Last updated on 2014-10-02 01:48:14.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.2 | 67.49 | 40.85 | 108.34 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.2 | 90.46 | 38.09 | 128.55 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.2 | 240.51 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.2 | 259.07 | ERROR | |||
| r-devel-osx-x86_64-clang | 0.2 | 158.72 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 0.2 | 230.00 | 96.00 | 326.00 | ERROR | |
| r-patched-linux-x86_64 | 0.2 | 88.84 | 43.50 | 132.34 | ERROR | |
| r-patched-solaris-sparc | 0.2 | 1941.70 | ERROR | |||
| r-patched-solaris-x86 | 0.2 | 325.30 | ERROR | |||
| r-release-linux-ix86 | 0.2 | 114.66 | 56.13 | 170.78 | ERROR | |
| r-release-linux-x86_64 | 0.2 | 89.98 | 44.58 | 134.56 | ERROR | |
| r-release-osx-x86_64-mavericks | 0.2 | NOTE | ||||
| r-release-osx-x86_64-snowleopard | 0.2 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 0.2 | 231.00 | 106.00 | 337.00 | ERROR | |
| r-oldrel-windows-ix86+x86_64 | 0.2 | 238.00 | 121.00 | 359.00 | ERROR |
Version: 0.2
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘bigrquery’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-ix86, r-release-linux-x86_64, r-release-osx-x86_64-snowleopard
Version: 0.2
Check: installed package size
Result: NOTE
installed size is 14.9Mb
sub-directories of 1Mb or more:
libs 13.2Mb
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86, r-release-linux-ix86, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-osx-x86_64-snowleopard, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: dependencies in R code
Result: NOTE
Namespaces in Imports field not imported from:
‘Lahman’ ‘hflights’ ‘magrittr’ ‘methods’
All declared Imports should be used.
See the information on DESCRIPTION files in the chapter ‘Creating R
packages’ of the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-osx-x86_64-clang, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86, r-release-linux-ix86, r-release-linux-x86_64, r-release-osx-x86_64-mavericks, r-release-osx-x86_64-snowleopard, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: R code for possible problems
Result: NOTE
anti_join.tbl_dt: no visible global function definition for ‘setkeyv’
auto_copy.tbl_dt: no visible global function definition for
‘as.data.table’
compare_tbls: no visible global function definition for ‘expect_true’
db_data_type.DBIConnection: no visible binding for global variable
‘dbDataType’
db_info: no visible global function definition for ‘dbGetInfo’
db_list_tables.DBIConnection: no visible global function definition for
‘dbListTables’
dbi_connect.DBIDriver: no visible global function definition for
‘dbConnect’
dbi_connect.SQLiteDriver: no visible global function definition for
‘dbConnect’
dt_col_compute: no visible global function definition for
‘is.data.table’
grouped_dt: no visible global function definition for ‘is.data.table’
grouped_dt: no visible global function definition for ‘setkeyv’
inner_join.tbl_dt: no visible global function definition for ‘setkeyv’
left_join.tbl_dt: no visible global function definition for ‘setkeyv’
qry_fetch.DBIConnection: no visible global function definition for
‘dbSendQuery’
qry_fetch.DBIConnection: no visible global function definition for
‘dbClearResult’
qry_fetch_paged: no visible global function definition for
‘dbSendQuery’
qry_fetch_paged: no visible global function definition for
‘dbClearResult’
qry_fetch_paged: no visible global function definition for
‘dbHasCompleted’
qry_fields.DBIConnection: no visible global function definition for
‘dbSendQuery’
qry_fields.DBIConnection: no visible global function definition for
‘dbClearResult’
qry_fields.DBIConnection: no visible global function definition for
‘dbGetInfo’
