[R 프로그래밍] 12. dplyr, tidyr

Author : tmlab / Date : 2016. 10. 4. 01:20 / Category : Lecture/R 프로그래밍

dplyr

When working with data you must:

  • Figure out what you want to do.

  • Describe those tasks in the form of a computer program.

  • Execute the program.

The dplyr package makes these steps fast and easy:

  • By constraining your options, it simplifies how you can think about common data manipulation tasks.

  • It provides simple “verbs”, functions that correspond to the most common data manipulation tasks, to help you translate those thoughts into code.

  • It uses efficient data storage backends, so you spend less time waiting for the computer.

This document introduces you to dplyr’s basic set of tools, and shows you how to apply them to data frames. Other vignettes provide more details on specific topics:

  • databases: Besides in-memory data frames, dplyr also connects to out-of-memory, remote databases. And by translating your R code into the appropriate SQL, it allows you to work with both types of data using the same set of tools.

  • benchmark-baseball: see how dplyr compares to other tools for data manipulation on a realistic use case.

  • window-functions: a window function is a variation on an aggregation function. Where an aggregate function uses n inputs to produce 1 output, a window function uses n inputs to produce n outputs.

Data: nycflights13

To explore the basic data manipulation verbs of dplyr, we’ll start with the built in nycflights13 data frame. This dataset contains all 336776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in?nycflights13

#install.packages("nycflights13")
library(nycflights13)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dim(flights)
## [1] 336776     19
head(flights)
## Source: local data frame [6 x 19]
## 
##    year month   day dep_time sched_dep_time dep_delay arr_time
##   (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1  2013     1     1      517            515         2      830
## 2  2013     1     1      533            529         4      850
## 3  2013     1     1      542            540         2      923
## 4  2013     1     1      544            545        -1     1004
## 5  2013     1     1      554            600        -6      812
## 6  2013     1     1      554            558        -4      740
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

dplyr can work with data frames as is, but if you’re dealing with large data, it’s worthwhile to convert them to a tbl_df: this is a wrapper around a data frame that won’t accidentally print a lot of data to the screen.

Single table verbs

Dplyr aims to provide a function for each basic verb of data manipulation:

  • filter() (and slice())
  • arrange()
  • select() (and rename())
  • distinct()
  • mutate() (and transmute())
  • summarise()
  • sample_n() and sample_frac()

If you’ve used plyr before, many of these will be familar.

Filter rows with filter()

filter() allows you to select a subset of rows in a data frame. The first argument is the name of the data frame. The second and subsequent arguments are the expressions that filter the data frame:

For example, we can select all flights on January 1st with:

filter(flights, month == 1, day == 1)
## Source: local data frame [842 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

This is equivalent to the more verbose code in base R:

flights[flights$month == 1 & flights$day == 1, ]

filter() works similarly to subset() except that you can give it any number of filtering conditions, which are joined together with & (not && which is easy to do accidentally!). You can also use other boolean operators:

filter(flights, month == 1 | month == 2)

To select rows by position, use slice():

slice(flights, 1:10)
## Source: local data frame [10 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

Arrange rows with arrange()

arrange() works similarly to filter() except that instead of filtering or selecting rows, it reorders them. It takes a data frame, and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:

arrange(flights, year, month, day)
## Source: local data frame [336,776 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

Use desc() to order a column in descending order:

arrange(flights, desc(arr_delay))
## Source: local data frame [336,776 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     9      641            900      1301     1242
## 2   2013     6    15     1432           1935      1137     1607
## 3   2013     1    10     1121           1635      1126     1239
## 4   2013     9    20     1139           1845      1014     1457
## 5   2013     7    22      845           1600      1005     1044
## 6   2013     4    10     1100           1900       960     1342
## 7   2013     3    17     2321            810       911      135
## 8   2013     7    22     2257            759       898      121
## 9   2013    12     5      756           1700       896     1058
## 10  2013     5     3     1133           2055       878     1250
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

dplyr::arrange() works the same way as plyr::arrange(). It’s a straighforward wrapper around order() that requires less typing. The previous code is equivalent to:

flights[order(flights$year, flights$month, flights$day), ]
flights[order(desc(flights$arr_delay)), ]

Select columns with select()

Often you work with large datasets with many columns but only a few are actually of interest to you. select() allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:

# Select columns by name

select(flights, year, month, day)
## Source: local data frame [336,776 x 3]
## 
##     year month   day
##    (int) (int) (int)
## 1   2013     1     1
## 2   2013     1     1
## 3   2013     1     1
## 4   2013     1     1
## 5   2013     1     1
## 6   2013     1     1
## 7   2013     1     1
## 8   2013     1     1
## 9   2013     1     1
## 10  2013     1     1
## ..   ...   ...   ...
select(flights, year:day)
## Source: local data frame [336,776 x 3]
## 
##     year month   day
##    (int) (int) (int)
## 1   2013     1     1
## 2   2013     1     1
## 3   2013     1     1
## 4   2013     1     1
## 5   2013     1     1
## 6   2013     1     1
## 7   2013     1     1
## 8   2013     1     1
## 9   2013     1     1
## 10  2013     1     1
## ..   ...   ...   ...
select(flights, -(year:day))
## Source: local data frame [336,776 x 16]
## 
##    dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
##       (int)          (int)     (dbl)    (int)          (int)     (dbl)
## 1       517            515         2      830            819        11
## 2       533            529         4      850            830        20
## 3       542            540         2      923            850        33
## 4       544            545        -1     1004           1022       -18
## 5       554            600        -6      812            837       -25
## 6       554            558        -4      740            728        12
## 7       555            600        -5      913            854        19
## 8       557            600        -3      709            723       -14
## 9       557            600        -3      838            846        -8
## 10      558            600        -2      753            745         8
## ..      ...            ...       ...      ...            ...       ...
## Variables not shown: carrier (chr), flight (int), tailnum (chr), origin
##   (chr), dest (chr), air_time (dbl), distance (dbl), hour (dbl), minute
##   (dbl), time_hour (time)

This function works similarly to the select argument in base::subset(). Because the dplyr philosophy is to have small functions that do one thing well, it’s its own function in dplyr.

