This may have solutions/answers available here, but I am unable to find.
Let us assume a simple data like this
x <- data.frame(id = rep(1:3, each = 2),
v1 = c('A', 'B', 'A', 'B', 'A', 'C'))
> x
id v1
1 1 A
2 1 B
3 2 A
4 2 B
5 3 A
6 3 C
Now I want an output of relation of V1 column with itself, but across group on id something like this
v1 A B C
1 A 0 2 1
2 B 2 0 0
3 C 1 0 0
So, I proceeded like this..
library(tidyverse)
#merged the V1 column by itself with all = TRUE
x <- merge(x, x, by = "id", all = T)
# removed same group rows
x <- x[x$v1.x != x$v1.y, ]
# final code
x %>% select(-id) %>%
group_by(v1.x, v1.y) %>%
summarise(val = n()) %>%
pivot_wider(names_from = v1.y, values_from = val, values_fill = 0L, names.sort = T)
# A tibble: 3 x 4
# Groups: v1.x [3]
v1.x A B C
<chr> <int> <int> <int>
1 A 0 2 1
2 B 2 0 0
3 C 1 0 0
My question is that any better/direct method to obtain the cross-table?
How about creating a contingency table with xtabs (which can work with large data sets as well). Then, you can use crossprod on the table and set the diagonal to zero for the final result.
ct <- xtabs(~ id + v1, data = x)
cp <- crossprod(ct, ct)
diag(cp) <- 0
cp
Instead of xtabs you can create a cross-table with simply table as well. As noted by #A5C1D2H2I1M1N2O1R2T1, you can simplify to a nice one-liner equivalent:
"diag<-"(crossprod(table(x)), 0)
Output
v1
v1 A B C
A 0 2 1
B 2 0 0
C 1 0 0
Related
I have a data frame which looks like this:
Value1 = c("1","2","1","3")
Letter = c("A","B","B","A")
A = c("2","2","0","1")
B = c("1","1","1","0")
data <- data.frame(Value1,Letter,A,B)
data
Value1 Letter A B
1 1 A 2 1
2 2 B 2 1
3 1 B 0 1
4 3 A 1 0
I'm trying to add a new column which is the multiplication of column Value1, by column A or B depending on what is in the Letter column. The expected result would be:
Value1 Letter A B Results
1 1 A 2 1 2
2 2 B 2 1 2
3 1 B 0 1 1
4 3 A 1 0 3
I'm trying to use the match() function, but without success.
Thanks!
With base R:
data <- type.convert(data, as.is = TRUE)
data$Results <- ifelse(data$Letter == 'A', data$A * data$Value1, data$B * data$Value1)
Output
Value1 Letter A B Results
1 1 A 2 1 2
2 2 B 2 1 2
3 1 B 0 1 1
4 3 A 1 0 3
Another option would be to pivot to long form, do the calculation, then pivot back to wide format.
library(tidyverse)
data %>%
type.convert(as.is = TRUE) %>%
pivot_longer(c(A, B)) %>%
mutate(Results = ifelse(Letter == name, value * Value1, NA_integer_)) %>%
pivot_wider(names_from = "name", values_from = "value") %>%
group_by(Value1, Letter) %>%
summarise_all(discard, is.na)
Output
Value1 Letter Results A B
<int> <chr> <int> <int> <int>
1 1 A 2 2 1
2 1 B 1 0 1
3 2 B 2 2 1
4 3 A 3 1 0
Use case_when or ifelse
library(dplyr)
data <- data %>%
type.convert(as.is = TRUE) %>%
mutate(Results = case_when(Letter == 'A' ~ A * Value1,
TRUE ~ B * Value1))
-output
data
Value1 Letter A B Results
1 1 A 2 1 2
2 2 B 2 1 2
3 1 B 0 1 1
4 3 A 1 0 3
Or use get with rowwise
data <- data %>%
type.convert(as.is = TRUE) %>%
rowwise %>%
mutate(Result = get(Letter) * Value1) %>%
# or may also use
# mutate(Result = cur_data()[[Letter]] * Value1) %>%
ungroup
-output
data
# A tibble: 4 × 5
Value1 Letter A B Result
<int> <chr> <int> <int> <int>
1 1 A 2 1 2
2 2 B 2 1 2
3 1 B 0 1 1
4 3 A 1 0 3
In base R, we may use row/column indexing as vectorized option
data <- type.convert(data, as.is = TRUE)
nm1 <- unique(data$Letter)
data$Results <-data[nm1][cbind(seq_len(nrow(data)),
match(data$Letter, nm1))] * data$Value1
I have two dataframes df1 and df2. They both have a column 'ID'. For each row in DF1, I would like to find out how many duplicates of its ID there are in df2 and add the count to that row. If there are no duplicates, the count should return as 0.
