1 Tidy data

In tidy data:

Each variable forms a column.
Each observation forms a row.
Each type of observational unit forms a table

An example of tidy data:

head(airquality, 10)

##    Ozone Solar.R Wind Temp Month Day
## 1     41     190  7.4   67     5   1
## 2     36     118  8.0   72     5   2
## 3     12     149 12.6   74     5   3
## 4     18     313 11.5   62     5   4
## 5     NA      NA 14.3   56     5   5
## 6     28      NA 14.9   66     5   6
## 7     23     299  8.6   65     5   7
## 8     19      99 13.8   59     5   8
## 9      8      19 20.1   61     5   9
## 10    NA     194  8.6   69     5  10

An example of a messy dataset:

2 Manipulate data

dplyr is the hero for most analysis tasks.

With these six functions you can accomplish just about anything you want with your data.

select() : select individual columns to keep or drop
arrange(): sort a table top-to-bottom based on the values of a column
mutate(): add new columns or dupate exsiting columns
filter(): keep only a subset of rows depending on the values of a column
summarize(): calculate a single summary for an entire table
group_by(): sort data into groups based on teh vales of a column

2.1 select()

Use the select() function to:

Pull-out a few columns to create a new table
Drop columns you no longer need
Rearrange or change the order of columns

2.1.1 Keep certain columns

library(dplyr)

select(airquality, Ozone, Month, Day) # keet Ozone, Month, and Day columns

select(airquality, c(Ozone:Temp))

2.1.2 drop one or more columns

colnames(airquality) # display column names

select(airquality, -Wind) # drop the wind column

select(airquality, -c(Solar.R:Temp)) # drop multiple columns

2.1.3 reorder columns

# Move Month and Day columns first and keep everthing else the same
select(airquality, Month, Day, everything())

Exercise 1

import ozone data from ozone_samples_demo.csv

drop the Latitude and Longitude columns

select SITE, Date, Ozone, UNITS columns

2.2 arrange()

Use arrange() to sort the rows of a data frame based on the values of selected columns

arrange(airquality, Ozone) # arrange rows based the value of ozone, the smallest on top

arrange(airquality, desc(Ozone)) # from high to low

Exercise 2

Try arranging more than one column, such as Ozone and Temp. What happens?

Create a new airquality data frame showing top 10 ozone days, with Month, Day and Ozone columns

2.3 mutate()

mutate() can edit existing columns in a data frame or add new values that are calculated from existing columns.

2.3.1 Add a column

First, let’s add a column with the city of ozone measurement.

airquality <- mutate(airquality, city = "New York") # add city as a colum

2.3.2 Add several columns

Let’s also add a new column to document the data measurement year.

# Add some information about city and year
airquality <- mutate(airquality, 
                     city = "New York",
                     year = 1973)

2.3.3 Change a column

#set city to lower case
airquality <- mutate(airquality,
                     city = tolower(city))

# toupper() changes all of the letters to upper-case.

2.3.4 Add calculated columns

We can use mutate() to convert the Wind column from mph to m/s. Multiply the Wind column by 0.447 to get new values for a column named wind_meter.per.second.

airquality <- mutate(airquality,
                     Wind_m.s = Wind * 0.447)

Exercise 3

Convert the Temp colum from Fahrenheit to Celsius using T(°C) = (T(°F) - 32) * 5/9

Week2-1

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