I want to do a stratified random sample of panel data. How to do it?
Example:
the most similar situation is the dataset Guns, included in the AER package of "R". it has 51 states, 13 variables over 23 years. Here 2 situations:
how to make a stratified random sample of 40 states?
how to make just a random sample of size=40 states?
I tried with this:
set.seed(2)
samp1=strata(Guns, ("levels(Guns$state)"), size=c(40), method = "srswor")
but an error is returned:
Error in strata(Guns, (levels(Guns$state)), size = c(40), method = "srswor") :
the names of the strata are wrong
THANKS!
For random sample do these simple steps
set.seed(2)
x <- sample(unique(Guns$state), 40)
sample <- Guns[Guns$state %in% x,]
> nrow(Guns)
[1] 1173
> nrow(sample)
[1] 920
920/1173 rows selected
check number of states in sample
> length(unique(sample$state))
[1] 40
For stratified sampling within this sample of 40 States say 50% selection per State, follow this code
library(tidyverse)
set.seed(2)
str_sample <- sample %>% group_by(state) %>%
sample_frac(size = 0.5)
If you'll see 480 rows are selected. Check each stratum size
> table(sample$state)
Alabama Alaska Arizona Arkansas California Colorado Connecticut
23 23 23 0 0 23 0
Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois
23 23 0 23 23 23 23
Indiana Iowa Kansas Kentucky Louisiana Maine Maryland
23 23 23 23 23 23 23
Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska
23 23 23 23 0 23 0
Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota
23 23 0 23 23 23 23
Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota
23 23 23 23 0 23 0
Tennessee Texas Utah Vermont Virginia Washington West Virginia
0 23 23 23 23 23 23
Wisconsin Wyoming
0 23
> table(str_sample$state)
Alabama Alaska Arizona Arkansas California Colorado Connecticut
12 12 12 0 0 12 0
Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois
12 12 0 12 12 12 12
Indiana Iowa Kansas Kentucky Louisiana Maine Maryland
12 12 12 12 12 12 12
Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska
12 12 12 12 0 12 0
Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota
12 12 0 12 12 12 12
Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota
12 12 12 12 0 12 0
Tennessee Texas Utah Vermont Virginia Washington West Virginia
0 12 12 12 12 12 12
Wisconsin Wyoming
0 12
Related
I am trying to merge two dataframes in r, and this error message keeps coming up even though the variable types all should be correct.
Here is my code:
team_info <- baseballr::mlb_teams(season = 2022)
team_info_mlb <- subset(team_info, sport_name == 'Major League Baseball')
tim2 <- team_info_mlb %>%
rename('home_team' = club_name)
tim3 <- subset(tim2, select = c('team_full_name', 'home_team'))
new_pf <- baseballr::fg_park(yr = 2022)
new_pf <- subset(new_pf, select = c('home_team', '1yr'))
info_pf <- merge(tim3, new_pf, by = 'home_team')
The final line is where the problems happen. Let me know if anyone has advice.
The problem is that the data have some fancy class attributes.
> class(tim3)
[1] "baseballr_data" "tbl_df" "tbl" "data.table" "data.frame"
> class(new_pf)
[1] "baseballr_data" "tbl_df" "tbl" "data.table" "data.frame"
Just wrap them in as.data.frame(). Since both data sets have the same by variable you may omit explicit specification.
