This question already has an answer here:
Using Reshape from wide to long in R [closed]
(1 answer)
Closed 2 years ago.
I'm trying to calculate the total number of matches played by each team in the year 2019 and put them in a table along with the corresponding team names
teams<-c("Sunrisers Hyderabad", "Mumbai Indians", "Gujarat Lions", "Rising Pune Supergiants",
"Royal Challengers Bangalore","Kolkata Knight Riders","Delhi Daredevils",
"Kings XI Punjab", "Deccan Chargers","Rajasthan Royals", "Chennai Super Kings",
"Kochi Tuskers Kerala", "Pune Warriors", "Delhi Capitals", " Gujarat Lions")
for (j in teams) {
print(j)
ipl_table %>%
filter(season==2019 & (team1==j | team2 ==j)) %>%
summarise(match_count=n())->kl
print(kl)
match_played<-data.frame(Teams=teams,Match_count=kl)
}
The match played by last team (i.e Gujarat Lions is 0 and its filling 0's for all other teams as well.
The output match_played can be found on the link given below.
I'd be really glad if someone could help me regarding this error as I'm very new to R.
filter for the particular season, get data in long format and then count number of matches.
library(dplyr)
matches %>%
filter(season == 2019) %>%
tidyr::pivot_longer(cols = c(team1, team2), values_to = 'team_name') %>%
count(team_name) -> result
result
# team_name n
# <chr> <int>
#1 Chennai Super Kings 17
#2 Delhi Capitals 16
#3 Kings XI Punjab 14
#4 Kolkata Knight Riders 14
#5 Mumbai Indians 16
#6 Rajasthan Royals 14
#7 Royal Challengers Bangalore 14
#8 Sunrisers Hyderabad 15
Here is an example
library(tidyr)
df_2019 <- matches[matches$season == 2019, ] # get the season you need
df_long <- gather(df_2019, Team_id, Team_Name, team1:team2) # Make it long format
final_count <- data.frame(t(table(df_long$Team_Name)))[-1] # count the number of matches
names(final_count) <- c("Team", "Matches")
Team Matches
1 Chennai Super Kings 17
2 Delhi Capitals 16
3 Kings XI Punjab 14
4 Kolkata Knight Riders 14
5 Mumbai Indians 16
6 Rajasthan Royals 14
7 Royal Challengers Bangalore 14
8 Sunrisers Hyderabad 15
Or by using base R
final_count <- data.frame(t(table(c(df_2019$team1, df_2019$team2))))[-1]
names(final_count) <- c("Team", "Matches")
final_count
Related
nubie here with a dataframe/mutate question... I want to update a dataframe (df1) based on data in another dataframe (df2). For one offs I've used MUTATE so I figure this is the way to go. Additionally I would like a check function added (TRUE/FALSE ?) to indicate if the the field in df1 was updated.
For Example..
df1-
State
<chr>
1 N.Y.
2 FL
3 AL
4 MS
5 IL
6 WS
7 WA
8 N.J.
9 N.D.
10 S.D.
11 CALL
df2
State New_State
<chr> <chr>
1 N.Y. New York
2 FL Florida
3 AL Alabama
4 MS Mississippi
5 IL Illinois
6 WS Wisconsin
7 WA Washington
8 N.J. New Jersey
9 N.D. North Dakota
10 S.D. South Dakota
11 CAL California
I want the output to look like this
df3
New_State Test
<chr>
1 New York TRUE
2 Florida TRUE
3 Alabama TRUE
4 Mississippi TRUE
5 Illinois TRUE
6 Wisconsin TRUE
7 Washington TRUE
8 New Jersey TRUE
9 North Dakota TRUE
10 South Dakota TRUE
11 CALL FALSE
In essence I want R to read the data in df1 and change df1 based on the match in df2 chaining out to the full state name and replace. Lastly if the data in df1 was update mark as "TRUE" (N.Y. to NEW YORK) and "FALSE" if not updated (CALL vs CAL)
Thanks in advance for any and all help.
