I need to find a way to sum columns by their index,I'm working on a bigread.csv file, I'll show here a sample of the problem; I'd like for example to sum from the 2nd to the 5th and from the 6th to the 7h the following matrix:
a 1 3 3 4 5 6
b 2 1 4 3 4 1
c 1 3 2 1 1 5
d 2 2 4 3 1 3
The result has to be like this:
a 11 11
b 10 5
c 7 6
d 8 4
The columns have all different names
We can use rowSums on the subset of columns i.e 2:5 and 6:7 separately and then create a new data.frame with the output.
data.frame(df1[1], Sum1=rowSums(df1[2:5]), Sum2=rowSums(df1[6:7]))
# id Sum1 Sum2
#1 a 11 11
#2 b 10 5
#3 c 7 6
#4 d 11 4
The package dplyr has a function exactly made for that purpose:
require(dplyr)
df1 = data.frame(a=c(1,2,3,4,3,3),b=c(1,2,3,2,1,2),c=c(1,2,3,21,2,3))
df2 = df1 %>% transmute(sum1 = a+b , sum2 = b+c)
df2 = df1 %>% transmute(sum1 = .[[1]]+.[[2]], sum2 = .[[2]]+.[[3]])
Related
So let's say I have two dataframes that look like this
df1 <- data.frame(ID = c("A","B","F","G","B","B","A","G","G","F","A","A","A","B","F"),
code = c(1,2,2,3,3,1,2,2,1,1,3,2,2,1,1),
class = c(2,4,5,5,2,3,2,5,1,2,4,5,3,2,1))
df2 <- data.frame(ID = c("G","F","C","F","B","A","F","C","A","B","A","B","C","A","G"),
code = c(1,2,2,3,3,1,2,2,1,1,3,2,2,1,1),
class = c(2,4,5,5,2,3,2,5,1,2,4,5,3,2,1))
I want to check the duplicates in df1$ID and df2$ID and remove all the rows from df2 if the IDs are not present in df1 so the new dataframe would look like this:
df3 <- data.frame(ID = c("G","F","F","B","A","F","A","B","A","B","A","G"),
code = c(1,2,3,3,1,2,1,1,3,2,1,1),
class = c(2,4,5,2,3,2,1,2,4,5,2,1))
With %in%:
df2[df2$ID %in% df1$ID, ]
ID code class
1 G 1 2
2 F 2 4
4 F 3 5
5 B 3 2
6 A 1 3
7 F 2 2
9 A 1 1
10 B 1 2
11 A 3 4
12 B 2 5
14 A 1 2
15 G 1 1
You can use the 'intersect' function to tackle the issue.
common_ids <- intersect(df1$ID, df2$ID)
df3 <- df2[df2$ID %in% common_ids, ]
ID code class
1 G 1 2
2 F 2 4
4 F 3 5
5 B 3 2
6 A 1 3
7 F 2 2
9 A 1 1
10 B 1 2
11 A 3 4
12 B 2 5
14 A 1 2
15 G 1 1
I want to throw semi_join in.
library(tidyverse)
df_test <- df2 |> semi_join(df1, by = "ID")
all.equal(df3, df_test)
#> [1] TRUE
I'm having trouble figuring out how to do the opposite of the answer to this question (and in R not python).
Count the amount of times value A occurs with value B
Basically I have a dataframe with a lot of combinations of pairs of columns like so:
df <- data.frame(id1 = c("1","1","1","1","2","2","2","3","3","4","4"),
id2 = c("2","2","3","4","1","3","4","1","4","2","1"))
I want to count, how often all the values in column A occur in the whole dataframe without the values from column B. So the results for this small example would be the output of:
df_result <- data.frame(id1 = c("1","1","1","2","2","2","3","3","4","4"),
id2 = c("2","3","4","1","3","4","1","4","2","1"),
count = c("4","5","5","3","5","4","2","3","3","3"))
The important criteria for this, is that the final results dataframe is collapsed by the pairs (so in my example rows 1 and 2 are duplicates, and they are collapsed and summed by the total frequency 1 is observed without 2). For tallying the count of occurances, it's important that both columns are examined. I.e. order of columns doesn't matter for calculating the frequency - if column A has 1 and B has 2, this counts the same as if column A has 2 and B has 1.
I can do this very slowly by filtering for each pair, but it's not really feasible for my real data where I have many many different pairs.
Any guidance is greatly appreciated.
First paste the two id columns together to id12 for later matching. Then use sapply to go through all rows to see the records where id1 appears in id12 but id2 doesn't. sum that value and only output the distinct records. Finally, remove the id12 column.
library(dplyr)
df %>% mutate(id12 = paste0(id1, id2),
count = sapply(1:nrow(.),
function(x)
sum(grepl(id1[x], id12) & !grepl(id2[x], id12)))) %>%
distinct() %>%
select(-id12)
Or in base R completely:
id12 <- paste0(df$id1, df$id2)
df$count <- sapply(1:nrow(df), function(x) sum(grepl(df$id1[x], id12) & !grepl(df$id2[x], id12)))
df <- df[!duplicated(df),]
Output
id1 id2 count
1 1 2 4
2 1 3 5
3 1 4 5
4 2 1 3
5 2 3 5
6 2 4 4
7 3 1 2
8 3 4 3
9 4 2 3
10 4 1 3
A full tidyverse version:
library(tidyverse)
df %>%
mutate(id = paste(id1, id2),
count = map(cur_group_rows(), ~ sum(str_detect(id, id1[.x]) & str_detect(id, id2[.x], negate = T))))
A more efficient approach would be to work on a tabulation format:
tab = crossprod(table(rep(seq_len(nrow(df)), ncol(df)), c(df$id1, df$id2)))
#tab
#
# 1 2 3 4
# 1 7 3 2 2
# 2 3 6 1 2
# 3 2 1 4 1
# 4 2 2 1 5
So, now, we have the times each value appears with another (irrespectively of their order in the two columns). Here on, we need a way to subset the above table by each pair and subtract the value of their cooccurence from the value of each id's total appearance.
