Suppose that we have two data frames as shown below:
df1 <- data.frame(Team1 = c("A","B","C"), Team2 = c("D","E","F"), Winner = c("A","E","F"))
df2 <- data.frame(Country = c("A","B","C","D","E","F"), Index = c(1,2,3,4,5,6))
What i want is create three columns in df2 as Team1_index, Team2_index, and Winner_index.
Team1 Team2 Winner Team1_index Team2_index Winner_index
A D A 1 4 1
B E E 2 5 5
C F F 3 6 6
I tried many ways but failed. Tips and advice!
If you just have a small number of columns, you can use the match function as in the example:
df1$Team1_index <- df2$Index[match(df1$Team1, df2$Country)]
df1$Team2_index <- df2$Index[match(df1$Team2, df2$Country)]
df1$Winner_index <- df2$Index[match(df1$Winner, df2$Country)]
df1
If you have more columns, you may look for more systematic solutions, but if it's really just three cases, this should do:
library("tidyverse")
df1 <- data.frame(Team1 = c("A","B","C"), Team2 = c("D","E","F"), Winner = c("A","E","F"))
df2 <- data.frame(Country = c("A","B","C","D","E","F"), Index = c(1,2,3,4,5,6))
df1 %>%
left_join(df2 %>% rename(Team1 = Country), by = "Team1") %>%
rename(Team1_Index = Index) %>%
left_join(df2 %>% rename(Team2 = Country), by = "Team2") %>%
rename(Team2_Index = Index) %>%
left_join(df2 %>% rename(Winner = Country), by = "Winner") %>%
rename(Winner_Index = Index)
#> Warning: Column `Team1` joining factors with different levels, coercing to
#> character vector
#> Warning: Column `Team2` joining factors with different levels, coercing to
#> character vector
#> Warning: Column `Winner` joining factors with different levels, coercing to
#> character vector
#> Team1 Team2 Winner Team1_Index Team2_Index Winner_Index
#> 1 A D A 1 4 1
#> 2 B E E 2 5 5
#> 3 C F F 3 6 6
You can safely ignore the warnings.
To get new columns as factors :
df1[paste0(colnames(df1),"_index")] <- lapply(df1,factor,df2$Country,df2$Index)
# Team1 Team2 Winner Team1_index Team2_index Winner_index
# 1 A D A 1 4 1
# 2 B E E 2 5 5
# 3 C F F 3 6 6
To get new columns as numeric :
df1[paste0(colnames(df1),"_index")] <-
lapply(df1,function(x) as.numeric(as.character(factor(x,df2$Country,df2$Index))))
# Team1 Team2 Winner Team1_index Team2_index Winner_index
# 1 A D A 1 4 1
# 2 B E E 2 5 5
# 3 C F F 3 6 6
Note that for this specific case (index from 1 incremented by 1), this shorter version works:
df1[paste0(colnames(df1),"_index")] <-
lapply(df1,function(x) as.numeric(factor(x,df2$Country)))
I have an almost solution with data.table, using melt and dacst to change shape
library(data.table)
df1 <- data.table(Team1 = c("A","B","C"), Team2 = c("D","E","F"), Winner = c("A","E","F"))
df2 <- data.table(Country = c("A","B","C","D","E","F"), Index = c(1,2,3,4,5,6))
melt(data = df1 , id.vars = )
plouf <- merge(df2,melt(df1,measure = 1:2), by.x = "Country", by.y = "value")
plouf[,winneridx := Index[Country == Winner]]
dcast(plouf,Country+winneridx~variable,value.var = "Index")
Country winneridx Team1 Team2
1: A 1 1 NA
2: B 5 2 NA
3: C 6 3 NA
4: D 1 NA 4
5: E 5 NA 5
6: F 6 NA 6
This is basically the same as giocomai's answer, just uses purrr to help eliminate duplication:
library(rlang)
library(dplyr)
getIndexCols <- function(df1, df2, colName){
idxColName <- sym(paste0(colName, "_Index"))
df1 %>% left_join(df2 %>% rename(!! sym(colName) := Country, !! idxColName := Index))
}
names(df1) %>% purrr::map(~ getIndexCols(df1, df2, .)) %>% reduce(~ left_join(.x, .y))
You can use chartr This will take into consideration both the country column and the index column:
df3=as.matrix(setNames(df1,paste0(names(df1),"_index")))
cbind(df1,chartr(paste0(df2$Country,collapse=""),paste0(df2$Index,collapse=""),df3))
Team1 Team2 Winner Team1_index Team2_index Winner_index
1 A D A 1 4 1
2 B E E 2 5 5
3 C F F 3 6 6
you can also do:
cbind(df1,do.call(chartr,c(as.list(sapply(unname(df2),paste,collapse="")),list(df3))))
Team1 Team2 Winner Team1_index Team2_index Winner_index
1 A D A 1 4 1
2 B E E 2 5 5
3 C F F 3 6 6
Here is another option for you that uses match and cbind.
