               month      trade_country   
 Length:2736        Length:2736        Jan    : 228   Min.   : 11194  
 Class :character   Class :character   Feb    : 228   1st Qu.: 28868  
 Mode  :character   Mode  :character   Mar    : 228   Median : 78635  
                                       Apr    : 228   Mean   :174271  
                                       May    : 228   3rd Qu.:161824  
                                       Jun    : 228   Max.   :950965  
                                       (Other):1368                   
     period           formatted_period       Type               Amount    
 Min.   :2020-01-01   Length:2736        Length:2736        Min.   :   0  
 1st Qu.:2020-09-23   Class :character   Class :character   1st Qu.: 128  
 Median :2021-06-16   Mode  :character   Mode  :character   Median : 457  
 Mean   :2021-06-16                                         Mean   :1210  
 3rd Qu.:2022-03-08                                         3rd Qu.:1445  
 Max.   :2022-12-01                                         Max.   :9216
Show the code 
glimpse(Trade_country)
Rows: 2,736
Columns: 8
Groups: country [38]
$ country          <chr> "Mainland China", "Mainland China", "Mainland China",…
$ year             <chr> "2021", "2021", "2022", "2022", "2021", "2021", "2021…
$ month            <ord> Dec, Dec, Mar, Mar, Sep, Sep, Oct, Oct, Nov, Nov, Aug…
$ trade_country    <dbl> 950965.3, 950965.3, 950965.3, 950965.3, 950965.3, 950…
$ period           <date> 2021-12-01, 2021-12-01, 2022-03-01, 2022-03-01, 2021…
$ formatted_period <chr> "2021 December", "2021 December", "2022 March", "2022…
$ Type             <chr> "import", "export", "import", "export", "import", "ex…
$ Amount           <dbl> 9216, 7876, 8912, 6636, 8785, 6017, 8462, 6026, 8281,…
Show the code 
summary(Trade_region)
   country              year               month     trade_country    
 Length:432         Length:432         Jan    : 36   Min.   :  95298  
 Class :character   Class :character   Feb    : 36   1st Qu.: 241065  
 Mode  :character   Mode  :character   Mar    : 36   Median : 723092  
                                       Apr    : 36   Mean   :1266720  
                                       May    : 36   3rd Qu.: 862475  
                                       Jun    : 36   Max.   :4955298  
                                       (Other):216                    
     period           formatted_period       Type               Amount       
 Min.   :2020-01-01   Length:432         Length:432         Min.   :  238.2  
 1st Qu.:2020-09-23   Class :character   Class :character   1st Qu.: 1540.2  
 Median :2021-06-16   Mode  :character   Mode  :character   Median : 4680.8  
 Mean   :2021-06-16                                         Mean   : 8796.7  
 3rd Qu.:2022-03-08                                         3rd Qu.: 6718.9  
 Max.   :2022-12-01                                         Max.   :46328.8
Show the code 
glimpse(Trade_region)
Rows: 432
Columns: 8
Groups: country [6]
$ country          <chr> "Asia ", "Asia ", "Asia ", "Asia ", "Asia ", "Asia ",…
$ year             <chr> "2022", "2022", "2022", "2022", "2022", "2022", "2022…
$ month            <ord> Jun, Jun, Jul, Jul, Sep, Sep, Mar, Mar, Dec, Dec, Aug…
$ trade_country    <dbl> 4955298, 4955298, 4955298, 4955298, 4955298, 4955298,…
$ period           <date> 2022-06-01, 2022-06-01, 2022-07-01, 2022-07-01, 2022…
$ formatted_period <chr> "2022 June", "2022 June", "2022 July", "2022 July", "…
$ Type             <chr> "import", "export", "import", "export", "import", "ex…
$ Amount           <dbl> 46328.8, 42507.2, 46129.5, 43214.2, 45605.1, 37696.3,…

5.0 Data Visualisation

5.1 Plotting the cycle plot

Show the code 
cp_region <- ggplot(data = Trade_region, aes(x = period, y = Amount)) +
  geom_line(aes(colour = Type)) +
  labs(x = "Date", y = "Amount", title = "Imports & Exports by Region,  2022") +
  facet_wrap(~ country) +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(cp_region)

From the above chart, we can see that Asia shows most significant import and export trade relation with Singapore since year 2020 to 2022, followed by Europe, European union, Oceania. Asia shows increasing trend during the mentioned time frame whereas Europe, European union, Oceania show stable trade trend. Hence, we will focus on about 80% of top trade with Singapore which are Asia and Europe.

Show the code 
cp_country <- ggplot(data = Trade_country, aes(x = period, y = Amount)) +
  geom_line(aes(colour = Type )) +
  labs(x = "Month", y = "Amount", title = "Imports & Exports by Region,  2022") +
  facet_wrap(~ country) +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(cp_country)

From the above chart, we can observe that, Hong Kong, Indonesia, Mainland China, Malaysia, Republic of Korean,Switzerland,Taiwan, Thailand, United Arab Emirates,United Kingdom, United States, Vietnam, Socialist Republic are the top 10 countries which has most significant trade relation with Singapore from 2020 to 2022. We will further analyze these countries.