There are a number of helper functions you can use within select(), like starts_with()ends_with()matches() and contains(). These let you quickly match larger blocks of variables that meet some criterion. See ?select for more details.

You can rename variables with select() by using named arguments:

select(flights, tail_num = tailnum)
## Source: local data frame [336,776 x 1]
## 
##    tail_num
##       (chr)
## 1    N14228
## 2    N24211
## 3    N619AA
## 4    N804JB
## 5    N668DN
## 6    N39463
## 7    N516JB
## 8    N829AS
## 9    N593JB
## 10   N3ALAA
## ..      ...

But because select() drops all the variables not explicitly mentioned, it’s not that useful. Instead, use rename():

rename(flights, tail_num = tailnum)
## Source: local data frame [336,776 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tail_num (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

Extract distinct (unique) rows

A common use of select() is to find the values of a set of variables. This is particularly useful in conjunction with the distinct() verb which only returns the unique values in a table.

distinct(select(flights, tailnum))
## Source: local data frame [4,044 x 1]
## 
##    tailnum
##      (chr)
## 1   N14228
## 2   N24211
## 3   N619AA
## 4   N804JB
## 5   N668DN
## 6   N39463
## 7   N516JB
## 8   N829AS
## 9   N593JB
## 10  N3ALAA
## ..     ...
distinct(select(flights, origin, dest))
## Source: local data frame [224 x 2]
## 
##    origin  dest
##     (chr) (chr)
## 1     EWR   IAH
## 2     LGA   IAH
## 3     JFK   MIA
## 4     JFK   BQN
## 5     LGA   ATL
## 6     EWR   ORD
## 7     EWR   FLL
## 8     LGA   IAD
## 9     JFK   MCO
## 10    LGA   ORD
## ..    ...   ...

(This is very similar to base::unique() but should be much faster.)

Add new columns with mutate()

Besides selecting sets of existing columns, it’s often useful to add new columns that are functions of existing columns. This is the job ofmutate():

mutate(flights,
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60)
## Source: local data frame [336,776 x 21]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time), gain (dbl),
##   speed (dbl)

dplyr::mutate() works the same way as plyr::mutate()and similarly to base::transform(). The key difference between mutate() andtransform() is that mutate allows you to refer to columns that you’ve just created:

mutate(flights,
  gain = arr_delay - dep_delay,
  gain_per_hour = gain / (air_time / 60)
)
## Source: local data frame [336,776 x 21]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time), gain (dbl),
##   gain_per_hour (dbl)
transform(flights,
  gain = arr_delay - dep_delay,
  gain_per_hour = gain / (air_time / 60)
)
## Error in eval(expr, envir, enclos): 객체 'gain'를 찾을 수 없습니다

If you only want to keep the new variables, use transmute():

transmute(flights,
  gain = arr_delay - dep_delay,
  gain_per_hour = gain / (air_time / 60)
)
## Source: local data frame [336,776 x 2]
## 
##     gain gain_per_hour
##    (dbl)         (dbl)
## 1      9      2.378855
## 2     16      4.229075
## 3     31     11.625000
## 4    -17     -5.573770
## 5    -19     -9.827586
## 6     16      6.400000
## 7     24      9.113924
## 8    -11    -12.452830
## 9     -5     -2.142857
## 10    10      4.347826
## ..   ...           ...

Summarise values with summarise()

The last verb is summarise(). It collapses a data frame to a single row (this is exactly equivalent to plyr::summarise()):

summarise(flights,
  delay = mean(dep_delay, na.rm = TRUE))
## Source: local data frame [1 x 1]
## 
##      delay
##      (dbl)
## 1 12.63907

Below, we’ll see how this verb can be very useful.

Randomly sample rows with sample_n() and sample_frac()

You can use sample_n() and sample_frac() to take a random sample of rows: use sample_n() for a fixed number and sample_frac() for a fixed fraction.

sample_n(flights, 10)
## Source: local data frame [10 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013    10    20     2028           2030        -2     2143
## 2   2013    12    13      730            730         0     1053
## 3   2013     3    10     1500           1505        -5     1817
## 4   2013    12     8     1617           1617         0     1914
## 5   2013     9    19      834            837        -3     1035
## 6   2013     4    28     1037           1010        27     1142
## 7   2013     2    22     1701           1700         1     1847
## 8   2013    12    22     1824           1724        60     2115
## 9   2013    12    23     1150           1125        25     1348
## 10  2013     2     8      606            610        -4      714
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)
sample_frac(flights, 0.01)
## Source: local data frame [3,368 x 19]
## 
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    (int) (int) (int)    (int)          (int)     (dbl)    (int)
## 1   2013     9    11     1105           1115       -10     1345
## 2   2013     3     5     2236           2129        67     2335
## 3   2013     8    24      857            900        -3     1114
## 4   2013     6    18     2159           2045        74       11
## 5   2013     5    23       NA           1724        NA       NA
## 6   2013     7    21      613            615        -2      755
## 7   2013    10    10     1700           1705        -5     1815
## 8   2013     9     4      819            825        -6     1010
## 9   2013     7    22     1038           1030         8     1302
## 10  2013     4    17      558            605        -7      722
## ..   ...   ...   ...      ...            ...       ...      ...
## Variables not shown: sched_arr_time (int), arr_delay (dbl), carrier (chr),
##   flight (int), tailnum (chr), origin (chr), dest (chr), air_time (dbl),
##   distance (dbl), hour (dbl), minute (dbl), time_hour (time)

Use replace = TRUE to perform a bootstrap sample. If needed, you can weight the sample with the weight argument.

