Sporcle

Note

https://www.sporcle.com/games/subcategory/mlb

You must show your code!!

Code

library(tidyverse)
library(Lahman)

1 Sporcle Quizzes

1.1 9/10: MLB Teams Last MVP

My solution

Code

# find all active teams (teams that played in 2023)
active_teams <- Teams |>
  filter(yearID == 2023) |>
  select(teamID, name)

# find all MVPs
AwardsPlayers |>
  filter(awardID == "Most Valuable Player") |>
  # find out which team(s) they played for in that year
  left_join(
    Appearances, 
    by = join_by("playerID", "yearID", "lgID")
  ) |>
  # sort them by year
  arrange(desc(yearID)) |>
  group_by(teamID) |>
  # take the first one, since they're already sorted
  summarize(
    playerID = first(playerID),
    yearID = first(yearID)
  ) |>
  # now reduce to only the active teams!
  right_join(active_teams, by = join_by(teamID)) |>
  arrange(name) |>
  # get the players' names
  left_join(People, by = join_by(playerID)) |>
  mutate(Player = paste(nameFirst, nameLast)) |>
  # make the headers match
  select(Team = name, Player, Year = yearID) |>
  knitr::kable()

Team	Player	Year
Arizona Diamondbacks	NA NA	NA
Atlanta Braves	Ronald Acuña	2023
Baltimore Orioles	Cal Ripken	1991
Boston Red Sox	Mookie Betts	2018
Chicago Cubs	Kris Bryant	2016
Chicago White Sox	Jose Abreu	2020
Cincinnati Reds	Joey Votto	2010
Cleveland Guardians	Al Rosen	1953
Colorado Rockies	Larry Walker	1997
Detroit Tigers	Miguel Cabrera	2013
Houston Astros	Jose Altuve	2017
Kansas City Royals	George Brett	1980
Los Angeles Angels of Anaheim	Shohei Ohtani	2023
Los Angeles Dodgers	Cody Bellinger	2019
Miami Marlins	Giancarlo Stanton	2017
Milwaukee Brewers	Christian Yelich	2018
Minnesota Twins	Joe Mauer	2009
New York Mets	NA NA	NA
New York Yankees	Aaron Judge	2024
Oakland Athletics	Miguel Tejada	2002
Philadelphia Phillies	Bryce Harper	2021
Pittsburgh Pirates	Andrew McCutchen	2013
San Diego Padres	Ken Caminiti	1996
San Francisco Giants	Buster Posey	2012
Seattle Mariners	Ichiro Suzuki	2001
St. Louis Cardinals	Paul Goldschmidt	2022
Tampa Bay Rays	NA NA	NA
Texas Rangers	Josh Hamilton	2010
Toronto Blue Jays	Josh Donaldson	2015
Washington Nationals	Bryce Harper	2015

My solution using SQL

Code

SELECT 
  CONCAT(nameFirst, ' ', nameLast) AS Player, 
  t.name, 
  mvp_year AS Year
FROM AwardsPlayers ap
  LEFT JOIN Appearances a ON a.playerID = ap.playerID AND a.yearID = ap.yearID AND a.lgID = ap.lgID
  RIGHT JOIN (
    SELECT a.teamID, MAX(ap.yearID) AS mvp_year
    FROM AwardsPlayers ap
    LEFT JOIN Appearances a ON a.playerID = ap.playerID AND a.yearID = ap.yearID AND a.lgID = ap.lgID
    WHERE ap.awardID = 'Most Valuable Player' 
    GROUP BY a.teamID
    ORDER BY a.teamID
  ) last_mvps ON last_mvps.teamID = a.teamID AND last_mvps.mvp_year = a.yearID
  LEFT JOIN Teams t ON t.teamID = last_mvps.teamID
  LEFT JOIN People p ON p.playerID = a.playerID
WHERE ap.awardID = 'Most Valuable Player' 
  AND t.yearID = 2023
ORDER BY t.name;

1.2 9/15: MLB Top 5 HR Hitters (1970-2009)

