Related
This is the data with the two columns 'weight' and 'group':
genderweight <- structure(list(weight = c(95.0626365041014, 65.9189881179415,
64.1289176345525, 66.1688823533661, 81.6245374434498, 85.1845386418439,
81.0348729928744, 92.161156464954, 86.3842380662202, 64.8582493776221,
62.3256566394621, 85.0980797936812, 80.0399859200671, 83.3698935236987,
62.8710960018134, 77.0097819307823, 62.9067362884316, 62.8505200797307,
62.2199243419118, 86.2430806667288, 83.8522826935738, 59.3086045947413,
82.578094058482, 62.9779809883867), group = structure(c(2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L), levels = c("F", "M"), class = "factor")), row.names = c(NA,
-24L), class = c("tbl_df", "tbl", "data.frame"))
Package and library needed:
install.packages("rstatix")
library(rstatix)
I would like to use a placeholder in the following function:
t_test(genderweight, weight ~ group, detailed = TRUE)
My placeholder could be named i, for example, and afterwards I would like to run:
i <- "weight"
t_test(genderweight, i ~ group, detailed = TRUE)
Or alternatively, i could be a number, e.g. i = 1 and then I would like to run:
t_test(genderweight,genderweight[,i] ~ group, detailed = TRUE)
For both ways, I get an error message of the following type:
Error in `vec_as_location2_result()`:
! Can't extract columns that don't exist.
✖ Column `genderweight[, 1]` doesn't exist.
Run `rlang::last_error()` to see where the error occurred.
Is there a way to tell the function in an indirect way which column you want for the t-test?
Is there any way to write the data in a block of a table line by line in R?
I'll be grateful can someone find me a solution.
Thank you.
This is the input data in tad.
structure(list(gene = structure(c(4L, 7L, 2L, 10L, 1L, 9L, 6L,
3L, 8L, 5L), .Label = c("ENSG00000065243", "ENSG00000084070",
"ENSG00000127423", "ENSG00000135801", "ENSG00000163909", "ENSG00000174950",
"ENSG00000183615", "ENSG00000197056", "ENSG00000203857", "ENSG00000204060"
), class = "factor"), domain = c(9L, 1L, 5L, 1L, 9L, 2L, 1L,
4L, 6L, 6L)), row.names = c(NA, 10L), class = "data.frame")
This is the code I used.
colnames(tad)<-c("gene", "domain")
domain_result= aggregate(gene~domain, tad, paste, collapse = ",")
This is the output
I need this to be line by line. For example in the first row the data is like this.
ENSG00000183615,ENSG00000204060,ENSG00000174950
But I need it to be line by line.
Okay, here is what you can do to get the output you want in a way I understand it. Basically, what you want to do is separate the column gene into multiple columns while maintaining the domain. There is a function in tidyr called separate_rows that can do that.
library(tidyr)
gene_domains_out <- separate_rows(gene_domains, gene,sep=",")
Sample input Data
Here is a dput of the image above
#dput(head(gene_domains))
structure(list(domain = c(1L, 3L, 4L, 5L, 6L, 7L), gene = c("ENSG00000230594,ENSG00000171155,ENSG00000224089,ENSG00000230347,ENSG00000236446,ENSG00000186471,ENSG00000101892,ENSG00000182890,ENSG00000232119,ENSG00000131721,ENSG00000101882,ENSG00000101883,ENSG00000242362,ENSG00000226685,ENSG00000125352,ENSG00000236126,ENSG00000237957,ENSG00000005893,ENSG00000125355,ENSG00000226600,ENSG00000125356,ENSG00000203989,ENSG00000226929,ENSG00000228517,ENSG00000177485,ENSG00000226023,ENSG00000236371,ENSG00000278646,ENSG00000158290",
"ENSG00000176774,ENSG00000176746,ENSG00000232030,ENSG00000188408",
"ENSG00000198205,ENSG00000215174,ENSG00000165591,ENSG00000198455,ENSG00000186787,ENSG00000204271,ENSG00000147059",
"ENSG00000029993,ENSG00000166049,ENSG00000183862,ENSG00000102181,ENSG00000013619,ENSG00000130032,ENSG00000171100,ENSG00000160131,ENSG00000063601,ENSG00000147378,ENSG00000102195",
"ENSG00000147099,ENSG00000067177,ENSG00000184388,ENSG00000225396,ENSG00000268994,ENSG00000198034,ENSG00000125931,ENSG00000269502,ENSG00000184911,ENSG00000275520",
"ENSG00000102081,ENSG00000176988")), row.names = c(NA, 6L), class = "data.frame")
Sample output
Here is a dput of the output data
#dput(head(gene_domains_out))
structure(list(domain = c(1L, 1L, 1L, 1L, 1L, 1L), gene = c("ENSG00000230594",
"ENSG00000171155", "ENSG00000224089", "ENSG00000230347", "ENSG00000236446",
"ENSG00000186471")), row.names = c(NA, 6L), class = "data.frame")
Hope that helps.