qry_fields.MonetDBConnection: no visible global function definition for
‘dbGetQuery’
qry_run: no visible global function definition for ‘dbBeginTransaction’
qry_run: no visible global function definition for ‘dbCommit’
qry_run: no visible global function definition for ‘dbSendQuery’
qry_run: no visible global function definition for
‘dbSendPreparedQuery’
qry_run: no visible global function definition for ‘dbClearResult’
res_warn_incomplete: no visible global function definition for
‘dbHasCompleted’
res_warn_incomplete: no visible global function definition for
‘dbGetRowCount’
reset.src_sql: no visible global function definition for
‘dbRemoveTable’
select.data.table: no visible global function definition for ‘setnames’
select.grouped_dt: no visible global function definition for ‘setnames’
semi_join.tbl_dt: no visible global function definition for ‘setkeyv’
sql_begin_trans.SQLiteConnection: no visible global function definition
for ‘dbBeginTransaction’
sql_commit.DBIConnection: no visible global function definition for
‘dbCommit’
sql_rollback: no visible global function definition for ‘dbRollback’
src_monetdb: no visible global function definition for ‘MonetDB.R’
src_mysql: no visible global function definition for ‘MySQL’
src_postgres: no visible global function definition for ‘PostgreSQL’
src_sqlite: no visible global function definition for ‘SQLite’
table_fields.DBIConnection: no visible global function definition for
‘dbListFields’
tbl_dt: no visible global function definition for ‘as.data.table’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-osx-x86_64-clang, r-devel-windows-ix86+x86_64
Version: 0.2
Check: examples
Result: ERROR
Running examples in ‘dplyr-Ex.R’ failed
The error most likely occurred in:
> ### Name: tbl_df
> ### Title: Create a data frame tble.
> ### Aliases: tbl_df
>
> ### ** Examples
>
> ds <- tbl_df(mtcars)
> ds
Source: local data frame [32 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
> as.data.frame(ds)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
>
> data("Batting", package = "Lahman")
> batting <- tbl_df(Batting)
> dim(batting)
[1] 97889 24
> colnames(batting)
[1] "playerID" "yearID" "stint" "teamID" "lgID" "G"
[7] "G_batting" "AB" "R" "H" "X2B" "X3B"
[13] "HR" "RBI" "SB" "CS" "BB" "SO"
[19] "IBB" "HBP" "SH" "SF" "GIDP" "G_old"
> head(batting)
Source: local data frame [6 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI SB CS
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0 0 0
Variables not shown: BB (int), SO (int), IBB (int), HBP (int), SH (int), SF
(int), GIDP (int), G_old (int)
>
> # Data manipulation verbs ---------------------------------------------------
> filter(batting, yearID > 2005, G > 130)
Source: local data frame [1,126 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 abreubo01 2007 1 NYA AL 158 158 605 123 171 40 5 16 101
2 abreubo01 2008 1 NYA AL 156 156 609 100 180 39 4 20 100
3 abreubo01 2009 1 LAA AL 152 152 563 96 165 29 3 15 103
4 abreubo01 2010 1 LAA AL 154 154 573 88 146 41 1 20 78
5 abreubo01 2011 1 LAA AL 142 142 502 54 127 30 1 8 60
6 ackledu01 2012 1 SEA AL 153 NA 607 84 137 22 2 12 50
7 alonsyo01 2012 1 SDN NL 155 NA 549 47 150 39 0 9 62
8 altuvjo01 2012 1 HOU NL 147 NA 576 80 167 34 4 7 37
9 alvarpe01 2012 1 PIT NL 149 NA 525 64 128 25 1 30 85
10 amezaal01 2006 1 FLO NL 132 132 334 42 87 9 3 3 19
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> select(batting, playerID:lgID)
Source: local data frame [97,889 x 5]
playerID yearID stint teamID lgID
1 aardsda01 2004 1 SFN NL
2 aardsda01 2006 1 CHN NL
3 aardsda01 2007 1 CHA AL
4 aardsda01 2008 1 BOS AL
5 aardsda01 2009 1 SEA AL
6 aardsda01 2010 1 SEA AL
7 aardsda01 2012 1 NYA AL
8 aaronha01 1954 1 ML1 NL
9 aaronha01 1955 1 ML1 NL
10 aaronha01 1956 1 ML1 NL
.. ... ... ... ... ...
