# data[].attributes.body
Using a line graph to show the trends of mobile data usage is the right choice as it shows clearly the volume of mobile data used in each quarter of the year.

# data[].attributes.attachment_link.readable_text
Mobile data usage reflects the amount of bi-directional (the aggregated total downloaded) mobile data sent over the mobile networks, including MMS and any other data upload/download but excludes video telephony and SMS.

The data usage figures are monthly average figures for each quarter, derived by taking the total data usage in a quarter, and then dividing by three.

# data[].attributes.attachment_link.description
Mobile data usage reflects the amount of bi-directional (the aggregated total downloaded) mobile data sent over the mobile networks, including MMS and any other data upload/download but excludes video telephony and SMS. The data usage figures are monthly average figures for each quarter, derived by taking the total data usage in a quarter, and then dividing by three.

# data[].attributes.body
It is the right chart to used as it shows the amount university graduates from various courses, managed to graduate in 2014.

# data[].attributes.attachment_link.readable_text
Prior to 2010, data are classified according to the Singapore Standard Educational Classification (SSEC) 2000. With effect from 2010, data are classified according to SSEC 2010. Covers full-time and part-time first degree courses. Data refer to academic year. From 2012, data includes students graduated from Singapore Institute of Technology.

APPLIED ARTS - Includes Industrial Design.

ARCHITECTURE & BUILDING - Includes Real Estate.

SERVICES - Includes Maritime Studies.

# data[].attributes.attachment_link.title
Graduates From University First Degree Courses By Type Of Course-Data.gov.sg

# data[].attributes.attachment_link.description
Prior to 2010, data are classified according to the Singapore Standard Educational Classification (SSEC) 2000. With effect from 2010, data are classified according to SSEC 2010. Covers full-time and part-time first degree courses. Data refer to academic year. From 2012, data includes students graduated from Singapore Institute of Technology.

# data[].attributes.body
Most popular items purchased online according to respondents in Singapore as of 2019

Using a bar graph to represent this dataset is the correct choice as we are able to see the percentage of each item purchased.

# data[].attributes.body
The line chart was the right chart that is used. This is because it illustrates both the genders clearly and displays the amount of male and female residents in Singapore over the years.

# data[].attributes.body
Using a bar graph to represent this dataset is the right choice as we are able to compare the variables easily.

# data[].attributes.body
The line chart used was the correct option as it allows people to see the trend of the flats constructed

# data[].attributes.body
Yes, the right chart type was used for this dataset. It clearly shows per annual on the x-axis and the number of residents on the y-axis. Moreover, it uses the bar graph to represent different colors for each ethnicity groups which makes it understandable and clear.

# data[].attributes.body
A line chart is used to show the flats constructed over time (annually). The line chart is most suitable as it clearly show the trend of flats constructed over the years.

# data[].attributes.attachment_link.description
Prior to 1982, include units sold by JTC, but exclude those by HUDC. Include middle housing units completed by URA in 1985 and 1986. In 1992, exclude 96 units due to reclassification.

# data[].attributes.body
The right chart is used as it shows the mobile data usage over the years

# data[].attributes.attachment_link.readable_text
Mobile data usage reflects the amount of bi-directional (the aggregated total downloaded) mobile data sent over the mobile networks, including MMS and any other data upload/download but excludes video telephony and SMS.

The data usage figures are monthly average figures for each quarter, derived by taking the total data usage in a quarter, and then dividing by three.

# data[].attributes.attachment_link.description
Mobile data usage reflects the amount of bi-directional (the aggregated total downloaded) mobile data sent over the mobile networks, including MMS and any other data upload/download but excludes video telephony and SMS. The data usage figures are monthly average figures for each quarter, derived by taking the total data usage in a quarter, and then dividing by three.

