Is it possible to use group_by to group one variable and count the target variable based on another variable?
For example,
x1
x2
x3
A
1
0
B
2
1
C
3
0
B
1
1
A
1
1
I want to count 0 and 1 of x3 with grouped x1
x1
x3=0
x3=1
A
1
1
B
0
2
C
1
0
Is it possible to use group_by and add something to summarize? I tried group_by both x1 and x3, but that gives x3 as the second column which is not what we are looking for.
If it's not possible to just use group_by, I was thinking we could group_by both x1 and x3, then split by x3 and cbind them, but the two dataframes after split have different lengths of rows, and there's no cbind_fill. What should I do to cbind them and fill the extra blanks?
using the data.table package:
library(data.table)
dat <- as.data.table(dataset)
dat[, x3:= paste0("x3=", x3)]
result <- dcast(dat, x1~x3, value.var = "x3", fun.aggregate = length)
A tidyverse approach to achieve your desired result using dplyr::count + tidyr::pivot_wider:
library(dplyr)
library(tidyr)
df %>%
count(x1, x3) %>%
pivot_wider(names_from = "x3", values_from = "n", names_prefix = "x3=", values_fill = 0)
#> # A tibble: 3 × 3
#> x1 `x3=0` `x3=1`
#> <chr> <int> <int>
#> 1 A 1 1
#> 2 B 0 2
#> 3 C 1 0
DATA
df <- data.frame(
x1 = c("A", "B", "C", "B", "A"),
x2 = c(1L, 2L, 3L, 1L, 1L),
x3 = c(0L, 1L, 0L, 1L, 1L)
)
Yes, it is possible. Here is an example:
dat = read.table(text = "x1 x2 x3
A 1 0
B 2 1
C 3 0
B 1 1
A 1 1", header = TRUE)
dat %>% group_by(x1) %>%
count(x3) %>%
pivot_wider(names_from = x3,
names_glue = "x3 = {x3}",
values_from = n) %>%
replace(is.na(.),0)
# A tibble: 3 x 3
# Groups: x1 [3]
# x1 `x3 = 0` `x3 = 1`
# <chr> <int> <int>
#1 A 1 1
#2 B 0 2
#3 C 1 0
Related
I have a dataframe with count information (df1)
rownames
sample1
sample2
sample3
m1
0
5
1
m2
1
7
5
m3
6
2
0
m4
3
1
0
and a second with sample information (df2)
rownames
batch
total count
sample1
a
10
sample2
b
15
sample3
a
6
I also have two lists with information about the m values (could easily be turned into another data frame if necessary but I would rather not add to the count information as it is quite large). No patterns (such as even and odd) exist, I am just using a very simplistic example
x <- c("m1", "m3") and y <- c("m2", "m4")
What I would like to do is add another two columns to the sample information. This is a count of each m per sample that has a value of above 5 and appears in list x or y
rownames
batch
total count
x
y
sample1
a
10
1
0
sample2
b
15
1
1
sample3
a
6
0
1
My current strategy is to make a list of values for both x and y and then append them to df2. Here are my attempts so far:
numX <- colSums(df1[sum(rownames(df1)>10 %in% x),]) and numX <- colSums(df1[sum(rownames(df1)>10 %in% x),]) both return a list of 0s
numX <- colSums(df1[rownames(df1)>10 %in% x,]) returns a list of the sum of count values meeting the conditions for each column
numX <- length(df1[rownames(df1)>10 %in% novel,]) returns the number of times the condition is met (in this example 2L)
I am not really sure how to approach this so I have just been throwing around attempts. I've tried looking for answers but maybe I am just struggling to find the proper wording.