> arrange(batting, playerID, desc(yearID))
Source: local data frame [97,889 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2013 1 NYN NL 43 43 0 0 0 0 0 0 0
2 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
3 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
4 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
5 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
6 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
7 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
8 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
9 aaronha01 1976 1 ML4 AL 85 85 271 22 62 8 0 10 35
10 aaronha01 1975 1 ML4 AL 137 137 465 45 109 16 2 12 60
.. ... ... ... ... ... ... ... ... .. ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> summarise(batting, G = mean(G), n = n())
Source: local data frame [1 x 2]
G n
1 51.65408 97889
> mutate(batting, rbi2 = if(is.null(AB)) 1.0 * R / AB else 0)
Source: local data frame [97,889 x 25]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
7 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
8 aaronha01 1954 1 ML1 NL 122 122 468 58 131 27 6 13 69
9 aaronha01 1955 1 ML1 NL 153 153 602 105 189 37 9 27 106
10 aaronha01 1956 1 ML1 NL 153 153 609 106 200 34 14 26 92
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int), rbi2 (dbl)
>
> # Group by operations -------------------------------------------------------
> # To perform operations by group, create a grouped object with group_by
> players <- group_by(batting, playerID)
> head(group_size(players), 100)
[1] 8 23 7 13 3 4 2 10 6 5 1 3 12 5 11 10 4 1 12 8 1 3 1 5 3
[26] 17 2 3 3 7 10 1 1 19 1 1 5 3 3 9 5 5 10 12 4 3 2 4 3 3
[51] 6 6 4 15 1 6 19 8 1 2 15 1 8 2 1 1 1 1 8 3 1 1 3 2 2
[76] 3 5 10 1 1 3 5 1 14 4 12 5 2 17 2 5 4 1 7 1 6 3 1 2 2
>
> summarise(players, mean_g = mean(G), best_ab = max(AB))
Source: local data frame [18,107 x 3]
playerID mean_g best_ab
1 aardsda01 37.25000 NA
2 aaronha01 143.39130 631
3 aaronto01 62.42857 334
4 aasedo01 34.46154 NA
5 abadan01 5.00000 17
6 abadfe01 31.50000 7
7 abadijo01 6.00000 45
8 abbated01 85.50000 610
9 abbeybe01 13.16667 75
10 abbeych01 90.20000 523
.. ... ... ...
> best_year <- filter(players, AB == max(AB) | G == max(G))
> progress <- mutate(players, cyear = yearID - min(yearID) + 1,
+ rank(desc(AB)), cumsum(AB))
>
> # When you group by multiple level, each summarise peels off one level
> per_year <- group_by(batting, playerID, yearID)
> stints <- summarise(per_year, stints = max(stint))
> # filter(stints, stints > 3)
> # summarise(stints, max(stints))
> # mutate(stints, cumsum(stints))
>
> # Joins ---------------------------------------------------------------------
> data("Master", "HallOfFame", package = "Lahman")
> player_info <- select(tbl_df(Master), playerID, hofID, birthYear)
Error in eval(expr, envir, enclos) : object 'hofID' not found
Calls: select ... select_vars -> select_vars_q -> lapply -> FUN -> eval
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-osx-x86_64-clang, r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86, r-release-linux-ix86, r-release-linux-x86_64
Version: 0.2
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking: ‘RMySQL’ ‘bigrquery’
Flavors: r-devel-osx-x86_64-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: running examples for arch 'i386'
Result: ERROR
Running examples in 'dplyr-Ex.R' failed
The error most likely occurred in:
> ### Name: tbl_df
> ### Title: Create a data frame tble.
