I have the following dataframe. And would like to get the desired output
data.frame(df)
num Name1 Result1 Name2 Result2 Name3 Result3
1 75% 74 100% 101 50% 50
2 75% 73 100% 101 50% 49
3 50% 50 100% 105 125% 128
I tried Dcast using the following
reshape2::dcast(df, num ~ Name1 + Name2 + Name3, value.var=c("Result1", "Result2", "Result3"))
The output from Dcast is close to my desired output but I would like only unique 'Name' values as my new columns. I can imagine that I can clean the table using aggregate before using Dcast but that seems excessive? I'm not sure if there's a faster way?
Desired output:
num 50% 75% 100% 125%
1 50 74 101 NA
2 49 73 101 NA
3 50 NA 100 128
I would appreciate any help
You can find more information on the steps here and here.
dat %>%
rename_at(vars(matches("[0-9]")),
~str_replace(.,"(\\d)(\\w*)","\\2_\\1")) %>%
pivot_longer(cols=matches("_"),names_to=c(".value","group"),
names_sep="_") %>%
dplyr::select(-group) %>%
pivot_wider(names_from = "Name",values_from="Result")
# A tibble: 3 x 5
num `75%` `100%` `50%` `125%`
<int> <int> <int> <int> <int>
1 1 74 101 50 NA
2 2 73 101 49 NA
3 3 NA 105 50 128
Alternately...
reshape(dat, idvar="num", direction="long",
varying=list(Name=c(2,4,6), Result=c(3,5,7)),
v.names = c("Name", "Result") ) %>%
dplyr::select(-time) %>%
dcast(num ~ Name)
num 50% 75% 100% 125%
1 1 50 74 101 NA
2 2 49 73 101 NA
3 3 50 NA 105 128
Get the data in long format so we have data in two columns Name and Result. We can then get the data in wide format.
library(dplyr)
library(tidyr)
df %>%
pivot_longer(cols = -num,
names_to = '.value',
names_pattern = '([A-Za-z]+)\\d+') %>%
arrange(readr::parse_number(Name)) %>%
pivot_wider(names_from = Name, values_from = Result)
# num `50%` `75%` `100%` `125%`
# <int> <int> <int> <int> <int>
#1 1 50 74 101 NA
#2 2 49 73 101 NA
#3 3 50 NA 105 128
Related
I have a dataset that contains the following values
Item Number Sales in Dollars
1 50 10
2 50 15
3 60 20
4 60 30
5 70 35
6 70 45
I would like to reshape the data such that the result would be
50 60 70
1 10 20 35
2 15 30 45
How could I go about achieving this?
in Base R:
unstack(df, Sales_in_Dollars~Item_Number)
X50 X60 X70
1 10 20 35
2 15 30 45
We could use pivot_wider:
The trick is to group_by and create an id in the group to get this output, otherwise you will get a list with NAs
library(dplyr)
library(tidyr)
df %>%
group_by(ItemNumber) %>%
mutate(id = row_number()) %>%
pivot_wider(names_from=ItemNumber, values_from = SalesinDollars) %>%
select(-id)
`50` `60` `70`
<int> <int> <int>
1 10 20 35
2 15 30 45
With data.table:
data.table::dcast(as.data.table(df),
rowid(`Item.Number`) ~ `Item.Number`,
value.var = "Sales.in.Dollars")[, -1]
Output
50 60 70
<int> <int> <int>
1: 10 20 35
2: 15 30 45
Another possible solution, based on tidyverse:
library(tidyverse)
df %>%
pivot_wider(names_from = item, values_from = sales, values_fn = list) %>%
unnest(everything())
#> # A tibble: 2 x 3
#> `50` `60` `70`
#> <int> <int> <int>
#> 1 10 20 35
#> 2 15 30 45
Let's say I have the following data:
set.seed(123)
test <- tibble(
ID = sample(rep(1:100, rpois(100,4))),
vals = abs(round(rnorm(length(ID), 10000, 5000)))
)
I would like to sort test first by vals and then by ID with the desired output looking like this:
# A tibble: 409 x 2
ID vals
<int> <dbl>
1 48 26522
2 48 14427
3 48 7570
4 48 5922
5 92 25286
6 92 10436
7 92 5705
8 92 4036
9 92 3399
10 64 22190
# ... with 399 more rows
i.e. it should group the sorting by ID and then in decreasing order vals.
