I have a program that gives me data in this format
toy
file_path Condition Trial.Num A B C ID A B C ID A B C ID
1 root/some.extension Baseline 1 2 3 5 car 2 1 7 bike 4 9 0 plane
2 root/thing.extension Baseline 2 3 6 45 car 5 4 4 bike 9 5 4 plane
3 root/else.extension Baseline 3 4 4 6 car 7 5 4 bike 68 7 56 plane
4 root/uniquely.extension Treatment 1 5 3 7 car 1 7 37 bike 9 8 7 plane
5 root/defined.extension Treatment 2 6 7 3 car 4 6 8 bike 9 0 8 plane
My goal is to tidy the format into something that at least can be easier to finally tidy with reshape having unique column names
tidy_toy
file_path Condition Trial.Num A B C ID
1 root/some.extension Baseline 1 2 3 5 car
2 root/thing.extension Baseline 2 3 6 45 car
3 root/else.extension Baseline 3 4 4 6 car
4 root/uniquely.extension Treatment 1 5 3 7 car
5 root/defined.extension Treatment 2 6 7 3 car
6 root/some.extension Baseline 1 2 1 7 bike
7 root/thing.extension Baseline 2 5 4 4 bike
8 root/else.extension Baseline 3 7 5 4 bike
9 root/uniquely.extension Treatment 1 1 7 37 bike
10 root/defined.extension Treatment 2 4 6 8 bike
11 root/some.extension Baseline 1 4 9 0 plane
12 root/thing.extension Baseline 2 9 5 4 plane
13 root/else.extension Baseline 3 68 7 56 plane
14 root/uniquely.extension Treatment 1 9 8 7 plane
15 root/defined.extension Treatment 2 9 0 8 plane
If I try to melt from toy it doesn't work because only the first ID column will get used for id.vars (hence everything will get tagged as cars). Identical variables will get dropped.
Here's the dput of both tables
structure(list(file_path = structure(c(3L, 4L, 2L, 5L, 1L), .Label = c("root/defined.extension",
"root/else.extension", "root/some.extension", "root/thing.extension",
"root/uniquely.extension"), class = "factor"), Condition = structure(c(1L,
1L, 1L, 2L, 2L), .Label = c("Baseline", "Treatment"), class = "factor"),
Trial.Num = c(1L, 2L, 3L, 1L, 2L), A = 2:6, B = c(3L, 6L,
4L, 3L, 7L), C = c(5L, 45L, 6L, 7L, 3L), ID = structure(c(1L,
1L, 1L, 1L, 1L), .Label = "car", class = "factor"), A = c(2L,
5L, 7L, 1L, 4L), B = c(1L, 4L, 5L, 7L, 6L), C = c(7L, 4L,
4L, 37L, 8L), ID = structure(c(1L, 1L, 1L, 1L, 1L), .Label = "bike", class = "factor"),
A = c(4L, 9L, 68L, 9L, 9L), B = c(9L, 5L, 7L, 8L, 0L), C = c(0L,
4L, 56L, 7L, 8L), ID = structure(c(1L, 1L, 1L, 1L, 1L), .Label = "plane", class = "factor")), .Names = c("file_path",
"Condition", "Trial.Num", "A", "B", "C", "ID", "A", "B", "C",
"ID", "A", "B", "C", "ID"), class = "data.frame", row.names = c(NA,
-5L))
structure(list(file_path = structure(c(3L, 4L, 2L, 5L, 1L, 3L,
4L, 2L, 5L, 1L, 3L, 4L, 2L, 5L, 1L), .Label = c("root/defined.extension",
"root/else.extension", "root/some.extension", "root/thing.extension",
"root/uniquely.extension"), class = "factor"), Condition = structure(c(1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L), .Label = c("Baseline",
"Treatment"), class = "factor"), Trial.Num = c(1L, 2L, 3L, 1L,
2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L, 2L), A = c(2L, 3L, 4L,
5L, 6L, 2L, 5L, 7L, 1L, 4L, 4L, 9L, 68L, 9L, 9L), B = c(3L, 6L,
4L, 3L, 7L, 1L, 4L, 5L, 7L, 6L, 9L, 5L, 7L, 8L, 0L), C = c(5L,
45L, 6L, 7L, 3L, 7L, 4L, 4L, 37L, 8L, 0L, 4L, 56L, 7L, 8L), ID = structure(c(2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L), .Label = c("bike",
"car", "plane"), class = "factor")), .Names = c("file_path",
"Condition", "Trial.Num", "A", "B", "C", "ID"), class = "data.frame", row.names = c(NA,
-15L))
You can use the make.unique-function to create unique column names. After that you can use melt from the data.table-package which is able to create multiple value-columns based on patterns in the columnnames:
# make the column names unique
names(toy) <- make.unique(names(toy))
# let the 'Condition' column start with a small letter 'c'
# so it won't be detected by the patterns argument from melt
names(toy)[2] <- tolower(names(toy)[2])
# load the 'data.table' package
library(data.table)
# tidy the data into long format
tidy_toy <- melt(setDT(toy),
measure.vars = patterns('^A','^B','^C','^ID'),
value.name = c('A','B','C','ID'))
which gives:
> tidy_toy
file_path condition Trial.Num variable A B C ID
1: root/some.extension Baseline 1 1 2 3 5 car
2: root/thing.extension Baseline 2 1 3 6 45 car
3: root/else.extension Baseline 3 1 4 4 6 car
4: root/uniquely.extension Treatment 1 1 5 3 7 car
5: root/defined.extension Treatment 2 1 6 7 3 car
6: root/some.extension Baseline 1 2 2 1 7 bike
7: root/thing.extension Baseline 2 2 5 4 4 bike
8: root/else.extension Baseline 3 2 7 5 4 bike
9: root/uniquely.extension Treatment 1 2 1 7 37 bike
10: root/defined.extension Treatment 2 2 4 6 8 bike
11: root/some.extension Baseline 1 3 4 9 0 plane
12: root/thing.extension Baseline 2 3 9 5 4 plane
13: root/else.extension Baseline 3 3 68 7 56 plane
14: root/uniquely.extension Treatment 1 3 9 8 7 plane
15: root/defined.extension Treatment 2 3 9 0 8 plane
Another option is to use a list of column-indexes for measure.vars:
tidy_toy <- melt(setDT(toy),
measure.vars = list(c(4,8,12), c(5,9,13), c(6,10,14), c(7,11,15)),
value.name = c('A','B','C','ID'))
Making the column-names unique isn't necessary then.
A more complicated method that creates names that are better distinguishable by the patterns argument:
# select the names that are not unique
tt <- table(names(toy))
idx <- which(names(toy) %in% names(tt)[tt > 1])
nms <- names(toy)[idx]
# make them unique
names(toy)[idx] <- paste(nms,
rep(seq(length(nms) / length(names(tt)[tt > 1])),
each = length(names(tt)[tt > 1])),
sep = '.')
# your columnnames are now unique:
> names(toy)
[1] "file_path" "Condition" "Trial.Num" "A.1" "B.1" "C.1" "ID.1" "A.2"
[9] "B.2" "C.2" "ID.2" "A.3" "B.3" "C.3" "ID.3"
# tidy the data into long format
tidy_toy <- melt(setDT(toy),
measure.vars = patterns('^A.\\d','^B.\\d','^C.\\d','^ID.\\d'),
value.name = c('A','B','C','ID'))
which will give the same end-result.
As mentioned in the comments, the janitor-package can be helpful for this problem as well. The clean_names() works similar as the make.unique function. See here for an explanation.
with tidyverse we can do :
library(tidyverse)
toy %>%
repair_names(sep="_") %>%
pivot_longer(-(1:3),names_to = c(".value","id"), names_sep="_") %>%
select(-id)
#> # A tibble: 15 x 7
#> file_path Condition Trial.Num A B C ID
#> <fct> <fct> <int> <int> <int> <int> <fct>
#> 1 root/some.extension Baseline 1 2 3 5 car
#> 2 root/some.extension Baseline 1 2 1 7 bike
#> 3 root/some.extension Baseline 1 4 9 0 plane
#> 4 root/thing.extension Baseline 2 3 6 45 car
#> 5 root/thing.extension Baseline 2 5 4 4 bike
#> 6 root/thing.extension Baseline 2 9 5 4 plane
#> 7 root/else.extension Baseline 3 4 4 6 car
#> 8 root/else.extension Baseline 3 7 5 4 bike
#> 9 root/else.extension Baseline 3 68 7 56 plane
#> 10 root/uniquely.extension Treatment 1 5 3 7 car
#> 11 root/uniquely.extension Treatment 1 1 7 37 bike
#> 12 root/uniquely.extension Treatment 1 9 8 7 plane
#> 13 root/defined.extension Treatment 2 6 7 3 car
#> 14 root/defined.extension Treatment 2 4 6 8 bike
#> 15 root/defined.extension Treatment 2 9 0 8 plane
#> Warning message:
#> Expected 2 pieces. Missing pieces filled with `NA` in 4 rows [1, 2, 3, 4].