df3 <- as.matrix(df1)
colnames(df3) <- paste0(colnames(df3), "_index")
# match the positions
df3[] <- match(df3, df2$Country)
cbind(df1, df3)
# Team1 Team2 Winner Team1_index Team2_index Winner_index
#1 A D A 1 4 1
#2 B E E 2 5 5
#3 C F F 3 6 6
df3 is created as a matrix, i.e. a vector with dimensions attribute, such that we can replace its entries with the result of match (a vector) right away and don't need to repeat the code for every column.
Or in one go
df1[paste0(colnames(df1), "_index")] <- match(as.matrix(df1), df2$Country)
Note however, that this ignores the index column of df2.
Thanks to #Moody_Mudskipper we could also write this more general as
df1[paste0(colnames(df1), "_index")] <- lapply(df1, function(x) df2$Index[match(x, df2$Country)])
Related
I have a dataframe in the following format with ID's and A/B's. The dataframe is very long, over 3000 ID's.
id
type
1
A
2
B
3
A
4
A
5
B
6
A
7
B
8
A
9
B
10
A
11
A
12
A
13
B
...
...
I need to remove all rows (A+B), where more than one A is behind another one or more. So I dont want to remove the duplicates. If there are a duplicate (2 or more A's), i want to remove all A's and the B until the next A.
id
type
1
A
2
B
6
A
7
B
8
A
9
B
...
...
Do I need a loop for this problem? I hope for any help,thank you!
This might be what you want:
First, define a function that notes the indices of what you want to remove:
row_sequence <- function(value) {
inds <- which(value == lead(value))
sort(unique(c(inds, inds + 1, inds +2)))
}
Apply the function to your dataframe by first extracting the rows that you want to remove into df1 and second anti_joining df1 with df to obtain the final dataframe:
library(dplyr)
df1 <- df %>% slice(row_sequence(type))
df2 <- df %>%
anti_join(., df1)
Result:
df2
id type
1 1 A
2 2 B
3 6 A
4 7 B
5 8 A
6 9 B
Data:
df <- data.frame(
id = 1:13,
type = c("A","B","A","A","B","A","B","A","B","A","A","A","B")
)
I imagined there is only one B after a series of duplicated A values, however if that is not the case just let me know to modify my codes:
library(dplyr)
library(tidyr)
library(data.table)
df %>%
mutate(rles = data.table::rleid(type)) %>%
group_by(rles) %>%
mutate(rles = ifelse(length(rles) > 1, NA, rles)) %>%
ungroup() %>%
mutate(rles = ifelse(!is.na(rles) & is.na(lag(rles)) & type == "B", NA, rles)) %>%
drop_na() %>%
select(-rles)
# A tibble: 6 x 2
id type
<int> <chr>
1 1 A
2 2 B
3 6 A
4 7 B
5 8 A
6 9 B
Data
df <- read.table(header = TRUE, text = "
id type
1 A
2 B
3 A
4 A
5 B
6 A
7 B
8 A
9 B
10 A
11 A
12 A
13 B")
Let' say I have two data.frames
name_df = read.table(text = "player_name
a
b
c
d
e
f
g", header = T)
game_df = read.table(text = "game_id winner_name loser_name
1 a b
2 b a
3 a c
4 a d
5 b c
6 c d
7 d e
8 e f
9 f a
10 g f
11 g a
12 f e
13 a d", header = T)
name_df contains a unique list of all the winner_name or loser_name values in game_df. I want to create a new data.frame that has, for each person in the name_df a row if a given name (e.g. a) appears in either the winner_name or loser_name column
So I essentially want to merge game_df with name_df, but the key column (name) can appear in either winner_name or loser_name.