# data[].attributes.body
Was appropriate to use pie chart but presentation could be better if legends of what colours meant what was included and the percentage for the pie chart was included

# data[].attributes.body
This is a line chart that shows the annual motor vehicle population by vehicle type. I think that the line chart is the right option. This is because line chart is ideal to see how the changes of a data attribute affect the other data attributes. Since we want to see how the motor vehicle population of different vehicle types change overtime.

# data[].attributes.attachment_link.title
Annual Motor Vehicle Population by Vehicle Type-Data.gov.sg

# data[].attributes.attachment_link.description
Annual motor vehicle population by vehicle type

# data[].attributes.body
This line chart was used to show the declining birth rate in Singapore. Since line charts are mainly used to display trends, I think it is appropriate to use line chart to display this dataset.

# data[].attributes.attachment_link.description
A summary of key indicators measuring births and fertility in Singapore.

# data[].attributes.body
The following pie chart represents the portion of the total amount of money spent by the Singapore Government. It is a good choice in chart type because there are few segments in the pie chart with distinctly different sizes, allowing the user to draw quick insights from the visualization without spending too much time but also allowing them to look further as mousing over each segment shows a tooltip of the amount of money spent in dollars. Because the data trying to be represented is part-in-whole data, this is a good visualization of the data and the correct chart was being used.

# data[].attributes.body
https://data.gov.sg/dataset/resident-population-by-ethnicity-gender-and-age-group
yes line chart is properly used

# data[].attributes.body
the right chart is being used as a line chart can allow the users to visualize the trend on the expenditures used on education

# data[].attributes.body
the pie chart shows how much the government spent in total. it is a good representation but perhaps it would be better if they include the percentages on the pie chart.

# data[].attributes.body
The chart used clearly show the proportionate differences between those with access and those without access to cooking fuels over the years, clearly showings its trend and differences between those two

# data[].attributes.attachment_link.title
Number of people with and without access to clean cooking fuels

# data[].attributes.attachment_link.description
Clean cooking fuels and technologies represent non-solid fuels such as natural gas, ethanol or electric technologies.

# data[].attributes.body
Yes shows the trends of flats constructed over time may be useful in seeing the development of housing

# data[].attributes.attachment_link.description
Prior to 1982, include units sold by JTC, but exclude those by HUDC. Include middle housing units completed by URA in 1985 and 1986. In 1992, exclude 96 units due to reclassification.

# data[].attributes.attachment_link.description
See all the wonderful things AISHWARYA SANTHOSH KUMAR has made

# data[].attributes.body
https://data.gov.sg/dataset/flats-constructed       line graph is used in an appropriate manner as it shows hwo the number of flats are being built as time goes by

# data[].attributes.body
https://data.gov.sg/dataset/resident-population-by-ethnicity-gender-and-age-group

the chart used in this data set are a line chart and a bar chart respectively. the aim of this chart is to understand the demographics of residences by their ethnicity, gender and age group. Both charts are used correctly and are able to display the right data. The charts have either 4 or 2  variables in 4 or 2 colours which represents each race and gender respectively. The increasing trend seen in the charts denotes the relationship between the population size of each race/gender as the years pass

# data[].attributes.body
The chart used here is correct as a line chart accurately shows the changes in the number of flats constructed over time

# data[].attributes.attachment_link.description
Prior to 1982, include units sold by JTC, but exclude those by HUDC. Include middle housing units completed by URA in 1985 and 1986. In 1992, exclude 96 units due to reclassification.

# data[].attributes.body
the line chart is correctly used to show the number of flats build every 5 years starting from 1980 to 2015. it also shows a trend on which year the government decide to build more flats

# data[].attributes.body
https://data.gov.sg/dataset/flats-constructed

# data[].attributes.body
https://data.gov.sg/dataset/flats-constructed

# data[].attributes.body
https://data.gov.sg/dataset/annual-motor-vehicle-population-by-vehicle-type  its a pe chart to show percentage of whole figures so its correct

# data[].attributes.body
https://data.gov.sg/dataset/annual-motor-vehicle-population-by-vehicle-type?view_id=6641b19a-f266-4039-becf-98be4e6779de&resource_id=dec53407-9f97-47b8-ba89-b2070569a09e  use bar chart is better

# data[].attributes.attachment_link.title
Annual Motor Vehicle Population by Vehicle Type-Data.gov.sg

# data[].attributes.attachment_link.description
Annual motor vehicle population by vehicle type

# data[].attributes.body
https://data.gov.sg/dataset/consumer-price-index-monthly
It is using the right line chart as it is being used to show trends of the consumer price index monthly.