Related
I have data like this:
df <-
a b c
1 2 3
1 2 4
1 2 5
1 2 9
2 3 3
2 3 4
2 3 5
2 3 9
3 4 3
3 4 4
3 4 5
3 4 9
I want to remove duplicate rows base on column a but keep the values in column c as in:
df2 <-
a b c c1 c2 c3
1 2 3 4 5 9
2 3 3 4 5 9
3 4 3 4 5 9
I know how to remove duplicates as in :
df2 <-df[!(df$a=="1"),]
But have now idea how to add the values to the kept row.
We can exclude c while subsettting the rows of the dataset, unlist, and then concatenate with the whole 'c' column
c(unlist(df[!duplicated(df$a), 1:2]), c = df$c)
# a b c1 c2 c3 c4
# 1 2 3 4 5 9
If we need the same names as in the expected
c(unlist(df[!duplicated(df$a), 1:2]),
setNames(df$c, make.unique(rep('c', nrow(df)), sep="")))
# a b c c1 c2 c3
# 1 2 3 4 5 9
With the new example
library(dplyr)
library(tidyr)
df2 %>%
group_by(a) %>%
summarise(b = first(b), c = list(as.list(c))) %>%
unnest_wider(c(c))%>%
rename_at(vars(starts_with('.')), ~ str_c('c', seq_along(.)))
# A tibble: 2 x 6
# a b c1 c2 c3 c4
# <int> <int> <int> <int> <int> <int>
#1 1 2 3 4 5 9
#2 2 2 3 4 5 9
Or with again updated example
df3 %>%
group_by(a) %>%
summarise(b = first(b), c = list(as.list(c))) %>%
unnest_wider(c(c))%>%
rename_at(vars(starts_with('.')), ~ str_c('c', seq_along(.)))
# A tibble: 3 x 6
# a b c1 c2 c3 c4
# <int> <int> <int> <int> <int> <int>
#1 1 2 3 4 5 9
#2 2 3 3 4 5 9
#3 3 4 3 4 5 9
Or with data.table
library(data.table)
setDT(df3)[, c(.(b = first(b)),
as.data.frame.list(setNames(c, rep('c', .N)))), a]
# a b c c.1 c.2 c.3
#1: 1 2 3 4 5 9
#2: 2 3 3 4 5 9
#3: 3 4 3 4 5 9
data
df <- structure(list(a = c(1L, 1L, 1L, 1L), b = c(2L, 3L, 3L, 4L),
c = c(3L, 4L, 5L, 9L)), class = "data.frame", row.names = c(NA,
-4L))
df2 <- structure(list(a = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), b = c(2L,
3L, 3L, 4L, 2L, 3L, 3L, 4L), c = c(3L, 4L, 5L, 9L, 3L, 4L, 5L,
9L)), class = "data.frame", row.names = c(NA, -8L))
df3 <- structure(list(a = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L), b = c(2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L
), c = c(3L, 4L, 5L, 9L, 3L, 4L, 5L, 9L, 3L, 4L, 5L, 9L)), class = "data.frame", row.names = c(NA,
-12L))
Within a group, I want to find the difference between that row and the first time that user appeared in the data. For example, I need to create the diff variable below. Users have different number of rows each as in the following data:
df <- structure(list(ID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 4L, 4L),
money = c(9L, 12L, 13L, 15L, 5L, 7L, 8L, 5L, 2L, 10L), occurence = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 1L, 1L, 2L), diff = c(NA, 3L, 4L,
6L, NA, 2L, 3L, NA, NA, 8L)), .Names = c("ID", "money", "occurence",
"diff"), class = "data.frame", row.names = c(NA, -10L))
ID money occurence diff
1 1 9 1 NA
2 1 12 2 3
3 1 13 3 4
4 1 15 4 6
5 2 5 1 NA
6 2 7 2 2
7 2 8 3 3
8 3 5 1 NA
9 4 2 1 NA
10 4 10 2 8
You can use ave(). We just remove the first value per group and replace it with NA, and subtract the first value from the rest of the values.
with(df, ave(money, ID, FUN = function(x) c(NA, x[-1] - x[1])))
# [1] NA 3 4 6 NA 2 3 NA NA 8
A dplyr solution, which uses the first function to get the first value and calculate the difference.