Commonalities

You may have noticed that the syntax and function of all these verbs are very similar:

  • The first argument is a data frame.

  • The subsequent arguments describe what to do with the data frame. Notice that you can refer to columns in the data frame directly without using $.

  • The result is a new data frame

Together these properties make it easy to chain together multiple simple steps to achieve a complex result.

These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (arrange()), pick observations and variables of interest (filter() and select()), add new variables that are functions of existing variables (mutate()), or collapse many values to a summary (summarise()). The remainder of the language comes from applying the five functions to different types of data. For example, I’ll discuss how these functions work with grouped data.

Grouped operations

These verbs are useful on their own, but they become really powerful when you apply them to groups of observations within a dataset. In dplyr, you do this by with the group_by() function. It breaks down a dataset into specified groups of rows. When you then apply the verbs above on the resulting object they’ll be automatically applied “by group”. Most importantly, all this is achieved by using the same exact syntax you’d use with an ungrouped object.

Grouping affects the verbs as follows:

  • grouped select() is the same as ungrouped select(), except that grouping variables are always retained.

  • grouped arrange() orders first by the grouping variables

  • mutate() and filter() are most useful in conjunction with window functions (like rank(), or min(x) == x). They are described in detail invignette("window-functions").

  • sample_n() and sample_frac() sample the specified number/fraction of rows in each group.

  • slice() extracts rows within each group.

  • summarise() is powerful and easy to understand, as described in more detail below.

In the following example, we split the complete dataset into individual planes and then summarise each plane by counting the number of flights (count = n()) and computing the average distance (dist = mean(Distance, na.rm = TRUE)) and arrival delay (delay = mean(ArrDelay, na.rm = TRUE)). We then use ggplot2 to display the output.

library(ggplot2)
by_tailnum <- group_by(flights, tailnum)
delay <- summarise(by_tailnum,
  count = n(),
  dist = mean(distance, na.rm = TRUE),
  delay = mean(arr_delay, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)

# 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()
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

You use summarise() with aggregate functions, which take a vector of values and return a single number. There are many useful examples of such functions in base R like min()max()mean()sum()sd()median(), and IQR(). dplyr provides a handful of others:

  • n(): the number of observations in the current group

  • n_distinct(x):the number of unique values in x.

  • first(x)last(x) and nth(x, n) - these work similarly to x[1]x[length(x)], and x[n] but give you more control over the result if the value is missing.

For example, we could use these to find the number of planes and the number of flights that go to each possible destination:

destinations <- group_by(flights, dest)
summarise(destinations,
  planes = n_distinct(tailnum),
  flights = n()
)
## Source: local data frame [105 x 3]
## 
##     dest planes flights
##    (chr)  (int)   (int)
## 1    ABQ    108     254
## 2    ACK     58     265
## 3    ALB    172     439
## 4    ANC      6       8
## 5    ATL   1180   17215
## 6    AUS    993    2439
## 7    AVL    159     275
## 8    BDL    186     443
## 9    BGR     46     375
## 10   BHM     45     297
## ..   ...    ...     ...

You can also use any function that you write yourself. For performance, dplyr provides optimised C++ versions of many of these functions. If you want to provide your own C++ function, see the hybrid-evaluation vignette for more details.

When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll-up a dataset:

daily <- group_by(flights, year, month, day)
(per_day   <- summarise(daily, flights = n()))
## Source: local data frame [365 x 4]
## Groups: year, month [?]
## 
##     year month   day flights
##    (int) (int) (int)   (int)
## 1   2013     1     1     842
## 2   2013     1     2     943
## 3   2013     1     3     914
## 4   2013     1     4     915
## 5   2013     1     5     720
## 6   2013     1     6     832
## 7   2013     1     7     933
## 8   2013     1     8     899
## 9   2013     1     9     902
## 10  2013     1    10     932
## ..   ...   ...   ...     ...
(per_month <- summarise(per_day, flights = sum(flights)))
## Source: local data frame [12 x 3]
## Groups: year [?]
## 
##     year month flights
##    (int) (int)   (int)
## 1   2013     1   27004
## 2   2013     2   24951
## 3   2013     3   28834
## 4   2013     4   28330
## 5   2013     5   28796
## 6   2013     6   28243
## 7   2013     7   29425
## 8   2013     8   29327
## 9   2013     9   27574
## 10  2013    10   28889
## 11  2013    11   27268
## 12  2013    12   28135
(per_year  <- summarise(per_month, flights = sum(flights)))
## Source: local data frame [1 x 2]
## 
##    year flights
##   (int)   (int)
## 1  2013  336776

However you need to be careful when progressively rolling up summaries like this: it’s ok for sums and counts, but you need to think about weighting for means and variances (it’s not possible to do this exactly for medians).

Chaining

The dplyr API is functional in the sense that function calls don’t have side-effects. You must always save their results. This doesn’t lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:

a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
a3 <- summarise(a2,
  arr = mean(arr_delay, na.rm = TRUE),
  dep = mean(dep_delay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)

Or if you don’t want to save the intermediate results, you need to wrap the function calls inside each other:

filter(
  summarise(
    select(
      group_by(flights, year, month, day),
      arr_delay, dep_delay
    ),
    arr = mean(arr_delay, na.rm = TRUE),
    dep = mean(dep_delay, na.rm = TRUE)
  ),
  arr > 30 | dep > 30
)
## Source: local data frame [49 x 5]
## Groups: year, month [11]
## 
##     year month   day      arr      dep
##    (int) (int) (int)    (dbl)    (dbl)
## 1   2013     1    16 34.24736 24.61287
## 2   2013     1    31 32.60285 28.65836
## 3   2013     2    11 36.29009 39.07360
## 4   2013     2    27 31.25249 37.76327
## 5   2013     3     8 85.86216 83.53692
## 6   2013     3    18 41.29189 30.11796
## 7   2013     4    10 38.41231 33.02368
## 8   2013     4    12 36.04814 34.83843
## 9   2013     4    18 36.02848 34.91536
## 10  2013     4    19 47.91170 46.12783
## ..   ...   ...   ...      ...      ...