My solution

Code

library(tidyverse)
library(Lahman)
Batting |>
  filter(yearID >= 1970 & yearID <= 2009) |>
  group_by(playerID, yearID) |>
  summarize(
    teamIDs = paste(unique(teamID), collapse = "/"),
    tHR = sum(HR)
  ) |>
  left_join(People, by = join_by(playerID)) |>
  mutate(player_name = paste(nameLast, nameFirst)) |>
  select(yearID, teamIDs, player_name, tHR) |>
  ungroup() |>
  group_by(yearID) |>
  group_split() |>
  map(~arrange(.x, desc(tHR))) |>
  map(head, n = 5) |>
  bind_rows() |>
  arrange(desc(yearID), desc(tHR)) |>
  select(-playerID) |>
  knitr::kable()

`summarise()` has grouped output by 'playerID'. You can override using the
`.groups` argument.
Adding missing grouping variables: `playerID`

yearID	teamIDs	player_name	tHR
2009	SLN	Pujols Albert	47
2009	MIL	Fielder Prince	46
2009	PHI	Howard Ryan	45
2009	ARI	Reynolds Mark	44
2009	SDN	Gonzalez Adrian	40
2008	PHI	Howard Ryan	48
2008	ARI/CIN	Dunn Adam	40
2008	NYN	Delgado Carlos	38
2008	MIL	Braun Ryan	37
2008	DET	Cabrera Miguel	37
2007	NYA	Rodriguez Alex	54
2007	MIL	Fielder Prince	50
2007	PHI	Howard Ryan	47
2007	TBA	Peña Carlos	46
2007	CIN	Dunn Adam	40
2006	PHI	Howard Ryan	58
2006	BOS	Ortiz David	54
2006	SLN	Pujols Albert	49
2006	WAS	Soriano Alfonso	46
2006	HOU	Berkman Lance	45
2005	ATL	Jones Andruw	51
2005	NYA	Rodriguez Alex	48
2005	BOS	Ortiz David	47
2005	CHN	Lee Derrek	46
2005	BOS	Ramirez Manny	45
2004	LAN	Beltre Adrian	48
2004	CIN	Dunn Adam	46
2004	SLN	Pujols Albert	46
2004	SFN	Bonds Barry	45
2004	BOS	Ramirez Manny	43
2003	TEX	Rodriguez Alex	47
2003	PHI	Thome Jim	47
2003	SFN	Bonds Barry	45
2003	MIL	Sexson Richie	45
2003	ATL	Lopez Javy	43
2002	TEX	Rodriguez Alex	57
2002	CLE	Thome Jim	52
2002	CHN	Sosa Sammy	49
2002	SFN	Bonds Barry	46
2002	TEX	Palmeiro Rafael	43
2001	SFN	Bonds Barry	73
2001	CHN	Sosa Sammy	64
2001	ARI	Gonzalez Luis	57
2001	TEX	Rodriguez Alex	52
2001	LAN	Green Shawn	49
2000	CHN	Sosa Sammy	50
2000	SFN	Bonds Barry	49
2000	HOU	Bagwell Jeff	47
2000	ANA	Glaus Troy	47
2000	MON	Guerrero Vladimir	44
1999	SLN	McGwire Mark	65
1999	CHN	Sosa Sammy	63
1999	SEA	Griffey Ken	48
1999	TEX	Palmeiro Rafael	47
1999	ATL	Jones Chipper	45
1998	SLN	McGwire Mark	70
1998	CHN	Sosa Sammy	66
1998	SEA	Griffey Ken	56
1998	SDN	Vaughn Greg	50
1998	CHA	Belle Albert	49
1997	OAK/SLN	McGwire Mark	58
1997	SEA	Griffey Ken	56
1997	COL	Walker Larry	49
1997	NYA	Martinez Tino	44
1997	HOU	Bagwell Jeff	43
1996	OAK	McGwire Mark	52
1996	BAL	Anderson Brady	50
1996	SEA	Griffey Ken	49
1996	CLE	Belle Albert	48
1996	COL	Galarraga Andres	47
1995	CLE	Belle Albert	50
1995	COL	Bichette Dante	40
1995	SEA	Buhner Jay	40
1995	CHA	Thomas Frank	40
1995	OAK	McGwire Mark	39
1994	SFN	Williams Matt	43
1994	SEA	Griffey Ken	40
1994	HOU	Bagwell Jeff	39
1994	CHA	Thomas Frank	38
1994	SFN	Bonds Barry	37
1993	SFN	Bonds Barry	46
1993	TEX	Gonzalez Juan	46
1993	SEA	Griffey Ken	45
1993	CHA	Thomas Frank	41
1993	ATL	Justice David	40
