I have a set of observational heath survey data. I want to modify the source identifier so that I can still have a number of sources identified, but the original ID is not the tracking ID to keep confidentiality. I am having trouble figuring it out.
Here's the basic layout of the dataframe
ID
30
30
30
30
24
24
24
I want to create a newID so that the data would look like the following
NewID ID
1 30
1 30
1 30
1 30
2 24
2 24
2 24
if your data frame is df then this should do it.
df$NewID <- as.numeric(factor(df$ID))
cbind(match(ID,unique(ID)),ID)
rleis useful here:
> ID <- rep(c(30,24), c(4,3)) # your data
> ind <- rle(ID)$lengths
> data.frame(ID, newID=rep(c(1,length(ind)), ind ))
ID newID
1 30 1
2 30 1
3 30 1
4 30 1
5 24 2
6 24 2
7 24 2
transform is also another alternative
> transform(ID, newID=rep(c(1,2), rle(ID)$lengths))
Related
I've got a mess of data and am trying to efficiently wrangle it into shape. Here's a simplified short sample of the general format of my data.frame right now. The main difference is that I have a few more data labels like Label1 for my sampling units - each has a set of data similar to the data.frame I'm including but in my situation they are all in the same data.frame. I don't think that will complicate the reformatting so I've just included the single sampling unit of mock data here. StatsType levels Ave, Max, and Min are effectively nested within MeasureType.
tastycheez<-data.frame(
Day=rep((1:3),9),
StatsType=rep(c(rep("Ave",3),rep("Max",3),rep("Min",3)),3),
MeasureType=rep(c("Temp","H2O","Tastiness"),each=9),
Data_values=1:27,
Label1=rep("SamplingU1",27))
Ultimately, I would like a data frame where for each sampling unit and each Day there are columns holding the Data_values for my categories, like this:
Day Label1 Ave.Temp Ave.H2O Ave.Tastiness Max.Temp ...
1 SamplingU1 1 10 19 4 ...
2 SamplingU1 2 11 20 5 ...
I think some combination of functions from reshape,dplyr,tidyr, and/or data.table could do the job but I can't figure out how to code it. Here's what I've tried:
First, I spread the tastycheez (yum!), and that got me partway:
test<-spread(tastycheez,StatsType,Data_values)
Now I'm trying to spread it again or to cast, but with no luck:
test2<-spread(test,MeasureType,(Ave,Max,Min))
test2 <- recast(Day ~ MeasureType+c(Ave,Max,Min), data=test)
(I also tried melting the tastycheez but the results were a sticky, gooey mess and my tongue got burnt. that doesn't seem to be the right function for this.)
If you hate my puns please excuse them, I really can't figure this out!
Here are a couple related questions:
Combining two subgroups of data in the same dataframe
How can I spread repeated measures of multiple variables into wide format?
reshape2 You could use dcast from reshape2:
library(reshape2)
dcast(tastycheez,
Day + Label1 ~ paste(StatsType, MeasureType, sep="."),
value.var = "Data_values")
which gives
Day Label1 Ave.H2O Ave.Tastiness Ave.Temp Max.H2O Max.Tastiness Max.Temp Min.H2O Min.Tastiness Min.Temp
1 1 SamplingU1 10 19 1 13 22 4 16 25 7
2 2 SamplingU1 11 20 2 14 23 5 17 26 8
3 3 SamplingU1 12 21 3 15 24 6 18 27 9
tidyr Stealing #DavidArenburg's comment, here's the tidyr way:
library(tidyr)
tastycheez %>%
unite(temp, StatsType, MeasureType, sep = ".") %>%
spread(temp, Data_values)
which gives
Day Label1 Ave.H2O Ave.Tastiness Ave.Temp Max.H2O Max.Tastiness Max.Temp Min.H2O Min.Tastiness Min.Temp
1 1 SamplingU1 10 19 1 13 22 4 16 25 7
2 2 SamplingU1 11 20 2 14 23 5 17 26 8
3 3 SamplingU1 12 21 3 15 24 6 18 27 9
I'm doing some cluster analysis on the MLTobs from the LifeTables package and have come across a tricky problem with the Year variable in the mlt.mx.info dataframe. Year contains the period that the life table was taken, in intervals. Here's a table of the data:
1751-1754 1755-1759 1760-1764 1765-1769 1770-1774 1775-1779 1780-1784 1785-1789 1790-1794
1 1 1 1 1 1 1 1 1
1795-1799 1800-1804 1805-1809 1810-1814 1815-1819 1816-1819 1820-1824 1825-1829 1830-1834
1 1 1 1 1 2 3 3 3
1835-1839 1838-1839 1840-1844 1841-1844 1845-1849 1846-1849 1850-1854 1855-1859 1860-1864
4 1 5 3 8 1 10 11 11
1865-1869 1870-1874 1872-1874 1875-1879 1876-1879 1878-1879 1880-1884 1885-1889 1890-1894
11 11 1 12 2 1 15 15 15
1895-1899 1900-1904 1905-1909 1908-1909 1910-1914 1915-1919 1920-1924 1921-1924 1922-1924
15 15 15 1 16 16 16 2 1
1925-1929 1930-1934 1933-1934 1935-1939 1937-1939 1940-1944 1945-1949 1947-1949 1948-1949
19 19 1 20 1 22 22 3 1
1950-1954 1955-1959 1956-1959 1958-1959 1960-1964 1965-1969 1970-1974 1975-1979 1980-1984
30 30 2 1 40 40 41 41 41
1983-1984 1985-1989 1990-1994 1991-1994 1992-1994 1995-1999 2000-2003 2000-2004 2005-2006
1 42 42 1 1 44 3 41 22
2005-2007
14
As you can see, some of the intervals sit within other intervals. Thankfully none of them overlap. I want to simplify the intervals so intervals such as 1992-1994 and 1991-1994 all go into 1990-1994.
