For the describe proposal, briefly describe your topic along with both an article/report/blog and a dataset. Submit both .Rmd and “knitted”.html files. You don’t need to upload your html file on RPubs.

You should not use the same topic as that for your midterm presentation. Please use a different topic for your final project.

1 Article

  • Include a link to access your article.

  • Provide your presentation title. It can be the same as the title of your article. Or, you can modify the title.

How to Sustainably Feed 10 billion people

As incomes rise, people will increasingly consume more resource-intensive, animal-based foods. At the same time, we urgently need to cut greenhouse gas (GHG) emissions from agricultural production and stop conversion of remaining forests to agricultural land. This article focuses on the various ways to create a sustainable food future for 10 billion during 2050. The article focusses on five areas of change: Reduce Growth In Demand for Food and Other Agricultural Products, Increase Food Production Without Expanding Agricultural Land, Protect and Restore Natural Ecosystems and Limit Agricultural Land-Shifting, Increase Fish Supply, Reduce Greenhouse Gas Emissions from Agricultural Production and Reduce Greenhouse Gas Emissions from Agricultural Production. I will be focusing on a subset of these 5 methods of improvement by supporting the reasons the article states for why we need to improve the sustainability of human activity rather than demonstrating how to improve these activities.

2 Data

  • Describe your dataset that is relevant, related to, and informative of the article.

The Food and Agriculture Organization of the United Nations provides free access to food and agriculture data for over 245 countries and territories, from the year 1961 to the most recent update (depends on the dataset). The Food Balance Sheets presents a comprehensive picture of the pattern of a country’s food supply during a specified reference period, the last time an update was loaded to the FAO database was in 2013. The food balance sheet shows for each food item the sources of supply and its utilization. The dataset is based on comparison between food produced for human consumption and feed produced for animals.

  • Print the first 6 rows using function head().
fao = read.csv("FAO.csv")
head(fao)
##   Area.Abbreviation Area.Code        Area Item.Code                     Item
## 1               AFG         2 Afghanistan      2511       Wheat and products
## 2               AFG         2 Afghanistan      2805 Rice (Milled Equivalent)
## 3               AFG         2 Afghanistan      2513      Barley and products
## 4               AFG         2 Afghanistan      2513      Barley and products
## 5               AFG         2 Afghanistan      2514       Maize and products
## 6               AFG         2 Afghanistan      2514       Maize and products
##   Element.Code Element        Unit latitude longitude Y1961 Y1962 Y1963 Y1964
## 1         5142    Food 1000 tonnes    33.94     67.71  1928  1904  1666  1950
## 2         5142    Food 1000 tonnes    33.94     67.71   183   183   182   220
## 3         5521    Feed 1000 tonnes    33.94     67.71    76    76    76    76
## 4         5142    Food 1000 tonnes    33.94     67.71   237   237   237   238
## 5         5521    Feed 1000 tonnes    33.94     67.71   210   210   214   216
## 6         5142    Food 1000 tonnes    33.94     67.71   403   403   410   415
##   Y1965 Y1966 Y1967 Y1968 Y1969 Y1970 Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977
## 1  2001  1808  2053  2045  2154  1819  1963  2215  2310  2335  2434  2512  2282
## 2   220   195   231   235   238   213   205   233   246   246   255   263   235
## 3    76    75    71    72    73    74    71    70    72    76    77    80    60
## 4   238   237   225   227   230   234   223   219   225   240   244   255   185
## 5   216   216   235   232   236   200   201   216   228   231   234   240   228
## 6   415   413   454   448   455   383   386   416   439   445   451   463   439
##   Y1978 Y1979 Y1980 Y1981 Y1982 Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990
## 1  2454  2443  2129  2133  2068  1994  1851  1791  1683  2194  1801  1754  1640
## 2   254   270   259   248   217   217   197   186   200   193   202   191   199
## 3    65    64    64    60    55    53    51    48    46    46    47    46    43
## 4   203   198   202   189   174   167   160   151   145   145   148   145   135
## 5   234   228   226   210   199   192   182   173   170   154   148   137   144
## 6   451   440   437   407   384   371   353   334   330   298   287   265   279
##   Y1991 Y1992 Y1993 Y1994 Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003
## 1  1539  1582  1840  1855  1853  2177  2343  2407  2463  2600  2668  2776  3095
## 2   197   249   218   260   319   254   326   347   270   372   411   448   460
## 3    43    40    50    46    41    44    50    48    43    26    29    70    48
## 4   132   120   155   143   125   138   159   154   141    84    83   122   144
## 5   126    90   141   150   159   108    90    99    72    35    48    89    63
## 6   245   170   272   289   310   209   173   192   141    66    93   170   117
##   Y2004 Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 1  3249  3486  3704  4164  4252  4538  4605  4711  4810  4895
## 2   419   445   546   455   490   415   442   476   425   422
## 3    58   236   262   263   230   379   315   203   367   360
## 4   185    43    44    48    62    55    60    72    78    89
## 5   120   208   233   249   247   195   178   191   200   200
## 6   231    67    82    67    69    71    82    73    77    76

3 Data Validation

  • Check the content of the attributes, traits, features, rows, and/or other properties specific to your dataset.

    • Is the data type correct for this field?

    • Is the value within the valid range or part of a domain or enumerated list?

    • Check for duplicates, for example of a unique key.

    • Check for nulls. Are there mandatory values, or are null / empty values allowed? Are the null types consistent (NaN, infinity, empty strings, etc.)?

  • Tips: for duplicates, use function unique(). For missing values, use function is.na(). To delete all rows with a missing, use function na.omit(). Feel free to use other functions to check duplicates and missing values.

4 Plot

  • You need to create at least one plot using shiny.

  • Describe your key plot(s) that you like to create in words.

5 Post on Discussion Board

  • Check your presentation date.

  • Create a post on the discussion board with your presentation title as the post title by 11:59 pm, Apr 5. You’ll edit your post on the discussion board to include your presentation files (Rmd file and your shiny-app URL).

  • You should upload your presentation files (Rmd file and your shiny-app URL) and dataset you used on the discussion board by the day before your presentation day. For example, if you’re a presenter on Apr 11, upload your presentation files by 11:59 pm, Apr 10.