We may do this with rowwise
library(dplyr)
df2 %>%
rowwise %>%
mutate(x = +(sum(df1[[rownames]][df1$rownames %in% x]) >= 5),
y = +(sum(df1[[rownames]][df1$rownames %in% y]) >= 5)) %>%
ungroup
-output
# A tibble: 3 × 5
rownames batch totalcount x y
<chr> <chr> <int> <int> <int>
1 sample1 a 10 1 0
2 sample2 b 15 1 1
3 sample3 a 6 0 1
Or based on the data, a base R option would be
out <- aggregate(. ~ grp, FUN = sum,
transform(df1, grp = c('x', 'y')[1 + (rownames %in% y)] )[-1])
df2[out$grp] <- +(t(out[-1]) >= 5)
-output
> df2
rownames batch totalcount x y
1 sample1 a 10 1 0
2 sample2 b 15 1 1
3 sample3 a 6 0 1
data
df1 <- structure(list(rownames = c("m1", "m2", "m3", "m4"), sample1 = c(0L,
1L, 6L, 3L), sample2 = c(5L, 7L, 2L, 1L), sample3 = c(1L, 5L,
0L, 0L)), class = "data.frame", row.names = c(NA, -4L))
df2 <- structure(list(rownames = c("sample1", "sample2", "sample3"),
batch = c("a", "b", "a"), totalcount = c(10L, 15L, 6L)),
class = "data.frame", row.names = c(NA,
-3L))
How about using using dplyr and reshape2::melt
df3 <- df1 %>%
melt %>%
filter(value >= 5) %>%
mutate(x = as.numeric(rownames %in% c("m1", "m3")),
y = as.numeric(rownames %in% c("m2", "m4"))) %>%
select(-rownames, - value) %>%
group_by(variable) %>%
summarise(x = sum(x), y = sum(y))
df2 %>% left_join(df3, by = c("rownames" = "variable"))
rownames batch total_count x y
1 sample1 a 10 1 0
2 sample2 b 15 1 1
3 sample3 a 6 0 1
You can create a named list of vectors and for each rownames count how many values of x and y in the respective sample is >= 5.
Base R option -
list_vec <- list(x = x, y = y)
cbind(df2, do.call(rbind, lapply(df2$rownames, function(x)
sapply(list_vec, function(y) {
sum(df1[[x]][df1$rownames %in% y] >= 5)
}))))
# rownames batch total.count x y
#1 sample1 a 10 1 0
#2 sample2 b 15 1 1
#3 sample3 a 6 0 1
Using tidyverse -
library(dplyr)
library(purrr)
list_vec <- lst(x, y)
df2 %>%
bind_cols(map_df(df2$rownames, function(x)
map(list_vec, ~sum(df1[[x]][df1$rownames %in% .x] >= 5))))
I'd like to find consecutive month by client. I thought this is easy but
still can't find solutions..
My goal is to find months' consecutive purchases for each client. Any
My data
Client Month consecutive
A 1 1
A 1 2
A 2 3
A 5 1
A 6 2
A 8 1
B 8 1
In base R, we can use ave
df$consecutive <- with(df, ave(Month, Client, cumsum(c(TRUE, diff(Month) > 1)),
FUN = seq_along))
df
# Client Month consecutive
#1 A 1 1
#2 A 1 2
#3 A 2 3
#4 A 5 1
#5 A 6 2
#6 A 8 1
#7 B 8 1
In dplyr, we can create a new group with lag to compare the current month with the previous month and assign row_number() in each group.
library(dplyr)
df %>%
group_by(Client,group=cumsum(Month-lag(Month, default = first(Month)) > 1)) %>%
mutate(consecutive = row_number()) %>%
ungroup %>%
select(-group)
We can create a grouping variable based on the difference in adjacent 'Month' for each 'Client' and use that to create the sequence
library(dplyr)
df1 %>%
group_by(Client) %>%
group_by(grp =cumsum(c(TRUE, diff(Month) > 1)), add = TRUE) %>%
mutate(consec = row_number()) %>%
ungroup %>%
select(-grp)
# A tibble: 7 x 4
# Client Month consecutive consec
# <chr> <int> <int> <int>
#1 A 1 1 1
#2 A 1 2 2
#3 A 2 3 3
#4 A 5 1 1
#5 A 6 2 2
#6 A 8 1 1
#7 B 8 1 1
Or using data.table
library(data.table)
setDT(df1)[, grp := cumsum(c(TRUE, diff(Month) > 1)), Client
][, consec := seq_len(.N), .(Client, grp)
][, grp := NULL][]
data
df1 <- structure(list(Client = c("A", "A", "A", "A", "A", "A", "B"),
Month = c(1L, 1L, 2L, 5L, 6L, 8L, 8L), consecutive = c(1L,
2L, 3L, 1L, 2L, 1L, 1L)), class = "data.frame", row.names = c(NA,
-7L))
I am trying to prep my data and I am stuck with one issue. Lets say I have the following data frame:
df1
Name C1 Val1
A a x1
A a x2
A b x3
A c x4
B d x5
B d x6
...
and I want to narrow down the df to
df2
Name C1 Val
A a,b,c x1+x2+x3+x4
B d x5+x6
...
while a is a character value and x is numeric value
I have been trying using sapply, rowsum and
df2<- aggregate(df1, list(df1[,1]), FUN= summary)
but it just can't put the character values in a list for each Name.