This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the %>% operator. x %>% f(y) turns into f(x, y) so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom:

flights %>%
  group_by(year, month, day) %>%
  select(arr_delay, dep_delay) %>%
  summarise(
    arr = mean(arr_delay, na.rm = TRUE),
    dep = mean(dep_delay, na.rm = TRUE)
  ) %>%
  filter(arr > 30 | dep > 30)
## Source: local data frame [49 x 5]
## Groups: year, month [11]
## 
##     year month   day      arr      dep
##    (int) (int) (int)    (dbl)    (dbl)
## 1   2013     1    16 34.24736 24.61287
## 2   2013     1    31 32.60285 28.65836
## 3   2013     2    11 36.29009 39.07360
## 4   2013     2    27 31.25249 37.76327
## 5   2013     3     8 85.86216 83.53692
## 6   2013     3    18 41.29189 30.11796
## 7   2013     4    10 38.41231 33.02368
## 8   2013     4    12 36.04814 34.83843
## 9   2013     4    18 36.02848 34.91536
## 10  2013     4    19 47.91170 46.12783
## ..   ...   ...   ...      ...      ...

Other data sources

As well as data frames, dplyr works with data that is stored in other ways, like data tables, databases and multidimensional arrays.

Data table

dplyr also provides data table methods for all verbs. If you’re using data.tables already this lets you to use dplyr syntax for data manipulation, and data.table for everything else.

For multiple operations, data.table can be faster because you usually use it with multiple verbs simultaneously. For example, with data table you can do a mutate and a select in a single step. It’s smart enough to know that there’s no point in computing the new variable for rows you’re about to throw away.

The advantages of using dplyr with data tables are:

  • For common data manipulation tasks, it insulates you from the reference semantics of data.tables, and protects you from accidentally modifying your data.

  • Instead of one complex method built on the subscripting operator ([), it provides many simple methods.

Databases

dplyr also allows you to use the same verbs with a remote database. It takes care of generating the SQL for you so that you can avoid the cognitive challenge of constantly switching between languages. See the databases vignette for more details.

Compared to DBI and the database connection algorithms:

  • it hides, as much as possible, the fact that you’re working with a remote database
  • you don’t need to know any SQL (although it helps!)
  • it abstracts over the many differences between the different DBI implementations

Multidimensional arrays / cubes

tbl_cube() provides an experimental interface to multidimensional arrays or data cubes. If you’re using this form of data in R, please get in touch so I can better understand your needs.

Comparisons

Compared to all existing options, dplyr:

  • abstracts away how your data is stored, so that you can work with data frames, data tables and remote databases using the same set of functions. This lets you focus on what you want to achieve, not on the logistics of data storage.

  • provides a thoughtful default print() method that doesn’t automatically print pages of data to the screen (this was inspired by data table’s output).

Compared to base functions:

  • dplyr is much more consistent; functions have the same interface. So once you’ve mastered one, you can easily pick up the others

  • base functions tend to be based around vectors; dplyr is based around data frames

Compared to plyr, dplyr:

  • is much much faster

  • provides a better thought out set of joins

  • only provides tools for working with data frames (e.g. most of dplyr is equivalent to ddply() + various functions, do() is equivalent todlply())

Compared to virtual data frame approaches:

  • it doesn’t pretend that you have a data frame: if you want to run lm etc, you’ll still need to manually pull down the data

  • it doesn’t provide methods for R summary functions (e.g. mean(), or sum())

tidyr

tidyr Operations

There are four fundamental functions of data tidying:

  • gather() takes multiple columns, and gathers them into key-value pairs: it makes “wide” data longer
  • spread() takes two columns (key & value) and spreads in to multiple columns, it makes “long” data wider
  • separate() splits a single column into multiple columns
  • unite() combines multiple columns into a single column

gather( ) function:

Objective: Reshaping wide format to long format

Description: There are times when our data is considered unstacked and a common attribute of concern is spread out across columns. To reformat the data such that these common attributes are gathered together as a single variable, the gather() function will take multiple columns and collapse them into key-value pairs, duplicating all other columns as needed.

Complement tospread()

tidyr

tidyr

Function:       gather(data, key, value, ..., na.rm = FALSE, convert = FALSE)
Same as:        data %>% gather(key, value, ..., na.rm = FALSE, convert = FALSE)

Arguments:
        data:           data frame
        key:            column name representing new variable
        value:          column name representing variable values
        ...:            names of columns to gather (or not gather)
        na.rm:          option to remove observations with missing values (represented by NAs)
        convert:        if TRUE will automatically convert values to logical, integer, numeric, complex or 
                        factor as appropriate

Example

We’ll start with the following data set:

