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 %>%
   14     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
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    ## 15    1932519  13 female            0
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    ## 20    1020296  13   male            0
    ## 21    1472643  13 female            0
    ## 22    2041297  13 female            0
    ## 23    1514978  13   male            0
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    ##  [ reached getOption("max.print") -- omitted 74004 rows ]

    filter( )

    Filter values based on defined conditions

    filter( )

    filter( )

    Can filter for multiple conditions

    filter( )

    We can apply multiple logic rules in the filter() function such as:

    < Less than != Not equal to
    > Greater than %in% Group membership
    == Equal to is.na is NA
    != Not equal to !is.na is not NA
    <= Less than or equal to &,|,! Boolean operators
    >= Greater than or equal to

    filter( )

    Additional subsetting functions:

    storms %>% distinct() Remove duplicate rows
    storms %>% sample_frac(0.5, replace = TRUE) Randomly select fraction of rows
    storms %>% sample_n(10, replace = TRUE) Randomly select n rows
    storms %>% slice(3:5) Select rows by position
    storms %>% top_n(storms, 2, date) Select and order top n entries

    Your Turn


    Continuing with our facebook.tsv data…


    1. Filter the facebook data for just males


    2. Select the following variables:

  • userid
  • age
  • gender
  • friend_count

  • …and then filter for just males

    Solution

    # 1. Filter the facebook data for just males
    fb_males <- facebook %>% filter(gender == "male")
    
    
    # 2. Select specified variables and then filter for males
    
    # Option 1
    facebook2 <- facebook %>% select(userid, age, gender, friend_count)
    fb2_males <- facebook %>% filter(gender == "male")
    
    # Option 2
    fb2_males <- facebook %>%
            select(userid, age, gender, friend_count) %>%
            filter(gender == "male")
    
    fb2_males
    ##        userid age gender friend_count
    ## 1     2094382  14   male            0
    ## 2     2083884  14   male            0
    ## 3     1733186  14   male            0
    ## 4     1524765  14   male            0
    ## 5     1136133  13   male            0
    ## 6     1365174  13   male            0
    ## 7     1712567  13   male            0
    ## 8     1612453  13   male            0
    ## 9     2104073  13   male            0
    ## 10    1918584  13   male            0
    ## 11    1704433  13   male            0
    ## 12    1020296  13   male            0
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    ##  [ reached getOption("max.print") -- omitted 33575 rows ]

    group_by( )

    Group data by categorical levels

    group_by( )

    group_by( )

    group_by( )

    group_by( )

    group_by( )

    • group_by() function is a silent function in which no observable manipulation of the data occurs
    • Alters underlying attributes → note Groups: city [3] in output below
    • Becomes useful when combined with other functions
    pollution %>% group_by(city)        
    ## Source: local data frame [6 x 3]
    ## Groups: city [3]
    ## 
    ##       city  size amount
    ##      <chr> <chr>  <dbl>
    ## 1 New York large     23
    ## 2 New York small     14
    ## 3   London large     22
    ## 4   London small     16
    ## 5  Beijing large    121
    ## 6  Beijing small     56

    Your Turn


    Continuing with our facebook.tsv data…


    Select the following variables:

  • userid
  • age
  • gender
  • friend_count

  • …and then group by gender

    Solution

    # 1. Select specified variables and then group by gender
    facebook %>%
            select(userid, age, gender, friend_count) %>%
            group_by(gender)
    ## Source: local data frame [99,003 x 4]
    ## Groups: gender [3]
    ## 
    ##     userid   age gender friend_count
    ##      <int> <int> <fctr>        <int>
    ## 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
    ## # ... with 98,993 more rows

    summarise( )

    Change unit of analysis

    summarise( )

    summarise( )

    summarise( )

    summarise( )

    summarise( )

    summarise( )

    summarise( )

    summarise( )

