August 6, 2016

because…


"Data sets contain more information than they display."


- Garrett Grolemund

Key Things to Remember

What to Remember from this Section

dplyr is a package that transforms and manipulates data


select(): select variables of concern


filter(): filter values based on conditions


group_by(): group data by categorical levels


summarise(): change unit of analysis


arrange(): order the data

What to Remember from this Section

dplyr is a package that transforms and manipulates data


mutate(): create new variables


join(): combine separate data sets

GR&A

That means grounds rules & assumptions

R package used…




install.packages("dplyr")


library(dplyr)

data used…




install.packages("EDAWR")


library(EDAWR)


Data sets: storms, tb, pollution, iris, a, b

%>% operator…

learn it, love it, leverage it



filter(data, variable == numeric_value)

or

data %>% filter(variable == numeric_value)

%>% operator…

learn it, love it, leverage it

arrange(
        summarize(
                filter(data, variable == "numeric_value"),
                Total = sum(variable)
        ),
        desc(Total)
)
a <- filter(data, variable == "numeric_value")
b <- summarise(a, Total = sum(variable))
c <- arrange(b, desc(Total))
data %>%
        filter(variable == "value") %>%
        summarise(Total = sum(variable)) %>%
        arrange(desc(Total))
Same results but the %>% operator is more efficient and legible

select( )

Select variables of concern

select( )

select( )

select( )

select( )

select( )

Code alternatives:

These produce the same results:
        storms %>% select(wind, pressure, date)
        storms %>% select(wind:pressure)
        storms %>% select(-storm)

Your Turn


Import the facebook.tsv file in the data folder


Create a new data frame that includes:

  • userid
  • age
  • gender
  • friend_count
  • Solution

    # Import the facebook.tsv file in the data folder
    facebook <- read.delim("data/facebook.tsv")
    
    facebook2 <- facebook %>% select(userid, age, gender, friend_count)
    
    facebook2
    ##        userid age gender friend_count
    ## 1     2094382  14   male            0
    ## 2     1192601  14 female            0
    ## 3     2083884  14   male            0
    ## 4     1203168  14 female            0
    ## 5     1733186  14   male            0
    ## 6     1524765  14   male            0
    ## 7     1136133  13   male            0
    ## 8     1680361  13 female            0
    ## 9     1365174  13   male            0
    ## 10    1712567  13   male            0
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    ## 15    1932519  13 female            0
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    ## 19    1537661  13 female            0
    ## 20    1020296  13   male            0
    ## 21    1472643  13 female            0
    ## 22    2041297  13 female            0
    ## 23    1514978  13   male            0
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