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INDIVIDUAL ASSIGNMENT
TECHNOLOGY PARK MALASIYA
CT127-3-2-PFDA
PROGRAMMING FOR DATA ANALYSIS
NP2F1909IT
HAND OUT DATE: - 5
th
November 2020 5
th
HAND IN DATE: - 21
th
February 2021
WEIGHTAGE: 50%
Submitted By: Submitted To: Purushottam Sah (NP000327) Ram Narayan Thakur INSTRUCTIONS TO CANDIDATES: 1 Submit your assignment at the administrative counter. 2 Students are advised to underpin their answers with the use of references (cited using the Harvard Name System of Referencing). 3 Late submission will be awarded zero (0) unless Extenuating Circumstances (EC) are upheld. 4 Cases of plagiarism will be penalized. 5 The assignment should be bound in an appropriate style (comb bound or stapled). 6 Where the assignment should be submitted in both hardcopy and softcopy, the softcopy of the written assignment and source code (where appropriate) should be on a CD in an envelope / CD cover and attached to the hardcopy. 7 You must obtain 50% overall to pass this module. i
Table of Contents
- Introduction.......................................................................................................................................
- Assumptions:.....................................................................................................................................
- 3.Screenshots........................................................................................................................................
- 3.1 Analysis 1:....................................................................................................................................
- 3.1.1 Output 1:...............................................................................................................................
- 3.2 Analysis 2:....................................................................................................................................
- 3.2.2 Output 2:...............................................................................................................................
- 3.3 Analysis 3:....................................................................................................................................
- 3.3.3 Output 3:...............................................................................................................................
- 3.4 Analysis 4:....................................................................................................................................
- 3.4.4 Output 4:...............................................................................................................................
- 3.5 Analysis 5:....................................................................................................................................
- 3.5.5 Output 5:...............................................................................................................................
- 3.6 Analysis 6:....................................................................................................................................
- 3.6.6 Output 6:...............................................................................................................................
- 3.7 Analysis 7:....................................................................................................................................
- 3.7.7 Output 7:...............................................................................................................................
- 3.8 Analysis 8:..................................................................................................................................
- 3.8.8 Output 8:.............................................................................................................................
- 3.9 Analysis 9:..................................................................................................................................
- 3.9.9 Output 9:.............................................................................................................................
- 3.10 Analysis 10:..............................................................................................................................
- 3.10.10 Output 10:.......................................................................................................................
- 3.11 Analysis 11:..............................................................................................................................
- 3.11.11 Output 10:.......................................................................................................................
- 3.12 Analysis 12:..............................................................................................................................
- 3.12.12 Output 12:.......................................................................................................................
- 3.13 Analysis 13:..............................................................................................................................
- 3.13.13 Output 13:.......................................................................................................................
- Analysis 14:................................................................................................................................
- 3.14.14 Output 14:.......................................................................................................................
- Conclusion.......................................................................................................................................
- References:......................................................................................................................................
2. Assumptions:
Through the R programming language, the given hourly weather data from the two airport such are John F. Kennedy International as well as LaGuardia is imported then analysis are done. By installing packages ggplot2 on R-Studio different plots of data is plotted. The data that is provided in hourly weather data that contains humidity, wind directions, pressure as well as visibility, wind speed and wind gust, temperature, dew points. All these given data used to analyze through the records all the effects of variable. During the increasing of wind gust, increase in wind speed is happened. After wind speed, the wind gust is suddenly changed. Both humidity as well as precipitation are linked with each other. If the humidity is more than the precipitation is also automatically high. The precipitation and visibility are inversely proportion to each other. When precipitation is increase then visibility becomes low. The given data help to analyzed to determine the relation between them. During the increasing of pressure automatically temperature also increasing.
3.Screenshots
3.1 Analysis 1: The commentary for the analysis 1 is shown in above diagram in which data of wind direction is plotted in boxplot where data is presented in color format for each data. In this diagram x-axis is named as wind direction as well as y-axis as temperature. 3.1.1 Output 1: The output for this analysis is given above where wind direction in x-axis as black and white color format. It represented the main significance of direction of wind. Every data is attached in one to another as cex which is not defined.