Group <- c(rep(1,4),rep(2,4),rep(3,4))
Year <- c(2006:2009,2006:2009,2006:2009)
Qtr.1 <- c(15,12,22,10,12,16,13,23,11,13,17,14)
Qtr.2 <- c(16,13,22,14,13,14,11,20,12,11,12,9)
Qtr.3 <- c(19,27,24,20,25,21,29,26,22,27,23,31)
Qtr.4 <- c(17,23,20,16,18,19,15,20,16,21,19,24)
DF <- data.frame(Group,Year,Qtr.1,Qtr.2,Qtr.3,Qtr.4)
DF
##    Group Year Qtr.1 Qtr.2 Qtr.3 Qtr.4
## 1      1 2006    15    16    19    17
## 2      1 2007    12    13    27    23
## 3      1 2008    22    22    24    20
## 4      1 2009    10    14    20    16
## 5      2 2006    12    13    25    18
## 6      2 2007    16    14    21    19
## 7      2 2008    13    11    29    15
## 8      2 2009    23    20    26    20
## 9      3 2006    11    12    22    16
## 10     3 2007    13    11    27    21
## 11     3 2008    17    12    23    19
## 12     3 2009    14     9    31    24

This data is considered wide since the time variable (represented as quarters) is structured such that each quarter represents a variable. To re-structure the time component as an individual variable, we can gather each quarter within one column variable and also gather the values associated with each quarter in a second column variable.

#install.packages("tidyr")
library(tidyr)
long_DF <- DF %>% gather(Quarter, Revenue, Qtr.1:Qtr.4)
head(long_DF, 24)  # note, for brevity, I only show the data for the first two years 
##    Group Year Quarter Revenue
## 1      1 2006   Qtr.1      15
## 2      1 2007   Qtr.1      12
## 3      1 2008   Qtr.1      22
## 4      1 2009   Qtr.1      10
## 5      2 2006   Qtr.1      12
## 6      2 2007   Qtr.1      16
## 7      2 2008   Qtr.1      13
## 8      2 2009   Qtr.1      23
## 9      3 2006   Qtr.1      11
## 10     3 2007   Qtr.1      13
## 11     3 2008   Qtr.1      17
## 12     3 2009   Qtr.1      14
## 13     1 2006   Qtr.2      16
## 14     1 2007   Qtr.2      13
## 15     1 2008   Qtr.2      22
## 16     1 2009   Qtr.2      14
## 17     2 2006   Qtr.2      13
## 18     2 2007   Qtr.2      14
## 19     2 2008   Qtr.2      11
## 20     2 2009   Qtr.2      20
## 21     3 2006   Qtr.2      12
## 22     3 2007   Qtr.2      11
## 23     3 2008   Qtr.2      12
## 24     3 2009   Qtr.2       9
These all produce the same results:
        DF %>% gather(Quarter, Revenue, Qtr.1:Qtr.4)
        DF %>% gather(Quarter, Revenue, -Group, -Year)
        DF %>% gather(Quarter, Revenue, 3:6)
        DF %>% gather(Quarter, Revenue, Qtr.1, Qtr.2, Qtr.3, Qtr.4)

Also note that if you do not supply arguments for na.rm or convert values then the defaults are used

separate( ) function:

Objective: Splitting a single variable into two

Description: Many times a single column variable will capture multiple variables, or even parts of a variable you just don’t care about. Some examples include:

##   Grp_Ind    Yr_Mo       City_State        First_Last Extra_variable
## 1     1.a 2006_Jan      Dayton (OH) George Washington   XX01person_1
## 2     1.b 2006_Feb Grand Forks (ND)        John Adams   XX02person_2
## 3     1.c 2006_Mar       Fargo (ND)  Thomas Jefferson   XX03person_3
## 4     2.a 2007_Jan   Rochester (MN)     James Madison   XX04person_4
## 5     2.b 2007_Feb     Dubuque (IA)      James Monroe   XX05person_5
## 6     2.c 2007_Mar Ft. Collins (CO)        John Adams   XX06person_6
## 7     3.a 2008_Jan   Lake City (MN)    Andrew Jackson   XX07person_7
## 8     3.b 2008_Feb    Rushford (MN)  Martin Van Buren   XX08person_8
## 9     3.c 2008_Mar          Unknown  William Harrison   XX09person_9

In each of these cases, our objective may be to separate characters within the variable string. This can be accomplished using the separate() function which turns a single character column into multiple columns.

Complement tounite()

Function:       separate(data, col, into, sep = " ", remove = TRUE, convert = FALSE)
Same as:        data %>% separate(col, into, sep = " ", remove = TRUE, convert = FALSE)

Arguments:
        data:           data frame
        col:            column name representing current variable
        into:           names of variables representing new variables
        sep:            how to separate current variable (char, num, or symbol)
        remove:         if TRUE, remove input column from output data frame
        convert:        if TRUE will automatically convert values to logical, integer, numeric, complex or 
                        factor as appropriate

Example

We can go back to our long_DF dataframe we created above in which way may desire to clean up or separate the Quarter variable.

##    Group Year Quarter Revenue
## 1      1 2006   Qtr.1      15
## 2      1 2007   Qtr.1      12
## 3      1 2008   Qtr.1      22
## 4      1 2009   Qtr.1      10
## 5      2 2006   Qtr.1      12
## 6      2 2007   Qtr.1      16
## 7      2 2008   Qtr.1      13
## 8      2 2009   Qtr.1      23
## 9      3 2006   Qtr.1      11
## 10     3 2007   Qtr.1      13
## 11     3 2008   Qtr.1      17
## 12     3 2009   Qtr.1      14
## 13     1 2006   Qtr.2      16
## 14     1 2007   Qtr.2      13
## 15     1 2008   Qtr.2      22
## 16     1 2009   Qtr.2      14
## 17     2 2006   Qtr.2      13
## 18     2 2007   Qtr.2      14
## 19     2 2008   Qtr.2      11
## 20     2 2009   Qtr.2      20
## 21     3 2006   Qtr.2      12
## 22     3 2007   Qtr.2      11
## 23     3 2008   Qtr.2      12
## 24     3 2009   Qtr.2       9
## 25     1 2006   Qtr.3      19
## 26     1 2007   Qtr.3      27
## 27     1 2008   Qtr.3      24
## 28     1 2009   Qtr.3      20
## 29     2 2006   Qtr.3      25
## 30     2 2007   Qtr.3      21
## 31     2 2008   Qtr.3      29
## 32     2 2009   Qtr.3      26
## 33     3 2006   Qtr.3      22
## 34     3 2007   Qtr.3      27
## 35     3 2008   Qtr.3      23
## 36     3 2009   Qtr.3      31
## 37     1 2006   Qtr.4      17
## 38     1 2007   Qtr.4      23
## 39     1 2008   Qtr.4      20
## 40     1 2009   Qtr.4      16
## 41     2 2006   Qtr.4      18
## 42     2 2007   Qtr.4      19
## 43     2 2008   Qtr.4      15
## 44     2 2009   Qtr.4      20
## 45     3 2006   Qtr.4      16
## 46     3 2007   Qtr.4      21
## 47     3 2008   Qtr.4      19
## 48     3 2009   Qtr.4      24