info_pf <- merge(as.data.frame(tim3), as.data.frame(new_pf))
info_pf
# home_team team_full_name 1yr
# 1 Angels Los Angeles Angels 102
# 2 Astros Houston Astros 99
# 3 Athletics Oakland Athletics 94
# 4 Blue Jays Toronto Blue Jays 106
# 5 Braves Atlanta Braves 105
# 6 Brewers Milwaukee Brewers 102
# 7 Cardinals St. Louis Cardinals 92
# 8 Cubs Chicago Cubs 103
# 9 Diamondbacks Arizona Diamondbacks 103
# 10 Dodgers Los Angeles Dodgers 98
# 11 Giants San Francisco Giants 99
# 12 Guardians Cleveland Guardians 97
# 13 Mariners Seattle Mariners 94
# 14 Marlins Miami Marlins 97
# 15 Mets New York Mets 91
# 16 Nationals Washington Nationals 97
# 17 Orioles Baltimore Orioles 108
# 18 Padres San Diego Padres 96
# 19 Phillies Philadelphia Phillies 98
# 20 Pirates Pittsburgh Pirates 101
# 21 Rangers Texas Rangers 98
# 22 Rays Tampa Bay Rays 89
# 23 Red Sox Boston Red Sox 111
# 24 Reds Cincinnati Reds 112
# 25 Rockies Colorado Rockies 112
# 26 Royals Kansas City Royals 108
# 27 Tigers Detroit Tigers 94
# 28 Twins Minnesota Twins 99
# 29 White Sox Chicago White Sox 100
# 30 Yankees New York Yankees 99
I'm trying to join two datasets together- a dataset from Natural Earth subset to contain only countries in Europe (europe_map) and a list of locations in Europe (europe_places).
Here is the headers of the datasets:
europe_places
Simple feature collection with 23 features and 4 fields
geometry type: POINT
dimension: XY
bbox: xmin: -9.1393 ymin: 38.7223 xmax: 24.1052 ymax: 58.97
geographic CRS: WGS 84
First 10 features:
Location Year Country Continent geometry
1 Paris 2008 France Europe POINT (2.3522 48.8566)
2 Stavanger 2009 Norway Europe POINT (5.7331 58.97)
3 Paris 2009 France Europe POINT (2.3522 48.8566)
4 Berlin 2010 Germany Europe POINT (13.405 52.52)
5 Prague 2011 Czechia Europe POINT (14.4378 50.0755)
6 Piancavallo 2012 Italy Europe POINT (12.5166 46.10768)
7 Budapest 2012 Hungary Europe POINT (19.0402 47.4979)
8 Aprica 2013 Italy Europe POINT (10.15177 46.15486)
9 Vienna 2014 Austria Europe POINT (16.3738 48.2082)
10 Folgaria 2014 Italy Europe POINT (11.17205 45.9162)
europe_map
Simple feature collection with 6 features and 94 fields
geometry type: GEOMETRY
dimension: XY
bbox: xmin: -8.144824 ymin: 41.89756 xmax: 40.12832 ymax: 60.83188
geographic CRS: WGS 84
featurecla scalerank LABELRANK Country SOV_A3 ADM0_DIF LEVEL TYPE
5 Admin-0 country 6 6 Vatican VAT 0 2 Sovereign country
28 Admin-0 country 4 6 United Kingdom GB1 1 2 Country
29 Admin-0 country 4 6 United Kingdom GB1 1 2 Country
30 Admin-0 country 3 6 United Kingdom GB1 1 2 Country
31 Admin-0 country 1 2 United Kingdom GB1 1 2 Country
33 Admin-0 country 1 3 Ukraine UKR 0 2 Sovereign country
ADMIN ADM0_A3 GEOU_DIF GEOUNIT GU_A3 SU_DIF SUBUNIT SU_A3 BRK_DIFF
5 Vatican VAT 0 Vatican VAT 0 Vatican VAT 0
28 Jersey JEY 0 Jersey JEY 0 Jersey JEY 0
29 Guernsey GGY 0 Guernsey GGY 0 Guernsey GGY 0
30 Isle of Man IMN 0 Isle of Man IMN 0 Isle of Man IMN 0
31 United Kingdom GBR 0 United Kingdom GBR 0 United Kingdom GBR 0