So, for just a and b the final output would look something like:
final_df = read.table(text = "player_name game_id winner_name loser_name
a 1 a b
a 2 b a
a 3 a c
a 4 a d
a 9 f a
a 11 g a
a 13 a d
b 1 a b
b 2 b a
b 5 b c", header = T)
We can loop over the elements in 'name_df' for 'player_name', filter the rows from 'game_df' for either the 'winner_name' or 'loser_name'
library(dplyr)
library(purrr)
map_dfr(setNames(name_df$player_name, name_df$player_name),
~ game_df %>%
filter(winner_name %in% .x|loser_name %in% .x), .id = 'player_name')
Or if there are many columns, use if_any
map_dfr(setNames(name_df$player_name, name_df$player_name),
~ {
nm1 <- .x
game_df %>%
filter(if_any(c(winner_name, loser_name), ~ . %in% nm1))
}, .id = 'player_name')
Dedicated to our teacher and mentor dear #akrun
I think we can also make use of the add_row() function you first taught me the other day. Unbelievable!!!
library(dplyr)
library(purrr)
library(tibble)
game_df %>%
rowwise() %>%
mutate(player_name = winner_name) %>%
group_split(game_id) %>%
map_dfr(~ add_row(.x, game_id = .x$game_id, winner_name = .x$winner_name,
loser_name = .x$loser_name, player_name = .x$loser_name)) %>%
arrange(player_name) %>%
relocate(player_name)
# A tibble: 26 x 4
player_name game_id winner_name loser_name
<chr> <int> <chr> <chr>
1 a 1 a b
2 a 2 b a
3 a 3 a c
4 a 4 a d
5 a 9 f a
6 a 11 g a
7 a 13 a d
8 b 1 a b
9 b 2 b a
10 b 5 b c
# ... with 16 more rows
This can be directly expressed in SQL:
library(sqldf)
sqldf("select *
from name_df
left join game_df on winner_name = player_name or loser_name = player_name")
Without using purrr. I think this is appropriate use case of tidyr::unite with argument remove = F where we can first unite the winners' and losers' names and then use tidyr::separate_rows to split new column into rows.
library(tidyr)
library(dplyr)
game_df %>% unite(Player_name, winner_name, loser_name, remove = F, sep = ', ') %>%
separate_rows(Player_name) %>%
relocate(Player_name) %>%
arrange(Player_name)
# A tibble: 26 x 4
Player_name game_id winner_name loser_name
<chr> <int> <chr> <chr>
1 a 1 a b
2 a 2 b a
3 a 3 a c
4 a 4 a d
5 a 9 f a
6 a 11 g a
7 a 13 a d
8 b 1 a b
9 b 2 b a
10 b 5 b c
# ... with 16 more rows
A Base R approach :
result <- do.call(rbind, lapply(name_df$player_name, function(x)
cbind(plaername = x,
subset(game_df, winner_name == x | loser_name == x))))
rownames(result) <- NULL
result
# playername game_id winner_name loser_name
#1 a 1 a b
#2 a 2 b a
#3 a 3 a c
#4 a 4 a d
#5 a 9 f a
#6 a 11 g a
#7 a 13 a d
#8 b 1 a b
#...
#...
I have two data frames as below and am trying to improve my code so the letters column in df1 should replaced with the letters column in df2 if they match.
df1 <- data.frame(ID = c(1,3,2,4,5), Letters = LETTERS[1:5], stringsAsFactors = F)
df2 <- data.frame(ID = c(1,3,4), Letters2 = "F", stringsAsFactors = F)
desired:
ID letters
1 F
2 B
3 F
4 D
5 F
It would be like doing the following by in one line:
desired <- merge(df1, df2, by = "ID", all.x = T)
desired$letters <- ifelse(is.na(desired$letters2), desired$letters, desired$letters2)
desired$letters2 <- NULL
Try this:
library(tidyverse)
df1%>%
left_join(df2)%>%
mutate(Letters=coalesce(letters2,Letters),letters2=NULL)
Joining, by = "ID"
ID Letters
1 1 F
2 2 B
3 3 F
4 4 F
5 5 E
We could use the numeric 'ID' as index to change the values in 'Letters' to those of 'letters2' (which are all 'F's)
df1$Letters[df2$ID] <- df2$letters2
df1
# ID Letters
#1 1 F
#2 2 B
#3 3 F
#4 4 F
#5 5 E
Or using data.table
library(data.table)
setDT(df1)[df2, Letters := Letters2, on = .(ID)]
df1
# ID Letters
#1: 1 F
#2: 3 F
#3: 2 C
#4: 4 F
#5: 5 E
I am trying to convert a matrix to a dataframe and use a column name and row name in the matrix with variables in the dataframe.