By applying the separate() function we get the following:

separate_DF <- long_DF %>% separate(Quarter, c("Time_Interval", "Interval_ID"))
head(separate_DF, 10)
##    Group Year Time_Interval Interval_ID Revenue
## 1      1 2006           Qtr           1      15
## 2      1 2007           Qtr           1      12
## 3      1 2008           Qtr           1      22
## 4      1 2009           Qtr           1      10
## 5      2 2006           Qtr           1      12
## 6      2 2007           Qtr           1      16
## 7      2 2008           Qtr           1      13
## 8      2 2009           Qtr           1      23
## 9      3 2006           Qtr           1      11
## 10     3 2007           Qtr           1      13
These produce the same results:
        long_DF %>% separate(Quarter, c("Time_Interval", "Interval_ID"))
        long_DF %>% separate(Quarter, c("Time_Interval", "Interval_ID"), sep = "\\.")

unite( ) function:

Objective: Merging two variables into one

Description: There may be a time in which we would like to combine the values of two variables. The unite() function is a convenience function to paste together multiple variable values into one. In essence, it combines two variables of a single observation into one variable.

Complement toseparate()

Function:       unite(data, col, ..., sep = " ", remove = TRUE)
Same as:        data %>% unite(col, ..., sep = " ", remove = TRUE)

Arguments:
        data:           data frame
        col:            column name of new "merged" column
        ...:            names of columns to merge
        sep:            separator to use between merged values
        remove:         if TRUE, remove input column from output data frame

Example

Using the separate_DF dataframe we created above, we can re-unite the Time_Interval and Interval_ID variables we created and re-create the original Quarter variable we had in the long_DF dataframe.

unite_DF <- separate_DF %>% unite(Quarter, Time_Interval, Interval_ID, sep = ".")
head(unite_DF, 10)
##    Group Year Quarter Revenue
## 1      1 2006   Qtr.1      15
## 2      1 2007   Qtr.1      12
## 3      1 2008   Qtr.1      22
## 4      1 2009   Qtr.1      10
## 5      2 2006   Qtr.1      12
## 6      2 2007   Qtr.1      16
## 7      2 2008   Qtr.1      13
## 8      2 2009   Qtr.1      23
## 9      3 2006   Qtr.1      11
## 10     3 2007   Qtr.1      13
These produce the same results:
        separate_DF %>% unite(Quarter, Time_Interval, Interval_ID, sep = "_")
        separate_DF %>% unite(Quarter, Time_Interval, Interval_ID)

If no spearator is identified, "_" will automatically be used

spread( ) function:

Objective: Reshaping long format to wide format

Description: There are times when we are required to turn long formatted data into wide formatted data. The spread() function spreads a key-value pair across multiple columns.

Complement togather()

Function:       spread(data, key, value, fill = NA, convert = FALSE)
Same as:        data %>% spread(key, value, fill = NA, convert = FALSE)

Arguments:
        data:           data frame
        key:            column values to convert to multiple columns
        value:          single column values to convert to multiple columns' values 
        fill:           If there isn't a value for every combination of the other variables and the key 
                        column, this value will be substituted
        convert:        if TRUE will automatically convert values to logical, integer, numeric, complex or 
                        factor as appropriate

Example

wide_DF <- unite_DF %>% spread(Quarter, Revenue)
head(wide_DF, 24)
##    Group Year Qtr.1 Qtr.2 Qtr.3 Qtr.4
## 1      1 2006    15    16    19    17
## 2      1 2007    12    13    27    23
## 3      1 2008    22    22    24    20
## 4      1 2009    10    14    20    16
## 5      2 2006    12    13    25    18
## 6      2 2007    16    14    21    19
## 7      2 2008    13    11    29    15
## 8      2 2009    23    20    26    20
## 9      3 2006    11    12    22    16
## 10     3 2007    13    11    27    21
## 11     3 2008    17    12    23    19
## 12     3 2009    14     9    31    24