33 Ukraine UKR 0 Ukraine UKR 0 Ukraine UKR 0
NAME NAME_LONG BRK_A3 BRK_NAME BRK_GROUP ABBREV POSTAL
5 Vatican Vatican VAT Vatican <NA> Vat. V
28 Jersey Jersey JEY Jersey Channel Islands Jey. JE
29 Guernsey Guernsey GGY Guernsey Channel Islands Guern. GG
30 Isle of Man Isle of Man IMN Isle of Man <NA> IoMan IM
31 United Kingdom United Kingdom GBR United Kingdom <NA> U.K. GB
33 Ukraine Ukraine UKR Ukraine <NA> Ukr. UA
FORMAL_EN FORMAL_FR NAME_CIAWF
5 State of the Vatican City <NA> Holy See (Vatican City)
28 Bailiwick of Jersey <NA> Jersey
29 Bailiwick of Guernsey <NA> Guernsey
30 <NA> <NA> Isle of Man
31 United Kingdom of Great Britain and Northern Ireland <NA> United Kingdom
33 Ukraine <NA> Ukraine
NOTE_ADM0 NOTE_BRK NAME_SORT NAME_ALT MAPCOLOR7 MAPCOLOR8 MAPCOLOR9
5 <NA> <NA> Vatican (Holy See) Holy See 1 3 4
28 U.K. crown dependency <NA> Jersey <NA> 6 6 6
29 U.K. crown dependency <NA> Guernsey <NA> 6 6 6
30 U.K. crown dependency <NA> Isle of Man <NA> 6 6 6
31 <NA> <NA> United Kingdom <NA> 6 6 6
33 <NA> <NA> Ukraine <NA> 5 1 6
MAPCOLOR13 POP_EST POP_RANK GDP_MD_EST POP_YEAR LASTCENSUS GDP_YEAR
5 2 1000 3 0 2015 NA 0
28 3 98840 8 5080 2017 2001 2015
29 3 66502 8 3465 2017 2001 2015
30 3 88815 8 7428 2017 2006 2014
31 3 64769452 16 2788000 2017 2011 2016
33 3 44033874 15 352600 2017 2001 2016
ECONOMY INCOME_GRP WIKIPEDIA FIPS_10_ ISO_A2 ISO_A3
5 2. Developed region: nonG7 2. High income: nonOECD 0 VT VA VAT
28 2. Developed region: nonG7 2. High income: nonOECD NA JE JE JEY
29 2. Developed region: nonG7 2. High income: nonOECD NA GK GG GGY
30 2. Developed region: nonG7 2. High income: nonOECD NA IM IM IMN
31 1. Developed region: G7 1. High income: OECD NA UK GB GBR
33 6. Developing region 4. Lower middle income NA UP UA UKR
ISO_A3_EH ISO_N3 UN_A3 WB_A2 WB_A3 WOE_ID WOE_ID_EH
5 VAT 336 336 <NA> <NA> 23424986 23424986
28 JEY 832 832 JG CHI 23424857 23424857
29 GGY 831 831 JG CHI 23424827 23424827
30 IMN 833 833 IM IMY 23424847 23424847
31 GBR 826 826 GB GBR -90 23424975
33 UKR 804 804 UA UKR 23424976 23424976
WOE_NOTE
5 Exact WOE match as country
28 Exact WOE match as country
29 Exact WOE match as country
30 Exact WOE match as country
31 Eh ID includes Channel Islands and Isle of Man. UK constituent countries of England (24554868), Wales (12578049), Scotland (12578048), and Northern Ireland (20070563).
33 Exact WOE match as country
ADM0_A3_IS ADM0_A3_US ADM0_A3_UN ADM0_A3_WB CONTINENT REGION_UN SUBREGION
5 VAT VAT NA NA Europe Europe Southern Europe
28 JEY JEY NA NA Europe Europe Northern Europe
29 GGY GGY NA NA Europe Europe Northern Europe
30 IMN IMN NA NA Europe Europe Northern Europe
31 GBR GBR NA NA Europe Europe Northern Europe
33 UKR UKR NA NA Europe Europe Eastern Europe
REGION_WB NAME_LEN LONG_LEN ABBREV_LEN TINY HOMEPART MIN_ZOOM MIN_LABEL
5 Europe & Central Asia 7 7 4 4 1 0 5.0
28 Europe & Central Asia 6 6 4 NA NA 0 5.0
29 Europe & Central Asia 8 8 6 NA NA 0 5.0
30 Europe & Central Asia 11 11 5 NA NA 0 5.0