Can someone help me how to receive df2?
m <- function(x) if(is.numeric(x<- type.convert(x)))sum(x) else toString(unique(x))
aggregate(.~Name,df1,m)
Name C1 Val1
1 A a, b, c 10
2 B d 11
where
df1
Name C1 Val1
1 A a 1
2 A a 2
3 A b 3
4 A c 4
5 B d 5
6 B d 6
This is your df, I give it numbers 1 to 6 in Val1
df <-
structure(list(Name = structure(c(1L, 1L, 1L, 1L, 2L, 2L), .Label = c("A",
"B"), class = "factor"), C1 = structure(c(1L, 1L, 2L, 3L, 4L,
4L), .Label = c("a", "b", "c", "d"), class = "factor"), Val1 = 1:6), row.names = c(NA,
-6L), class = "data.frame")
We just use summarise:
df %>%
group_by(Name) %>%
summarise(C1=paste(unique(C1),collapse=","),Val1=sum(Val1))
# A tibble: 2 x 3
Name C1 Val1
<fct> <chr> <int>
1 A a,b,c 10
2 B d 11
Quick and easy dplyr solution:
library(dplyr)
library(stringr)
df1 %>%
mutate(Val1_num = as.numeric(str_extract(Val1, "\\d+"))) %>%
group_by(Name) %>%
summarise(C1 = paste(unique(C1), collapse = ","),
Val1 = paste(unique(Val1), collapse = "+"),
Val1_num = sum(Val1_num))
#> # A tibble: 2 x 4
#> Name C1 Val1 Val1_num
#> <chr> <chr> <chr> <dbl>
#> 1 A a,b,c x1+x2+x3+x4 10
#> 2 B d x5+x6 11
Or in base:
df2 <- aggregate(df1, list(df1[,1]), FUN = function(x) {
if (all(grepl("\\d", x))) {
sum(as.numeric(gsub("[^[:digit:]]", "", x)))
} else {
paste(unique(x), collapse = ",")
}
})
df2
#> Group.1 Name C1 Val1
#> 1 A A a,b,c 10
#> 2 B B d 11
data
df1 <- read.csv(text = "
Name,C1,Val1
A,a,x1
A,a,x2
A,b,x3
A,c,x4
B,d,x5
B,d,x6", stringsAsFactors = FALSE)
I have some poorly formatted data that I must work with. It contains two identifiers in the first two rows, followed by the data. The data looks like:
V1 V2 V3
1 Date 12/16/18 12/17/18
2 Equip a b
3 x1 1 2
4 x2 3 4
5 x3 5 6
I want to gather the data to make it tidy, but gathering only works when you have single column names. I've tried looking at spreading as well. The only solutions I've come up with are very hacky and don't feel right. Is there an elegant way to deal with this?
Here's what I want:
Date Equip metric value
1 12/16/18 a x1 1
2 12/16/18 a x2 3
3 12/16/18 a x3 5
4 12/17/18 b x1 2
5 12/17/18 b x2 4
6 12/17/18 b x3 6
This approach gets me close, but I don't know how to deal with the poor formatting (no header, no row names). It should be easy to gather if the formatting was proper.
> as.data.frame(t(df))
V1 V2 V3 V4 V5
V1 Date Equip x1 x2 x3
V2 12/16/18 a 1 3 5
V3 12/17/18 b 2 4 6
And here's the dput
structure(list(V1 = c("Date", "Equip", "x1", "x2", "x3"), V2 = c("12/16/18",
"a", "1", "3", "5"), V3 = c("12/17/18", "b", "2", "4", "6")), class = "data.frame", .Names = c("V1",
"V2", "V3"), row.names = c(NA, -5L))
Thanks for posting a nicely reproducible question. Here's some gentle tidyr/dplyr massaging.
library(tidyr)
df %>%
gather(key = measure, value = value, -V1) %>%
spread(key = V1, value = value) %>%
dplyr::select(-measure) %>%
gather(key = metric, value = value, x1:x3) %>%
dplyr::arrange(Date, Equip, metric)
#> Date Equip metric value
#> 1 12/16/18 a x1 1
#> 2 12/16/18 a x2 3
#> 3 12/16/18 a x3 5
#> 4 12/17/18 b x1 2
#> 5 12/17/18 b x2 4
#> 6 12/17/18 b x3 6
Updated for tidyr v1.0.0:
This is just a little bit cleaner syntax with the pivot functions.