1992	TEX	Gonzalez Juan	43
1992	OAK	McGwire Mark	42
1992	DET	Fielder Cecil	35
1992	SDN	McGriff Fred	35
1992	CLE	Belle Albert	34
1991	OAK	Canseco Jose	44
1991	DET	Fielder Cecil	44
1991	NYN	Johnson Howard	38
1991	BAL	Ripken Cal	34
1991	SFN	Williams Matt	34
1990	DET	Fielder Cecil	51
1990	CHN	Sandberg Ryne	40
1990	OAK	McGwire Mark	39
1990	OAK	Canseco Jose	37
1990	NYN	Strawberry Darryl	37
1989	SFN	Mitchell Kevin	47
1989	NYN	Johnson Howard	36
1989	TOR	McGriff Fred	36
1989	CLE	Carter Joe	35
1989	CIN	Davis Eric	34
1988	OAK	Canseco Jose	42
1988	NYN	Strawberry Darryl	39
1988	TOR	McGriff Fred	34
1988	OAK	McGwire Mark	32
1988	HOU	Davis Glenn	30
1987	CHN	Dawson Andre	49
1987	OAK	McGwire Mark	49
1987	TOR	Bell George	47
1987	ATL	Murphy Dale	44
1987	NYN	Strawberry Darryl	39
1986	TOR	Barfield Jesse	40
1986	PHI	Schmidt Mike	37
1986	OAK	Kingman Dave	35
1986	MIN	Gaetti Gary	34
1986	OAK	Canseco Jose	33
1985	DET	Evans Darrell	40
1985	CHA	Fisk Carlton	37
1985	ATL	Murphy Dale	37
1985	KCA	Balboni Steve	36
1985	NYA	Mattingly Don	35
1984	BOS	Armas Tony	43
1984	ATL	Murphy Dale	36
1984	PHI	Schmidt Mike	36
1984	OAK	Kingman Dave	35
1984	OAK	Murphy Dwayne	33
1983	PHI	Schmidt Mike	40
1983	BOS	Rice Jim	39
1983	BOS	Armas Tony	36
1983	ATL	Murphy Dale	36
1983	CHA	Kittle Ron	35
1982	CAL	Jackson Reggie	39
1982	ML4	Thomas Gorman	39
1982	NYN	Kingman Dave	37
1982	NYA	Winfield Dave	37
1982	ATL	Murphy Dale	36
1981	PHI	Schmidt Mike	31
1981	MON	Dawson Andre	24
1981	OAK	Armas Tony	22
1981	BOS	Evans Dwight	22
1981	CIN	Foster George	22
1980	PHI	Schmidt Mike	48
1980	NYA	Jackson Reggie	41
1980	ML4	Oglivie Ben	41
1980	ML4	Thomas Gorman	38
1980	OAK	Armas Tony	35
1979	CHN	Kingman Dave	48
1979	PHI	Schmidt Mike	45
1979	ML4	Thomas Gorman	45
1979	BOS	Lynn Fred	39
1979	BOS	Rice Jim	39
1978	BOS	Rice Jim	46
1978	CIN	Foster George	40
1978	PHI	Luzinski Greg	35
1978	CAL	Baylor Don	34
1978	ML4	Hisle Larry	34
1977	CIN	Foster George	52
1977	ATL	Burroughs Jeff	41
1977	PHI	Luzinski Greg	39
1977	BOS	Rice Jim	39
1977	PHI	Schmidt Mike	38
1976	PHI	Schmidt Mike	38
1976	NYN	Kingman Dave	37
1976	CHN	Monday Rick	32
1976	NYA	Nettles Graig	32
1976	CIN	Foster George	29
1975	PHI	Schmidt Mike	38
1975	OAK	Jackson Reggie	36
1975	NYN	Kingman Dave	36
1975	ML4	Scott George	36
1975	PHI	Luzinski Greg	34
1974	PHI	Schmidt Mike	36
1974	CIN	Bench Johnny	33
1974	CHA	Allen Dick	32
1974	LAN	Wynn Jim	32
1974	OAK	Jackson Reggie	29
1973	PIT	Stargell Willie	44
1973	ATL	Johnson Davey	43
1973	ATL	Evans Darrell	41
1973	ATL	Aaron Hank	40
1973	SFN	Bonds Bobby	39
1972	CIN	Bench Johnny	40
1972	SDN	Colbert Nate	38
1972	CHA	Allen Dick	37
1972	CHN	Williams Billy	37
1972	ATL	Aaron Hank	34
1971	PIT	Stargell Willie	48
1971	ATL	Aaron Hank	47
1971	CIN	May Lee	39
1971	PHI	Johnson Deron	34
1971	SFN	Bonds Bobby	33
1970	CIN	Bench Johnny	45
1970	WS2	Howard Frank	44
1970	CHN	Williams Billy	42
1970	MIN	Killebrew Harmon	41
1970	CIN	Perez Tony	40

