I have a data frame that looks as follows:
WORD CATEGORY n
<fct> <fct> <int>
1 A X 4
2 B X 3
3 C X 6
4 C Y 3
5 D X 2
6 E X 2
7 F Y 2
I want to add a column sum that adds together values in the column n based on CATEGORY. So in rows 3 and 4, for instance, the value of the sum column would be 9.
Here is what the full dataset would look like:
WORD CATEGORY n sum
<fct> <fct> <int> <int>
1 A X 4 4
2 B X 3 3
3 C X 6 9
4 C Y 3 9
5 D X 2 2
6 E X 2 2
7 F Y 2 2
How do I do this in the tidyverse?
If we count the number of unique values in CATEGORY and add it to the grouping variables we can directly sum up the n's:
dt %>%
group_by(WORD) %>%
mutate(uni=length(unique(CATEGORY))) %>%
group_by(WORD,uni) %>%
mutate(sum=sum(n)) %>%
ungroup %>%
select(-uni)
# A tibble: 7 x 4
WORD CATEGORY n sum
<fct> <fct> <int> <int>
1 A X 4 4
2 B X 3 3
3 C X 6 9
4 C Y 3 9
5 D X 2 2
6 E X 2 2
7 F Y 2 2
Related
I have a data frame with ten columns, but five columns of concern: A, B, C, D, E. I also have a list of values. What's the best way to subset the rows whose values in column A, B, C, D, OR, E is included in the list of values?
If I were only concerned with a single column, I know I can use left_join(list_of_values, df$A) but I'm not sure how to do something similar with multiple columns.
The key here is if_any.
library(tidyverse)
set.seed(26)
sample_df <- tibble(col = rep(LETTERS[1:8], each = 5),
val = sample(1:10, 40, replace = TRUE),
ID = rep(1:5, 8)) |>
pivot_wider(names_from = col, values_from = val)
sample_df
#> # A tibble: 5 x 9
#> ID A B C D E F G H
#> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 8 4 10 7 2 7 4 3
#> 2 2 3 2 3 3 4 10 2 3
#> 3 3 9 6 6 8 2 10 10 3
#> 4 4 7 6 8 9 3 5 8 3
#> 5 5 6 3 4 1 9 7 9 1
vals <- c(1, 7)
#solution
sample_df |>
filter(if_any(A:E, ~. %in% vals))
#> # A tibble: 3 x 9
#> ID A B C D E F G H
#> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 8 4 10 7 2 7 4 3
#> 2 4 7 6 8 9 3 5 8 3
#> 3 5 6 3 4 1 9 7 9 1
or any and apply with base R:
#base solution
indx <- apply(sample_df[,which(colnames(sample_df) %in% LETTERS[1:5])], 1, \(x) any(x %in% vals))
sample_df[indx,]
#> # A tibble: 3 x 9
#> ID A B C D E F G H
#> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 8 4 10 7 2 7 4 3
#> 2 4 7 6 8 9 3 5 8 3
#> 3 5 6 3 4 1 9 7 9 1
DATA = data.frame(STUDENT=c(1,1,1,1,2,2,2,3,3,3,3,3),
T = c(1,2,3,4,1,2,3,1,2,3,4,5),
SCORE=c(NA,1,5,2,3,4,4,1,4,5,2,2),
WANT=c('N','N','P','P','N','N','N','N','N','P','P','P'))
)
I have 'DATA' and wish to create 'WANT' variable where is 'N' but within each 'STUDENT' when there is a score of '5' OR HIGHER than the 'WANT' value is 'P' and stays that way I seek a dplyr solutions
You can use cumany:
library(dplyr)
DATA %>%
group_by(STUDENT) %>%
mutate(WANT2 = ifelse(cumany(ifelse(is.na(SCORE), 0, SCORE) == 5),
"N", "P"))
# A tibble: 12 × 5
# Groups: STUDENT [3]
STUDENT T SCORE WANT WANT2
<dbl> <dbl> <dbl> <chr> <chr>
1 1 1 NA N N
2 1 2 1 N N
3 1 3 5 P P
4 1 4 2 P P
5 2 1 3 N N
6 2 2 4 N N
7 2 3 4 N N
8 3 1 1 N N
9 3 2 4 N N
10 3 3 5 P P
11 3 4 2 P P
12 3 5 2 P P
You can use cummax():
library(dplyr)
DATA %>%
group_by(STUDENT) %>%
mutate(WANT = c("N", "P")[cummax(SCORE >= 5 & !is.na(SCORE))+1])
# A tibble: 12 × 4
# Groups: STUDENT [3]
STUDENT T SCORE WANT
<dbl> <dbl> <dbl> <chr>
1 1 1 NA N
2 1 2 1 N
3 1 3 5 P
4 1 4 2 P
5 2 1 3 N
6 2 2 4 N
7 2 3 4 N
8 3 1 1 N
9 3 2 4 N
10 3 3 5 P
11 3 4 2 P
12 3 5 2 P
I am using the separate_rows function from tidyr.