I am attempting to populate two newly empty columns in a data frame with data from other columns in the same data frame in different ways depending on if they are populated.
I am trying to populate the values of HIGH_PRCN_LAT and HIGH_PRCN_LON (previously called F_Lat and F_Lon) which represent the final latitudes and londitudes for those rows this will be based off the values of the other columns in the table.
Case 1: Lat/Lon2 are populated (like in IDs 1 & 2), using the great
circle algorithm a midpoint between them should be calculated and
then placed into F_Lat & F_Lon.
Case 2: Lat/Lon2 are empty, then the values of Lat/Lon1 should be put
into F_Lat and F_Lon (like with IDs 3 & 4).
My code is as follows but doesn't work (see previous versions, removed in an edit).
The preperatory code I am using is as follows:
incidents <- structure(list(id = 1:9, StartDate = structure(c(1L, 3L, 2L,
2L, 2L, 3L, 1L, 3L, 1L), .Label = c("02/02/2000 00:34", "02/09/2000 22:13",
"20/01/2000 14:11"), class = "factor"), EndDate = structure(1:9, .Label = c("02/04/2006 20:46",
"02/04/2006 22:38", "02/04/2006 23:21", "02/04/2006 23:59", "03/04/2006 20:12",
"03/04/2006 23:56", "04/04/2006 00:31", "07/04/2006 06:19", "07/04/2006 07:45"
), class = "factor"), Yr.Period = structure(c(1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 3L), .Label = c("2000 / 1", "2000 / 2", "2000 /3"
), class = "factor"), Description = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = "ENGLISH TEXT", class = "factor"),
Location = structure(c(2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L
), .Label = c("Location 1", "Location 1 : Location 2"), class = "factor"),
Location.1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = "Location 1", class = "factor"), Postcode.1 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Postcode 1", class = "factor"),
Location.2 = structure(c(2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L), .Label = c("", "Location 2"), class = "factor"), Postcode.2 = structure(c(2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L), .Label = c("", "Postcode 2"
), class = "factor"), Section = structure(c(2L, 2L, 3L, 1L,
4L, 4L, 2L, 1L, 4L), .Label = c("East", "North", "South",
"West"), class = "factor"), Weather.Category = structure(c(1L,
2L, 4L, 2L, 2L, 2L, 4L, 1L, 3L), .Label = c("Animals", "Food",
"Humans", "Weather"), class = "factor"), Minutes = c(13L,
55L, 5L, 5L, 5L, 522L, 1L, 11L, 22L), Cost = c(150L, 150L,
150L, 20L, 23L, 32L, 21L, 11L, 23L), Location.1.Lat = c(53.0506727,
53.8721035, 51.0233529, 53.8721035, 53.6988355, 53.4768766,
52.6874562, 51.6638245, 51.4301359), Location.1.Lon = c(-2.9991256,
-2.4004125, -3.0988341, -2.4004125, -1.3031529, -2.2298073,
-1.8023421, -0.3964916, 0.0213837), Location.2.Lat = c(52.7116187,
53.746791, NA, 53.746791, 53.6787167, 53.4527824, 52.5264907,
NA, NA), Location.2.Lon = c(-2.7493169, -2.4777984, NA, -2.4777984,
-1.489026, -2.1247029, -1.4645023, NA, NA)), class = "data.frame", row.names = c(NA, -9L))
#gpsColumns is used as the following line of code is used for several data frames.
