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Updated by Matthew Van Dyke on 01/06/20 1920 U3L4p1 Creating Bar Graphs Unit 3 Lab 4: Building Data Visualization Tools Creating Bar Graphs2.0 Explore the blocks already written for you and understand how they work.These data sets are also given. Look inside.You will write these blocks below. Right click and edit.3.0 Develop code for the draw axes block. It should draw the horizontal and vertical axes based on the given stage coordinates of the origin x0, y0 and the given length, height information.4.0 Explore the basic label block included in the starter project. Try changing the location and direction of the drawing sprite.5.0 Use the basic label block to develop code for the more elaborate label block that will place a label with the inputted specifications (text, size and direction.)6.0 Develop code for the draw bar block. It should draw a vertical bar (a line with the given width and height) straight up from the current location with a label placed underneath the bar.7.0 Develop code for the draw bars block, which should draw a series of labelled bars of the desired width and an appropriate height that start on the left and continue to the right. In order to properly level the bars and the labels you may need to refer to the global variables x-origin, y-origin.8.0 Develop code for the label vertical axis block so that it labels the vertical axis based on the specified inputs. For simplicity, you can assume that the labels will always split the vertical axis into 10 equal parts (using 11 tick marks) like this:9.0 Now you are ready to put everything together and develop code for the Bar Graph drawing block for a given data list input. Fill in the blanks.10.0 When everything is done, the following code, with a few extra labels for the graph title, should produce a bar graph as shown below: 11.0 Your coding should be general enough to accommodate a new set of data (also included in your starter file):12.0 In collaboration with your partner, compare the two bar graphs. What insights does each graph provide about CO2 emissions? Is one better than the other?

Fill in the blanks

LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

Hello!12

1datamapmany

data lists

110i

121

cont

catchtag

cont

catchtag

Sprite

A direction is missing in this code.

Not all parts are present and not in correct order.

Not all pieces of the puzzle are given.

Not all items are presentMake a tick mark.

Not all items are present.

-200-90