library(dplyr)
df2 <- df %>%
group_by(ID) %>%
mutate(diff = money - first(money)) %>%
mutate(diff = replace(diff, diff == 0, NA)) %>%
ungroup()
df2
# # A tibble: 10 x 4
# ID money occurence diff
# <int> <int> <int> <int>
# 1 1 9 1 NA
# 2 1 12 2 3
# 3 1 13 3 4
# 4 1 15 4 6
# 5 2 5 1 NA
# 6 2 7 2 2
# 7 2 8 3 3
# 8 3 5 1 NA
# 9 4 2 1 NA
# 10 4 10 2 8
Update
Here is a data.table solution provided by Sotos. Notice that no need to replace 0 with NA.
library(data.table)
setDT(df)[, money := money - first(money), by = ID][]
# ID money occurence diff
# 1: 1 0 1 NA
# 2: 1 3 2 3
# 3: 1 4 3 4
# 4: 1 6 4 6
# 5: 2 0 1 NA
# 6: 2 2 2 2
# 7: 2 3 3 3
# 8: 3 0 1 NA
# 9: 4 0 1 NA
# 10: 4 8 2 8
DATA
dput(df)
structure(list(ID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 4L, 4L),
money = c(9L, 12L, 13L, 15L, 5L, 7L, 8L, 5L, 2L, 10L), occurence = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 1L, 1L, 2L)), .Names = c("ID", "money",
"occurence"), row.names = c(NA, -10L), class = "data.frame")
Is there a way to fill in for implicit missingness for future dates based on id?
For example, imagine a experiment that starts in Jan-2016. I have 3 participants that join in at different periods. Subject 1 joins me in Jan and continues to stay until Aug. Subj 2 joins me in March, and stays in the experiment until August. Subject 3 also joins me in March, but drops out sometime in in May, so no observations are recorded for periods May-Aug.
The question is, how do I fill in the dates when subject 3 dropped out of the experiment? Here is some mock data for how things look like:
Subject Date
1 1 Jan-16
2 1 Feb-16
3 1 Mar-16
4 1 Apr-16
5 1 May-16
6 1 Jun-16
7 1 Jul-16
8 1 Aug-16
9 2 Mar-16
10 2 Apr-16
11 2 May-16
12 2 Jun-16
13 2 Jul-16
14 2 Aug-16
15 3 Mar-16
16 3 Apr-16
structure(list(Subject = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L), Date = structure(c(5L, 4L, 8L, 2L,
9L, 7L, 6L, 3L, 8L, 2L, 9L, 7L, 6L, 3L, 8L, 2L), .Label = c("",
"Apr-16", "Aug-16", "Feb-16", "Jan-16", "Jul-16", "Jun-16", "Mar-16",
"May-16"), class = "factor")), class = "data.frame", row.names = c(NA,
-16L), .Names = c("Subject", "Date"))
And here is how the data should look like:
Subject Date
1 1 Jan-16
2 1 Feb-16
3 1 Mar-16
4 1 Apr-16
5 1 May-16
6 1 Jun-16
7 1 Jul-16
8 1 Aug-16
9 2 Mar-16
10 2 Apr-16
11 2 May-16
12 2 Jun-16
13 2 Jul-16
14 2 Aug-16
15 3 Mar-16
16 3 Apr-16
17 3 May-16
18 3 Jun-16
19 3 Jul-16
20 3 Aug-16
structure(list(Subject = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L), Date = structure(c(4L,
3L, 7L, 1L, 8L, 6L, 5L, 2L, 7L, 1L, 8L, 6L, 5L, 2L, 7L, 1L, 8L,
6L, 5L, 2L), .Label = c("Apr-16", "Aug-16", "Feb-16", "Jan-16",
"Jul-16", "Jun-16", "Mar-16", "May-16"), class = "factor")), class = "data.frame", row.names = c(NA,
-20L), .Names = c("Subject", "Date"))
I tried using expand from tidyr and TimeFill from DataCombine package, but the issue with those approaches is that I would get dates for periods before a participant joined an experiment. In this particular instance, I only want the periods to be filled for cases when a participant drops out of an experiment.