gpsColumns <- c("HIGH_PRCN_LAT", "HIGH_PRCN_LON")
incidents [ , gpsColumns] <- NA
#create separate variable(?) containing a list of which rows are complete
ind <- complete.cases(incidents [,17])
#populate rows with a two Lat/Lons with great circle middle of both values
incidents [ind, c("HIGH_PRCN_LON_2","HIGH_PRCN_LAT_2")] <-
with(incidents [ind,,drop=FALSE],
do.call(rbind, geosphere::midPoint(cbind.data.frame(Location.1.Lon, Location.1.Lat), cbind.data.frame(Location.2.Lon, Location.2.Lat))))
#populate rows with one Lat/Lon with those values
incidents[!ind, c("HIGH_PRCN_LAT","HIGH_PRCN_LON")] <- incidents[!ind, c("Location.1.Lat","Location.1.Lon")]
I will use the geosphere::midPoint function based off a recommendation here: http://r.789695.n4.nabble.com/Midpoint-between-coordinates-td2299999.html.
Unfortunately, it doesn't appear that this way of populating the column will work when there are several cases.
The current error that is thrown is:
Error in `$<-.data.frame`(`*tmp*`, F_Lat, value = integer(0)) :
replacement has 0 rows, data has 178012
Edit: also posted to reddit: https://www.reddit.com/r/Rlanguage/comments/bdvavx/conditional_updating_column_in_dataframe/
Edit: Added clarity on the parts of the code I do not understand.
#replaces the F_Lat2/F_Lon2 columns in rows with a both sets of input coordinates
dataframe[ind, c("F_Lat2","F_Lon2")] <-
#I am unclear on what this means, specifically what the "with" function does and what "drop=FALSE" does and also why they were used in this case.
with(dataframe[ind,,drop=FALSE],
#I am unclear on what do.call and rbind are doing here, but the second half (geosphere onwards) is binding the Lats and Lons to make coordinates as inputs for the gcIntermediate function.
do.call(rbind, geosphere::gcIntermediate(cbind.data.frame(Lat1, Lon1),
cbind.data.frame(Lat2, Lon2), n = 1)))
Though your code doesn't work as-written for me, and I cannot calculate the same precise values your expect, I suspect the error your seeing can be fixed with these steps. (Data is down at the bottom here.)
Pre-populate the empty columns.
Pre-calculate the complete.cases step, it'll save time.
Use cbind.data.frame for inside gcIntermediate.
I'm inferring from
gcIntermediate([dataframe...
^
this is an error in R
that you are binding those columns together, so I'll use cbind.data.frame. (Using cbind itself produced some ignorable warnings from geosphere, so you can use it instead and perhaps suppressWarnings, but that function is a little strong in that it'll mask other warnings as well.)
Also, since it appears you want one intermediate value for each pair of coordinates, I added the gcIntermediate(..., n=1) argument.
The use of do.call(rbind, ...) is because gcIntermediate returns a list, so we need to bring them together.
dataframe$F_Lon2 <- dataframe$F_Lat2 <- NA_real_
ind <- complete.cases(dataframe[,4])
dataframe[ind, c("F_Lat2","F_Lon2")] <-
with(dataframe[ind,,drop=FALSE],
do.call(rbind, geosphere::gcIntermediate(cbind.data.frame(Lat1, Lon1),
cbind.data.frame(Lat2, Lon2), n = 1)))
dataframe[!ind, c("F_Lat2","F_Lon2")] <- dataframe[!ind, c("Lat1","Lon1")]
dataframe
# ID Lat1 Lon1 Lat2 Lon2 F_Lat F_Lon F_Lat2 F_Lon2
# 1 1 19.05067 -3.999126 92.71332 -6.759169 55.88200 -5.379147 55.78466 -6.709509
# 2 2 58.87210 -1.400413 54.74679 -4.479840 56.80945 -2.940126 56.81230 -2.942029
# 3 3 33.02335 -5.098834 NA NA 33.02335 -5.098834 33.02335 -5.098834
# 4 4 54.87210 -4.400412 NA NA 54.87210 -4.400412 54.87210 -4.400412
Update, using your new incidents data and switching to geosphere::midPoint.