My (partial) solution using SQL

Code

SELECT 
  CONCAT(ANY_VALUE(p.nameFirst), ' ', ANY_VALUE(p.nameLast)) AS Player, 
  yearID, 
  GROUP_CONCAT(DISTINCT teamID) AS teamIDs, 
  SUM(HR) AS tHR
FROM Batting b
LEFT JOIN People p ON p.playerID = b.playerID
WHERE yearID >= 1970 AND yearID < 2010
  AND yearID = 2009
GROUP BY b.playerID, yearID
ORDER BY tHR DESC
LIMIT 5;

1.3 9/17: MLB 40-HRs by 4-Letter Players

My solution

Code

Batting |>
  group_by(playerID, yearID) |>
  summarize(
    tHR = sum(HR)
  ) |>
  filter(tHR >= 40) |>
  group_by(playerID) |>
  summarize(
    n = n(),
    last = max(yearID)
  ) |>
  arrange(desc(n)) |>
  left_join(People, by = join_by(playerID)) |>
  filter(nchar(nameLast) <= 4) |>
  select(n, last, nameLast, nameFirst) |>
  knitr::kable()

`summarise()` has grouped output by 'playerID'. You can override using the
`.groups` argument.

n	last	nameLast	nameFirst
11	1932	Ruth	Babe
7	2003	Sosa	Sammy
6	2012	Dunn	Adam
6	1965	Mays	Willie
5	1938	Foxx	Jimmie
4	2019	Cruz	Nelson
3	1948	Mize	Johnny
1	1987	Bell	George
1	1961	Cash	Norm
1	2006	Dye	Jermaine
1	2005	Lee	Derrek
1	1929	Ott	Mel
1	2007	Peña	Carlos
1	1955	Post	Wally
1	1978	Rice	Jim
1	2024	Soto	Juan

My solution using SQL

Code

SELECT 
  COUNT(DISTINCT yearID) AS N, 
  MAX(yearID) AS most_recent, 
  ANY_VALUE(p.nameLast) AS last_name
FROM
  (SELECT playerID, yearID, SUM(HR) AS tHR
  FROM Batting
  GROUP BY playerID, yearID
  HAVING SUM(HR) >= 40) AS hr40
LEFT JOIN People p ON p.playerID = hr40.playerID
GROUP BY hr40.playerID
HAVING LEN(ANY_VALUE(p.nameLast)) <= 4
ORDER BY N DESC;

1.4 9/22: Multiple Cy Young Winners

My solution

Code

AwardsPlayers |>
  filter(str_detect(awardID, "Cy Young")) |>
  group_by(playerID) |>
  summarize(
    n = n(),
    years = paste(yearID, collapse = ", ")
  ) |>
  filter(n > 1) |>
  arrange(n)

# A tibble: 22 × 3
   playerID      n years     
   <chr>     <int> <chr>     
 1 degroja01     2 2018, 2019
 2 gibsobo01     2 1968, 1970
 3 glavito02     2 1991, 1998
 4 hallaro01     2 2003, 2010
 5 klubeco01     2 2014, 2017
 6 linceti01     2 2008, 2009
 7 mclaide01     2 1968, 1969
 8 perryga01     2 1972, 1978
 9 saberbr01     2 1985, 1989
10 santajo01     2 2004, 2006
# ℹ 12 more rows

My solution using SQL

Code

SELECT playerID, COUNT(*) AS num_awards, GROUP_CONCAT(yearID) AS years
FROM AwardsPlayers
WHERE awardID = 'Cy Young Award'
GROUP BY playerID
HAVING COUNT(*) > 1
ORDER BY COUNT(*);