과제 - pr12

  • 위에 제공된 소스코드를 그대로 작성해보시오
  • 밑에 제공된 다음의 문제들을 단계별로 푸시오

  • 제공된 데이터는 전국 2015년 고등교육기관 일람표입니다.
    • -는 NA값입니다.
univ <- read.csv("https://raw.githubusercontent.com/halrequin/data/master/data.csv",stringsAsFactors=F,na.strings="-")
  • 제가 제공하는 결과값과 같은 값이 나와야합니다.
  • 저는 칸이 너무 길어지기 때문에 head()를 적용하므로 오해 없으시길 바랍니다
  1. 제공된 데이터를 가지고 filter()를 사용하여 경기 지역의 대학교(학제)를 kyung_gi에 할당하시오.
##   조사회차   학제 학교코드       학교명    본분교 학교상태 지역 설립
## 1   201501 대학교 51028000   한경대학교      본교     기존 경기 국립
## 2   201501 대학교 53003000 가톨릭대학교      본교     기존 경기 사립
## 3   201501 대학교 53005000   강남대학교      본교     기존 경기 사립
## 4   201501 대학교 53008000   경기대학교      본교     기존 경기 사립
## 5   201501 대학교 53010000   경동대학교 제4캠퍼스     신설 경기 사립
## 6   201501 대학교 53013000   가천대학교      본교     기존 경기 사립
##   우편번호                                                주소
## 1  456-749                      경기 안성시 중앙로 327(석정동)
## 2  420-743             경기도 부천시 원미구 지봉로 43 (역곡동)
## 3  446-702 경기도 용인시 기흥구 강남로 40 (구갈동, 강남대학교)
## 4  443-760       경기 수원시 영통구 광교산로 154-42 경기대학교
## 5  482-010                    경기도 양주시 청담로 95 (고암동)
## 6  461-701           경기 성남시 수정구 성남대로 1342 (복정동)
##   재적.학생.총합 재학생.총합 휴학생.총합 총장.및.전임교원수
## 1           5580        3901        1679                168
## 2          10174        7380        2794                247
## 3          10020        7279        2741                222
## 4          17614       12354        5260                418
## 5            877         620         257                 32
## 6          26411       19011        7400                650
  1. kyung_gi를 가지고 arrange()를 사용하여 재적학생 총합을 기준으로 내림차순 정렬하여 kyung_gi에 재할당 하시오.
##   조사회차   학제 학교코드     학교명 본분교 학교상태 지역 설립 우편번호
## 1   201501 대학교 53013000 가천대학교   본교     기존 경기 사립  461-701
## 2   201501 대학교 53008000 경기대학교   본교     기존 경기 사립  443-760
## 3   201501 대학교 53030000 단국대학교   본교     기존 경기 사립  448-701
## 4   201501 대학교 53072000 수원대학교   본교     기존 경기 사립  445-743
## 5   201501 대학교 53078000 아주대학교   본교     기존 경기 사립  443-749
## 6   201501 대학교 53123000 한양대학교  분교1     기존 경기 사립  426-791
##                                                             주소
## 1                      경기 성남시 수정구 성남대로 1342 (복정동)
## 2                  경기 수원시 영통구 광교산로 154-42 경기대학교
## 3 경기도 용인시 수지구 죽전로 152 (죽전동, 단국대학교죽전캠퍼스)
## 4                 경기도 화성시 와우안길 17 (봉담읍, 수원대학교)
## 5         경기도 수원시 영통구 월드컵로 206 (원천동, 아주대학교)
## 6          경기도 안산시 상록구 한양대학로 55 (사동, 한양대학교)
##   재적.학생.총합 재학생.총합 휴학생.총합 총장.및.전임교원수
## 1          26411       19011        7400                650
## 2          17614       12354        5260                418
## 3          16946       11975        4971                440
## 4          14978       10705        4273                358
## 5          14141        9954        4187                624
## 6          12910        9232        3678                390
  1. kyung_gi를 가지고 select()를 사용하여 학제, 학교명, 학교상태, 지역, 설립 정보를 추출하시오.
##     학제     학교명 학교상태 지역 설립
## 1 대학교 가천대학교     기존 경기 사립
## 2 대학교 경기대학교     기존 경기 사립
## 3 대학교 단국대학교     기존 경기 사립
## 4 대학교 수원대학교     기존 경기 사립
## 5 대학교 아주대학교     기존 경기 사립
## 6 대학교 한양대학교     기존 경기 사립
  • 다음은 재학생 총합 및 총장 및 전임교원수 열에서 NA값이 있는 행들을 제거하여 재저장하는 코드입니다.
    • kyung_gi1에 할당하세요
kyung_gi1 <- kyung_gi[complete.cases(kyung_gi$재학생.총합,kyung_gi$총장.및.전임교원수),]
# 숫자로 만들기 위해 자리 수를 나타내는 "," 제거
  1. kyung_gi1에 mutate()를 사용하여 총장 및 전임교원 수 대비 재학생 총합 값(재학생 총합/총장 및 전임교원 수)을 rate라는 열에 저장하여kyung_gi1에 재할당하시오.
##   조사회차   학제 학교코드     학교명 본분교 학교상태 지역 설립 우편번호
## 1   201501 대학교 53013000 가천대학교   본교     기존 경기 사립  461-701
## 2   201501 대학교 53008000 경기대학교   본교     기존 경기 사립  443-760
## 3   201501 대학교 53030000 단국대학교   본교     기존 경기 사립  448-701
## 4   201501 대학교 53072000 수원대학교   본교     기존 경기 사립  445-743
## 5   201501 대학교 53078000 아주대학교   본교     기존 경기 사립  443-749
## 6   201501 대학교 53123000 한양대학교  분교1     기존 경기 사립  426-791
##                                                             주소
## 1                      경기 성남시 수정구 성남대로 1342 (복정동)
## 2                  경기 수원시 영통구 광교산로 154-42 경기대학교
## 3 경기도 용인시 수지구 죽전로 152 (죽전동, 단국대학교죽전캠퍼스)
## 4                 경기도 화성시 와우안길 17 (봉담읍, 수원대학교)
## 5         경기도 수원시 영통구 월드컵로 206 (원천동, 아주대학교)
## 6          경기도 안산시 상록구 한양대학로 55 (사동, 한양대학교)
##   재적.학생.총합 재학생.총합 휴학생.총합 총장.및.전임교원수     rate
## 1          26411       19011        7400                650 29.24769
## 2          17614       12354        5260                418 29.55502
## 3          16946       11975        4971                440 27.21591
## 4          14978       10705        4273                358 29.90223
## 5          14141        9954        4187                624 15.95192
## 6          12910        9232        3678                390 23.67179
  1. kyung_gi1에 summarise()를 사용하여 경기 지역 대학교 재학생 평균을 구해보세요
##   stu_mean
## 1 4959.286
  1. univ 객체에서 10%만큼 sample을 추출하여 univ_sam에 할당하시오.
    • dplyr에서 제공하는 sample_n()이나 sample_frac()을 사용하시오.
##      조사회차           학제 학교코드                            학교명
## 1895   201501 대학부설대학원 63202C54           남서울대학교 특수대학원
## 602    201501 대학부설대학원 51011B55       부산대학교 치의학전문대학원
## 1287   201501 대학부설대학원 53068D74     성신여자대학교 생애복지대학원
## 978    201501 대학부설대학원 53026650            국민대학교디자인대학원
## 1091   201501 대학부설대학원 53041820       동국대학교 영상대학원(전문)
## 1577   201501 대학부설대학원 53104B57 추계예술대학교 문화예술경영대학원
##      본분교 학교상태 지역 설립 우편번호
## 1895   본교     폐교 충남 사립  330-707
## 602    본교     기존 경남 국립  626-870
## 1287   본교     기존 서울 사립  136-742
## 978    본교     기존 서울 사립  136-702
## 1091   본교     기존 서울 사립  100-715
## 1577   본교     기존 서울 사립  120-763
##                                                                                          주소
## 1895                                               충남 천안시 성환읍 대학로 91 남서울대학교 
## 602  경상남도 양산시 부산대학로 49 (물금읍, 부산대학교양산캠퍼스) 부산대학교 치의학전문대학원
## 1287                                서울특별시 성북구 보문로34다길 2 (돈암동, 성신여자대학교)
## 978                                                     서울 성북구 정릉3동 국민대학교 조형관
## 1091                                     서울특별시 중구 필동로1길 30 (장충동2가, 동국대학교)
## 1577                          서울특별시 서대문구 북아현로11가길 7 (북아현동, 추계예술대학교)
##      재적.학생.총합 재학생.총합 휴학생.총합 총장.및.전임교원수
## 1895             NA          NA          NA                 NA
## 602             366         346          20                 48
## 1287             79          73           6                  1
## 978             300         222          78                 NA
## 1091            290         216          74                 16
## 1577             79          65          14                  2
  1. univ 객체를 사용하여 지역으로 grouping을 하여 모든 교육기관의 갯수와 재학생 평균, 총장 및 전임교원수 평균을 구하여 korean_univ 객체에 할당하시오.
    • NA값 제거 하셔야 합니다.
    • 위에서 제시한 방법으로 하지 않아도 됩니다.(e.g., na.rm = T 사용)
## Source: local data frame [6 x 4]
## 
##    지역 count stu_mean staff_mean
##   (chr) (int)    (dbl)      (dbl)
## 1  강원    75 1414.971  124.17647
## 2  경기   319 1317.207   71.95597
## 3  경남    86 1379.620  120.67742
## 4  경북   141 1440.458  104.28814
## 5  광주    74 1480.123  128.72414
## 6  대구    51 2129.625  141.04167
  1. 다음의 코드를 체이닝 연산자를 사용한 코드로 변경하시오.
    • 학제에 따른 각 기관의 갯수를 세어 산점도로 표현
library(ggplot2)
univ_gateg <- summarise(group_by(univ,학제),
                        count = n(),
                        stu_mean=mean(재적.학생.총합,na.rm=T))