Try this:
incidents$F_Lon2 <- incidents$F_Lat2 <- NA_real_
ind <- complete.cases(incidents[,4])
incidents[ind, c("F_Lat2","F_Lon2")] <-
with(incidents[ind,,drop=FALSE],
geosphere::midPoint(cbind.data.frame(Location.1.Lat,Location.1.Lon),
cbind.data.frame(Location.2.Lat,Location.2.Lon)))
incidents[!ind, c("F_Lat2","F_Lon2")] <- dataframe[!ind, c("Lat1","Lon1")]
One (big) difference is that geosphere::gcIntermediate(..., n=1) returns a list of results, whereas geosphere::midPoint(...) (no n=) returns just a matrix, so no rbinding required.
Data:
dataframe <- read.table(header=T, stringsAsFactors=F, text="
ID Lat1 Lon1 Lat2 Lon2 F_Lat F_Lon
1 19.0506727 -3.9991256 92.713318 -6.759169 55.88199535 -5.3791473
2 58.8721035 -1.4004125 54.746791 -4.47984 56.80944725 -2.94012625
3 33.0233529 -5.0988341 NA NA 33.0233529 -5.0988341
4 54.8721035 -4.4004125 NA NA 54.8721035 -4.4004125")
I've been trying to make a graph that looks like this (but nicer)
based on what I found in this discussion using the transitionPlot() function from the Gmiscpackage.
However, I can't get my transition_matrix right and I also can't seem to plot the different state classes in separate third column.
My data is based on the symptomatic improvement of patients following surgery. The numbers in the boxes are the number of patients in each "state" pre vs. post surgery. Please note the (LVAD) is not a necessity.
The data for this plot is this called df and is as follows
dput(df)
structure(list(StudyID = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L), .Label = c("P1", "P2", "P3",
"P4", "P5", "P6", "P7"), class = "factor"), MeasureTime = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Postoperative",
"Preoperative"), class = "factor"), NYHA = c(3L, 3L, 3L, 3L,
3L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L)), .Names = c("StudyID",
"MeasureTime", "NYHA"), row.names = c(NA, -14L), class = "data.frame")
I've made a plot in ggplot2 that looked like this
but my supervisor didn't like it, because I had to jitterthe lines so that they didn't overlap and so one could see what was happening with each patient and thus the points/lines aren't exactly lined up with the y-axis.
So I was wondering if anyone had an idea, how I'd be able to do this using the Gmisc package making what seems to me to be a transitionPlot.
Your help and time is much appreciated.
Thanks.
Using your sample df data, here are some pretty low-level plotting function that can re-create your sample image. It should be straigtforward to customize however you like
First, make sure pre comes before post
df$MeasureTime<-factor(df$MeasureTime, levels=c("Preoperative","Postoperative"))
then define some plot helper functions
textrect<-function(x,y,text,width=.2) {
rect(x-width, y-width, x+width, y+width)
text(x,y,text)
}
connect<-function(x1,y1,x2,y2, width=.2) {
segments(x1+width,y1,x2-width,y2)
}
now draw the plot
plot.new()
par(mar=c(0,0,0,0))
plot.window(c(0,4), c(0,4))
with(unique(reshape(df, idvar="StudyID", timevar="MeasureTime", v.names="NYHA", direction="wide")[,-1]),
connect(2,NYHA.Preoperative,3,NYHA.Postoperative)
)
with(as.data.frame(with(df, table(NYHA, MeasureTime))),
textrect(as.numeric(MeasureTime)+1,as.numeric(as.character(NYHA)), Freq)
)
text(1, 1:3, c("I","II","III"))
text(1:3, 3.75, c("NYHA","Pre-Op","Post-Op"))
text(3.75, 2, "(LVAD)")
which results in
I have a data frame with 18 columns and about 12000 rows. I want to find the outliers for the first 17 columns and compare the results with the column 18. The column 18 is a factor and contains data which can be used as indicator of outlier.