What I tried:
test %>% arrange(ID, desc(vals))
test %>% arrange(desc(vals), ID)
test %>% arrange(ID) %>% arrange(desc(vals))
I think you're missing some clarity:
sort first by each ID's maximum value, descending;
Try this:
library(dplyr)
test %>%
mutate(valrank = dense_rank(-vals)) %>%
group_by(ID) %>%
mutate(valrank = min(valrank)) %>%
ungroup() %>%
arrange(valrank, ID, desc(vals))
# # A tibble: 409 x 3
# ID vals valrank
# <int> <dbl> <int>
# 1 48 26522 1
# 2 48 14427 1
# 3 48 7570 1
# 4 48 5922 1
# 5 92 25286 2
# 6 92 10436 2
# 7 92 5705 2
# 8 92 4036 2
# 9 92 3399 2
# 10 64 22190 3
# # ... with 399 more rows
(I kept valrank just for demonstration.)
I am applying a user defined function on numeric variables from a dataset but instead of getting their name's I am getting x when applied using map function. How do I replace x with variable name in map functions?
dataset: hd_trn
age sex cp trestbps chol fbs restecg thalach exang
<int> <fctr> <fctr> <int> <int> <fctr> <fctr> <int> <fctr>
63 1 1 145 233 1 2 150 0
67 1 4 160 286 0 2 108 1
67 1 4 120 229 0 2 129 1
37 1 3 130 250 0 0 187 0
41 0 2 130 204 0 2 172 0
56 1 2 120 236 0 0 178 0
user defined function to calculate high freq elements column wise
top_freq_elements <- function(x){
table(x) %>% as.data.frame() %>% top_n(5, Freq) %>% arrange(desc(Freq))
}
Applying function
hd_trn %>% select_if(is.numeric) %>% map(., .f = top_freq_elements)
######### output #########
x Freq
<fctr> <int>
54 51
58 43
55 41
56 38
57 38
desired: In the above output I am looking to get variable name instead of x
Tried reconstructing code below using imap but that is also not giving variable name:
hd_trn %>%
select_if(is.numeric) %>%
imap(function(feature_value, feature_name){
table(feature_value) %>%
as.data.frame() %>% #head()
rename(feature_name = feature_value) %>%
top_n(5, Freq) %>%
arrange(desc(Freq))
})
######### output #########
feature_name Freq
<fctr> <int>
54 51
58 43
55 41
56 38
57 38
You can rename the 1st column in each list :
library(dplyr)
library(purrr)
iris %>%
select(where(is.numeric)) %>%
imap(function(feature_value, feature_name){
table(feature_value) %>%
as.data.frame() %>%
rename_with(~feature_name, 1) %>%
slice_max(n = 5, Freq) %>%
arrange(desc(Freq))
})
This could be achieved using e.g. curly-curly {{ and := in rename like so:
top_freq_elements <- function(x){
table(x) %>% as.data.frame() %>% top_n(5, Freq) %>% arrange(desc(Freq))
}
library(dplyr)
library(purrr)
hd_trn %>%
select_if(is.numeric) %>%
imap(function(feature_value, feature_name){
table(feature_value) %>%
as.data.frame() %>% #head()
rename({{feature_name}} := feature_value) %>%
top_n(5, Freq) %>%
arrange(desc(Freq))
})
#> $age
#> age Freq
#> 1 67 2
#> 2 37 1
#> 3 41 1
#> 4 56 1
#> 5 63 1
#>
#> $sex
#> sex Freq
#> 1 1 5
#> 2 0 1
#>
#> $cp
#> cp Freq
#> 1 2 2
#> 2 4 2
#> 3 1 1
#> 4 3 1
#>
#> $trestbps
#> trestbps Freq
#> 1 120 2
#> 2 130 2
#> 3 145 1
#> 4 160 1
I'm trying to split columns into new rows keeping the data of the first two columns.
d1 <- data.frame(a=c(100,0,78),b=c(0,137,117),c.1=c(111,17,91), d.1=c(99,66,22), c.2=c(11,33,44), d.2=c(000,001,002))
d1
a b c.1 d.1 c.2 d.2
1 100 0 111 99 11 0
2 0 137 17 66 33 1
3 78 117 91 22 44 2
Expected results would be:
a b c d
1 100 0 111 99
2 100 0 11 0
3 0 137 17 66
4 0 137 33 1
5 78 117 91 22
6 78 117 44 2
Multiple tries with dplyr, but in sees is not the right approach.
If you want to stay in dplyr/tidyverse, you want tidyr::pivot_longer with a special reference to .value -- see the pivot vignette for more:
library(tidyverse)
d1 <- data.frame(
a = c(100, 0, 78),
b = c(0, 137, 117),
c.1 = c(111, 17, 91),
d.1 = c(99, 66, 22),
c.2 = c(11, 33, 44),
d.2 = c(000, 001, 002)
)
d1 %>%
pivot_longer(
cols = contains("."),
names_to = c(".value", "group"),
names_sep = "\\."