The complete function from tidyr is designed for turning implicit missing values into explicit missing values. We will have to do some filtering to not include past completion. The easiest way seems to be to do a join on a table with starting values:
library(dplyr)
library(tidyr)
df <- df %>%
filter(Date != '') %>%
droplevels() %>%
group_by(Subject)
df2 <- summarise(df, start = first(Date))
df %>%
complete(Subject, Date) %>%
left_join(df2) %>%
mutate(Date2 = as.Date(paste0('01-', Date), format = '%d-%b-%y'),
start = as.Date(paste0('01-', start), format = '%d-%b-%y')) %>%
filter(Date2 >= start) %>%
arrange(Subject, Date2) %>%
select(-start, -Date2)
Result:
Source: local data frame [20 x 2]
Groups: Subject [3]
Subject Date
<int> <fctr>
1 1 Jan-16
2 1 Feb-16
3 1 Mar-16
4 1 Apr-16
5 1 May-16
6 1 Jun-16
7 1 Jul-16
8 1 Aug-16
9 2 Mar-16
10 2 Apr-16
11 2 May-16
12 2 Jun-16
13 2 Jul-16
14 2 Aug-16
15 3 Mar-16
16 3 Apr-16
17 3 May-16
18 3 Jun-16
19 3 Jul-16
20 3 Aug-16
I use date conversion to do a reliable comparison with the starting date, but you might be able to use the row_numbers somehow. A problem is that complete will rearrange the data.
p.s. Note that your example dput has an empty factor level (""), so I filter that out first.
This question already has answers here:
How to sum a variable by group
(18 answers)
Closed 6 years ago.
I have the following data frame:
Event Scenario Year Cost
1 1 1 10
2 1 1 5
3 1 2 6
4 1 2 6
5 2 1 15
6 2 1 12
7 2 2 10
8 2 2 5
9 3 1 4
10 3 1 5
11 3 2 6
12 3 2 5
I need to produce a pivot table/ frame that will sum the total cost per year for each scenario. So the result will be.
Scenario Year Cost
1 1 15
1 2 12
2 1 27
2 2 15
3 1 9
3 2 11
I need to produce a ggplot line graph that plot the cost of each scenario per year. I know how to do that, I just can't get the right data frame.
Try
library(dplyr)
df %>% group_by(Scenario, Year) %>% summarise(Cost=sum(Cost))
Or
library(data.table)
setDT(df)[, list(Cost=sum(Cost)), by=list(Scenario, Year)]
Or
aggregate(Cost~Scenario+Year, df,sum)
data
df <- structure(list(Event = 1:12, Scenario = c(1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L), Year = c(1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L), Cost = c(10L, 5L, 6L, 6L, 15L, 12L,
10L, 5L, 4L, 5L, 6L, 5L)), .Names = c("Event", "Scenario", "Year",
"Cost"), class = "data.frame", row.names = c(NA, -12L))
The following does it:
library(plyr)
ddply(df, .(Scenario, Year), summarize, Cost = sum(Cost))
#Scenario Year Cost
#1 1 1 15
#2 1 2 12
#3 2 1 27
#4 2 2 15
#5 3 1 9
#6 3 2 11
I can't find a good title for this question so feel free to edit it please.
I have this data.frame
section time to from
1 a 9 1 2
2 a 9 2 1
3 a 12 2 3
4 a 12 2 4
5 a 12 3 2
6 a 12 3 4
7 a 12 4 2
8 a 12 4 3
I want to remove duplicated rows that have the same to and from simultaneously, without computing permutations of the 2 columns: e.g (1,2) and (2,1) are duplicated.
So final output would be:
section time to from
1 a 9 1 2
3 a 12 2 3
4 a 12 2 4
6 a 12 3 4
I have a solution by constructing a new column key e.g
key <- paste(min(to,from),max(to,from))
and remove duplicated key using duplicated, but I think this is dirty solution.
here the dput of my data
structure(list(section = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = "a", class = "factor"), time = c(9L, 9L, 12L,
12L, 12L, 12L, 12L, 12L), to = c(1L, 2L, 2L, 2L, 3L, 3L, 4L,
4L), from = c(2L, 1L, 3L, 4L, 2L, 4L, 2L, 3L)), .Names = c("section",
"time", "to", "from"), row.names = c(NA, -8L), class = "data.frame")
mn <- pmin(s$to, s$from)
mx <- pmax(s$to, s$from)
int <- as.numeric(interaction(mn, mx))
s[match(unique(int), int),]
section time to from
1 a 9 1 2
3 a 12 2 3
4 a 12 2 4
6 a 12 3 4
Credit for the idea goes to this question: Remove consecutive duplicates from dataframe and specifically #MatthewPlourde's answer.
You can try using sort within the apply function to order the combinations.
mydf[!duplicated(t(apply(mydf[3:4], 1, sort))), ]
# section time to from
# 1 a 9 1 2
# 3 a 12 2 3
# 4 a 12 2 4
# 6 a 12 3 4