An idea might be to get the modulo of each interval and sort them into their new intervals that way but I'm unsure how to do this with the interval data type. If anyone has any ideas I'd really appreciate the help. Ultimately I want to create a histogram or barplot to illustrate the nicely.
If I understand your problem, you'll want something like this:
bottom <- seq(1750, 2010, 5)
library(dplyr)
new_df <- mlt.mx.info %>%
arrange(Year) %>%
mutate(year2 = as.numeric(substr(Year, 6, 9))) %>%
mutate(new_year = paste0(bottom[findInterval(year2, bottom)], "-",(bottom[findInterval(year2, bottom) + 1] - 1)))
View(new_df)
So what this does, it creates bins, and outputs a new column (new_year) that is the bottom of the bin. So everything from 1750-1754 will correspond to a new value of 1750-1754 (in string form; the original is an integer type, not sure how to fix that). Does this do what you want? Double check the results, but it looks right to me.
I'm at a loss to cycle through a data frame and calculate a variable that is a function of different/multiple rows. Please see the following data as an example.
date var1 var2 var3
12/29/2013 10 34 0
12/30/2013 10 34 15
12/31/2013 8 27 15
1/1/2014 8 27 0
1/2/2014 2 7 10
1/3/2014 10 35 20
1/4/2014 13 45 10
I would like to create a variable that is a function of the current row and the next row. For example,
var4(12/31/2013) = var1(12/31/2013) + var2(1/1/2014) + var3(12/31/2013)
For the last element in the dataframe, there is no (n+1) variable, so I'd like to assign a missing value/exception value in that case. Any guidance you could provide would be wonderful. Thank you in advance!
you could try
library(dplyr)
df %>%
mutate(var4=var1+lead(var2)+var3)
This question already has an answer here:
Summarizing multiple columns with data.table
(1 answer)
Closed 3 years ago.
I am trying to write a function that will sum the column(s) in the data frame according to the values in the first two columns.For example I have a matrix M,
Crs gr P_7 P_8
38 1 3 16
38 1 12 45
38 1 9 28
40 2 3 9
40 2 14 29
40 1 4 3
40 2 8 2
I want to sum the columns according to column1(crs) first and then column2(gr). Result will be,
Crs gr P_7 P_8
38 1 24 89
40 2 25 40
40 1 4 3
Currently I am using,
M <- M[, list(sum(P_7),sum(P_8)), by=list(Crs,gr)]
But the problem with this, is that I have to define the names of columns which wont be fixed. So, I was wondering how can I do this without defining the names of the columns.
Thanks in advance!
You're looking for this:
M[, lapply(.SD, sum), by = list(Crs, gr)]
The package plyr has some magic for situations just like this. Use a combination of ddply and numcolwise, like this:
library(plyr)
ddply(dat, .(Crs, gr), numcolwise(sum))
results in:
Crs gr P_7 P_8
1 38 1 24 89
2 40 1 4 3
3 40 2 25 40
I have data that illustrates hurricane tracks crossing through a series of "gates". How would I code it to output the GateID, and the count of times that each GateID occurs in the total data frame?
track_id day hour month year rate gate_id pres_inter vmax_inter
9 10 0 7 1 9.6451E-06 2 97809 23.545
9 10 0 7 1 9.6451E-06 17 100170 13.843
10 3 6 7 1 9.6451E-06 2 96662 31.568
13 22 12 8 1 9.6451E-06 1 94449 48.466
13 22 12 8 1 9.6451E-06 17 96749 30.55
16 13 0 8 1 9.6451E-06 4 98702 19.205
16 13 0 8 1 9.6451E-06 16 98585 18.143
19 27 6 9 1 9.6451E-06 9 98838 20.053
header <- read.table(fname_in, nrows=1)
track <- read.table(fname_in, sep=',', skip=1)
colnames(track) <- c("ID", "day", "month", "year", "hour", "rate", "gate_id", "pres_inter", "vmax_inter")
I think I would like to count the occurrence of each gate_id, and also perhaps output the maximum wind per gate (vmax_inter), etc....
Totally reading your mind, since you provide nothing concrete to go on. But if GateID is one of your data frame columns, you can get the count for each unique GateID along with other parameters using count from package plyr.
install.packages("plyr")
library("plyr")
count(mydf, vars = "GateID")
See ?count after installing for further details.
For the 2nd part of your question, see ?aggregate and consider the formula interface. For example,
aggregate(gate_id ~ vmax_inter, data = mydf, FUN = max)
or something similar. By the way, you can combine your two read.table steps with 'read.csv`