Having a data frame like this:
df <- data.frame(id = c(1,2,3,4,5), keywords = c("google, yahoo, air, cookie", "cookie, air", "air, cookie", "google", "yahoo, google"))
How is it possible to extract a table like
df_binary_exist <- data.frame(id = c(1,2,3,4,5), google = c(1,0,0,1,1), yahoo = c(1,0,0,0,1), air = c(1,1,1,0,0), cookie = c(1,1,1,0,0))
df_binary_exist
id google yahoo air cookie
1 1 1 1 1 1
2 2 0 0 1 1
3 3 0 0 1 1
4 4 1 0 0 0
5 5 1 1 0 0
and from this table find the most frequent couples?
df_frequency <- data.frame(couple = c("yahoo-google", "cookie-air"), freq = c(2,3))
df_frequency
couple freq
1 yahoo-google 2
2 cookie-air 3
The first part can be achieved by using separate_rows, count and spread
library(dplyr)
library(tidyr)
df1 <- df %>% separate_rows(keywords)
df1 %>%
dplyr::count(id, keywords) %>%
spread(keywords, n, fill = 0)
# id air cookie google yahoo
# <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 1 1 1 1
#2 2 1 1 0 0
#3 3 1 1 0 0
#4 4 0 0 1 0
#5 5 0 0 1 1
For second part I used a base R method where we first split keywords based on id, paste combination of every 2 elements and count their frequency using table.
data.frame(sort(table(unlist(sapply(split(df1$keywords, df1$id), function(x)
combn(sort(x), pmin(2, length(x)), paste, collapse = "-")))), decreasing = TRUE))
# Var1 Freq
#1 air-cookie 3
#2 google-yahoo 2
#3 air-google 1
#4 air-yahoo 1
#5 cookie-google 1
#6 cookie-yahoo 1
#7 google 1
One tidyverse possibility could be:
df %>%
mutate(keywords = strsplit(keywords, ", ", fixed = TRUE)) %>%
unnest() %>%
full_join(df %>%
mutate(keywords = strsplit(keywords, ", ", fixed = TRUE)) %>%
unnest(), by = c("id" = "id")) %>%
filter(keywords.x != keywords.y) %>%
count(keywords.x, keywords.y) %>%
transmute(keywords = paste(pmax(keywords.x, keywords.y), pmin(keywords.x, keywords.y), sep = "-"),
n) %>%
distinct(keywords, .keep_all = TRUE)
keywords n
<chr> <int>
1 cookie-air 3
2 google-air 1
3 yahoo-air 1
4 google-cookie 1
5 yahoo-cookie 1
6 yahoo-google 2
It, first, splits the "keywords" column on , and then performs a full join with itself. Second, it filters out the rows where the values are the same as the OP is interested in pairs of values. Third, it counts the number of occurrences of pairs. Finally, it creates an ordered variable of pairs and keeps only the distinct rows based on this variable.
Or the same using separate_rows():
df %>%
separate_rows(keywords) %>%
full_join(df %>%
separate_rows(keywords), by = c("id" = "id")) %>%
filter(keywords.x != keywords.y) %>%
count(keywords.x, keywords.y) %>%
transmute(keywords = paste(pmax(keywords.x, keywords.y), pmin(keywords.x, keywords.y), sep = "-"),
n) %>%
distinct(keywords, .keep_all = TRUE)
We can do this easily with
library(qdapTools)
cbind(df[1], mtabulate(strsplit(as.character(df$keywords), ", ")))
# id air cookie google yahoo
#1 1 1 1 1 1
#2 2 1 1 0 0
#3 3 1 1 0 0
#4 4 0 0 1 0
#5 5 0 0 1 1
Related
In the below reproducible R code, I'd like to add a column "adjust" that results from a series of calculations that in Excel would use cumulative countifs, max, and match (actually, to make this more complete the adjust column should have used the match formula since there could be more than 1 element in the list starting in row 15, but I think it's clear what I'm doing without actually using match) formulas as shown below in the illustration. The yellow shading shows what the reproducible code generates, and the blue shading shows my series of calculations in Excel that derive the desired values in the "adjust" column. Any suggestions for doing this, in dplyr if possible?
I am a long-time Excel user trying to migrate all of my work to R.