# data[].attributes.body
https://data.gov.sg/dataset/enrolment-primary-by-level?resource_id=b4b01dcd-9262-44e6-bf60-6d0126b7b2bb  The right chart was used as it is showing the trend of the number of enrolment over time.

# data[].attributes.body
https://data.gov.sg/dataset/enrolment-primary-by-level?resource_id=b4b01dcd-9262-44e6-bf60-6d0126b7b2bb

# data[].attributes.body
https://data.gov.sg/dataset/government-fiscal-position-annual?view_id=42b84b79-19aa-4681-af29-96bd3e742190&resource_id=98856a60-33cd-482a-9dc4-1ed52e562d5d

# data[].attributes.body
number of raining days

# data[].attributes.attachment_link.title
Rainfall - Monthly Number of Rain Days-Data.gov.sg

# data[].attributes.attachment_link.description
The number of rain days (day with rainfall amount of 0.2mm or more) in a month recorded at the Changi Climate Station.

# data[].attributes.body
The line chart was used and it is appropriate since it shows trends over time. From the chart, we can see very clearly the type of vehicles on the road that are increasing over time and which ones are decreasing.   https://data.gov.sg/dataset/annual-motor-vehicle-population-by-vehicle-type

# data[].attributes.body
https://data.gov.sg/dataset/births-and-fertility-annual

As we are comparing the change over continuous time, a line chart was best suited for the data as it visualize the change showing increase or decrease making it easy for the readers to understand.

# data[].attributes.body
How do you pick the right chart or graph for your data?
f you have data you want to visualize, make sure you use the right charts. While your data might work with multiple chart types, it’s up to you to select the one that ensures your message is clear and accurate. Remember, data is only valuable if you know how to visualize it and give context.

# data[].attributes.body
The line chart was the appropriate chart to use. this is because it is very clear representation of the trend in the domestic exports.  https://data.gov.sg/dataset/domestic-exports-by-area

# data[].attributes.body
It is correct to use a horizontal bar graph  to display the

Graduates From University First Degree Courses By Type Of Course as it does creates a comparison, when it is ranked according to the highest to lowest. This can provide in-sights to what course are actually high in demand and which is a common skill set. In which it can be used to predict what are the market needs in terms of the technical skill sets popularity.

# data[].attributes.body
https://data.gov.sg/dataset/graduates-from-university-first-degree-courses-by-type-of-course

# data[].attributes.body
The chart below shows the hawker centre around Singapore. They are using a map chart which is the best chart that can be used to represent locations.

# data[].attributes.body
The chart used here for the amount of rainy days in a month is not suitable as it is hard to visualize, an interactive chart should be used so that when users select a certain year/month they can visualize it easily

# data[].attributes.body
I feel like this was a line graph because it can be clearly shown what the GDP is for each year, quarterly

# data[].attributes.body
This line graph showing the patents applied, awarded, and owned by sector is well-made. It even has different colours and it's deliberately do so that users are able to distinguish between the different elements

# data[].attributes.body
A line chart is being used to show annual domestic exports by area. I think they chose the right type of graph as a line chart is good at showing time series data.

# data[].attributes.attachment_link.readable_text
Prior to 2003, merchandise trade data exclude Singapore's merchandise trade with Indonesia. Data prior to 1999 are based on Standard International Trade Classification (SITC) 3. Data from 1999 onwards are based on SITC 4.1. Trade data has been revised arising from periodic reviews based on International standard.