# # A tibble: 4 x 3
# ID a b
# <dbl> <dbl> <dbl>
# 1 1_234 1 1
# 2 1_235 1 2
# 3 2_222 1 1
# 4 2_654 1 2
# # A tibble: 4 x 3
# ID a b
# <dbl> <dbl> <dbl>
# 1 1_234 1 1
# 2 1_235 1 2
# 3 1_234 1 1
# 4 3_234 1 2
Using dplyr:
Your data:
df1 <- data.frame(ID = c("1_234","1_235","2_222","2_654"),
a = c(1,1,1,1),
b = c(1,2,1,2))
df2 <- data.frame(ID = c("1_234","1_235","1_234","3_235"),
a = c(1,1,1,1),
b = c(1,2,1,2))
Edit: considering only the IDs:
output <- left_join(df1,
as.data.frame(table(df2$ID)),
by = c("ID" = "Var1")) %>%
mutate(Freq = ifelse(is.na(Freq), 0, Freq))
Output:
ID a b Freq
1 1_234 1 1 2
2 1_235 1 2 1
3 2_222 1 1 0
4 2_654 1 2 0
A base R option using subset + aggregate
subset(
aggregate(
n ~ .,
rbind(
cbind(df1, n = 1),
cbind(df2, n = 1)
), function(x) length(x) - 1
), ID %in% df1$ID
)
gives
ID a b n
1 1_234 1 1 2
2 2_222 1 1 0
3 1_235 1 2 1
4 2_654 1 2 0
I think you can do it with a simple sapply() and base r (no extra packages).
df1$count <- sapply(df1$ID, function(x) sum(df2$ID == x))
We may also use outer
df1$count <- rowSums(outer(df1$ID, df2$ID, FUN = `==`))
df1$count
[1] 2 1 0 0
We could use semi_join and n() to get the count of duplicates:
library(dplyr)
df1 %>%
semi_join(df2, by="ID") %>%
summarise(duplicates_df1_df2 = n())
Output:
duplicates_df1_df2
1 2
I am running the following code to count the amount of days that sites are over 90.
temp2 <- temp %>%
filter(ds > '2017-12-31') %>%
filter(over90 == 1) %>%
group_by(site) %>%
tally()
However, this drops sites which do not have days over 90. I want my output to include these sites with a value of zero, instead of simply not existing. Any way I can do this?
Pretend data:
site over90
a 1
a 0
a 1
b 0
What happens:
site n
a 2
What I want:
site n
a 2
b 0
If your site is a factor variable you can use tidyr::complete to fill in the blanks at the end:
filter(dd, over90 == 1) %>%
group_by(site) %>%
tally() %>%
complete(site, fill = list(n = 0))
# A tibble: 2 x 2
site n
<fctr> <dbl>
1 a 2
2 b 0
data
dd <- read.table(text =
"site over90
a 1
a 0
a 1
b 0",
header = TRUE)
tally isn't doing quite what you want it to. Since you just want to count the number of rows where over_90 is equal to one and that variable is binary, you can sum that column to get the effect of counting.
df = data_frame(site=sample(letters[1:3], 10, replace=TRUE),
over_90=sample(0:1, 10, replace=TRUE))
df
# A tibble: 10 x 2
# site over_90
# <chr> <int>
# 1 c 0
# 2 b 1
# 3 c 0
# 4 b 0
# 5 c 1
# 6 b 0
# 7 a 0
# 8 a 1
# 9 c 1
#10 c 0
df$over_90[df$site == 'c'] = 0
df %>% group_by(site) %>% summarise(n = sum(over_90))
# A tibble: 3 x 2
# site n
# <chr> <dbl>
#1 a 1
#2 b 1
#3 c 0
One option is to use table instead. But you have first convert site in factor so that count for all factors is included after calling table function.
library(dplyr)
df %>% mutate(site = as.factor(site)) %>% #This line is very important.
filter(over90 ==1) %>%
group_by(site) %>%
table()
# site 1
# a 2
# b 0
Data:
df <- read.table(text =
"site over90
a 1
a 0
a 1
b 0",
header = TRUE, stringsAsFactors = FALSE)
I have a data frame where each row is an observation and I have two columns:
the group membership of the observation
the outcome for the observation.
I'm trying to create a new variable outcome_change that takes a value of 1 if outcome is NOT identical for all observations in a given group and 0 otherwise.
Shown in the below code (dat) is an example of the data I have. Meanwhile, dat_out1 shows what I'm looking for the code to produce in the presence of no NA values. The dat_out2 is identical except it shows that the same results arise when there are missing values in a group's values.