    Summary functions:

    first() First value of a vector min() Min value in vector
    last() Last value of a vector max() Max value in vector
    nth() Nth value of a vector mean() Mean value of vector
    n() # of values in a vector median() Median value of vector
    n_distinct() # of distinct values var() Variance of vector
    IQR() IQR of a vector sd() St. dev. of vector

    Your Turn

    Continuing with our facebook.tsv data…


    1. Calculate the mean and median for tenure and friend_count


    2. Select the following variables:

  • gender
  • age
  • friend_count

  • group by gender and calculate the mean and median for age and friend_count

    Solution

    # 1. Calculate the mean and median for tenure and friend_count
    facebook %>% summarise(tenure_avg = mean(tenure, na.rm = TRUE),
                           tenure_med = median(tenure, na.rm = TRUE),
                           fc_avg = mean(friend_count, na.rm = TRUE),
                           fc_med = median(friend_count, na.rm = TRUE))
    ##   tenure_avg tenure_med   fc_avg fc_med
    ## 1   537.8874        412 196.3508     82
    # 2. Select specified variables, group by gender, and calculate mean and median
    facebook %>%
            select(gender, age, friend_count) %>%
            group_by(gender) %>%
            summarise_each(funs(mean, median))
    ## # A tibble: 3 × 5
    ##   gender age_mean friend_count_mean age_median friend_count_median
    ##   <fctr>    <dbl>             <dbl>      <dbl>               <dbl>
    ## 1 female 39.45990          241.9699         31                  96
    ## 2   male 35.67025          165.0355         27                  74
    ## 3     NA 74.77714          184.4114         74                  81

    arrange( )

    Order data

    arrange( )

    arrange( )

    Use desc(x) to sort variable(s) in descending order

    Your Turn

    Continuing with our facebook.tsv data…


    1. Group the data by age


    2. Calculate the median friend_count for each age


    3. Arrange the median calculated friend_count variable in descending order to find the ages with the greatest median friend count

    Solution

    facebook %>% 
            group_by(age) %>% 
            summarise(friend_count = median(friend_count, na.rm = TRUE)) %>% 
            arrange(desc(friend_count))
    ## # A tibble: 101 × 2
    ##      age friend_count
    ##    <int>        <dbl>
    ## 1    104        275.0
    ## 2    103        268.5
    ## 3     97        260.0
    ## 4     95        252.0
    ## 5     94        251.0
    ## 6    105        249.5
    ## 7    110        243.0
    ## 8    102        229.0
    ## 9     99        216.0
    ## 10   107        215.0
    ## # ... with 91 more rows

    mutate( )

    Create new variables

    mutate( )

    mutate( )

    mutate( )

    mutate( )

    mutate( )

    Window functions that can be applied within mutate_each(funs()):

    lead() ntile() cumsum()
    lag() between() cummax()
    dense_rank() cume_dist() cummin()
    min_rank() cumall() cumprod()
    percent_rank() cumany() pmax()
    row_number() cumean() pmin()


    • Can also create own functions to pass through mutate_each(funs())
    • Be aware that some functions will cause errors when applied to non-numeric variables

    Your Turn

    Continuing with our facebook.tsv data…


    1. Create a new variable friend_ratio that equals

    \[\text{friend_ratio} = \frac{\text{friendships_initiated}}{\text{friend_count}}\]


    2. Find the median friend_ratio for males and females.

    Solution

    # 1. Create a new variable `friend_ratio` that equals `friendships_initiated` / `friend_count`
    facebook2 <- facebook %>% mutate(friend_ratio = friendships_initiated / friend_count)
    # 2. Find the median `friend_ratio` for males and females.
    facebook2 %>% group_by(gender) %>%
              summarise(fr_median = median(friend_ratio, na.rm = TRUE))
    ## # A tibble: 3 × 2
    ##   gender fr_median
    ##   <fctr>     <dbl>
    ## 1 female 0.5681818
    ## 2   male 0.6666667
    ## 3     NA 0.4513274
    # 1 & 2 in one fell swoop
    facebook %>% mutate(friend_ratio = friendships_initiated / friend_count) %>%
             group_by(gender) %>%
             summarise(fr_median = median(friend_ratio, na.rm = TRUE))
    ## # A tibble: 3 × 2
    ##   gender fr_median
    ##   <fctr>     <dbl>
    ## 1 female 0.5681818
    ## 2   male 0.6666667
    ## 3     NA 0.4513274

    join( )