# data[].attributes.attachment_link.description
Prior to 2003, merchandise trade data exclude Singapore's merchandise trade with Indonesia. Data prior to 1999 are based on Standard International Trade Classification (SITC) 3. Data from 1999 onwards are based on SITC 4.1. Trade data has been revised arising from periodic reviews based on International standard.

# data[].attributes.body
A line chart would be good to see trends for individual the individual values. However, as the bars are split into revenue, expenditure and balance, the right chart was used.

# data[].attributes.attachment_link.readable_text
Fiscal position may not be fully comparable across financial years due to the reclassification of revenue and expenditure items over time.

The Government's financial year begins on 1 April of the current year and ends on 31 March of the following year.

Figures may not add up due to rounding.

# data[].attributes.attachment_link.title
Government Budget and Fiscal Position, Annual-Data.gov.sg

# data[].attributes.attachment_link.description
Fiscal position may not be fully comparable across financial years due to the reclassification of revenue and expenditure items over time. The Government's financial year begins on 1 April of the current year and ends on 31 March of the following year. Figures may not add up due to rounding.

# data[].attributes.body
I feel that the wrong chart was used as it would be easier to see a trend from a line chart.

# data[].attributes.body
Choosing the pie chart is correct is it visually shows the proportion of each single data given. This will makes it easier for viewers to compare and analyse the data

# data[].attributes.body
The pie chart was the right choice in this case. It allows for quick analysis of the different things the government spends on. This pie chart has only a few segments, which is not to overwhelming to see. It shows that the proportion of each areas of government spending is not similar in proportion to one another, hence a clear distinction between each factor.

# data[].attributes.body
https://data.gov.sg/dataset/domestic-exports-by-area It is correct to use line graph as it shows a continuous data of over 30 years condensed into stacked line graphs for easier comparison, and allows for an easier trend to be seen where Asia constantly and remains the largest export by area for Singapore with growing exports in Oceania in recent times compared to Americas in the 1995s and early 2000s

# data[].attributes.body
https://data.gov.sg/dataset/resident-population-by-ethnicity-gender-and-age-group?view_id=b8e25fcd-2cae-4093-a935-c86496482774&resource_id=f9dbfc75-a2dc-42af-9f50-425e4107ae84
It is correct to use the double line graph to show the different number of males and females respectively with each line annually at the end of June.

# data[].attributes.body
This line graph was used correctly to show the years of which the price of housing in Singapore has been since the year 2000, by using the graph we can infer that shortly after the start of 2020 the prices started to drop as the housing index points during the start of 2020 during the covid-19 phenomenon.

# data[].attributes.body
This data representation method is accurate because of its use of a line chart in the representation of a trend taking place over a certain time frame.

# data[].attributes.body
https://data.gov.sg/dataset/number-of-physiotherapists
the chart used is right because it shows how the number of Physiotherapists who are in public sector, private sector, and Physiotherapists that are not in active practice have changed during a continuous period of time.

# data[].attributes.body
https://data.gov.sg/dataset/rainfall-monthly-total
I think that the chart used is not right. It will be hard to see using line chart.
I think it will be better to use scatter plot chart to show.

# data[].attributes.body
The line chart used to display the percentage improvements of energy consumption per gdp by year is accurate & effective. This is due to the percentage improvements depicted on the Y-axis do not start at 0 & that the X-axis is depicted by years, which give solid justification as to why a line chart would be the preferred choice in this scenario.

Link:  https://data.gov.sg/dataset/climate-change-and-energy-energy-consumption-per-dollar-gdp-improvement-from-2005-levels

# data[].attributes.attachment_link.title
Climate Change and Energy - Energy Consumption Per Dollar GDP (% Improvement From 2005 Levels)-Data.gov.sg

# data[].attributes.attachment_link.description
Energy Consumption Per Dollar GDP (% Improvement From 2005 Levels)

# data[].attributes.body
The bar chart used to show the Resource Conservation can be displayed with the help of a Pie Chart because it can show how many percent is available there to begin with. A hierchal pie chart can be used to  show the different types of materials from general to a more in-depth version.