31 Europe & Central Asia 14 14 4 NA 1 0 1.7
33 Europe & Central Asia 7 7 4 NA 1 0 3.0
MAX_LABEL NE_ID WIKIDATAID NAME_AR NAME_BN NAME_DE
5 10.0 1159321407 Q237 الفاتيكان ভ্যাটিকান সিটি Vatikanstadt
28 10.0 1159320725 Q785 جيرزي জার্সি Jersey
29 10.0 1159320715 Q25230 غيرنزي <NA> Guernsey
30 10.0 1159320721 Q9676 جزيرة مان আইল অব ম্যান Isle of Man
31 6.7 1159320713 Q145 المملكة المتحدة যুক্তরাজ্য Vereinigtes Königreich
33 7.0 1159321345 Q212 أوكرانيا ইউক্রেন Ukraine
NAME_EN NAME_ES NAME_FR NAME_EL NAME_HI
5 Vatican City Ciudad del Vaticano Vatican Βατικανό वैटिकन नगर
28 Jersey Jersey Jersey Τζέρσεϊ जर्सी
29 Guernsey Guernsey Guernesey Γκέρνσεϊ ग्वेर्नसे
30 Isle of Man Isla de Man île de Man Νήσος του Μαν मनुष्य का टापू
31 United Kingdom Reino Unido Royaume-Uni Ηνωμένο Βασίλειο यूनाइटेड किंगडम
33 Ukraine Ucrania Ukraine Ουκρανία युक्रेन
NAME_HU NAME_ID NAME_IT NAME_JA NAME_KO
5 Vatikán Vatikan Città del Vaticano バチカン 바티칸 시국
28 Jersey Jersey Baliato di Jersey ジャージー 저지 섬
29 Guernsey Bailiffség Guernsey Guernsey ガーンジー 건지 섬
30 Man Pulau Man Isola di Man マン島 맨 섬
31 Egyesült Királyság Britania Raya Regno Unito イギリス 영국
33 Ukrajna Ukraina Ucraina ウクライナ 우크라이나
NAME_NL NAME_PL NAME_PT NAME_RU NAME_SV
5 Vaticaanstad Watykan Vaticano Ватикан Vatikanstaten
28 Jersey Jersey Jersey Джерси Jersey
29 Guernsey Guernsey Guernsey Гернси Guernsey
30 Man Wyspa Man Ilha de Man остров Мэн Isle of Man
31 Verenigd Koninkrijk Wielka Brytania Reino Unido Великобритания Storbritannien
33 Oekraïne Ukraina Ucrânia Украина Ukraina
NAME_TR NAME_VI NAME_ZH
5 Vatikan Thành Vatican 梵蒂冈
28 Jersey Jersey 澤西島
29 Guernsey Guernsey 根西岛
30 Man Adası Đảo Man 马恩岛
31 Birleşik Krallık Vương quốc Liên hiệp Anh và Bắc Ireland 英国
33 Ukrayna Ukraina 乌克兰
geometry
5 POLYGON ((12.43916 41.89839...
28 POLYGON ((-2.018652 49.2312...
29 POLYGON ((-2.512305 49.4945...
30 POLYGON ((-4.412061 54.1853...
31 MULTIPOLYGON (((-2.667676 5...
33 MULTIPOLYGON (((38.21436 47...
I used the following code to join the datasets together:
europe.map1<-st_join(europe_places, europe_map, by="Country")
But when I did the entries for Venice, Lisbon and Copenhagen had NA values despite the entry for Country containing values that matched those in the europe_map dataset.
Picking up on the comments above, you have not specified the spatial join correctly. I think this is what you are looking for:
europe.map1<- st_join(europe_places, europe_map,
join=st_within, # always best to specify the method
left=TRUE)
This should work for you. That said, you may want to switch the order of europe_places and europe_map. I am not sure about your goal. You can find more information about the different types of spatial joins within the sf package here.
I have a weird data frame where the Player column has the names of the players. The problem is that the first name is shown twice. So Roy Sievers is RoyRoy Sievers, and I want the name to obviously be Roy Sievers.
Would anybody know how to do this?
