This is my dataset:
df <- data.frame(PatientID = c("3454","3454","3454","345","345","345"), date = c("05/01/2001", "02/06/1997", "29/03/2004", "05/2/2021", "01/06/1960", "29/03/2003"),
infarct1 = c(TRUE, NA, TRUE, NA, NA, TRUE),infarct2 = c(TRUE, TRUE, TRUE, TRUE, NA, TRUE, stringsAsFactors = F)
Basically I need to keep just 1 patient ID (aka, eliminate duplicated PatientID), based on the most recent infarct (last infarct==TRUE [but any kind of infarct] based on date).
So the outcome I want would look like:
df <- data.frame(PatientID = c("3454","345"), date = c("29/03/2004", "05/2/2021"),
infarct = c(TRUE,TRUE), stringsAsFactors = F)
Hope this makes sense.
Thanks
Try this:
library(dplyr)
df <- df %>%
mutate(infarct = infarct1 | infarct2) %>%
filter(infarct == TRUE) %>%
group_by(PatientID, infarct) %>%
summarise(date=max(date))
Create infarct variable.
Filter TRUE infarct.
Group.
Look for last time.
You can turn the date to date class, arrange the data by PatientID and date and get the last date where infarct = TRUE.
library(dplyr)
df %>%
mutate(date = lubridate::dmy(date)) %>%
arrange(PatientID, date) %>%
group_by(PatientID) %>%
summarise(date = date[max(which(infarct))],
infract = TRUE)
# PatientID date infract
# <chr> <date> <lgl>
#1 345 2003-03-29 TRUE
#2 3454 2004-03-29 TRUE
For multiple columns get the data in long format.
df %>%
mutate(date = lubridate::dmy(date)) %>%
tidyr::pivot_longer(cols = starts_with('infarct')) %>%
arrange(PatientID, date) %>%
group_by(PatientID) %>%
slice(max(which(value))) %>%
ungroup
# PatientID date name value
# <chr> <date> <chr> <lgl>
#1 345 2021-02-05 infarct2 TRUE
#2 3454 2004-03-29 infarct2 TRUE
data
I think you need quotes around data in date column.
df <- data.frame(PatientID = c("3454","3454","3454","345","345","345"),
date = c("05/01/2001", "02/06/1997", "29/03/2004", "05/2/2021", "01/06/1960", "29/03/2003"),
infarct = c(TRUE, NA, TRUE, NA, NA, TRUE), stringsAsFactors = FALSE)
Related
I have found this dataframe in an Excel file, very disorganized. This is just a sample of a bigger dataset, with many jobs.
df <- data.frame(
Job = c("Frequency", "Driver", "Operator"),
Gloves = c("Daily", 1,2),
Aprons = c("Weekly", 2,0),
)
Visually it's
I need it to be in this format, something that I can work in a database:
df <- data.frame(
Job = c("Driver", "Driver", "Operator", "Operator"),
Frequency= c("Daily", "Weekly", "Daily", "Weekly"),
Item= c("Gloves", "Aprons", "Gloves", "Aprons"),
Quantity= c(1,2,2,0)
)
Visually it's
Any thoughts in how do we have to manipulate the data? I have tried without any luck.
We could use tidyverse methods by doing this in three steps
Remove the first row - slice(-1), reshape to 'long' format (pivot_longer)
Keep only the first row - slice(1), reshape to 'long' format (pivot_longer)
Do a join with both of the reshaped datasets
library(dplyr)
library(tidyr)
df %>%
slice(-1) %>%
pivot_longer(cols = -Job, names_to = 'Item',
values_to = 'Quantity') %>%
left_join(df %>%
slice(1) %>%
pivot_longer(cols= -Job, values_to = 'Frequency',
names_to = 'Item') %>%
select(-Job) )
-output
# A tibble: 4 x 4
Job Item Quantity Frequency
<chr> <chr> <chr> <chr>
1 Driver Gloves 1 Daily
2 Driver Aprons 2 Weekly
3 Operator Gloves 2 Daily
4 Operator Aprons 0 Weekly
data
df <- data.frame(
Job = c("Frequency", "Driver", "Operator"),
Gloves = c("Daily", 1,2),
Aprons = c("Weekly", 2,0))
I have a df that looks like the following:
ID DATE
12 10-20-20
12 10-22-20
10 10-15-20
9 10-10-20
11 11-01-20
7 11-02-20
I would like to group by month and then create a column for unique id count and repeat id count like below:
MONTH Unique_Count Repeat_Count
10-1-20 2 2
11-1-20 2 0
I am able to get the date down to the first of the month and group by ID but I am not sure how to count unique instances within the months.
