I have a dataframe df containing count data at different sites, across two days:
day site count
1 A 2
1 B 3
2 A 10
2 B 12
I would like to add a new column day1count that represents the count value at day 1, for each unique site. So, on rows where day==1, count and day1count would be identical. The new df would look like:
day site count day1count
1 A 2 2
1 B 3 3
2 A 10 2
2 B 12 3
So far I've created a new column that has duplicate values for day 1 rows, and NA for everything else:
df$day1count= ifelse(df$day==1, df$count, NA)
day site count day1count
1 A 2 2
1 B 3 3
2 A 10 NA
2 B 12 NA
How can I now replace the NA entries with values corresponding to each unique site from day 1?
I figured it out. It's not very elegant (and I invite others to submit a more efficient approach) but...
Do NOT create the new column with df$day1count= ifelse(df$day==1, df$count, NA) as I did in the original example. Instead, start by making a duplicate of df, but which only contains rows from day 1
tmpdf = df[df$day==1,]
Rename count as day1count, and remove day column
tmpdf = rename(tmpdf, c("count"="day1count"))
tmpdf$day = NULL
Merge the two dataframes by site
newdf = merge(x=df,y=tmpdf, by="site")
newdf
site day count day1count
1 A 1 2 2
2 A 2 10 2
3 B 1 3 3
4 B 2 12 3
With tidyverse you could do the following:
library(tidyverse)
df %>%
group_by(site) %>%
mutate(day1count = first(count))
Output
# A tibble: 4 x 4
# Groups: site [2]
day site count day1count
<int> <fct> <int> <int>
1 1 A 2 2
2 1 B 3 3
3 2 A 10 2
4 2 B 12 3
Data
df <- read.table(
text =
"day site count
1 A 2
1 B 3
2 A 10
2 B 12", header = T
)
Related
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
my question is:
I have a matrix of 200.000 rows and 3 different columns (productID, week, order).
I want to put the productID (starting with 1) in the product column and create 26 rows for each ID. Than I want to put 1-26 in the week column for every ID.
I know it's not that hard, but I keep making mistakes.
Thank you so much for your help!
Do you look for something like this:
tibble(productID = 1:4, week = 5:8, order = "Test") %>%
tidyr::complete(week = 1:26, productID = 1:4, fill = list(order = NA_character_))
# A tibble: 104 x 3
week productID order
<int> <int> <chr>
1 1 1 NA
2 1 2 NA
3 1 3 NA
4 1 4 NA
5 2 1 NA
6 2 2 NA
7 2 3 NA
8 2 4 NA
9 3 1 NA
10 3 2 NA
# ... with 94 more rows
This question already has answers here:
How to create a consecutive group number
(13 answers)
Closed 2 years ago.
I have data from an experiment that has multiple rows per item (each row has the reading time for one word of a sentence of n words), and multiple items per subject. Items can be varying numbers of rows. Items were presented in a random order, and their order in the data as initially read in reflects the sequence they saw the items in. What I'd like to do is add a column that contains the order in which the subject saw that item (i.e., 1 for the first item, 2 for the second, etc.).
Here's an example of some input data that has the relevant properties:
d <- data.frame(Subject = c(1,1,1,1,1,2,2,2,2,2),
Item = c(2,2,2,1,1,1,1,2,2,2))
Subject Item
1 2
1 2
1 2
1 1
1 1
2 1
2 1
2 2
2 2
2 2
And here's the output I want:
Subject Item order
1 2 1
1 2 1
1 2 1
1 1 2
1 1 2
2 1 1
2 1 1
2 2 2
2 2 2
2 2 2
I know I can do this by setting up a temp data frame that filters d to unique combinations of Subject and Item, adding order to that as something like 1:n() or row_number(), and then using a join function to put it back together with the main data frame. What I'd like to know is whether there's a way to do this without having to create a new data frame just to store the order---can this be done inside dplyr's mutate somehow if I group by Subject and Item, for instance?
Here's one way:
d %>%
group_by(Subject) %>%
mutate(order = match(Item, unique(Item))) %>%
ungroup()
# # A tibble: 10 x 3
# Subject Item order
# <dbl> <dbl> <int>
# 1 1 2 1
# 2 1 2 1
# 3 1 2 1
# 4 1 1 2
# 5 1 1 2
# 6 2 1 1
# 7 2 1 1
# 8 2 2 2
# 9 2 2 2
# 10 2 2 2
Here is a base R option
transform(d,
order = ave(Item, Subject, FUN = function(x) as.integer(factor(x, levels = unique(x))))
)
or
transform(d,
order = ave(Item, Subject, FUN = function(x) match(x, unique(x)))
)
both giving
Subject Item order
1 1 2 1
2 1 2 1
3 1 2 1
4 1 1 2
5 1 1 2
6 2 1 1
7 2 1 1
8 2 2 2
9 2 2 2
10 2 2 2
I have a long data frame with players' decisions who worked in groups.