Here is the full data frame, it's not very long:
Year Player Team Position
1 1949 RoyRoy Sievers St. Louis Browns OF
2 1950 WaltWalt Dropo Boston Red Sox 1B
3 1951 GilGil McDougald New York Yankees 3B
4 1952 HarryHarry Byrd Philadelphia Athletics P
5 1953 HarveyHarvey Kuenn Detroit Tigers SS
6 1954 BobBob Grim New York Yankees P
7 1955 HerbHerb Score Cleveland Indians P
8 1956 LuisLuis Aparicio Chicago White Sox SS
9 1957 TonyTony Kubek New York Yankees SS
10 1958 AlbieAlbie Pearson Washington Senators OF
11 1959 BobBob Allison Washington Senators OF
12 1960 RonRon Hansen Baltimore Orioles SS
13 1961 DonDon Schwall Boston Red Sox P
14 1962 TomTom Tresh New York Yankees SS
15 1963 GaryGary Peters Chicago White Sox P
16 1964 TonyTony Oliva Minnesota Twins OF
17 1965 CurtCurt Blefary Baltimore Orioles OF
18 1966 TommieTommie Agee Chicago White Sox OF
19 1967 RodRod Carew Minnesota Twins 2B
20 1968 StanStan Bahnsen New York Yankees P
21 1969 LouLou Piniella Kansas City Royals OF
22 1970 ThurmanThurman Munson New York Yankees C
23 1971 ChrisChris Chambliss Cleveland Indians 1B
24 1972 CarltonCarlton Fisk Boston Red Sox C
25 1973 AlAl Bumbry Baltimore Orioles OF
26 1974 MikeMike Hargrove Texas Rangers 1B
27 1975 FredFred Lynn Boston Red Sox OF
28 1976 MarkMark Fidrych Detroit Tigers P
29 1977 EddieEddie Murray Baltimore Orioles DH
30 1978 LouLou Whitaker Detroit Tigers 2B
31 1979* JohnJohn Castino Minnesota Twins 3B
32 1979* AlfredoAlfredo Griffin Toronto Blue Jays SS
33 1980 JoeJoe Charboneau Cleveland Indians OF
34 1981 DaveDave Righetti New York Yankees P
35 1982 CalCal Ripken Baltimore Orioles SS
36 1983 RonRon Kittle Chicago White Sox OF
37 1984 AlvinAlvin Davis Seattle Mariners 1B
38 1985 OzzieOzzie Guillén Chicago White Sox SS
39 1986 JoseJose Canseco Oakland Athletics OF
40 1987 MarkMark McGwire Oakland Athletics 1B
41 1988 WaltWalt Weiss Oakland Athletics SS
42 1989 GreggGregg Olson Baltimore Orioles P
43 1990 Sandy Alomar Jr Cleveland Indians C
44 1991 ChuckChuck Knoblauch Minnesota Twins 2B
45 1992 PatPat Listach Milwaukee Brewers SS
46 1993 TimTim Salmon California Angels OF
47 1994 BobBob Hamelin Kansas City Royals DH
48 1995 MartyMarty Cordova Minnesota Twins OF
49 1996 DerekDerek Jeter New York Yankees SS
50 1997 NomarNomar Garciaparra Boston Red Sox SS
51 1998 BenBen Grieve Oakland Athletics OF
52 1999 CarlosCarlos Beltrán Kansas City Royals OF
53 2000 KazuhiroKazuhiro Sasaki Seattle Mariners P
54 2001 IchiroIchiro Suzuki Seattle Mariners OF
55 2002 EricEric Hinske Toronto Blue Jays 3B
56 2003 ÁngelÁngel Berroa Kansas City Royals SS
57 2004 BobbyBobby Crosby Oakland Athletics SS
58 2005 HustonHuston Street Oakland Athletics P
59 2006 JustinJustin Verlander Detroit Tigers P
60 2007 DustinDustin Pedroia Boston Red Sox 2B
61 2008 EvanEvan Longoria Tampa Bay Rays 3B
62 2009 Andrew Bailey Oakland Athletics P
63 2010 NeftalíNeftalí Feliz Texas Rangers P
64 2011 JeremyJeremy Hellickson Tampa Bay Rays P
65 2012 MikeMike Trout Los Angeles Angels OF
66 2013 WilWil Myers Tampa Bay Rays OF
67 2014 JoséJosé Abreu Chicago White Sox 1B
68 2015 CarlosCarlos Correa Houston Astros SS
69 2016 MichaelMichael Fulmer Detroit Tigers P
You can fix this by finding a repeated pattern of at least three letters and replacing it with one copy like this:
gsub("(\\w{3,})\\1", "\\1", Players$Player)
If you want to overwrite the old version, just
Players$Player = gsub("(\\w{3,})\\1", "\\1", Players$Player)