df %>%
mutate(month = floor_date(as.Date(DATE), "month")) %>%
group_by(ID) %>%
mutate(count = n())
Are you perhaps looking for:
df %>%
mutate(month = strftime(floor_date(as.Date(DATE, "%m-%d-%y"), "month"),
"%m-%d-%y")) %>%
group_by(month) %>%
summarize(unique_count = length(which(table(ID) == 1)),
repeat_count = sum(table(ID)[(which(table(ID) > 1))]))
#> # A tibble: 2 x 3
#> month unique_count repeat_count
#> <chr> <int> <int>
#> 1 10-01-20 2 2
#> 2 11-01-20 2 0
Here's a shot at it:
library(lubridate)
library(dplyr)
dates <- as.Date(c("2020-10-15", "2020-10-15", "2020-11-16", "2020-11-16", "2020-11-16"))
ids <- c(12, 12, 13, 13, 14)
df <- data.frame(dates, ids)
duplicates <- df %>%
group_by(dates_floored = floor_date(dates, unit = "month"), ids) %>%
mutate(duplicate_count = n()) %>%
filter(duplicate_count > 1) %>%
distinct(ids, .keep_all = TRUE)
uniques <- df %>%
group_by(dates_floored = floor_date(dates, unit = "month"), ids) %>%
mutate(unique_count = n()) %>%
filter(unique_count < 2) %>%
distinct(ids, .keep_all = TRUE)
df_cleaned <- full_join(uniques, duplicates, by = c("ids", "dates", "dates_floored")) %>%
group_by(dates_floored) %>%
summarize(count_duplicates = sum(duplicate_count, na.rm = TRUE),
count_unique = sum(unique_count, na.rm = TRUE))
df_cleaned
I have data similar to below :
df=data.frame(
company=c("McD","McD","McD","KFC","KFC"),
Title=c("Crew Member","Manager","Trainer","Crew Member","Manager"),
Manhours=c(12,NA,5,13,10)
)
df
I would wish to manipulate it and obtain the data frame as below:
df=data.frame(
company=c("KFC", "McD"),
Manager=c(1,1),
Surbodinate=c(1,2),
TotalEmp=c(2,3),
TotalHours=c(23,17)
)
I have managed to manipulate and categorise the employees as well as their count as below:
df<- df %>%
mutate(Role = if_else((Title=="Manager" ),
"Manager","Surbodinate"))%>%
count(company, Role) %>%
spread(Role, n, fill=0)%>%
as.data.frame() %>%
mutate(TotalEmp= select(., Manager:Surbodinate) %>%
apply(1, sum, na.rm=TRUE))
Also, I have summarised the man hours as below:
df <- df %>%group_by(company) %>%
summarize(TotalHours = sum(Manhours, na.rm = TRUE))
How would I combine these two steps at once or is there a cleaner/simpler way of getting the desired output?
dplyr solution:
df %>%
mutate(Title = if_else((Title=="Manager" ),
"Manager","Surbodinate")) %>%
group_by(company) %>%
summarise(Manager = sum(Title == "Manager"), Subordinate = sum(Title == "Surbodinate"), TotalEmp = n(), Manhours = sum(Manhours, na.rm = TRUE))
company Manager Subordinate TotalEmp Manhours
<fct> <int> <int> <int> <dbl>
1 KFC 1 1 2 23
2 McD 1 2 3 17
how about something like this:
df %>%
mutate(Role = ifelse(Title=="Manager" ,
"Manager", "Surbodinate"))%>%
group_by(company) %>%
mutate(TotalEmp = n(),
TotalHours = sum(Manhours, na.rm=TRUE)) %>%
reshape2::dcast(company + TotalEmp + TotalHours ~ Role)
This is not tidyverse nor is it a one step process. But if you use data.table you could do:
library(data.table)
setDT(df, key = "company")
totals <- DT[, .(TotalEmp = .N, TotalHours = sum(Manhours, na.rm = TRUE)), by = company]
dcast(DT, company ~ ifelse(Title == "Manager", "Manager", "Surbodinate"))[totals]
# company Manager Surbodinate TotalEmp TotalHours
# 1 KFC 1 1 2 23
# 2 McD 1 2 3 17
I need to rename a dataframe by days in analysis.
names(dados) <- c("name", "day_1","Freq_1","Percent_1","day_2","Freq_2","Percent_2",
"day_3","Freq_3","Percent_3","day_4","Freq_4","Percent_4",
"day_5","Freq_5","Percent_5","day_6","Freq_6","Percent_6",
"day_7","Freq_7","Percent_7","day_8","Freq_8","Percent_8",
"day_9","Freq_9","Percent_9")
I'm doing an analysis that the data I get is in a list of dataframes, where each dataframe represents a day of analysis. I combine the dataframes and I have the columns 'name' unique and 'day_X', 'Freq_X' and 'Percent_X' for each dataframe as a return.