    Combine separate data sets

    join( )

    join( )

    Join matching rows from b to a

    join( )

    Join matching rows from b to a

    join( )

    Join matching rows from b to a

    join( )

    Join matching rows from a to b

    join( )

    Join data. Retain only matching rows in both sets.

    join( )

    Join data. Retain all values, all rows.

    join( )

    Keep all rows in a that have a match in b.

    join( )

    Keep all rows in a that do not have a match in b.

    join( )

    Additional dplyr functions for merging data sets:

    intersect(y, z) Rows that appear in both y and z
    union(y, z) Rows that appear in either or both y and z
    setdiff(y, z) Rows that appear in y but not z
    bind_rows(y, z) Append z to y as new rows
    bind_cols(y, z) Append z to y as new columns


    Use extreme caution when applying bind_ functions.

    Your Turn


    Import the reddit.csv and regions.csv files in the data folder


    Join the regions data to the reddit data











    HINT: the common variable between the two data frames is state

    Solution

    reddit <- read.csv("../data/reddit.csv", stringsAsFactors = FALSE)
    regions <- read.csv("../data/regions.csv", stringsAsFactors = FALSE)
    
    reddit <- reddit %>% left_join(regions)
    
    str(reddit)
    ## 'data.frame':    32754 obs. of  16 variables:
    ##  $ id               : int  1 2 3 4 5 6 7 8 9 10 ...
    ##  $ gender           : int  0 0 1 0 1 0 0 0 0 0 ...
    ##  $ age.range        : chr  "25-34" "25-34" "18-24" "25-34" ...
    ##  $ marital.status   : chr  NA NA NA NA ...
    ##  $ employment.status: chr  "Employed full time" "Employed full time" "Freelance" "Freelance" ...
    ##  $ military.service : chr  NA NA NA NA ...
    ##  $ children         : chr  "No" "No" "No" "No" ...
    ##  $ education        : chr  "Bachelor's degree" "Bachelor's degree" "Some college" "Bachelor's degree" ...
    ##  $ country          : chr  "United States" "United States" "United States" "United States" ...
    ##  $ state            : chr  "New York" "New York" "Virginia" "New York" ...
    ##  $ income.range     : chr  "$150,000 or more" "$150,000 or more" "Under $20,000" "$150,000 or more" ...
    ##  $ fav.reddit       : chr  "getmotivated" "gaming" "snackexchange" "spacedicks" ...
    ##  $ dog.cat          : chr  NA NA NA NA ...
    ##  $ cheese           : chr  NA NA NA NA ...
    ##  $ state.abreviated : chr  "(NY)" "(NY)" "(VA)" "(NY)" ...
    ##  $ division         : int  2 2 5 2 9 2 NA 9 2 NA ...

    DB connections

    PostgreSQL Flights

    Introduction to dplyr

    library(DBI)
    library(RPostgreSQL)
    con <-
      dbConnect(drv = "PostgreSQL", dbname = "szgdlszd", host = "horton.elephantsql.com",
                port = 5432, user = "szgdlszd", password = Sys.getenv("ELEPHANTSQLPW"))
    
    ## flights <- copy_to(con, nycflights13::flights, "flights")
    flights_db <- tbl(con, "flights")
    delay_db <- flights_db %>%
      group_by(tailnum) %>%
      summarise(count = n(),
                dist = mean(distance, na.rm = TRUE),
                delay = mean(arr_delay, na.rm = TRUE)) %>%
      filter(count > 20, dist < 2000)
    