1.5 9/24: MLB Win Leaders by Team

My solution

Code

Pitching |>
  group_by(playerID, teamID) |>
  summarize(wins = sum(W)) |>
  group_by(teamID) |>
  group_split() |>
  map(arrange, desc(wins)) |>
  map(slice_head, n = 1) |>
  bind_rows() |>
  right_join(
    Teams |>
      filter(yearID == 2023) |>
      select(teamID, name),
    by = join_by(teamID)
  ) |>
  arrange(name) |>
  knitr::kable()

`summarise()` has grouped output by 'playerID'. You can override using the
`.groups` argument.

playerID	teamID	wins	name
johnsra05	ARI	118	Arizona Diamondbacks
niekrph01	ATL	266	Atlanta Braves
palmeji01	BAL	268	Baltimore Orioles
clemero02	BOS	192	Boston Red Sox
rootch01	CHN	201	Chicago Cubs
lyonste01	CHA	260	Chicago White Sox
rixeyep01	CIN	179	Cincinnati Reds
fellebo01	CLE	266	Cleveland Guardians
delarjo01	COL	86	Colorado Rockies
daussho01	DET	222	Detroit Tigers
niekrjo01	HOU	144	Houston Astros
splitpa01	KCA	166	Kansas City Royals
weaveje02	LAA	150	Los Angeles Angels of Anaheim
suttodo01	LAN	233	Los Angeles Dodgers
alcansa01	MIA	41	Miami Marlins
gallayo01	MIL	89	Milwaukee Brewers
kaatji01	MIN	189	Minnesota Twins
seaveto01	NYN	198	New York Mets
fordwh01	NYA	236	New York Yankees
hunteca01	OAK	131	Oakland Athletics
carltst01	PHI	241	Philadelphia Phillies
coopewi01	PIT	202	Pittsburgh Pirates
shower01	SDN	100	San Diego Padres
maricju01	SFN	238	San Francisco Giants
hernafe02	SEA	169	Seattle Mariners
gibsobo01	SLN	251	St. Louis Cardinals
shielja02	TBA	87	Tampa Bay Rays
houghch01	TEX	139	Texas Rangers
stiebda01	TOR	175	Toronto Blue Jays
strasst01	WAS	113	Washington Nationals

1.6 9/24: MLB Most Wins by Decade

My solution

1.7 9/24: MLB Home Run Leaders (A-Z)

My solution

2 Appendix

2.1 How to create a SQL duckdb database from the Lahman package

2.1.1 Educate yourself

Read R for Data Science, Ch. 21
See also Modern Data Science with R, Ch. 15

2.1.2 Instantiate the database

Create a duckdb instance

Code

library(dbplyr)
library(duckdb)
con <- DBI::dbConnect(duckdb::duckdb())

2.1.3 Populate the database

Start populating it with data from the Lahman package.

Code

# Have to do this for every table...
dbWriteTable(con, "Teams", Lahman::Teams)
dbWriteTable(con, "People", Lahman::People)
dbWriteTable(con, "Appearances", Lahman::Appearances)
dbWriteTable(con, "AwardsPlayers", Lahman::AwardsPlayers)
dbWriteTable(con, "Batting", Lahman::Batting)
dbWriteTable(con, "TeamsFranchises", Lahman::TeamsFranchises)

Tip

Challenge: Use a map() statement to do them all!

2.1.4 Step 4: Inpsect the database

Code

dbListTables(con)

[1] "Appearances"     "AwardsPlayers"   "Batting"         "People"         
[5] "Teams"           "TeamsFranchises"

2.1.5 Access

Now, you can access the data in your duckdb SQL database in several ways.

2.1.5.1 Using `dbplyr`

Code

teams <- tbl(con, "Teams")

active_teams <- teams |>
  filter(yearID == 2023) |>
  select(teamID, name)

active_teams

# Source:   SQL [?? x 2]
# Database: DuckDB 1.4.0 [unknown@Linux 6.11.0-1018-azure:R 4.5.1/:memory:]
   teamID name                         
   <fct>  <chr>                        
 1 LAA    Los Angeles Angels of Anaheim
 2 ARI    Arizona Diamondbacks         
 3 ATL    Atlanta Braves               
 4 BAL    Baltimore Orioles            
 5 BOS    Boston Red Sox               
 6 CHN    Chicago Cubs                 
 7 CHA    Chicago White Sox            
 8 CIN    Cincinnati Reds              
 9 CLE    Cleveland Guardians          
10 COL    Colorado Rockies             
# ℹ more rows

And you can see the translated SQL query!