Surely there is somewhat to do this with dplyr::group_by()? I don't know how to make these comparisons within groups.
# Input (2 groups: 1 with identical values of outcome
# in the group (group a) and 1 with differing values of
# outcome in the group (group b)
dat <- data.frame(group = c("a","a","a","b","b","b"),
outcome = c(1,1,1,3,2,2))
# Output 1: add a variable for all observations belonging to
# a group where the outcome changed within each group
dat_out1 <- data.frame(group = c("a","a","a","b","b","b"),
outcome = c(1,1,1,3,2,2),
outcome_change = c(0,0,0,1,1,1))
# Output 2: same as Output 1, but able to ignore NA values
dat_out2 <- data.frame(group = c("a","a","a","b","b","b"),
outcome = c(1,1,NA,3,2,NA),
outcome_change = c(0,0,0,1,1,1))
Here is an aproach:
library(tidyverse)
dat %>%
group_by(group) %>%
mutate(outcome_change = ifelse(length(unique(outcome[!is.na(outcome)])) > 1, 1, 0))
#output
# A tibble: 6 x 3
# Groups: group [2]
group outcome outcome_change
<fctr> <dbl> <dbl>
1 a 1 0
2 a 1 0
3 a 1 0
4 b 3 1
5 b 2 1
6 b 2 1
with dat2
# A tibble: 6 x 3
# Groups: group [2]
group outcome outcome_change
<fctr> <dbl> <dbl>
1 a 1 0
2 a 1 0
3 a NA 0
4 b 3 1
5 b 2 1
6 b NA 1
library(dplyr)
dat <- data.frame(group = c("a","a","a","b","b","b"),
outcome = c(1,1,1,3,2,2))
dat2 <- data.frame(group = c("a","a","a","b","b","b"),
outcome = c(1,1,NA,3,2,NA))
dat_out1 <- dat %>% group_by(group) %>%
mutate(outcome_change = ifelse(min(outcome) == max(outcome), 0, 1))
dat_out2 <- dat2 %>% group_by(group) %>%
mutate(outcome_change = ifelse(min(outcome, na.rm = TRUE) == max(outcome, na.rm = TRUE), 0, 1))
Here is an option using data.table
library(data.table)
setDT(dat1)[, outcome_change := as.integer(uniqueN(outcome[!is.na(outcome)])>1), group]
dat1
# group outcome outcome_change
#1: a 1 0
#2: a 1 0
#3: a 1 0
#4: b 3 1
#5: b 2 1
#6: b 2 1
If we apply the same with 'dat2'
dat2
# group outcome outcome_change2
#1: a 1 0
#2: a 1 0
#3: a NA 0
#4: b 3 1
#5: b 2 1
#6: b NA 1
Suppose I have a dataframe like this
testtbl <- data.frame(ID = c('1','2','3','4'),
A = c(1,0,1,1),
B = c(1,1,1,1),
C = c(0,0,0,1),
D = c(0,1,1,1))
> testtbl
ID A B C D
1 1 1 1 0 0
2 2 0 1 0 1
3 3 1 1 0 1
4 4 1 1 1 1
Where columns A-D are flags that can either be 1 or 0. I would like to consolidate these columns into 1 column, where the new dataframe would look something like:
> testtbl
ID flag
1 1 A,B
2 2 B,D
3 3 A,B,D
4 4 A,B,C,D
At little confused on how I would approach this and would appreciate any hints or help.
A solution from dplyr and tidyr.
library(dplyr)
library(tidyr)
testtbl2 <- testtbl %>%
gather(Col, Val, -ID) %>%
filter(Val == 1) %>%
select(-Val) %>%
group_by(ID) %>%
summarise(flag = toString(Col))
testtbl2
# # A tibble: 4 x 2
# ID flag
# <fctr> <chr>
# 1 1 A, B
# 2 2 B, D
# 3 3 A, B, D
# 4 4 A, B, C, D
You can also do it without any libraries with an apply:
testtbl <- data.frame(ID = c('1','2','3','4'),
A = c(1,0,1,1),
B = c(1,1,1,1),
C = c(0,0,0,1),
D = c(0,1,1,1))
test<-data.frame(ID=testtbl$ID, flag=(apply(testtbl[,-1], 1, function(x) paste0(names(x)[which(x==1)], collapse=','))))
Base R
do.call(rbind, lapply(split(testtbl, testtbl$ID), function(x)
data.frame(ID = x[1],
flag = paste(sort(names(x)[-1][x[-1] > 0]),
collapse = ","))))
# ID flag
#1 1 A,B
#2 2 B,D
#3 3 A,B,D
#4 4 A,B,C,D