> ### Aliases: tbl_df
>
> ### ** Examples
>
> ds <- tbl_df(mtcars)
> ds
Source: local data frame [32 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
> as.data.frame(ds)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
>
> data("Batting", package = "Lahman")
> batting <- tbl_df(Batting)
> dim(batting)
[1] 97889 24
> colnames(batting)
[1] "playerID" "yearID" "stint" "teamID" "lgID" "G"
[7] "G_batting" "AB" "R" "H" "X2B" "X3B"
[13] "HR" "RBI" "SB" "CS" "BB" "SO"
[19] "IBB" "HBP" "SH" "SF" "GIDP" "G_old"
> head(batting)
Source: local data frame [6 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI SB CS
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0 0 0
Variables not shown: BB (int), SO (int), IBB (int), HBP (int), SH (int), SF
(int), GIDP (int), G_old (int)
>
> # Data manipulation verbs ---------------------------------------------------
> filter(batting, yearID > 2005, G > 130)
Source: local data frame [1,126 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 abreubo01 2007 1 NYA AL 158 158 605 123 171 40 5 16 101
2 abreubo01 2008 1 NYA AL 156 156 609 100 180 39 4 20 100
3 abreubo01 2009 1 LAA AL 152 152 563 96 165 29 3 15 103
4 abreubo01 2010 1 LAA AL 154 154 573 88 146 41 1 20 78
5 abreubo01 2011 1 LAA AL 142 142 502 54 127 30 1 8 60
6 ackledu01 2012 1 SEA AL 153 NA 607 84 137 22 2 12 50
7 alonsyo01 2012 1 SDN NL 155 NA 549 47 150 39 0 9 62
8 altuvjo01 2012 1 HOU NL 147 NA 576 80 167 34 4 7 37
9 alvarpe01 2012 1 PIT NL 149 NA 525 64 128 25 1 30 85
10 amezaal01 2006 1 FLO NL 132 132 334 42 87 9 3 3 19
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> select(batting, playerID:lgID)
Source: local data frame [97,889 x 5]
playerID yearID stint teamID lgID
1 aardsda01 2004 1 SFN NL
2 aardsda01 2006 1 CHN NL
3 aardsda01 2007 1 CHA AL
4 aardsda01 2008 1 BOS AL
5 aardsda01 2009 1 SEA AL
6 aardsda01 2010 1 SEA AL
7 aardsda01 2012 1 NYA AL
8 aaronha01 1954 1 ML1 NL
9 aaronha01 1955 1 ML1 NL
10 aaronha01 1956 1 ML1 NL
.. ... ... ... ... ...
> arrange(batting, playerID, desc(yearID))
Source: local data frame [97,889 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2013 1 NYN NL 43 43 0 0 0 0 0 0 0
2 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
3 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
4 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
5 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
6 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
7 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
8 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
9 aaronha01 1976 1 ML4 AL 85 85 271 22 62 8 0 10 35
10 aaronha01 1975 1 ML4 AL 137 137 465 45 109 16 2 12 60
.. ... ... ... ... ... ... ... ... .. ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> summarise(batting, G = mean(G), n = n())
Source: local data frame [1 x 2]
G n
1 51.65408 97889
> mutate(batting, rbi2 = if(is.null(AB)) 1.0 * R / AB else 0)
Source: local data frame [97,889 x 25]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
7 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
8 aaronha01 1954 1 ML1 NL 122 122 468 58 131 27 6 13 69
9 aaronha01 1955 1 ML1 NL 153 153 602 105 189 37 9 27 106
10 aaronha01 1956 1 ML1 NL 153 153 609 106 200 34 14 26 92
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int), rbi2 (dbl)
>
> # Group by operations -------------------------------------------------------
> # To perform operations by group, create a grouped object with group_by
> players <- group_by(batting, playerID)
> head(group_size(players), 100)
[1] 8 23 7 13 3 4 2 10 6 5 1 3 12 5 11 10 4 1 12 8 1 3 1 5 3
[26] 17 2 3 3 7 10 1 1 19 1 1 5 3 3 9 5 5 10 12 4 3 2 4 3 3
[51] 6 6 4 15 1 6 19 8 1 2 15 1 8 2 1 1 1 1 8 3 1 1 3 2 2
[76] 3 5 10 1 1 3 5 1 14 4 12 5 2 17 2 5 4 1 7 1 6 3 1 2 2
>
> summarise(players, mean_g = mean(G), best_ab = max(AB))
Source: local data frame [18,107 x 3]
playerID mean_g best_ab
1 aardsda01 37.25000 NA
2 aaronha01 143.39130 631
3 aaronto01 62.42857 334
4 aasedo01 34.46154 NA
5 abadan01 5.00000 17
6 abadfe01 31.50000 7
7 abadijo01 6.00000 45
8 abbated01 85.50000 610
9 abbeybe01 13.16667 75
10 abbeych01 90.20000 523
.. ... ... ...