G5W's answer gets you most of the way there, but would miss two-letter first names like "Al". This version relies on capitalization, and not character count:
myData$Player <- gsub('([A-Z][a-z]+)\\1', '\\1', myData$Player)
For the not so regex savvy---
library(stringr)
fun1<-function(string){
g<-str_split(g," ")
h<-str_length(m<-g[[1]][1])
l<-str_sub(m,start = 1,end = h/2)
return(paste(l,g[[1]][2]))
}
fun1(df$Player)
I am trying to create a Sankey diagram in R, using the googleVis package. (The data.frame that I am using can be found below) What I want the diagram to do is go from the Type, to the Organization, then to the team (Tm), while the size represents the number of (Name) players. From what I have read, one can only three columns. I, therefore, did that using this code
BrewersDraft <- sqldf("SELECT Type, Organization, COUNT(Name) AS PLAYERS
FROM df
GROUP BY 1,2
UNION ALL
SELECT Type, (Tm) AS MLB_TEAM, COUNT(Name) AS PLAYERS
FROM df
GROUP BY 1,2")
The data now looks like this:
Type Organization
1 College/University Bradley University (Peoria, IL)
2 College/University California State University Fullerton (Fullerton, CA)
3 College/University Clemson University (Clemson, SC)
4 College/University East Tennessee State University (Johnson City, TN)
5 College/University Faulkner University (Montgomery, AL)
6 College/University Felician College (Lodi, NJ)
PLAYERS
1 1
2 1
3 1
4 1
5 1
6 1
The "Brewers" value is also in the Organization value. Then I used this code to create the Sankey Diagram:
plot(gvisSankey(BrewersDraft, from = "Type", to="Organization_Type", weight = "PLAYERS",
options = list(height=800, width=850,
sankey="{
link:{color:{fill: 'lightblue'}}}")))
The problem is that the Brewers value in the Sankey diagram is with all of the Organization variables when I want the Organization variables to flow to the Brewers variable.
It should look similar to the example on this website, https://thedatagame.com.au/2015/12/14/visualising-the-2015-nba-draft-in-r/
Only difference being that all of the Organization is only going to one team, instead of many.
Can anybody help me? Thank you, it would be much appreciated.
The original data frame.
Year Rnd OvPck RdPck Tm Name Pos
1 2016 1 5 5 Brewers Corey Ray (minors) OF
2 2016 2 46 5 Brewers Lucas Erceg (minors) 3B
3 2016 2 75 34 Brewers Mario Feliciano (minors) C
4 2016 3 82 5 Brewers Braden Webb (minors) RHP
5 2016 4 111 5 Brewers Corbin Burnes (minors) RHP
6 2016 5 141 5 Brewers Zack Brown (minors) RHP
7 2016 6 171 5 Brewers Payton Henry (minors) C
8 2016 7 201 5 Brewers Daniel Brown (minors) LHP
9 2016 8 231 5 Brewers Francisco Thomas (minors) SS
10 2016 9 261 5 Brewers Trey York (minors) 2B
11 2016 10 291 5 Brewers Blake Fox (minors) LHP
12 2016 11 321 5 Brewers Chad McClanahan (minors) 3B
13 2016 12 351 5 Brewers Trever Morrison (minors) SS
14 2016 13 381 5 Brewers Thomas Jankins (minors) RHP
15 2016 14 411 5 Brewers Gabriel Garcia (minors) C
16 2016 15 441 5 Brewers Scott Serigstad (minors) RHP
17 2016 16 471 5 Brewers Louie Crow (minors) RHP
18 2016 17 501 5 Brewers Weston Wilson (minors) 3B
19 2016 18 531 5 Brewers Cooper Hummel (minors) C
20 2016 19 561 5 Brewers Zach Clark (minors) CF
21 2016 20 591 5 Brewers Jared Horn (minors) RHP
22 2016 21 621 5 Brewers Nathan Rodriguez (minors) C
23 2016 22 651 5 Brewers Cam Roegner (minors) LHP
24 2016 23 681 5 Brewers Ronnie Gideon (minors) 1B
25 2016 24 711 5 Brewers Michael Gonzalez (minors) RHP
26 2016 25 741 5 Brewers Blake Lillis (minors) LHP