This should give you the result you're looking for:
match_vec <- match(df1$State, table = df2$State)
This vector should match all the abbreviated state names in df1 with those in df2. Where there's no match, you end up with a missing value:
Then the following code using dplyr should produce the df3 you requested.
library(dplyr)
df3 <- df1 %>%
mutate(New_State = df2$New_State[match_vec]) %>%
mutate(Test = !is.na(match_vec)) %>%
mutate(New_State = ifelse(is.na(New_State),
State, New_State)) %>%
select(New_State, Test)
I have a df of 17 variables (my samples) with the condition location which I would like to plot based on a single gene "photosystem II protein D1 1"
View(metadata)
sample location
<chr> <chr>
1 X1344 West
2 X1345 West
3 X1365 West
4 X1366 West
5 X1367 West
6 X1419 West
7 X1420 West
8 X1421 West
9 X1473 Mid
10 X1475 Mid
11 X1528 Mid
12 X1584 East
13 X1585 East
14 X1586 East
15 X1678 East
16 X1679 East
17 X1680 East
View(countdata)
func X1344 X1345 X1365 X1366 X1367 X1419 X1420 X1421 X1473 X1475 X1528 X1584 X1585 X1586 X1678 X1679 X1680
photosystem II protein D1 1 11208 6807 3483 4091 12198 7229 7404 5606 6059 7456 4007 2514 5709 2424 2346 4447 5567
countdata contains thousands of genes but I am only showing the headers and gene of interest
ddsMat has been created like this:
ddsMat <- DESeqDataSetFromMatrix(countData = countdata,
colData = metadata,
design = ~ location)
When plotting:
library(DeSeq2)
plotCounts(ddsMat, "photosystem II protein D1 1", intgroup=c("location"))
By default, the function plots the "conditions" alphabetically eg: East-Mid-West. But I would like to order them so I can see them on the graph West-Mid-East.
Check plotCountsIMAGEhere
Is there a way of doing this?
Thanks,
I have found that you can manually change the order like this:
ddsMat$location <- factor(ddsMat$location, levels=c("West", "Mid", "East"))
I have dataset as follow
EstablishmentName Freq
bahria university 20
bahria university islamabad 12
arid agriculture 3
arid agriculture university 15
arid rawalpindi 9
college of e&me, nust 20
college of e & me (nust) 15
college of eme 30
As you can see above that Bahria University and Bahria University Islamabad are almost same, so goes for other strings. I want to unify them into one such that
Expected Output
EstablishmentName Freq
Bahria University 32
Arid Agriculture 27
College of EME 30
I have tried the following solution but it doesn't seems to work.
library(SnowballC)
library(dplyr)
mutate(df, word = wordStem(EstablishmentName)) %>%
group_by(EstablishmentName) %>%
summarise(total = sum(Freq))
This question already has answers here:
Arrange a grouped_df by group variable not working
(2 answers)
Closed 6 years ago.
This should be easy, but I can't find a straight forward way to achieve it. My dataset looks like the following:
DisplayName Nationality Gender Startyear
1 Alfred H. Barr, Jr. American Male 1929
2 Paul C\216zanne French Male 1929
3 Paul Gauguin French Male 1929
4 Vincent van Gogh Dutch Male 1929
5 Georges-Pierre Seurat French Male 1929
6 Charles Burchfield American Male 1929
7 Charles Demuth American Male 1929
8 Preston Dickinson American Male 1929
9 Lyonel Feininger American Male 1929
10 George Overbury ("Pop") Hart American Male 1929
...
I want to group by DisplayName and Gender, and get the counts for for each of the names (they are repeated several times on the list, with different year information).
The following 2 commands give me the same output, but they are not sorted by the count output "n". Any ideas on how to achieve this?
artists <- data %>%
filter(!is.na(Gender) & Gender != "NULL") %>%
group_by(DisplayName, Gender) %>%
tally(sort = T) %>%
arrange(desc(n))
artists <- data %>%
filter(!is.na(Gender) & Gender != "NULL") %>%
count(DisplayName, Gender, sort = T)
DisplayName Gender n
(chr) (chr) (int)
1 A. F. Sherman Male 1
2 A. G. Fronzoni Male 2
3 A. Lawrence Kocher Male 3
4 A. M. Cassandre Male 21
5 A. R. De Ycaza Female 1
6 A.R. Penck (Ralf Winkler) Male 20
7 Aaron Siskind Male 25
8 Abigail Perlmutter Female 1
9 Abraham Rattner Male 5
10 Abraham Walkowitz Male 17
.. ... ... ...