Essentially, I would like to change the value of the data that is copied -- in the example below, it would read: "everytime a new row is created, multiply z by 0.5"
I already added an index in the default df. so it could be "everytime the index N is the same as [-1], multiply z by 0.5"
df <- tibble(
x = 1:4,
y = c("a", "b,c,d", "e,f"),
z = 1:4
)
# A tibble: 3 x 3
x y z
<int> <chr> <int>
1 1 a 1
2 2 b,c,d 2
3 3 e,f 3
what we get:
> separate_rows(df, y)
# A tibble: 6 x 3
x y z
<int> <chr> <int>
1 1 a 1
2 2 b 2
3 2 c 2
4 2 d 2
5 3 e 3
6 3 f 3
what I would need (the z values that have a new row multipled by 0.5:
# A tibble: 6 x 3
x y z
<int> <chr> <int>
1 1 a 1
2 2 b 1
3 2 c 1
4 2 d 1
5 3 e 1.5
6 3 f 1.5
You can group by z and multiply if n > 1.
df %>%
separate_rows(y) %>%
group_by(z) %>%
mutate(z = ifelse(n() > 1, z*0.5, z))
x y z
<int> <chr> <dbl>
1 1 a 1
2 2 b 1
3 2 c 1
4 2 d 1
5 3 e 1.5
6 3 f 1.5
An option is also to multiply 'z' by 0.5, get the pmax with 1 and then use separate_rows
library(dplyr)
library(tidyr)
df %>%
mutate(z = pmax(1, z * 0.5)) %>%
separate_rows(y)
-output
# A tibble: 6 × 3
x y z
<int> <chr> <dbl>
1 1 a 1
2 2 b 1
3 2 c 1
4 2 d 1
5 3 e 1.5
6 3 f 1.5
I want to built a dataframe like df2 from df1, looking always for the name of the column where the value is closet to 0: Where clossets_1 - closer value to 0 of the columns x,y and z. clossets_2 - closer value to 0 of the columns x and a, because x is the most received value in clossets_1. clossets_3 - closer value to 0 of the columns a and b, because a is the most received value in clossets_2.
df1
df1
# x y z a b
#1 1 2 3 4 3
#2 2 3 4 1 2
#3 3 2 4 2 1
#4 4 3 2 3 6
Desire output:
df2
# x y z clossets_1 a clossets_2 b clossets_3
#1 1 2 3 x 4 x 3 b
#2 2 3 4 x 1 a 2 a
#3 3 2 4 y 2 a 1 b
#4 4 3 2 z 3 a 2 b
Here is the first step to get you started:
cols = c("x","y","z")
df2 = df1
df2$clossets_1 = cols[apply(df1[,cols], 1, function(x) {which(x == min(x))})]
df2
## x y z a b clossets_1
## 1 1 2 3 4 3 x
## 2 2 3 4 1 2 x
## 3 3 2 4 2 1 y
## 4 4 3 2 3 6 z
I solved it this way, using the first step of #BigFinger answer and the mlv() function from the package modeest to find the most repeated value in the closests columns
library(DescTools)
library(modeest)
library(tibble)
df1 = tibble(x = c(1,2,3,4),
y = c(2,3,2,3),
z = c(3,4,4,2),
clossest_1 = c("x","y","z")[apply(data.frame(x,y,z),1,function(x){which(x == Closest(x,0))})],
a = c(4,1,2,3),
clossest_2 = c(mlv(clossest_1),"a")[apply(data.frame(get(mlv(clossest_1)),a),1,function(x){which(x == Closest(x,0))})],
b = c(3,2,1,2),
clossest_3 = c(mlv(clossest_2),"b")[apply(data.frame(get(mlv(clossest_2)),b),1,function(x){which(x == Closest(x,0))})])
df1
# A tibble: 4 x 8
# x y z clossest_1 a clossest_2 b clossest_3
# <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <chr>
#1 1 2 3 x 4 x 3 b
#2 2 3 4 x 1 a 2 a
#3 3 2 4 y 2 a 1 b
#4 4 3 2 z 3 a 2 b
For demonstration purpose, I have dataset as following:
df <- data.frame(A = as.factor(floor(runif(20,1,6))),
B = as.factor(floor(runif(20,1,6))),
C = as.factor(floor(runif(20,1,6))),
D = c(rep('X',3), rep("Y",7), rep('Z',10)))
How can I iterate through column A, B and C to get counts for
count(df, D, A), count(df, D, B) and count(df, D, C)
This is a simplified version, if I need to do this for 20 or more variables, how can I automate the process?
