[1] "X"
[2] "Y"
[3] "Unique.Squirrel.ID"
[4] "Hectare"
[5] "Shift"
[6] "Date"
[7] "Hectare.Squirrel.Number"
[8] "Age"
[9] "Primary.Fur.Color"
[10] "Highlight.Fur.Color"
[11] "Combination.of.Primary.and.Highlight.Color"
[12] "Color.notes"
[13] "Location"
[14] "Above.Ground.Sighter.Measurement"
[15] "Specific.Location"
[16] "Running"
[17] "Chasing"
[18] "Climbing"
[19] "Eating"
[20] "Foraging"
[21] "Other.Activities"
[22] "Kuks"
[23] "Quaas"
[24] "Moans"
[25] "Tail.flags"
[26] "Tail.twitches"
[27] "Approaches"
[28] "Indifferent"
[29] "Runs.from"
[30] "Other.Interactions"
[31] "Lat.Long"
Assignment 1 – Loading Data into a Data Frame
Introduction
For this assignment I chose to use a csv from NYC OpenData called 2018 Central Park Squirrel Census - Squirrel Data provided by “The Squirrel Census”.
Approach
Since the assignment is to transform this dataframe, I want to drop a couple of columns. Below is a list of all the columns:
I want to create a dataframe with only these columns:
[1] "Unique.Squirrel.ID"
[2] "Primary.Fur.Color"
[3] "Highlight.Fur.Color"
[4] "Combination.of.Primary.and.Highlight.Color"
[5] "Running"
[6] "Chasing"
[7] "Climbing"
[8] "Eating"
[9] "Foraging"
[10] "X"
[11] "Y"
[12] "Lat.Long"
Afterwards I want to create the following:
A binary “Active” squirrel column using the “Running,” “Chasing,” “Climbing,” “Eating,” and “Foraging” columns.
Convert the “Above Ground Sighter Measurement” column to numeric (INT/FLOAT) values only.
The motivation to use this dataset is simple, I just chose the first interesting popular dataset I found on NYC OpenData. This encourages an exploratory approach, which might be useful when learning new skills.
Code-base
TODO: Code-base
Video
TODO: Video