I have been trying to do the following within the dplyr package but without success.
I need to find which levels of a certain factor column are present in every level of another factor column, in my case, it is a Year column. This could be an example dataset:
ID Year
1568 2013
1341 2013
1568 2014
1341 2014
1261 2014
1348 2015
1568 2015
1341 2015
So I would like a list of the ID names that are present in every year. In the above example would be:
"1568", "1341"
I have been trying with dplyr to first grou_by column Year and then summarise the data somehow, but withouth achieving it.
Thanks
Using dplyr, we can group_by ID and select the groups which has same number of unique Year as the complete data.
library(dplyr)
df %>%
group_by(ID) %>%
filter(n_distinct(Year) == n_distinct(.$Year)) %>%
pull(ID) %>%unique()
#[1] 1568 1341
Here is a base R solution using intersect() + split()
comm <- Reduce(intersect,split(df$ID,df$Year))
such that
> comm
[1] 1568 1341
Related
I'm struggling to perform basic indexing on a data frame to perform mathematical operations. I have a data frame containing all 50 US states with an entry for each month of the year, so there are 600 observations. I wish to find the difference between a value for the month of December minus the January value for each of the states. My data looks like this:
> head(df)
state year month value
1 AL 2020 01 2.7
2 AK 2020 01 5
3 AZ 2020 01 4.8
4 AR 2020 01 3.7
5 CA 2020 01 4.2
7 CO 2020 01 2.7
For instance, AL has a value in Dec of 4.7 and Jan value of 2.7 so I'd like to return 2 for that state.
I have been trying to do this with the group_by and summarize functions, but can't figure out the indexing piece of it to grab values that correspond to a condition. I couldn't find a resource for performing these mathematical operations using indexing on a data frame, and would appreciate assistance as I have other transformations I'll be using.
With dplyr:
library(dplyr)
df %>%
group_by(state) %>%
summarize(year_change = value[month == "12"] - value[month == "01"])
This assumes that your data is as you describe--every state has a single value for every month. If you have missing rows, or multiple observations in for a state in a given month, I would not expect this code to work.
Another approach, based row order rather than month value, might look like this:
library(dplyr)
df %>%
## make sure things are in the right order
arrange(state, month) %>%
group_by(state) %>%
summarize(year_change = last(value) - first(value))
This question already has answers here:
Calculate the mean by group
(9 answers)
Closed 5 years ago.
I have a dataset that looks approximately like this:
> dataSet
month detrend
1 Jan 315.71
2 Jan 317.45
3 Jan 317.5
4 Jan 317.1
5 Jan 315.71
6 Feb 317.45
7 Feb 313.5
8 Feb 317.1
9 Feb 314.37
10 Feb 315.41
11 March 316.44
12 March 315.73
13 March 318.73
14 March 315.55
15 March 312.64
.
.
.
How do I compute the average by month? E.g., I want something like
> by_month
month ave_detrend
1 Jan 315.71
2 Feb 317.45
3 March 317.5
What you need to focus on is a means to group your column of interest (the "detrend") by the month. There are ways to do this within "vanilla R", but the most effective way is to use tidyverse's dplyr.
I will use the example taken directly from that page:
mtcars %>%
group_by(cyl) %>%
summarise(disp = mean(disp), sd = sd(disp))
In your case, that would be:
by_month <- dataSet %>%
group_by(month) %>%
summarize(avg = mean(detrend))
This new "tidyverse" style looks quite different, and you seem quite new, so I'll explain what's happening (sorry if this is overly obvious):
First, we are grabbing the dataframe, which I'm calling dataSet.
Then we are piping that dataset to our next function, which is group_by. Piping means that we're putting the results of the last command (which in this case is just the dataframe dataSet) and using it as the first parameter of our next function. The function group_by has a dataframe provided as its first function.
Then the results of that group by are piped to the next function, which is summarize (or summarise if you're from down under, as the author is). summarize simply calculates using all the data in the column, however, the group_by function creates partitions in that column. So we now have the mean calculated for each partition that we've made, which is month.
This is the key: group_by creates "flags" so that summarize calculates the function (mean, in this case) separately on each group. So, for instance, all of the Jan values are grouped together and then the mean is calculated only on them. Then for all of the Feb values, the mean is calculated, etc.
HTH!!
R has an inbuilt mean function: mean(x, trim = 0, na.rm = FALSE, ...)
I would do something like this:
january <- dataset[dataset[, "month"] == "january",]
januaryVector <- january[, "detrend"]
januaryAVG <- mean(januaryVector)
I got a dataframe like this:
id Date value
a 2016 400
a 2017 300
a 2018 200
a 2019 100
and so on. I got multiple identifiers.
How can I get a dataframe like this
id 2016 2017 2018 2019
a 400 300 200 100
I have tried different solutions with merge and transposing the dataframe but it won't work. Is there a solution to this?
Thank you guys a lot in advance
Using spread from the tidyr package, if your data frame is called d:
library(tidyr)
result <- spread(d, Date, value)
Sorry relative R novice here trying to do more with dplyr. I have a large data frame with an id column, end (dates as POSIXct) and D (codes the outcome ~6 different types), sample here:
id end D
1143 1996-08-10 KT
1148 2000-07-27 KT
1150 2004-07-02 KT
1158 2001-11-03 KT
I want to create a subset for outcome KT. Within this outcome, many id's have 1-4 outcomes with separate dates. I want to create a wide data frame that looks like this:
id datetx1 datetx2 datetx3 datetx4
1915 2014-10-13 2014-10-18 <NA> <NA>
2715 2006-09-17 2006-09-17 2006-09-17 2008-02-01
5089 2007-02-02 2007-02-11 <NA> <NA>
5266 2007-04-16 2010-07-14 2010-07-14 <NA>
I have been trying to use dplyr and tidyr with some success at going from long to wide, but am getting stuck with the dates with this approach:
transplant<-filter(outcomes, D == "KT") %>%
filter(!is.na(date)) %>%
group_by(id) %>%
arrange((end)) %>%
mutate(number = row_number()) %>%
spread(number, date)
The problem is with spread, which creates numeric values in the other columns that I can’t seem to coerce into dates. I have resorted to coding the date as.character and then recoding as.Date after the spread which works but am I missing something about tidyr and date coding?
Data like so:
data <- read.table(text="year level items
2014 a 12
2014 b 16
2014 c 7")
Would like to run that through mutate() and I guess group_by so I have a year and a total, so a row that's just:
year items
2014 35
Feel like it should be 101 simple but I can't quite figure this one out.
library(dplyr)
out <- data %>% group_by(year) %>%
summarize(items= sum(items, na.rm=T))