# data[].attributes.body
this chart is right as it shows singaporean residents by their races effectively

# data[].attributes.attachment_link.readable_text
Data for 1970 and from 1980 onwards refer to Singapore residents (citizens and permanent residents).

Data prior to 1980 (except 1970) refer to total population.

Data for 1970 and 1980 refer to all residents present in Singapore on Census day.

Data from 2000 onwards are based on the register-based approach.

Data from 2003 onwards exclude residents who are overseas for a continuous period of 12 months or longer as at the reference period.

# data[].attributes.attachment_link.title
Singapore Residents By Age Group, Ethnic Group And Gender, End June, Annual-Data.gov.sg

# data[].attributes.attachment_link.description
Data for 1970 and from 1980 onwards refer to Singapore residents (citizens and permanent residents). Data prior to 1980 (except 1970) refer to total population. Data for 1970 and 1980 refer to all residents present in Singapore on Census day. Data from 2000 onwards are based on the register-based approach.

# data[].attributes.body
I think that the revenue of infocomm industry using bar chart to display the different market segment is a good choice for visualization as the data communicates the message clearly and is well-organized.
Source: https://data.gov.sg/dataset/revenue-of-infocomm-industry-by-market-segment

# data[].attributes.body
Although the line chart is used, i feel that using a bar chart would have been better as it allow us to better visualize the by percentage

# data[].attributes.body
https://data.gov.sg/dataset/domestic-exports-by-area

# data[].attributes.body
I think this is a good choice to use pie chart to display

Annual Motor Vehicle Population by Vehicle Type. It clearly shows the proportion of each sector which gives clear view for people to understand and do further analyse.

# data[].attributes.body
i think that  the right choice will be a line chart to explain a domestic exports to tell the difference of different countries

# data[].attributes.body
https://data.gov.sg/dataset/flats-constructed This shows the correct chart being used as it shows how much buildings is being build yearly using a time-series chart but it would be better to use a bar chart to show a better visualization

# data[].attributes.body
Correctly used to show the average ridership over the years for different transport mode.

# data[].attributes.body
Correctly used to show how many flats have been constructed over the years

# data[].attributes.body
the map chart is used correctly as it shows real time data of the taxi location in different parts of singapore, this would allow the reader to better understand whether there are taxi available in their area of vicinity by looking  at the chart

# data[].attributes.attachment_link.readable_text
Average daily number of trips made islandwide on MRT, LRT, bus & taxi.

Only data from 2001 onwards had been updated when the methodology of estimating taxi ridership was revised in 2003.

# data[].attributes.attachment_link.title
Public Transport Utilisation - Average Daily Public Transport Ridership-Data.gov.sg

# data[].attributes.attachment_link.description
Average daily number of trips made islandwide on MRT, LRT, bus & taxi. * Only data from 2001 onwards had been updated when the methodology of estimating taxi ridership was revised in 2003.

# data[].attributes.body
I think the chart use here is incorrect, we should have used Stacked Column Charts.  Stacked Column Charts can show us the composition of patents, this will allow us to see the total number of patents in each year.

# data[].attributes.attachment_link.title
Patents Applied, Awarded and Owned by Sector-Data.gov.sg

# data[].attributes.attachment_link.description
To collect data that provide useful indication on the scale and direction of R&D in Singapore. This will facilitate the development of national science and technology policies and programmes, which will benefit the whole R&D community. For more information, please refer to https://www.a-star.edu.sg/News-and-Events/Publications/National-Survey-of-R-D.aspx

# data[].attributes.body
This shows the progression over time the Total R&D expenditure in technology using line graph. https://data.gov.sg/dataset/total-research-and-development-expenditure

# data[].attributes.body
This representation of data is accurate as in uses a line chart to represent a trend taking place over a period of time.