Reproducible code:
library(dplyr)
myData <-
data.frame(
Element = c("A","B","B","B","B","B","B","B"),
Group = c(0,1,1,1,2,2,3,3)
)
myDataGroups <- myData %>%
mutate(origOrder = row_number()) %>%
group_by(Element) %>%
mutate(ElementCnt = row_number()) %>%
ungroup() %>%
mutate(Group = factor(Group, unique(Group))) %>%
arrange(Group) %>%
mutate(groupCt = cumsum(Group != lag(Group, 1, Group[[1]])) - 1L) %>%
as.data.frame()
myDataGroups
We may use rowid to get the sequence to update the 'Group', and then create a logical vector on 'Group' to create the binary and use cumsum on the 'excessOver2' and take the lag
library(dplyr)
library(data.table)
myDataGroups %>%
mutate(Group = rowid(Element, Group),
excessOver2 = +(Group > 2), adjust = lag(cumsum(excessOver2),
default = 0))
-output
Element Group origOrder ElementCnt groupCt excessOver2 adjust
1 A 1 1 1 -1 0 0
2 B 1 2 1 0 0 0
3 B 2 3 2 0 0 0
4 B 3 4 3 0 1 0
5 B 1 5 4 1 0 1
6 B 2 6 5 1 0 1
7 B 1 7 6 2 0 1
8 B 2 8 7 2 0 1
library(dplyr)
myData %>%
group_by(Element, Group) %>%
summarize(ElementCnt = row_number(), over2 = 1 * (ElementCnt > 2),
.groups = "drop_last") %>%
mutate(adjust = cumsum(lag(over2, default = 0))) %>%
ungroup()
Result
# A tibble: 8 × 5
Element Group ElementCnt over2 adjust
<chr> <dbl> <int> <dbl> <dbl>
1 A 0 1 0 0
2 B 1 1 0 0
3 B 1 2 0 0
4 B 1 3 1 0
5 B 2 1 0 1
6 B 2 2 0 1
7 B 3 1 0 1
8 B 3 2 0 1
I have the following dataframe:
df <-read.table(header=TRUE, text="id code
1 A
1 B
1 C
2 A
2 A
2 A
3 A
3 B
3 A")
Per id, I would love to find those individuals that have at least 2 conditions, namely:
conditionA = "A"
conditionB = "B"
conditionC = "C"
and create a new colum with "index", 1 if there are two or more conditions met and 0 otherwise:
df_output <-read.table(header=TRUE, text="id code index
1 A 1
1 B 1
1 C 1
2 A 0
2 A 0
2 A 0
3 A 1
3 B 1
3 A 1")
So far I have tried the following:
df_output = df %>%
group_by(id) %>%
mutate(index = ifelse(grepl(conditionA|conditionB|conditionC, code), 1, 0))
and as you can see I am struggling to get the threshold count into the code.
You can create a vector of conditions, and then use %in% and sum to count the number of occurrences in each group. Use + (or ifelse) to convert logical into 1 and 0:
conditions = c("A", "B", "C")
df %>%
group_by(id) %>%
mutate(index = +(sum(unique(code) %in% conditions) >= 2))
id code index
1 1 A 1
2 1 B 1
3 1 C 1
4 2 A 0
5 2 A 0
6 2 A 0
7 3 A 1
8 3 B 1
9 3 A 1
You could use n_distinct(), which is a faster and more concise equivalent of length(unique(x)).
df %>%
group_by(id) %>%
mutate(index = +(n_distinct(code) >= 2)) %>%
ungroup()
# # A tibble: 9 × 3
# id code index
# <int> <chr> <int>
# 1 1 A 1
# 2 1 B 1
# 3 1 C 1
# 4 2 A 0
# 5 2 A 0
# 6 2 A 0
# 7 3 A 1
# 8 3 B 1
# 9 3 A 1
You can check conditions using intersect() function and check whether resulting list is of minimal (eg- 2) length.
conditions = c('A', 'B', 'C')
df_output2 =
df %>%
group_by(id) %>%
mutate(index = as.integer(length(intersect(code, conditions)) >= 2))
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 a large data set and I'm trying to filter the days following a specific event for each subject. This issue is that the "event" of interest may happen multiple times for some subjects and for a few subjects the event doesn't happen at all (in which case they could just be removed from the summarized data).
Here is an example of the data and what I've tried:
library(tidyverse)
set.seed(355)
subject <- c(rep(LETTERS[1:4], each = 40), rep("E", times = 40))
event <- c(sample(0:1, size = length(subject)-40, replace = T, prob = c(0.95, 0.05)), rep(0, times = 40))
df <- data.frame(subject, event)
df %>%
filter(event == 1) %>%
count(subject, event, sort = T)
# A tibble: 4 x 3
subject event n
<fct> <dbl> <int>
1 D 1 3
2 A 1 2
3 B 1 2
4 C 1 2
So we see that subject D has had the event 3 times while subjects A, B, and C have had the event 2 times. Subject E has not had the event at all.
My next step was to create an "event" tag that identifies where each event happened and then produced an NA for all over rows. I also created an event sequence, which sequences along between events, because I thought it might be useful, but I didn't end up trying to use it.
df_cleaned <- df %>%
group_by(subject, event) %>%
mutate(event_seq = seq_along(event == 1),
event_detail = ifelse(event == 1, "event", NA)) %>%
as.data.frame()
I tried two different approaches using a filter() and between() to get each event and the 2 rows following each event. Both of these approaches create an error because of the multiple events within subject. I can't figure out a good workaround for it.