> best_year <- filter(players, AB == max(AB) | G == max(G))
> progress <- mutate(players, cyear = yearID - min(yearID) + 1,
+ rank(desc(AB)), cumsum(AB))
>
> # When you group by multiple level, each summarise peels off one level
> per_year <- group_by(batting, playerID, yearID)
> stints <- summarise(per_year, stints = max(stint))
> # filter(stints, stints > 3)
> # summarise(stints, max(stints))
> # mutate(stints, cumsum(stints))
>
> # Joins ---------------------------------------------------------------------
> data("Master", "HallOfFame", package = "Lahman")
> player_info <- select(tbl_df(Master), playerID, hofID, birthYear)
Error in eval(expr, envir, enclos) : object 'hofID' not found
Calls: select ... select_vars -> select_vars_q -> lapply -> FUN -> eval
Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: running examples for arch 'x64'
Result: ERROR
Running examples in 'dplyr-Ex.R' failed
The error most likely occurred in:
> ### Name: tbl_df
> ### Title: Create a data frame tble.
> ### Aliases: tbl_df
>
> ### ** Examples
>
> ds <- tbl_df(mtcars)
> ds
Source: local data frame [32 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
> as.data.frame(ds)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
>
> data("Batting", package = "Lahman")
> batting <- tbl_df(Batting)
> dim(batting)
[1] 97889 24
> colnames(batting)
[1] "playerID" "yearID" "stint" "teamID" "lgID" "G"
[7] "G_batting" "AB" "R" "H" "X2B" "X3B"
[13] "HR" "RBI" "SB" "CS" "BB" "SO"
[19] "IBB" "HBP" "SH" "SF" "GIDP" "G_old"
> head(batting)
Source: local data frame [6 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI SB CS
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0 0 0
Variables not shown: BB (int), SO (int), IBB (int), HBP (int), SH (int), SF
(int), GIDP (int), G_old (int)
>
> # Data manipulation verbs ---------------------------------------------------
> filter(batting, yearID > 2005, G > 130)
Source: local data frame [1,126 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 abreubo01 2007 1 NYA AL 158 158 605 123 171 40 5 16 101
2 abreubo01 2008 1 NYA AL 156 156 609 100 180 39 4 20 100
3 abreubo01 2009 1 LAA AL 152 152 563 96 165 29 3 15 103
4 abreubo01 2010 1 LAA AL 154 154 573 88 146 41 1 20 78
5 abreubo01 2011 1 LAA AL 142 142 502 54 127 30 1 8 60
6 ackledu01 2012 1 SEA AL 153 NA 607 84 137 22 2 12 50
7 alonsyo01 2012 1 SDN NL 155 NA 549 47 150 39 0 9 62
8 altuvjo01 2012 1 HOU NL 147 NA 576 80 167 34 4 7 37
9 alvarpe01 2012 1 PIT NL 149 NA 525 64 128 25 1 30 85
10 amezaal01 2006 1 FLO NL 132 132 334 42 87 9 3 3 19
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> select(batting, playerID:lgID)
Source: local data frame [97,889 x 5]
playerID yearID stint teamID lgID
1 aardsda01 2004 1 SFN NL
2 aardsda01 2006 1 CHN NL
3 aardsda01 2007 1 CHA AL
4 aardsda01 2008 1 BOS AL
5 aardsda01 2009 1 SEA AL
6 aardsda01 2010 1 SEA AL
7 aardsda01 2012 1 NYA AL
8 aaronha01 1954 1 ML1 NL
9 aaronha01 1955 1 ML1 NL
10 aaronha01 1956 1 ML1 NL
.. ... ... ... ... ...