27 2016 26 771 5 Brewers Nick Roscetti (minors) SS
28 2016 27 801 5 Brewers Nick Cain (minors) RF
29 2016 28 831 5 Brewers Andrew Vernon (minors) RHP
30 2016 29 861 5 Brewers Brennan Price (minors) RHP
31 2016 30 891 5 Brewers Dalton Brown (minors) RHP
32 2016 31 921 5 Brewers Ryan Aguilar (minors) 1B
33 2016 32 951 5 Brewers Wilson Adams (minors) RHP
34 2016 33 981 5 Brewers Emerson Gibbs (minors) RHP
35 2016 34 1011 5 Brewers Matt Smith (minors) RHP
36 2016 35 1041 5 Brewers Chase Williams (minors) RHP
37 2016 36 1071 5 Brewers Parker Bean (minors) RHP
38 2016 37 1101 5 Brewers Jomar Cortes (minors) SS
39 2016 38 1131 5 Brewers Caleb Whalen (minors) CF
40 2016 39 1161 5 Brewers Jose Gomez (minors) CF
41 2016 40 1191 5 Brewers Kyle Serrano (minors) RHP
Type Organization
1 College/University University of Louisville (Louisville, KY)
2 College/University Menlo College (Atherton, CA)
3 High School Carlos Beltran Baseball Academy (Florida, PR)
4 College/University University of South Carolina (Columbia, SC)
5 College/University St. Mary's College of California (Moraga, CA)
6 College/University University of Kentucky (Lexington, KY)
7 High School Pleasant Grove HS (Pleasant Grove, UT)
8 College/University Mississippi State University (Mississippi State, MS)
9 High School Osceola HS (Kissimmee, FL)
10 College/University East Tennessee State University (Johnson City, TN)
11 College/University Rice University (Houston, TX)
12 High School Brophy College Preparatory (Phoenix, AZ)
13 College/University Oregon State University (Corvallis, OR)
14 College/University Quinnipiac College (Hamden, CT)
15 Junior College Broward Community College (Fort Lauderdale, FL)
16 College/University California State University Fullerton (Fullerton, CA)
17 High School Buena Park HS (Buena Park, CA)
18 College/University Clemson University (Clemson, SC)
19 College/University University of Portland (Portland, OR)
20 Junior College Pearl River Community College (Poplarville, MS)
21 High School Vintage HS (Napa, CA)
22 Junior College Cypress College (Cypress, CA)
23 College/University Bradley University (Peoria, IL)
24 College/University Texas A&M University (College Station, TX)
25 High School Norwalk HS (Norwalk, CT)
26 High School St. Thomas Aquinas HS (Overland Park, KS)
27 College/University University of Iowa (Iowa City, IA)
28 College/University Faulkner University (Montgomery, AL)
29 College/University North Carolina Central University (Durham, NC)
30 College/University Felician College (Lodi, NJ)
31 College/University Texas Tech University (Lubbock, TX)
32 College/University University of Arizona (Tucson, AZ)
33 College/University University of Alabama in Huntsville (Huntsville, AL)
34 College/University Tulane University (New Orleans, LA)
35 College/University Georgetown University (Washington, DC)
36 College/University Wichita State University (Wichita, KS)
37 College/University Liberty University (Lynchburg, VA)
38 High School Carlos Beltran Baseball Academy (Florida, PR)
39 College/University University of Portland (Portland, OR)
40 College/University St. Thomas University (Miami Gardens, FL)
41 College/University University of Tennessee (Knoxville, TN)
If I understand correctly you have 3 states: type, organization and team. Type is always the origin, team is the final destination and organization is at first a destination and then an origin.
In the second SQL statement you use "Type" again as the origin, when the origin should be "Organization".