My data frame is ufo and I remove the column 18 as follow:
ufo2 <- ufo[,1:17]
and then convert 3 non0numeric columns to numeric values:
ufo2$Weight <- as.numeric(ufo2$Weight)
ufo2$InvoiceValue <- as.numeric(ufo2$InvoiceValue)
ufo2$Score <- as.numeric(ufo2$Score)
and then use the following command for outlier detection:
outlier.scores <- lofactor(ufo2, k=5)
But all of the elements of the outlier.scores are NA!!!
Do I have any mistake in this code?
Is there another way to find outlier for such a data frame?
All of my code:
setwd(datadirectory)
library(doMC)
registerDoMC(cores=8)
library(DMwR)
# load data
load("data_9802-f2.RData")
ufo2 <- ufo[,2:17]
ufo2$Weight <- as.numeric(ufo2$Weight)
ufo2$InvoiceValue <- as.numeric(ufo2$InvoiceValue)
ufo2$Score <- as.numeric(ufo2$Score)
outlier.scores <- lofactor(ufo2, k=5)
The output of the dput(head(ufo2)) is:
structure(list(Origin = c(2L, 2L, 2L, 2L, 2L, 2L), IO = c(2L,
2L, 2L, 2L, 2L, 2L), Lot = c(1003L, 1003L, 1003L, 1012L, 1012L,
1013L), DocNumber = c(10069L, 10069L, 10087L, 10355L, 10355L,
10382L), OperatorID = c(5698L, 5698L, 2015L, 246L, 246L, 4135L
), Month = c(1L, 1L, 1L, 1L, 1L, 1L), LineNo = c(1L, 2L, 1L,
1L, 2L, 1L), Country = c(1L, 1L, 1L, 1L, 11L, 1L), ProduceCode = c(63456227L,
63455714L, 33687427L, 32686627L, 32686627L, 791614L), Weight = c(900,
850, 483, 110000, 5900, 1000), InvoiceValue = c(637, 775, 2896,
48812, 1459, 77), InvoiceValueWeight = c(707L, 912L, 5995L, 444L,
247L, 77L), AvgWeightMonth = c(1194.53, 1175.53, 7607.17, 311.667,
311.667, 363.526), SDWeightMonth = c(864.931, 780.247, 3442.93,
93.5818, 93.5818, 326.238), Score = c(0.56366535234262, 0.33775439984787,
0.46825476121676, 1.414092583904, 0.69101737288291, 0.87827342721894
), TransactionNo = c(47L, 47L, 6L, 3L, 3L, 57L)), .Names = c("Origin",
"IO", "Lot", "DocNumber", "OperatorID", "Month", "LineNo", "Country",
"ProduceCode", "Weight", "InvoiceValue", "InvoiceValueWeight",
"AvgWeightMonth", "SDWeightMonth", "Score", "TransactionNo"), row.names = c(NA,
6L), class = "data.frame")
First of all, you need to spend a lot more time preprocessing your data.
Your axes have completely different meaning and scale. Without care, the outlier detection results will be meaningless, because they are based on a meaningless distance.
For example produceCode. Are you sure, this should be part of your similarity?
Also note that I found the lofactor implementation of the R DMwR package to be really slow. Plus, it seems to be hard-wired to Euclidean distance!
Instead, I recommend using ELKI for outlier detection. First of all, it comes with a much wider choice of algorithms, secondly it is much faster than R, and third, it is very modular and flexible. For your use case, you may need to implement a custom distance function instead of using Euclidean distance.
Here's the link to the ELKI tutorial on implementing a custom distance function.