p<-ggplot(univ_gateg, aes(학제,count))

p + geom_point(aes(colour=학제)) + theme(axis.text.x = element_text(angle=270,vjust=1))

  1. kyung_gi객체에서 각 학교별 본분교 상황, 재적 학생 총합, 재학생 총합, 휴학생 총합, 총장 및 전임교원수를 select()를 사용하여 test 객체에 할당하세요
##       학교명 본분교 재적.학생.총합 재학생.총합 휴학생.총합
## 1 가천대학교   본교          26411       19011        7400
## 2 경기대학교   본교          17614       12354        5260
## 3 단국대학교   본교          16946       11975        4971
## 4 수원대학교   본교          14978       10705        4273
## 5 아주대학교   본교          14141        9954        4187
## 6 한양대학교  분교1          12910        9232        3678
##   총장.및.전임교원수
## 1                650
## 2                418
## 3                440
## 4                358
## 5                624
## 6                390
  1. test 객체를 사용하여 학교명을 key로 재적 학생 총합, 재학생 총합, 휴학생 총합, 총장 및 전임교원수를 value로 하여 gather()를 적용,test_gat 객체에 할당하시오
##       학교명 본분교       학생구분 tot_num
## 1 가천대학교   본교 재적.학생.총합   26411
## 2 경기대학교   본교 재적.학생.총합   17614
## 3 단국대학교   본교 재적.학생.총합   16946
## 4 수원대학교   본교 재적.학생.총합   14978
## 5 아주대학교   본교 재적.학생.총합   14141
## 6 한양대학교  분교1 재적.학생.총합   12910
  1. test_gat을 spread()를 사용하여 원래 test와 같은 모습으로 만들어 출력하시오.
    • spread된 열의 순서는 상관 없습니다
##         학교명    본분교 재적.학생.총합 재학생.총합 총장.및.전임교원수
## 1   가천대학교      본교          26411       19011                650
## 2 가톨릭대학교      본교          10174        7380                247
## 3   강남대학교      본교          10020        7279                222
## 4   경기대학교      본교          17614       12354                418
## 5   경동대학교 제4캠퍼스            877         620                 32
## 6   단국대학교      본교          16946       11975                440
##   휴학생.총합
## 1        7400
## 2        2794
## 3        2741
## 4        5260
## 5         257
## 6        4971


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