Code

active_teams |>
  show_query()

<SQL>
SELECT teamID, "name"
FROM Teams
WHERE (yearID = 2023.0)

2.1.5.2 Using `DBI`

If you want to practice writing the raw SQL query

Code

con |>
  dbGetQuery(
"
SELECT teamID, name
FROM teams
WHERE yearID = 2023;
"
)

   teamID                          name
1     LAA Los Angeles Angels of Anaheim
2     ARI          Arizona Diamondbacks
3     ATL                Atlanta Braves
4     BAL             Baltimore Orioles
5     BOS                Boston Red Sox
6     CHN                  Chicago Cubs
7     CHA             Chicago White Sox
8     CIN               Cincinnati Reds
9     CLE           Cleveland Guardians
10    COL              Colorado Rockies
11    DET                Detroit Tigers
12    MIA                 Miami Marlins
13    HOU                Houston Astros
14    KCA            Kansas City Royals
15    LAN           Los Angeles Dodgers
16    MIL             Milwaukee Brewers
17    MIN               Minnesota Twins
18    NYN                 New York Mets
19    NYA              New York Yankees
20    OAK             Oakland Athletics
21    PHI         Philadelphia Phillies
22    PIT            Pittsburgh Pirates
23    SDN              San Diego Padres
24    SEA              Seattle Mariners
25    SFN          San Francisco Giants
26    SLN           St. Louis Cardinals
27    TBA                Tampa Bay Rays
28    TEX                 Texas Rangers
29    TOR             Toronto Blue Jays
30    WAS          Washington Nationals

2.1.5.3 Using Quarto/Markdown

If you want SQL syntax highlighting, and to use the data back in R, make an SQL chunk

Code

```{sql}
#| connection: con
#| output.var: "teams_sql"
SELECT teamID, name
FROM teams
WHERE yearID = 2023;
```

Code

```{r}
teams_sql
```

   teamID                          name
1     LAA Los Angeles Angels of Anaheim
2     ARI          Arizona Diamondbacks
3     ATL                Atlanta Braves
4     BAL             Baltimore Orioles
5     BOS                Boston Red Sox
6     CHN                  Chicago Cubs
7     CHA             Chicago White Sox
8     CIN               Cincinnati Reds
9     CLE           Cleveland Guardians
10    COL              Colorado Rockies
11    DET                Detroit Tigers
12    MIA                 Miami Marlins
13    HOU                Houston Astros
14    KCA            Kansas City Royals
15    LAN           Los Angeles Dodgers
16    MIL             Milwaukee Brewers
17    MIN               Minnesota Twins
18    NYN                 New York Mets
19    NYA              New York Yankees
20    OAK             Oakland Athletics
21    PHI         Philadelphia Phillies
22    PIT            Pittsburgh Pirates
23    SDN              San Diego Padres
24    SEA              Seattle Mariners
25    SFN          San Francisco Giants
26    SLN           St. Louis Cardinals
27    TBA                Tampa Bay Rays
28    TEX                 Texas Rangers
29    TOR             Toronto Blue Jays
30    WAS          Washington Nationals

1 Sporcle Quizzes

1.1 9/10: MLB Teams Last MVP

1.2 9/15: MLB Top 5 HR Hitters (1970-2009)

1.3 9/17: MLB 40-HRs by 4-Letter Players

1.4 9/22: Multiple Cy Young Winners

1.5 9/24: MLB Win Leaders by Team

1.6 9/24: MLB Most Wins by Decade

1.7 9/24: MLB Home Run Leaders (A-Z)

2 Appendix

2.1 How to create a SQL duckdb database from the Lahman package

2.1.1 Educate yourself

2.1.2 Instantiate the database

2.1.3 Populate the database

2.1.4 Step 4: Inpsect the database

2.1.5 Access

2.1.5.1 Using dbplyr

2.1.5.2 Using DBI

2.1.5.3 Using Quarto/Markdown

2.1.5.1 Using `dbplyr`

2.1.5.2 Using `DBI`