I need to convert the data in such a way that each row (individual observation) would contain all group members decisions (so we basically can see whether they are interdependent).
Let's say the generating code is:
group_id <- c(rep(1, 3), rep(2, 3))
player_id <- c(rep(seq(1, 3), 2))
player_decision <- seq(10,60,10)
player_contribution <- seq(6,1,-1)
df <-
data.frame(group_id, player_id, player_decision, player_contribution)
So the initial data looks like:
group_id player_id player_decision player_contribution
1 1 1 10 6
2 1 2 20 5
3 1 3 30 4
4 2 1 40 3
5 2 2 50 2
6 2 3 60 1
But I need to convert it to wide per each group, but only for some of these variables, (in this example specifically for player_contribution, but in such a way that the rest of the data remains. So the head of the converted data would be:
data.frame(group_id=c(1,1),
player_id=c(1,2),
player_decision=c(10,20),
player_1_contribution=c(6,6),
player_2_contribution=c(5,5),
player_3_contribution=c(4,6)
)
group_id player_id player_decision player_1_contribution player_2_contribution player_3_contribution
1 1 1 10 6 5 4
2 1 2 20 6 5 6
I suspect I need to group_by in dplyr and then somehow gather per group but only for player_contribution (or a vector of variables). But I really have no clue how to approach it. Any hints would be welcome!
Here is solution using tidyr and dplyr.
Make a dataframe with the columns for the players contributions. Then join this dataframe back onto the columns of interest from the original Dataframe.
library(tidyr)
library(dplyr)
wide<-pivot_wider(df, id_cols= - player_decision,
names_from = player_id,
values_from = player_contribution,
names_prefix = "player_contribution_")
answer<-left_join(df[, c("group_id", "player_id", "player_decision") ], wide)
answer
group_id player_id player_decision player_contribution_1 player_contribution_2 player_contribution_3
1 1 1 10 6 5 4
2 1 2 20 6 5 4
3 1 3 30 6 5 4
4 2 1 40 3 2 1
5 2 2 50 3 2 1
6 2 3 60 3 2 1
I have a data frame in which each individual (row) has two data points per variable.
Example data:
df1 <- read.table(text = "IID L1.1 L1.2 L2.1 L2.2
1 1 38V1 38V1 48V1 52V1
2 2 36V1 38V2 50V1 48Y1
3 3 37Y1 36V1 50V2 48V1
4 4 38V2 36V2 52V1 50V2",
stringsAsFactor = FALSE, header = TRUE)
I have many more columns than this in the full dataset and would like to recode these values to label unique identifiers across the two columns. I know how to get identifiers and relabel a single column from previous questions (Creating a unique ID and How to assign a unique ID number to each group of identical values in a column) but I don't know how to include the information for two columns, as R identifies and labels factors per column.
Ultimately I want something that would look like this for the above data:
(df2)
IID L1.1 L1.2 L2.1 L2.2
1 1 1 1 1 4
2 2 2 4 2 5
3 3 3 2 3 1
4 4 1 5 4 3
It doesn't really matter what the numbers are, as long as they indicate unique values across both columns. I've tried creating a function based on the output from:
unique(df1[,1:2])
but am struggling as this still looks at unique entries per column, not across the two.
Something like this would work...
pairs <- (ncol(df1)-1)/2
for(i in 1:pairs){
refs <- unique(c(df1[,2*i],df1[,2*i+1]))
df1[,2*i] <- match(df1[,2*i],refs)
df1[,2*i+1] <- match(df1[,2*i+1],refs)
}
df1
IID L1.1 L1.2 L2.1 L2.2
1 1 1 1 1 4
2 2 2 4 2 5
3 3 3 2 3 1
4 4 4 5 4 3
You could reshape it to long format, assign the groups and then recast it to wide:
library(data.table)
df_m <- melt(df, id.vars = "IID")
setDT(df_m)[, id := .GRP, by = .(gsub("(.*).","\\1", df_m$variable), value)]
dcast(df_m, IID ~ variable, value.var = "id")
# IID L1.1 L1.2 L2.1 L2.2
#1 1 1 1 6 9
#2 2 2 4 7 10
#3 3 3 2 8 6
#4 4 1 5 9 8
This should also be easily expandable to multiple groups of columns. I.e. if you have L3. it should work with that as well.