)
#> # A tibble: 6 x 5
#> a b group c d
#> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 100 0 1 111 99
#> 2 100 0 2 11 0
#> 3 0 137 1 17 66
#> 4 0 137 2 33 1
#> 5 78 117 1 91 22
#> 6 78 117 2 44 2
Created on 2020-05-11 by the reprex package (v0.3.0)
This could solve your issue:
#Try this
a1 <- d1[,c(1:4)]
a2 <- d1[,c(1,2,5,6)]
names(a1) <- names(a2) <- c('a','b','c','d')
DF <- rbind(a1,a2)
The posted answers are good, here's my attempt:
df <- data.frame(a=c(100,0,78),b=c(0,137,117),
c.1=c(111,17,91), d.1=c(99,66,22),
c.2=c(11,33,44), d.2=c(000,001,002))
# Make 2 pivot long operations
df_c <- df %>% select(-d.1, -d.2) %>%
pivot_longer(cols = c("c.1", "c.2"), values_to = "c") %>% select(-name)
df_d <- df %>% select(-c.1, -c.2) %>%
pivot_longer(cols=c("d.1","d.2"), values_to = "d") %>% select(-name)
# bind them without the "key" colums
bind_cols(df_c, select(df_d, -a, -b))
Which produces
# A tibble: 6 x 4
a b c d
<dbl> <dbl> <dbl> <dbl>
1 100 0 111 99
2 100 0 11 0
3 0 137 17 66
4 0 137 33 1
5 78 117 91 22
6 78 117 44 2
I have a data frame set up similar to this:
id <- c(123,234,123,234)
task <- c(54,23,12,58)
a <- c(23,67,45,89)
b <- c(78,45,65,45)
df <- data.frame(id,task,a,b)
> df
id task a b
1 123 54 23 78
2 234 23 67 45
3 123 12 45 65
4 234 58 89 45
where I score a and b for each ID:
df$score <- rowMeans(subset(df, select = c(3:4)), na.rm = TRUE)
> df
id task a b score
1 123 54 23 78 50.5
2 234 23 67 45 56.0
3 123 12 45 65 55.0
4 234 58 89 45 67.0
for each id I got an aggregate score like such:
out <- ddply(df, 1, summarise,
overall = mean(score, na.rm = TRUE))
> out
id overall
1 123 52.75
2 234 61.50
but what I want my final output to have is a new column that has the scores that went into the overall and their task id like this:
id overall meta
1 123 52.75 "task_scores":[{"54":50.5,"12":55}]
2 234 61.50 "task_scores":[{"23":56,"58":67}]
how would I go about doing that using R?
We could make use of jsonlite to create the structure
library(jsonlite)
library(plyr)
ddply(df, "id", summarise, overall = mean(score, na.rm = TRUE),
meta = paste0('"task_scores":',
toJSON(setNames(as.data.frame.list(score), task))))
# id overall meta
#1 123 52.75 "task_scores":[{"54":50.5,"12":55}]
#2 234 61.50 "task_scores":[{"23":56,"58":67}]
I don't know how to make that metadata dictionary offhand, but you could do something like this:
library(dplyr)
library(magrittr)
out <- df %>% group_by(id) %>% mutate(overall = mean(score))
> out
# A tibble: 4 x 6
# Groups: id [2]
id task a b score overall
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 123 54 23 78 50.5 52.8
2 234 23 67 45 56 61.5
3 123 12 45 65 55 52.8
4 234 58 89 45 67 61.5
So the df would have both the aggregated scores and preserve the data in the original rows.
You can do it with a few mutates. Paste your tallies, get your row average, then your group average.
library(dplyr)
df %>%
mutate(score = rowMeans(subset(., select = c(3:4)), na.rm = TRUE)) %>%
group_by(id) %>%
mutate(overall = mean(score)) %>%
mutate(tally = paste(task, score, sep = ":", collapse = ","))
# A tibble: 4 x 7
# Groups: id [2]
id task a b score overall tally
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 123 54 23 78 50.5 52.8 54:50.5,12:55
2 234 23 67 45 56 61.5 23:56,58:67
3 123 12 45 65 55 52.8 54:50.5,12:55
4 234 58 89 45 67 61.5 23:56,58:67
And to get your desired final output, just select and slice.
df %>%
mutate(score = rowMeans(subset(., select = c(3:4)), na.rm = TRUE)) %>%
group_by(id) %>%
mutate(overall = mean(score)) %>%
mutate(tally = paste(task, score, sep = ":", collapse = ",")) %>%
select(id, overall, tally) %>%
slice(1)
# A tibble: 1 x 3
id overall tally
<dbl> <dbl> <chr>
1 123 52.8 54:50.5,12:55
2 234 61.5 23:56,58:67