> arrange(batting, playerID, desc(yearID))
Source: local data frame [97,889 x 24]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2013 1 NYN NL 43 43 0 0 0 0 0 0 0
2 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
3 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
4 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
5 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
6 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
7 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
8 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
9 aaronha01 1976 1 ML4 AL 85 85 271 22 62 8 0 10 35
10 aaronha01 1975 1 ML4 AL 137 137 465 45 109 16 2 12 60
.. ... ... ... ... ... ... ... ... .. ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int)
> summarise(batting, G = mean(G), n = n())
Source: local data frame [1 x 2]
G n
1 51.65408 97889
> mutate(batting, rbi2 = if(is.null(AB)) 1.0 * R / AB else 0)
Source: local data frame [97,889 x 25]
playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR RBI
1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0 0
2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0 0
3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0 0
4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0 0
5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0 0
6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0 0
7 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA NA
8 aaronha01 1954 1 ML1 NL 122 122 468 58 131 27 6 13 69
9 aaronha01 1955 1 ML1 NL 153 153 602 105 189 37 9 27 106
10 aaronha01 1956 1 ML1 NL 153 153 609 106 200 34 14 26 92
.. ... ... ... ... ... ... ... ... ... ... ... ... .. ...
Variables not shown: SB (int), CS (int), BB (int), SO (int), IBB (int), HBP
(int), SH (int), SF (int), GIDP (int), G_old (int), rbi2 (dbl)
>
> # Group by operations -------------------------------------------------------
> # To perform operations by group, create a grouped object with group_by
> players <- group_by(batting, playerID)
> head(group_size(players), 100)
[1] 8 23 7 13 3 4 2 10 6 5 1 3 12 5 11 10 4 1 12 8 1 3 1 5 3
[26] 17 2 3 3 7 10 1 1 19 1 1 5 3 3 9 5 5 10 12 4 3 2 4 3 3
[51] 6 6 4 15 1 6 19 8 1 2 15 1 8 2 1 1 1 1 8 3 1 1 3 2 2
[76] 3 5 10 1 1 3 5 1 14 4 12 5 2 17 2 5 4 1 7 1 6 3 1 2 2
>
> summarise(players, mean_g = mean(G), best_ab = max(AB))
Source: local data frame [18,107 x 3]
playerID mean_g best_ab
1 aardsda01 37.25000 NA
2 aaronha01 143.39130 631
3 aaronto01 62.42857 334
4 aasedo01 34.46154 NA
5 abadan01 5.00000 17
6 abadfe01 31.50000 7
7 abadijo01 6.00000 45
8 abbated01 85.50000 610
9 abbeybe01 13.16667 75
10 abbeych01 90.20000 523
.. ... ... ...
> best_year <- filter(players, AB == max(AB) | G == max(G))
> progress <- mutate(players, cyear = yearID - min(yearID) + 1,
+ rank(desc(AB)), cumsum(AB))
>
> # When you group by multiple level, each summarise peels off one level
> per_year <- group_by(batting, playerID, yearID)
> stints <- summarise(per_year, stints = max(stint))
> # filter(stints, stints > 3)
> # summarise(stints, max(stints))
> # mutate(stints, cumsum(stints))
>
> # Joins ---------------------------------------------------------------------
> data("Master", "HallOfFame", package = "Lahman")
> player_info <- select(tbl_df(Master), playerID, hofID, birthYear)
Error in eval(expr, envir, enclos) : object 'hofID' not found
Calls: select ... select_vars -> select_vars_q -> lapply -> FUN -> eval
Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
‘RMySQL’ ‘bigrquery’ ‘microbenchmark’
Flavor: r-patched-solaris-sparc
Version: 0.2
Check: R code for possible problems
Result: NOTE
src_mysql: no visible global function definition for ‘MySQL’
Flavors: r-patched-solaris-sparc, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.2
Check: Rd cross-references
Result: NOTE
Package unavailable to check Rd xrefs: ‘microbenchmark’
Flavor: r-patched-solaris-sparc
Version: 0.2
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
‘RMySQL’ ‘RPostgreSQL’ ‘bigrquery’
Flavor: r-release-osx-x86_64-mavericks
Version: 0.2
Check: R code for possible problems
Result: NOTE
src_mysql: no visible global function definition for ‘MySQL’
src_postgres: no visible global function definition for ‘PostgreSQL’
Flavor: r-release-osx-x86_64-mavericks