Your SQL has to be modified to look like this:
BrewersDraft <- sqldf("SELECT Type, Organization, COUNT(Name) AS PLAYERS
FROM df
GROUP BY 1,2
UNION ALL
SELECT Organization, (Tm) AS MLB_TEAM, COUNT(Name) AS PLAYERS
FROM df
GROUP BY 1,2")
Here is my data
x i
1 D W MCMILLAN MEMORIAL HOSPITAL AL
2 <NA> AK
3 JOHN C LINCOLN DEER VALLEY HOSPITAL AZ
4 ARKANSAS METHODIST MEDICAL CENTER AR
5 SHERMAN OAKS HOSPITAL CA
6 SKY RIDGE MEDICAL CENTER CO
7 MIDSTATE MEDICAL CENTER CT
8 <NA> DE
9 <NA> DC
10 SOUTH FLORIDA BAPTIST HOSPITAL FL
11 UPSON REGIONAL MEDICAL CENTER GA
12 <NA> HI
13 LOST RIVERS DISTRICT HOSPITAL ID
14 JESSE BROWN VA MEDICAL CENTER - VA CHICAGO HEALTHCARE SYSTEM IL
15 COMMUNITY HOSPITAL IN
16 COVENANT MEDICAL CENTER IA
17 COFFEYVILLE REGIONAL MEDICAL CENTER KS
18 KING'S DAUGHTERS' MEDICAL CENTER KY
19 NORTH OAKS MEDICAL CENTER, LLC LA
20 RUMFORD HOSPITAL ME
21 CIVISTA MEDICAL CENTER MD
22 HEYWOOD HOSPITAL MA
23 GENESYS REGIONAL MEDICAL CENTER - HEALTH PARK MI
24 HEALTHEAST WOODWINDS HOSPITAL MN
25 MARION GENERAL HOSPITAL MS
26 LIBERTY HOSPITAL MO
27 FRANCES MAHON DEACONESS HOSPITAL MT
28 ALEGENT HEALTH MEMORIAL HOSPITAL NE
29 BANNER CHURCHILL COMMUNITY HOSPITAL NV
30 FRANKLIN REGIONAL HOSPITAL NH
31 CAPITAL HEALTH MEDICAL CENTER - HOPEWELL NJ
32 ESPANOLA HOSPITAL NM
33 METROPOLITAN HOSPITAL CENTER NY
34 MEDWEST HAYWOOD NC
35 LISBON AREA HEALTH SERVICES ND
36 CINCINNATI VA MEDICAL CENTER OH
37 JACKSON COUNTY MEMORIAL HOSPITAL OK
38 ST ALPHONSUS MEDICAL CENTER - BAKER CITY, INC OR
39 UPMC PASSAVANT PA
40 HOSPITAL METROPOLITANO DR TITO MATTEI PR
41 <NA> RI
42 PALMETTO HEALTH BAPTIST SC
43 BLACK HILLS SURGICAL HOSPITAL LLP SD
44 INDIAN PATH MEDICAL CENTER TN
45 NIX HEALTH CARE SYSTEM TX
46 BEAR RIVER VALLEY HOSPITAL UT
47 <NA> VT
48 <NA> VI
49 CARILION GILES COMMUNITY HOSPITAL VA
50 SWEDISH MEDICAL CENTER WA
51 PLATEAU MEDICAL CENTER WV
52 ST CROIX REG MED CTR WI
53 POWELL VALLEY HOSPITAL WY
54 <NA> GU
I want to order this list by column i, but for some reason it throws GU at the bottom.
When I run
order(z$i)
(z is my table)
I get this as a result
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
> str(z)
'data.frame': 54 obs. of 2 variables:
$ x: Factor w/ 46 levels "D W MCMILLAN MEMORIAL HOSPITAL",..: 1 NA 2 3 4 5 6 NA NA 7 ...
$ i: Factor w/ 54 levels "AL","AK","AZ",..: 1 2 3 4 5 6 7 8 9 10 ...
Which to me means that it thinks that GU belongs at the bottom of the list. Also there is a problem at the top of the list, AL is before AK and AZ is before AR.
Any suggestion why it would do this?
Thanks
z[order(as.character(z$i)), ]
will do the trick.