# data[].attributes.body
This representation of data is appropriate as it can clearly convey the idea of this data in visual form with percentages and visual agaration of the continents of the domestic helper

# data[].attributes.body
This bar graph on recycling rates by waste type should be plotted with tables or tables with embedded charts to show the percentages easily across for the user to understand quicker. With the right table, the user can see the comparison between the variables across other categories.

https://data.gov.sg/group/environment

# data[].attributes.body
This dataset is suitable for a bar chart as the data consists mainly of long category names and needs more space. Adding on, the number of categories is greater than seven but not more than fifteen so a bar chart would be more suitable than a column chart in this case. The distribution of the respective category can also be seen clearly so I think the right chart was used.

# data[].attributes.body
The following bar graph is used to effectively show the annual amount of waste that were disposed of and recycled. The X-axis represents the year and Y-axis represents the number of tonnes (in millions) disposed/recycled in that respective period and the two categories are differentiated by color (red or orange). With this chart, it is relatively easy to observe the trends.

However, it may not be the most ideal way to present this data if the data were to increase (more years added) and may end up looking cluttered. Thus, while bar charts can be used for easy comparisons in value changes over a period of time (and between sub-categories), it should be used for reasonably-low number of data points.

https://data.gov.sg/dataset/waste-disposed-of-and-recycled-annual

# data[].attributes.body
This chart is the most ideal chart that can be used. Since line chart helps to effectively establish trends, this allows the viewer to have a better insight on the current trend of the Cornova Virus deaths among all the countries. With the background being black, allowing the graph to pop out. Its Y-axis are also placed horizontally and not vertically, allowing for better viewing experience and give the viewer an easier time to understand the graph. It also has a legend which further helps the user to isolate which line is either increasing at an experiential rate or are slowing down based on the colors highlighted.

# data[].attributes.body
Public Transport Utilisation - Average Daily Public Transport Ridership

It uses the correct chart to display the amount of people using different transportation. Showing the difference.

# data[].attributes.body
The bar graph is suitable as it shows the number of hours and clearly state which device is used.

# data[].attributes.body
It used the right graph as it shows the average amount of passengers taking MRT, LRT, bus and taxi everyday clearly.

# data[].attributes.body
Line chart can effective shows the pattern of the graph.

# data[].attributes.body
This pie chart is suitable to show proportional data clearly so I think it is right to use it in this case.

# data[].attributes.body
This data on the number of epidemic cases is represented as a bar chart. This gives a clear representation as well as a good explanation just by the chart itself that h1n1 has the highest cases up till this date, compared to other epidemic cases.

# data[].attributes.body
The line chart was used correctly for the dataset below. The x-axis shows the different weeks of the year and the y-axis shows the number of dengue cases. Color indication for the lines in the chart had also been used to differentiate between the weeks from different years.

By having this line graph, we can see that there is a high number of cases in 2014 week 27 with a total number of cases at 891. Thus, the graph used is correct and data can be view hassle freely.

https://data.gov.sg/dataset/weekly-infectious-disease-bulletin-cases?view_id=3ca8dd9d-9e88-428e-bb87-252f3ecd28ba&resource_id=ef7e44f1-9b14-4680-a60a-37d2c9dda390

# data[].attributes.body
Line graphs are used to monitor changes over both short and long time periods. The line chart is the right chart to use as it shows the trend of mobile data usage throughout the years in Singapore

# data[].attributes.body
This line chart was used correctly. The x-axis shows a time series (years), whereas the y-axis shows the domestic exports in millions, which are appropriate axes for a line chart to show changes overtime. The colours used should be changed, as the shades of blue for Oceania, Africa and the European Union are too similar, and it would be hard to differenciate amongst them while looking at the lines.