As return I need the columns to have the following names:
"name", "day_1","Freq_1","Percent_1","day_2","Freq_2","Percent_2","day_3","Freq_3","Percent_3"
How do I go about analyzing 50 days?
reproducible example:
day1 <- data.frame(name = c("jose", "mary", "julia"), freq = c(1,5,3), percent = c(40,30,20))
day2 <- data.frame(name = c("abner", "jose", "mary"), freq = c(3,5,4), percent = c(20,30,20))
day3 <- data.frame(name = c("abner", "jose", "mike"), freq = c(6,2,3), percent = c(40,30,70))
day4 <- data.frame(name = c("andre", "joseph", "ana"), freq = c(1,5,8), percent = c(40,30,20))
day5 <- data.frame(name = c("abner", "poli", "joseph"), freq = c(4,3,3), percent = c(10,30,10))
dates <- list(day1,day2,day4,day5)
data <- Reduce(function(x, y) merge(x, y, by = "name", all = TRUE), dates)
Here's a way to get what you want using the tidyverse suite of packages. We start by putting the data in the "long" format - but add a column with the date:
long_form <- dates %>%
imap_dfr(function(x, y) dplyr::mutate(x, day_num = y))
Now, to get the wide format you are after, we need to reformat things a bit, as done in the following code. I'm not sure what is supposed to go in the day_# variables, as #useR mentioned in the comments, so it's missing. If you have a variable called day, the code should automatically do the right thing as written.
wide_form <- long_form %>%
gather(key, value, -name,-day_num) %>%
dplyr::mutate(
key = paste(key, day_num, sep = "_")
) %>%
select(-day_num) %>%
spread(key, value)
One can use dplyr::bind_rows to merge all data frames form the list to a data frame. Please provide name to list so that day1, day2 etc can set beforehand. Finally, gather and spread is used to transform the data.
names(dates) <- paste("day", seq_along(dates), sep = "")
library(tidyverse)
bind_rows(dates,.id = "Name") %>%
group_by(Name) %>%
mutate(rn = row_number()) %>%
ungroup() %>%
gather(Key, value, -Name,-rn) %>%
unite("Key", c("Key", "Name")) %>%
spread(Key, value) %>%
select(-rn)
Result:
# # A tibble: 3 x 12
# freq_day1 freq_day2 freq_day3 freq_day4 name_day1 name_day2 name_day3 name_day4 percent_day1 percent_day2 percent~ percent~
# * <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 1 3 1 4 jose abner andre abner 40 20 40 10
# 2 5 5 5 3 mary jose joseph poli 30 30 30 30
# 3 3 4 8 3 julia mary ana joseph 20 20 20 10
#
Data:
Data is slightly modified from OP. I have included stringsAsFactors = FALSE argument as part of data.frame to avoid a mutate_at call to convert factor to character.
day1 <- data.frame(name = c("jose", "mary", "julia"), freq = c(1,5,3), percent = c(40,30,20), stringsAsFactors = FALSE)
day2 <- data.frame(name = c("abner", "jose", "mary"), freq = c(3,5,4), percent = c(20,30,20), stringsAsFactors = FALSE)
day3 <- data.frame(name = c("abner", "jose", "mike"), freq = c(6,2,3), percent = c(40,30,70), stringsAsFactors = FALSE)
day4 <- data.frame(name = c("andre", "joseph", "ana"), freq = c(1,5,8), percent = c(40,30,20), stringsAsFactors = FALSE)
day5 <- data.frame(name = c("abner", "poli", "joseph"), freq = c(4,3,3), percent = c(10,30,10), stringsAsFactors = FALSE)
dates <- list(day1,day2,day4,day5)
As described in numerous questions on here, I should be able to take a data.frame, group it, sort by date, and then apply cumsum, to get the cumulative sum over time per grouping.
Instead, with dplyr 0.8.0, I'm getting cumulative sums that ignore the grouping.
Example code:
data.frame(
cat = sample(c("a", "b", "c"), size = 1000, replace = T),
date = sample(seq(as.Date('1999/01/01'), as.Date('2000/01/01'), by="day"), 1000, replace=T)
) %>%
mutate(
x = 1
) %>%
arrange(date) %>%
group_by(cat) %>%
mutate(x = cumsum(x)) %>%
tail()
Now, I'd expect the last few rows to have x equal to around 300-something, for each group.
Instead I get:
# A tibble: 6 x 3
# Groups: cat [2]
cat date x
<chr> <date> <dbl>
1 a 1999-12-31 995
2 a 1999-12-31 996
3 c 2000-01-01 997
4 a 2000-01-01 998
5 c 2000-01-01 999
6 a 2000-01-01 1000
What am I doing wrong?
I'm guessing this is a classic problem when you load plyr after dplyr, nothing to do with your version of dplyr. For example:
tmp1<- data.frame(cat = sample(c("a", "b", "c"), size = 1000, replace = T),
date = sample(seq(as.Date('1999/01/01'), as.Date('2000/01/01'), by="day"), 1000, replace=T)) %>% mutate(x = 1)
see difference between
tmp1 %>%
arrange(date) %>%
group_by(cat) %>%
plyr::mutate(x = cumsum(x)) %>%
tail()
and
tmp1 %>%
arrange(date) %>%
group_by(cat) %>%
dplyr::mutate(x = cumsum(x)) %>%
tail()
plyr's mutate doesn't understand grouping.
You can verify if this is the problem using search()