here is the sample
sample = matrix(c(1,NA,NA,2,NA,3,NA,NA,5,NA,NA,6,NA,NA,NA,NA,8,NA,3,1),ncol = 4)
colnames(sample) = letters[1:4]
row.names(sample) = letters[22:26]
My dataset has a lot of NA so I am trying to remove all the NA in the dataframe.
so here is my desiring output,
data.frame(col = c("v","v","w","w","y","y","y","z"),
row = c("a","b","c","c","a","b","d","d"),
value = c(1,3,6,8,2,5,3,1))
Use melt from reshape2 package for reshaping, then clear NA. Finally, do some formating stuff to get your desired output (ordering, setting colnames...).
> library(reshape2)
> df <- na.omit(melt(sample)) # reshaping
> df <- df[order(df$Var1), ] # ordering
> colnames(df) <- c("col", "row", "value") # setting colnames
> df # getting desired output
col row value
1 v a 1
6 v b 3
12 w c 6
17 w d 8
4 y a 2
9 y b 5
19 y d 3
20 z d 1
With dplyr and magrittr
> library(magrittr)
> library(dplyr)
> sample %>% melt %>%
na.omit %>%
arrange(., Var1) %>%
setNames(c('col', 'row', 'value'))
col row value
1 v a 1
2 v b 3
3 w c 6
4 w d 8
5 y a 2
6 y b 5
7 y d 3
8 z d 1
Here is a base R method by replicating the row names and column names
out <- na.omit(data.frame(col = rownames(sample)[row(sample)],
row = colnames(sample)[col(sample)], value = c(sample)))
out <- out[order(out$col),]
row.names(out) <- NULL
out
# col row value
#1 v a 1
#2 v b 3
#3 w c 6
#4 w d 8
#5 y a 2
#6 y b 5
#7 y d 3
#8 z d 1
New to R, please guide !
Dataframe1 contain:
df1
Col1 Col2 Col3 Col4 Col5
A=5 C=1 E=5 F=4 G=2 --Row1
A=6 B=3 D=6 E=4 F=4 --Row2
B=2 C=3 D=3 E=3 F=7 --Row3
Dataframe2 contain one row with each parameters as field names:
df2 = A B C D E F g .....'n'
Example Output (if values not found the null to be printed):
df2:
A B C D E F G
5 1 5 4 2
6 3 6 4 4
2 3 3 3 7
How to populate values of each parameter from df1 to df2 under same parameter which are present in first row as fields?
Create a row number column (rownames_to_column), gather into 'long' format, separate the 'val' column into two (by splitting at =- automatically picked up) and then spread into 'wide' format. By default, the elements that are missing are filled by NA. There is also a fill argument to change it to the desired fill value
library(tidyverse)
res <- df1 %>%
rownames_to_column('rn') %>%
gather(key, val, -rn) %>%
separate(val, into = c('val1', 'val2')) %>%
select(-key) %>%
spread(val1, val2) %>%
select(-rn)
res
# A B C D E F G
#1 5 <NA> 1 <NA> 5 4 2
#2 6 3 <NA> 6 4 4 <NA>
#3 <NA> 2 3 3 3 7 <NA>
If there is a second dataset containing some values and want to replace the non-NA elements in 'df2'
df2[!is.na(df2)] <- res[!is.na(df2)][names(df2)]
Or another option is dcast from data.table
library(data.table)
dcast(setDT(df1)[, tstrsplit(unlist(.SD), "="), .(grp = 1:nrow(df1))
], grp ~ V1, value.var = 'V2')[, grp := NULL][]
# A B C D E F G
#1: 5 NA 1 NA 5 4 2
#2: 6 3 NA 6 4 4 NA
#3: NA 2 3 3 3 7 NA
data
df1 <- structure(list(Col1 = c("A=5", "A=6", "B=2"), Col2 = c("C=1",
"B=3", "C=3"), Col3 = c("E=5", "D=6", "D=3"), Col4 = c("F=4",
"E=4", "E=3"), Col5 = c("G=2", "F=4", "F=7")), .Names = c("Col1",
"Col2", "Col3", "Col4", "Col5"), class = "data.frame", row.names = c(NA,
-3L))