We can see that over the years from 1980 to 2014, there is a steady increase (with a few minor drops) in the domestic exports for all areas except for Asia. Going in depth, we can see that between the years 2008 and 2009, these areas all had a drop, which means that the drop does not impact one area individually. For Asia, it is largely a steady growth from 1980 to 1998. After the year 1998, there is a huge increase with only one drop in the year 2009.

https://data.gov.sg/dataset/domestic-exports-by-area?view_id=98954d67-971b-4528-a450-11de26f3695e&resource_id=f5542e9d-58d5-48dc-b8c6-4ba692318b41

# data[].attributes.body
The line chart shown below is suitable in displaying the trend of recycling rate by waste type.
It clearly shows that this chart is clear overview.
It can tell us the differences and we can compare the recycling rate by waste type.

# data[].attributes.body
CANCEL

upload

link

search_web

camera

more_horiz
POST

# data[].attributes.body
CANCEL

upload

link

search_web

camera

more_horiz
POST

# data[].attributes.body
CANCEL

upload

link

search_web

camera

more_horiz
POST

# data[].attributes.body
This line chart show the trends of domestic exports per millions over the course of 1980 to 2014.  There is a larger increase in Asia's exports over time as compared to the other countries. However, there seemed to be a minor dip in exports from 2009 to 2010 in Asia, but quickly picking back up in 2011.

https://data.gov.sg/dataset/domestic-exports-by-area

# data[].attributes.body
CANCEL

upload

link

search_web

camera

more_horiz
POST

# data[].attributes.body
CANCEL

upload

link

search_web

camera

more_horiz
POST

# data[].attributes.body
The right chart has been used as with the Line chart, it is easy to look at the rate of deaths the corona virus has incurred as well as clearly compare the rate between each country. Luckily the rate is stagnating which can only be a good thing

# data[].attributes.body
The right chart has been used for the dataset. This is because the usage of a line graph would allow people to view the trend of the data from the graph based on the month.

# data[].attributes.body
A bar chart could be better in displaying the data here, as there are too many parts in the pie chart and the chart seems to be too congested. I feel that using a bar chart here would be more visually appealing and present the data more efficiently.

# data[].attributes.body
The chart selected to show the number of graduates from each course in the year 2014 is a Bar chart which is correct. I feel that it is the correct chart used as it met the requirements of the bar chart. The first requirement met is that it has long category name and also it has 15 courses and not more than 15. It also shows a clear overview of the number of student graduated from each course.

# data[].attributes.body
The line chart selected is suitable in displaying the trend of students enrolling into Primary School over the years. It clearly shows how the number of students enrolled into Primary One has changed over the years, with the highest number of students enrolled was in 2000, and there is a decline in number of students enrolled following the years after 2000.

# data[].attributes.body
This chart is using the bar chart perfectly as i can understand it well and is displayed in a manner that is understandable.

# data[].attributes.body
by grouping  Ethnic Group make it easier to see for each  race in year

# data[].attributes.body
The chart selected (Pie Chart) for showing 'Annual Motor Vehicle Population by Vehicle Type (2016)' is correct. Pie Charts are generally used to represent items under the category in numbers, or in percentages (For Percentage wise, all should add up to 100%). Using of Pie Charts for the category helps users to better understand and help to develop business insights on types of vehicles used/ owed by people in Singapore.

# data[].attributes.body
This pie chart was the right chart to use in this context. Pie charts are generally used to show % or  proportional data in a form of slice and also in category so in this case, Domestic Exports By Area (2014) pie chart categorized exports earning by countries and the slices makes it easy and readable to regular people without analysis background.

# data[].attributes.body
The data is displayed with a line chart. Which i personally think is a right choise as the data is collected annually and it makes it easer to compare

# data[].attributes.attachment_link.title
Annual Motor Vehicle Population by Vehicle Type-Data.gov.sg

# data[].attributes.attachment_link.description
Annual motor vehicle population by vehicle type