Approach 1:
df_cleaned %>%
group_by(subject) %>%
filter(., between(row_number(),
left = which(!is.na(event_detail)),
right = which(!is.na(event_detail)) + 1))
Approach 2:
df_cleaned %>%
group_by(subject) %>%
mutate(event_group = cumsum(!is.na(event_detail))) %>%
filter(., between(row_number(), left = which(event_detail == "event"), right = which(event_detail == "event") + 2))
If you want to get rows with 1 in event and the following two rows, you can do the following. Thanks to Ananda Mahto who is the author of splitstackshape package, we can handle this type of operation with getMyRows(), which returns a list. You can specify a range of rows in the function. Here I said 0:2. So I am asking R to take each row with 1 in event and the following two rows. I used bind_rows() to return a data frame. But if you need to work with a list, you do not have to do that.
install_github("mrdwab/SOfun")
library(SOfun)
library(dplyr)
ind <- which(x = df$event == 1)
bind_rows(getMyRows(data = df, pattern = ind, range = 0:2))
subject event
1 A 1
2 A 0
3 A 0
4 A 1
5 A 0
6 A 0
7 B 1
8 B 0
9 B 0
10 B 1
11 B 0
12 B 0
13 C 1
14 C 0
15 C 0
16 C 1
17 C 0
18 C 0
19 D 1
20 D 0
21 D 0
22 D 1
23 D 0
24 D 0
25 D 1
26 D 0
27 D 0
Here is a tidyverse approach which uses cumsum() to create groups of rows after (and including) an event and which picks the top 3 rows of each group:
df %>%
group_by(subject) %>%
mutate(event_group = cumsum(event == 1L)) %>%
group_by(event_group, add = TRUE) %>%
filter(event_group > 0 & row_number() <= 3L)
# A tibble: 27 x 3
# Groups: subject, event_group [9]
subject event event_group
<fct> <dbl> <int>
1 A 1 1
2 A 0 1
3 A 0 1
4 A 1 2
5 A 0 2
6 A 0 2
7 B 1 1
8 B 0 1
9 B 0 1
10 B 1 2
# … with 17 more rows
For testing an edge case, here is a modified data set where subject A starts with three subsequent events. Furthermore, I have added row numbers rn in order to check that the correct rows are picked:
df2 <- df %>%
mutate(event = ifelse(row_number() <= 2L, 1L, event),
rn = row_number())
Now we get
df2 %>%
group_by(subject) %>%
mutate(event_group = cumsum(event == 1L)) %>%
group_by(event_group, add = TRUE) %>%
filter(event_group > 0 & row_number() <= 3L)
# A tibble: 29 x 4
# Groups: subject, event_group [11]
subject event rn event_group
<fct> <dbl> <int> <int>
1 A 1 1 1
2 A 1 2 2
3 A 1 3 3
4 A 0 4 3
5 A 0 5 3
6 A 1 22 4
7 A 0 23 4
8 A 0 24 4
9 B 1 59 1
10 B 0 60 1
# … with 19 more rows
which is in line with my expectations for this edge case.
Here is a base R option which looks similar to #jazzurro's attempt. We get the row indices where event == 1, then select next two rows from each index, use unique so in case there are overlapping indices we select only the unique ones and subset it from the original df.
inds <- which(df$event == 1)
df[unique(c(sapply(inds, `+`, 0:2))), ]
# subject event
#3 A 1
#4 A 0
#5 A 0
#22 A 1
#23 A 0
#24 A 0
#59 B 1
#60 B 0
#61 B 0
#62 B 1
#63 B 0
#64 B 0
#....
Another option using dplyr, could be using lag
library(dplyr)
df %>%
group_by(subject) %>%
filter(event == 1 | lag(event) == 1 | lag(event, 2) == 1)
I am running the following code to count the amount of days that sites are over 90.
temp2 <- temp %>%
filter(ds > '2017-12-31') %>%
filter(over90 == 1) %>%
group_by(site) %>%
tally()
However, this drops sites which do not have days over 90. I want my output to include these sites with a value of zero, instead of simply not existing. Any way I can do this?
Pretend data:
site over90
a 1
a 0
a 1
b 0
What happens:
site n
a 2
What I want:
site n
a 2
b 0
If your site is a factor variable you can use tidyr::complete to fill in the blanks at the end:
filter(dd, over90 == 1) %>%
group_by(site) %>%
tally() %>%
complete(site, fill = list(n = 0))
# A tibble: 2 x 2
site n
<fctr> <dbl>
1 a 2
2 b 0
data
dd <- read.table(text =
"site over90
a 1
a 0
a 1
b 0",
header = TRUE)
tally isn't doing quite what you want it to. Since you just want to count the number of rows where over_90 is equal to one and that variable is binary, you can sum that column to get the effect of counting.
df = data_frame(site=sample(letters[1:3], 10, replace=TRUE),
over_90=sample(0:1, 10, replace=TRUE))
df
# A tibble: 10 x 2
# site over_90
# <chr> <int>
# 1 c 0
# 2 b 1
# 3 c 0
# 4 b 0
# 5 c 1
# 6 b 0
# 7 a 0
# 8 a 1
# 9 c 1
#10 c 0
df$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)