Your data is grouped by two variables. So after tally, your dataframe is still grouped by Display name. So arrange(desc(n)) is sorting but by Disply name. If you want to sort the all dataframe by column n, just ungroup before sorting. try this :
artists <- data %>%
filter(!is.na(Gender) & Gender != "NULL") %>%
group_by(DisplayName, Gender) %>%
tally(sort = T) %>%
ungroup() %>%
arrange(desc(n))
I am working with data in the following form:
Country Player Goals
"USA" "Tim" 0
"USA" "Tim" 0
"USA" "Dempsey" 3
"USA" "Dempsey" 5
"Brasil" "Neymar" 6
"Brasil" "Neymar" 2
"Brasil" "Hulk" 5
"Brasil" "Luiz" 2
"England" "Rooney" 4
"England" "Stewart" 2
Each row represents the number of goals that a player scored per game, and also contains that player's country. I would like to have the data in the form such that I can run pairwise correlations to see whether being from the same country has some association with the number of goals that a player scores. The data would look like this:
Player_1 Player_2
0 8 # Tim Dempsey
8 5 # Neymar Hulk
8 2 # Neymar Luiz
5 2 # Hulk Luiz
4 2 # Rooney Stewart
(You can ignore the comments, they are there simply to clarify what each row contains).
How would I do this?
table(df$player)
gets me the number of goals per player, but then how to I generate these pairwise combinations?
This is a pretty classic self-join problem. I'm gonna start by summarizing your data to get the total goals for each player. I like dplyr for this, but aggregate or data.table work just fine too.
library(dplyr)
df <- df %>% group_by(Player, Country) %>% dplyr::summarize(Goals = sum(Goals))
> df
Source: local data frame [7 x 3]
Groups: Player
Player Country Goals
1 Dempsey USA 8
2 Hulk Brasil 5
3 Luiz Brasil 2
4 Neymar Brasil 8
5 Rooney England 4
6 Stewart England 2
7 Tim USA 0
Then, using good old merge, we join it to itself based on country, and then so we don't get each row twice (Dempsey, Tim and Tim, Dempsey---not to mention Dempsey, Dempsey), we'll subset it so that Player.x is alphabetically before Player.y. Since I already loaded dplyr I'll use filter, but subset would do the same thing.
df2 <- merge(df, df, by.x = "Country", by.y = "Country")
df2 <- filter(df2, as.character(Player.x) < as.character(Player.y))
> df2
Country Player.x Goals.x Player.y Goals.y
2 Brasil Hulk 5 Luiz 2
3 Brasil Hulk 5 Neymar 8
6 Brasil Luiz 2 Neymar 8
11 England Rooney 4 Stewart 2
15 USA Dempsey 8 Tim 0
The self-join could be done in dplyr if we made a little copy of the data and renamed the Player and Goals columns so they wouldn't be joined on. Since merge is pretty smart about the renaming, it's easier in this case.
There is probably a smarter way to get from the aggregated data to the pairs, but assuming your data is not too big (national soccer data), you can always do something like:
A<-aggregate(df$Goals~df$Player+df$Country,data=df,sum)
players_in_c<-table(A[,2])
dat<-NULL
for(i in levels(df$Country)) {
count<-players_in_c[i]
pair<-combn(count,m=2)
B<-A[A[,2]==i,]
dat<-rbind(dat, cbind(B[pair[1,],],B[pair[2,],]) )
}
dat
> dat
df$Player df$Country df$Goals df$Player df$Country df$Goals
1 Hulk Brasil 5 Luiz Brasil 2
1.1 Hulk Brasil 5 Neymar Brasil 8
2 Luiz Brasil 2 Neymar Brasil 8
4 Rooney England 4 Stewart England 2
6 Dempsey USA 8 Tim USA 0