df %>%
pivot_longer(cols = -V1) %>%
pivot_wider(names_from = V1) %>%
pivot_longer(cols = matches("x\\d"), names_to = "metric") %>%
dplyr::select(-name)
You can using reshape
library(reshape)
row.names(df) = df$V1
df$V1 = NULL
df = melt(data.frame(t(df)),id.var = c('Date','Equip'))
df[order(df$Date),]
Date Equip variable value
1 12/16/18 a x1 1
3 12/16/18 a x2 3
5 12/16/18 a x3 5
2 12/17/18 b x1 2
4 12/17/18 b x2 4
6 12/17/18 b x3 6
Here's another way starting from your approach using t(). We can replace the headers from the first row and then drop the first row, allowing just a single gather which might be more intuitive.
library(tidyverse)
df <- structure(list(V1 = c("Date", "Equip", "x1", "x2", "x3"), V2 = c(
"12/16/18",
"a", "1", "3", "5"
), V3 = c("12/17/18", "b", "2", "4", "6")), class = "data.frame", .Names = c(
"V1",
"V2", "V3"
), row.names = c(NA, -5L))
df %>%
t() %>%
`colnames<-`(.[1, ]) %>%
`[`(-1, ) %>%
as_tibble() %>%
gather("metric", "value", x1:x3) %>%
arrange(Date, Equip, metric)
#> # A tibble: 6 x 4
#> Date Equip metric value
#> <chr> <chr> <chr> <chr>
#> 1 12/16/18 a x1 1
#> 2 12/16/18 a x2 3
#> 3 12/16/18 a x3 5
#> 4 12/17/18 b x1 2
#> 5 12/17/18 b x2 4
#> 6 12/17/18 b x3 6
Created on 2018-04-20 by the reprex package (v0.2.0).
I have the following data frame:
df <- structure(list(x = c(0.389794300700167, -1.20807617542949, -0.363676017470862,
-1.62667268170309, -0.256478394123992, 1.10177950308713, 0.755781508027337,
-0.238233556018718, 0.98744470341339, 0.741390128383824), y = c(0.0893472664958216,
-0.954943856152377, -0.195150384667239, 0.92552126209408, 0.482978524836611,
-0.596310636720207, -2.18528683816953, -0.674865937875116, -2.11906119191017,
-1.2651980215309), fac = structure(c(2L, 1L, 2L, 3L, 1L, 1L,
1L, 1L, 2L, 2L), .Label = c("A", "B", "C"), class = "factor")), .Names = c("x",
"y", "fac"), row.names = c(NA, -10L), class = "data.frame")
df
#> x y fac manual_assignment
#> 1 0.3897943 0.08934727 B b.x
#> 2 -1.2080762 -0.95494386 A a.y
#> 3 -0.3636760 -0.19515038 B b.y
#> 4 -1.6266727 0.92552126 C c.y
#> 5 -0.2564784 0.48297852 A a.y
#> 6 1.1017795 -0.59631064 A a.x
#> 7 0.7557815 -2.18528684 A a.x
#> 8 -0.2382336 -0.67486594 A a.x
#> 9 0.9874447 -2.11906119 B b.x
#> 10 0.7413901 -1.26519802 B b.x
What I want to do is to count rows based on comparing values in x and y. For every row, if the value x is larger than y, we increase the count of fac member by 1. So the end result is this:
x.count y.count
A 3 2 (# a.y)
B 3 1
C 0 1
How can I achieve that? Possible with dplyr?
This is more straight forward with table:
with(df, table(fac, ifelse(x > y, "x.count", "y.count")))
#fac x.count y.count
# A 3 2
# B 3 1
# C 0 1
With dplyr/tidyr, you need a few more lines of code:
library(tidyverse)
df %>%
group_by(fac, measure = if_else(x > y, "x.count", "y.count")) %>%
tally() %>%
spread(measure, n, fill = 0)
#Source: local data frame [3 x 3]
#Groups: fac [3]
# fac x.count y.count
#* <fctr> <dbl> <dbl>
#1 A 3 2
#2 B 3 1
#3 C 0 1
With dplyr we can group by fac and count the length of values where x and y values are greater.
library(dplyr)
df %>%
group_by(fac) %>%
summarise(x.count = length(which(x > y)),
y.count = length(which(x < y)))
# fac x.count y.count
# <fctr> <int> <int>
#1 A 3 2
#2 B 3 1
#3 C 0 1
With data.table, we can use
library(data.table)
dcast(setDT(df)[, .N, .(fac, measure = c('y.count', 'x.count')[(x > y) + 1])],
fac ~measure, fill = 0)
# fac x.count y.count
#1: A 3 2
#2: B 3 1
#3: C 0 1