Make a grid of all combinations:
gr = expand.grid(id1 = colnames(tab), id2 = rownames(tab), stringsAsFactors = FALSE)
Create 2-column matrices to subset the table:
id1.ij = cbind(match(gr$id1, colnames(tab)),
match(gr$id1, rownames(tab)))
id2.ij = cbind(match(gr$id1, colnames(tab)),
match(gr$id2, rownames(tab)))
Subtract the respective values:
cbind(gr, count = tab[id1.ij] - tab[id2.ij])
# id1 id2 count
#1 1 1 0
#2 2 1 3
#3 3 1 2
#4 4 1 3
#5 1 2 4
#6 2 2 0
#7 3 2 3
#8 4 2 3
#9 1 3 5
#10 2 3 5
#11 3 3 0
#12 4 3 4
#13 1 4 5
#14 2 4 4
#15 3 4 3
#16 4 4 0
Of course, if we do not need the full grid of values, we can set:
gr = unique(df)
which results in:
# id1 id2 count
#1 1 2 4
#3 1 3 5
#4 1 4 5
#5 2 1 3
#6 2 3 5
#7 2 4 4
#8 3 1 2
#9 3 4 3
#10 4 2 3
#11 4 1 3
I'm trying to create a dummy variable based on if df1 is contained within df2. Note that df2 has columns more than just the columns in df1.
e.g.:
df1:
A
B
C
1
2
3
4
5
6
7
8
0
df2:
A
B
C
D
1
2
3
E
4
5
6
F
7
8
9
G
Resulting in:
df2:
A
B
C
D
Dummy
1
2
3
E
1
4
5
6
F
1
7
8
9
G
0
Any good approaches I should consider?
I've tried using an ifelse function applied to the dataframe, but I suspect I've coded it wrong. Any tips would be appreciated!
One approach would be to add a column called "dummy" to df1, then join with df2 on all variables of df1.
df1$dummy <- 1
library(dplyr)
dplyr::left_join(df2, df1) %>%
mutate(dummy = ifelse(is.na(dummy), 0, dummy))
# Joining, by = c("A", "B", "C")
# A B C D dummy
# 1 2 3 E 1
# 4 5 6 F 1
# 7 8 9 G 0
By default left_join joins using all commonly named variables, but this can be modified as required.
I am writing a code for analysis a set of dplyr data.
here is how my table_1 looks:
1 A B C
2 5 2 3
3 9 4 1
4 6 3 8
5 3 7 3
And my table_2 looks like this:
1 D E F
2 2 9 3
I would love to based on table 1 column"A", if A>6, then create a column "G" in table1, equals to "C*D+C*E"
Basically, it's like make table 2 as a factor...
Is there any way I can do it?
I can apply a filter to Column "A" and multiply Column"C" with a set number instead of a factor from table_2
table_1_New <- mutate(Table_1,G=if_else(A<6,C*2+C*9))
You could try
#Initialize G column with 0
df1$G <- 0
#Get index where A value is greater than 6
inds <- df1$A > 6
#Multiply those values with D and E from df2
df1$G[inds] <- df1$C[inds] * df2$D + df1$C[inds] * df2$E
df1
# A B C G
#2 5 2 3 0
#3 9 4 1 11
#4 6 3 8 0
#5 3 7 3 0
Using dplyr, we can do
df1 %>% mutate(G = ifelse(A > 6, C*df2$D + C*df2$E, 0))
I have a dataframe in the following format
> x <- data.frame("a" = c(1,1),"b" = c(2,2),"c" = c(3,4))
> x
a b c
1 1 2 3
2 1 2 4
I'd like to add 3 new columns which is a cumulative product of the columns a b c, however I need a reverse cumulative product i.e. the output should be
row 1:
result_d = 1*2*3 = 6 , result_e = 2*3 = 6, result_f = 3
and similarly for row 2
The end result will be
a b c result_d result_e result_f
1 1 2 3 6 6 3
2 1 2 4 8 8 4
the column names do not matter this is just an example. Does anyone have any idea how to do this?
as per my comment, is it possible to do this on a subset of columns? e.g. only for columns b and c to return:
a b c results_e results_f
1 1 2 3 6 3
2 1 2 4 8 4
so that column "a" is effectively ignored?
One option is to loop through the rows and apply cumprod over the reverse of elements and then do the reverse
nm1 <- paste0("result_", c("d", "e", "f"))
x[nm1] <- t(apply(x, 1,
function(x) rev(cumprod(rev(x)))))
x
# a b c result_d result_e result_f
#1 1 2 3 6 6 3
#2 1 2 4 8 8 4
Or a vectorized option is rowCumprods
library(matrixStats)
x[nm1] <- rowCumprods(as.matrix(x[ncol(x):1]))[,ncol(x):1]
temp = data.frame(Reduce("*", x[NCOL(x):1], accumulate = TRUE))
setNames(cbind(x, temp[NCOL(temp):1]),
c(names(x), c("res_d", "res_e", "res_f")))
# a b c res_d res_e res_f
#1 1 2 3 6 6 3
#2 1 2 4 8 8 4