3.3 Analysis 3: Figure 5 For this third analysis which is shown in above diagram in which wind direction is plotted in boxplot, x-lab as wind direction as well as y-lab as temperature where horizontal kept as true and las as 2. The color for each data kept as red, green, violet, sky-blue, gray. 3.3.3 Output 3: From the above output, where wind direction data is printed out in red, green, violet, sky- blue, gray which is attached each other. This diagram shows data kept as dewp.
3.4 Analysis 4: For this forth analysis which is shown in above diagram in which temperature is plotted in histogram. Data kept as hour in which main kept as temperature histogram, x-lab as temperature and y-lab as frequency temp, las as 2. Color kept for each data as cyan and orange. The output for this is printed out as bar diagram. 3.4.4 Output 4: After analysis forth the above output is printed out in which data are attached with each other with two color such are orange and cyan. Temperature is range between 0 to 20 in x-axis. Frequency temp is ranges from 0 to 2000 in y-axis.
3.6 Analysis 6: For this fifth analysis which is shown in above diagram in which temperature is plotted in histogram in which temp data is imported. For this analysis, x-lab value kept as temperature and y-lab value kept as density temp, las as 1 lwd as 5. 3.6.6 Output 6: After analysis sixth the above diagram is obtained that contain two different data in which temperature x-axis assigned ass temperature that is ranges from 20 to 100 and y-axis as density temp which is range from 0.000 to 0.020. The data of temperature is printed in red and green color form. Each data in this diagram is attached to each other as shown in output.
3.7 Analysis 7: The script for analysis 7 is shown in above diagram in which data of both day and temp is plotted where data is read from weather data. In the above command x-axis is name as day and y-axis named as temp. 3.7.7 Output 7: After analysis 7 is done the above output is obtained where data of day is printed ranges from 0 to 30 and data of temp ranges from 20 to 80. This diagram shown as sometimes temperature is low sometimes temperature is high
3.9 Analysis 9: The script for the 9th^ analysis is shown in the above figure in which relationships of both data humidity as well as precipitation is plotted through point graph. 3.9.9 Output 9: After analysis the 9th^ the above output is displayed. In the output data of humidity is more where precipitation is more. In the x-axis humid is assigned and y-axis precipitation is assigned. According to the output it shows that high precipitation is occurred at higher humidity.
3.10 Analysis 10: The script for the 10th^ analysis is shown in the above figure in which pressure is plotted in boxplot in each month. Along with both airport pressure is plotted of each and every month. 3.10.10 Output 10: After analysis 10 is done the above diagram is printed out in which x-axis is assigned as month and y-axis as pressure. The pressure in both airports increase of decrease almost similarly through each month.
3.12 Analysis 12: The script for the 11analysis is shown in the above figure in which plotting is doing precipitation against visibility in boxplot. If the precipitation is high then the visibility becomes less. 3.12.12 Output 12: After the analysis 12 is done the above output is printed out that shows the plot of precipitation against months in graph. Graphs explains that increase in visibility, precipitation is low. Precipitation data display at high, and visibility display at bottom in the graph. The boxplot of precipitation against visibility shows that x-axis assigned as visible (Miles) and y- axis assigned as precipitation(inch) as per data rate.
3.13 Analysis 13: The script for the 11 analysis is shown in the above figure in which from the both LaGuardia Airport as well as John F. Kennedy International Airport precipitation in each and every month is plotted. 3.13.13 Output 13: After the analysis 12 is done the above output is printed out. The above graphs precipitation against month shows that precipitation was increased in the month of September in both airports. The precipitation on the months of September becomes 0.8 of one airport.
4. Conclusion
This module taught me that how a real data is presented in real world. The assignment attached with this module was very helpful in understanding the concept of packages and libraries in R programming language. The given hour weather data is plotted that helps to show the relationship between different element. After the plotting all the given data thus it concluded that in both airports having similar weather. The plotted data shows the relation of different elements.
5. References:
R-project.org. 2021. R: What is R?. [online] Available at: https://www.r-project.org/about.html [Accessed 21 February 2021]. DataMentor. 2021. R Tutorial - Learn R Programming. [online] Available at: https://www.datamentor.io/r-programming/ [Accessed 10 January 2021]. Holtz, Y., 2021. The R Graph Gallery – Help and inspiration for R charts. [online] The R Graph Gallery. Available at: https://www.r-graph-gallery.com/ [Accessed 20 January 2021].