I have tried:
f <- function(x) count(df, D, x)
result <- bind_rows(lapply(df[ , c('A','B','C')], f))
and I got the following error:
Error in grouped_df_impl(data, unname(vars), drop) :
Column `x` is unknown
Would using tidyr::gather first work for you so you can do the count all at once for the different variables? As #alistaire noted in the comments, this can be done using
df %>% gather(key, value, -D) %>% count(D, key, value)
which results in the same output as my unnecessary extra use of group_by
df %>% gather(key, value, -D) %>% group_by(D, key) %>% count(value)
Worked Solution
library(tidyverse)
df %>% gather(key, value, -D) %>% group_by(D, key) %>% count(value)
#> # A tibble: 34 x 4
#> # Groups: D, key [9]
#> D key value n
#> <fctr> <chr> <chr> <int>
#> 1 X A 2 1
#> 2 X A 3 1
#> 3 X A 4 1
#> 4 X B 4 2
#> 5 X B 5 1
#> 6 X C 1 1
#> 7 X C 3 2
#> 8 Y A 1 1
#> 9 Y A 3 3
#> 10 Y A 5 3
#> # ... with 24 more rows
Source data
set.seed(123)
df<-data.frame(A=as.factor(floor(runif(20,1,6))),
B=as.factor(floor(runif(20,1,6))),
C=as.factor(floor(runif(20,1,6))),
D=c(rep('X',3),rep("Y",7),rep('Z',10)))
We can use map2 to do the individual count of the subset of columns that involve columns other than 'D' with that of 'D'
library(tidyverse)
lst <- map2(names(df)[1:3], names(df)[4], ~count(df[c(.x, .y)],
!!!rlang::syms(c(.x, .y))))
lst
#[[1]]
# A tibble: 11 x 3
# A D n
# <fctr> <fctr> <int>
# 1 1 Z 2
# 2 2 X 1
# 3 2 Y 1
# 4 2 Z 2
# 5 3 X 2
# 6 3 Y 2
# 7 3 Z 4
# 8 4 Y 2
# 9 4 Z 1
#10 5 Y 2
#11 5 Z 1
#[[2]]
# A tibble: 11 x 3
# B D n
# <fctr> <fctr> <int>
# 1 1 Y 2
# 2 1 Z 2
# 3 2 Y 1
# 4 2 Z 1
# 5 3 Y 1
# 6 3 Z 2
# 7 4 X 3
# 8 4 Y 2
# 9 4 Z 3
#10 5 Y 1
#11 5 Z 2
#[[3]]
# A tibble: 12 x 3
# C D n
# <fctr> <fctr> <int>
# 1 1 Y 1
# 2 1 Z 1
# 3 2 X 2
# 4 2 Y 1
# 5 2 Z 4
# 6 3 X 1
# 7 3 Y 2
# 8 3 Z 1
# 9 4 Y 2
#10 4 Z 3
#11 5 Y 1
#12 5 Z 1
It is not clear whether to have a single dataset or a list of datasets