Show the code 
Trade_top10<- Trade_country %>% 
  filter(country %in% c("Mainland China", "Malaysia", "Republic of Korean", "Switzerland","Taiwan", "Thailand","United Arab Emirates","United Kingdom", "United States", "Vietnam, Socialist Republic Of" ))
Show the code 
cp_top10 <- ggplot(data = Trade_top10, aes(x = period, y = Amount)) +
  geom_line(aes(colour = Type)) +
  labs(x = "Month", y = "Amount", title = "Imports & Exports by Region,  2022") +
  facet_wrap(~ country) +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(cp_top10)

The above chart shows top 10 countries which have the most significant trade relation with Singapore. Mainland China, Malaysia, and Taiwan are the top 3 countries which showed increasing import and export trade.Whereas the Thailand, United Arab Emirates, United Kingdom, United States, Vietnam showed stable import and export trend.

5.2 Plotting the time series plot

Show the code 
ts_region <- TradeM%>%
  group_by(country) %>%
  plot_time_series(period, trade_balance,.color_var = year,  .facet_ncol = 2, .facet_scales = "free", .interactive = TRUE)
ggplotly(ts_region)

The following is the time series plot for trade balance(import-export) by region.

Show the code 
ts_top10 <- TradeK%>%
  group_by(country) %>%
  filter(country %in% c("Mainland China", "Malaysia", "Republic of Korean", "Switzerland","Taiwan", "Thailand","United Arab Emirates","United Kingdom", "United States", "Vietnam, Socialist Republic Of" ))%>%
  plot_time_series(period, trade_balance,.color_var = year,  .facet_ncol = 2, .facet_scales = "free", .interactive = TRUE)
ggplotly(ts_top10)

The following is the time series plot for trade balance(import-export) by top10 countries.

5.3 Plotting the slopegraph

Show the code 
Trade_region %>%
  filter(Type == "import")%>%
  group_by(country, year) %>%
  summarise(Amount = sum(Amount)) %>%
  mutate(year = factor(year)) %>%
  filter(year %in% c(2020, 2021,2022)) %>%
  newggslopegraph(year, Amount, country,
                  Title = "Imports by Region",
                  SubTitle = "2020 to 2022",
                  Caption = "Source: Department of Statistics, Singapore")

The above chart shows Asia has increasing trend of imports from year 2020 to 2022.

Show the code 
Trade_region %>%
  filter(Type == "export")%>%
  group_by(country, year) %>%
  summarise(Amount = sum(Amount)) %>%
  mutate(year = factor(year)) %>%
  filter(year %in% c(2020, 2021,2022)) %>%
  newggslopegraph(year, Amount, country,
                  Title = "Exports by Region",
                  SubTitle = "2020 to 2022",
                  Caption = "Source: Department of Statistics, Singapore")

The above chart shows Asia has increasing trend of exports from year 2020 to 2022.

Show the code 
Trade_top10 %>%
  filter(Type == "import")%>%
  group_by(country, year) %>%
  summarise(Amount = sum(Amount)) %>%
  mutate(year = factor(year)) %>%
  filter(year %in% c(2020, 2021,2022)) %>%
  newggslopegraph(year, Amount, country,
                  Title = "Imports by top10 countries",
                  SubTitle = "2020 to 2022",
                  Caption = "Source: Department of Statistics, Singapore")

The above chart shows the imports trend of top10 countries which have most trade relation with Singapore.

Show the code 
Trade_top10 %>%
  filter(Type == "export")%>%
  group_by(country, year) %>%
  summarise(Amount = sum(Amount)) %>%
  mutate(year = factor(year)) %>%
  filter(year %in% c(2020, 2021,2022)) %>%
  newggslopegraph(year, Amount, country,
                  Title = "Exports by top10 countries",
                  SubTitle = "2020 to 2022",
                  Caption = "Source: Department of Statistics, Singapore")

The above chart shows the exports trend of top10 countries which have most trade relation with Singapore.

5.4 Plotting Horizon Plot

Show the code 
TradeM %>% 
 
  filter(period >= "2020-01-01") %>%
  ggplot() +
  geom_horizon(aes(x = period, y=trade_balance), 
               origin = "midpoint", 
               horizonscale = 6)+
  facet_grid(`country`~.) +
    theme_few() +
  scale_fill_hcl(palette = 'RdBu') +
  theme(panel.spacing.y=unit(0, "lines"), strip.text.y = element_text(
    size = 5, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=7),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank()
    ) +
    scale_x_date(expand=c(0,0), date_breaks = "3 month", date_labels = "%b%y") +
  ggtitle('Trade blance by region (Jan 2020 to Dec 2022)')

The chart shows there is large trade balance in Asia. The darker area, the larger balance.

Show the code 
TradeK %>% 
  filter(country %in% c("Mainland China", "Malaysia", "Republic of Korean", "Switzerland","Taiwan", "Thailand","United Arab Emirates","United Kingdom", "United States", "Vietnam, Socialist Republic Of" ))%>%
  filter(period >= "2020-01-01") %>%
  ggplot() +
  geom_horizon(aes(x = period, y=trade_balance), 
               origin = "midpoint", 
               horizonscale = 6)+
  facet_grid(`country`~.) +
    theme_few() +
  scale_fill_hcl(palette = 'RdBu') +
  theme(panel.spacing.y=unit(0, "lines"), strip.text.y = element_text(
    size = 5, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=7),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank()
    ) +
    scale_x_date(expand=c(0,0), date_breaks = "3 month", date_labels = "%b%y") +
  ggtitle('Top 10 countries Trade blance (Jan 2020 to Dec 2022)')

The chart shows there is large trade balance in Mainland China, Malaysia and Taiwan. The darker area, the larger balance.