    # delay_db %>% explain()
    # delay_db %>% show_query()
    
    delay <- delay_db %>% collect()

    PostgreSQL Flights

    # Interestingly, the average delay is only slightly related to the
    # average distance flown by a plane.
    ggplot(delay, aes(dist, delay)) + geom_point(aes(size = count), alpha = 1/2) +
      geom_smooth() + scale_size_area()

    substitute( )

    Programming with dplyr

    substitute( )

    friend_ratio <- function(data, nom, denom) {
      call <- substitute(data_new <- data %>%
                           transmute(friend_ratio = nom / denom),
                         list(nom = as.name(nom), denom = as.name(denom)))
      eval(call); return(as.vector(data_new$friend_ratio)) }
    facebook$friend_ratio <- friend_ratio(data = facebook, "friendships_initiated", "friend_count")
    facebook %>% tbl_df %>% select(userid, age, gender, "friendships_initiated", "friend_count", friend_ratio) %>%
      arrange(desc(friend_count), desc(friend_ratio))
    ## # A tibble: 99,003 x 6
    ##     userid   age gender friendships_initiated friend_count friend_ratio
    ##      <int> <int> <fctr>                 <int>        <int>        <dbl>
    ##  1 2090699   103 female                    96         4923  0.019500305
    ##  2 1660276    66   male                   431         4917  0.087655074
    ##  3 1926655    19 female                   241         4863  0.049557886
    ##  4 1685573    38   male                   942         4845  0.194427245
    ##  5 1386477    61   male                   561         4844  0.115813377
    ##  6 1604095    15 female                    97         4826  0.020099461
    ##  7 2042824    18   male                    32         4817  0.006643139
    ##  8 1529639    96 female                   555         4803  0.115552780
    ##  9 1587911   108 female                   535         4797  0.111528038
    ## 10 1205425    60 female                  3538         4794  0.738005841
    ## # ... with 98,993 more rows

    substitute( )

    # facebook %>% group_by(gender) %>% summarise(mean_fr = mean(friend_ratio, na.rm = TRUE))
    ggplot(data = filter(facebook, !is.na(gender)), aes(gender, friend_ratio, fill = gender)) +
      geom_boxplot() + coord_flip()

    substitute ( )

    group_median <- function(data, id_vars, var, na.rm) {
      id_vars_sym <- lapply(id_vars, as.symbol)
      call <- substitute(data_new <- data %>%
                           group_by_(.dots = id_vars) %>%
                           summarise(var_median = median(var, na.rm = na.rm)),
                         list(id_vars = id_vars_sym,
                              var = as.name(var)))
      eval(call)
      return(data_new)
    }
    facebook %>% mutate(minor = ifelse(age < 18, TRUE, FALSE)) %>%
                        group_median(id_vars = c("gender", "minor"),
                              var = "friend_ratio", na.rm = TRUE)
    ## # A tibble: 6 x 3
    ## # Groups:   gender [?]
    ##   gender minor var_median
    ##   <fctr> <lgl>      <dbl>
    ## 1 female FALSE  0.5700935
    ## 2 female  TRUE  0.5574885
    ## 3   male FALSE  0.6666667
    ## 4   male  TRUE  0.7058824
    ## 5   <NA> FALSE  0.4514702
    ## 6   <NA>  TRUE  0.4479167

    Key Things to Remember

    Remember These Functions!

    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

    Remember These Functions!

    dplyr is a package that transforms and manipulates data


    mutate(): create new variables


    join(): combine separate data sets

    Hands-on script 6-dplyr-data.R

    Start RStudio from \\asap1\em_apps$\R

    or

    Log on to server and select R installation:

    \\asap1\em_apps$\R\R-3.3.1


    setwd("T:/DKI/rtrainings/bootcamp/code-scripts")