The data file FAO_grains_NA.csv will be used in this exercise. This dataset consists of grain yield and harvest year by North American country. The dataset was downloaded from http://faostat3.fao.org/ in June of 2014.
Run the following line to load the FAO data file into your current R session.
Make sure to load the
dplyr package before proceeding with the following examples.
group_by function will split any operations applied to the dataframe into groups defined by one or more columns. For example, if we wanted to get the minimum and maximum years from the
Year column for which crop data are available by crop type, we would type the following:
# A tibble: 11 x 3 Crop yr_min yr_max <fct> <int> <int> 1 Barley 1961 2012 2 Buckwheat 1961 2012 3 Canary seed 1980 2012 4 Grain, mixed 1961 2012 5 Maize 1961 2012 6 Millet 1961 2012 7 Oats 1961 2012 8 Popcorn 1961 1982 9 Rye 1961 2012 10 Sorghum 1961 2012 11 Triticale 1989 2012
In this example, we are identifying the number of records by
Crop type. There are two ways this can be accomplished:
# A tibble: 7 x 2 Crop Count <fct> <int> 1 Barley 6 2 Buckwheat 6 3 Maize 6 4 Millet 6 5 Oats 6 6 Rye 6 7 Sorghum 6
The former uses the
count() function and the latter uses the
Here’s another example where two variables are summarized in a single pipe.
# A tibble: 7 x 3 Crop Yield `Number of Years` <fct> <dbl> <int> 1 Barley 35471. 5 2 Buckwheat 10418. 5 3 Maize 96151. 5 4 Millet 16548. 5 5 Oats 22619. 5 6 Rye 17132. 5 7 Sorghum 42258. 5
In this example, we are subtracting each value in a group by that group’s median. This can be useful in identifying which year yields are higher than or lower than the median yield value within each crop group. We will concern ourselves with US yields only and sort the output by crop type. We’ll save the output dataframe as
Let’s plot the normalized yields by year for
Barley and add a
0 line representing the (normalized) central value.
The relative distribution of points does not change, but the values do (they are re-scaled) allowing us to compare values based on some localized (group) context. This technique will prove very useful later on in the course when EDA topics are explored.
dplyr’s output data structure
dplyr’s functions such as
summarise generate a tibble data table. For example, the
dat.grp object created in the last chunk of code is associated with a
tb_df (a tibble).
 "tbl_df" "tbl" "data.frame"
A tibble table will behave a little differently than a data frame table when printing to a screen or subsetting its elements. In most cases, a tibble rendering of the table will not pose a problem in a workflow, however, this format may prove problematic with some older functions. To remedy this, you can force the
dat.grp object to a standalone
dataframe as follows: