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I have a list of column names that look like this...
colnames(dat)
1 subject
2 e.type
3 group
4 boxnum
5 edate
6 file.name
7 fr
8 active
9 inactive
10 reward
11 latency.to.first.active
12 latency.to.first.inactive
13 act0.600
14 act600.1200
15 act1200.1800
16 act1800.2400
17 act2400.3000
18 act3000.3600
19 inact0.600
20 inact600.1200
21 inact1200.1800
22 inact1800.2400
23 inact2400.3000
24 inact3000.3600
25 rew0.600
26 rew600.1200
27 rew1200.1800
28 rew1800.2400
29 rew2400.3000
30 rew3000.3600
I want to get the row sum for the columns that list act#, inact#, and reward#
This works...
for (row in 1:nrow(dat)) {
dat[row, "active"] = rowSums(dat[row,c(13:18)])
dat[row, "inactive"] = rowSums(dat[row,c(19:24)])
dat[row, "reward"] = rowSums(dat[row,c(25:30)])
}
But I don't want to hard coded it since the number of columns for the 3 sections may change. How can I do this without hard coding the column indexes?
Also, for example, I tried searching for the "act" named columns but it was also including the "active" column.
sub_dat <- dat[, 13:30]
result <- sapply(split.default(sub_dat, substr(names(sub_dat), 1, 3)), rowSums)
dat[, c('active', 'inactive', 'reward')] <- result
Easy-cheesy with witch select & matches from the tidyverse.
library(tidyverse)
data %>%
mutate(
sum_act = rowSums(select(., matches("act[0-9]"))),
sum_inact = rowSums(select(., matches("inact[0-9]"))),
sum_rew = rowSums(select(., matches("rew[0-9]")))
)
I made an example how it could be done:
t <- data.frame(c(1,2,3),c("a","b","c"))
colnames(t) <- c("num","char")
#with function append() you make a list of rows that fulfill your logical argument
whichRows <- append(which(t$char == "a"),which(t$char == "b"))
sum(t$num[whichRows])
or if I misunderstood you and you want to sum for every column separately then:
sum(t$num[which(t$char == "a")])
sum(t$num[which(t$char == "b")])
I am having trouble figuring out how to trim the end off of a string in a data frame.
I want to trim everything to a "base" name, after #s and letters, a period, then a number. My goal is trim everything in my dataframe to this "base" name, then sum the values with the same "base." I was thinking it would be possible to trim, then merge and sum the values.
ie/
Gene_name Values
B0222.5 4
B0222.6 16
B0228.7.1 2
B0228.7.2 12
B0350.2h.1 30
B0350.2h.2 2
B0350.2i 15
2RSSE.1a 3
2RSSE.1b 10
R02F11.11 4
to
Gene_name Values
B0222.5 4
B0222.6 16
B0228.7 14
B0350.2 47
2RSSE.1 13
R02F11.11 4
Thank you for any help!
Here is a solution using the dplyr and stringr packages. You first create a column with your extracted base pattern, and then use the group_by and summarise functions from dplyr to get the sum of values for each name:
library(dplyr)
library(stringr)
df2 = df %>% mutate(Gene_name = str_extract(Gene_name,"[[:alnum:]]+\\.\\d+")) %>%
group_by(Gene_name) %>% summarise(Values = sum(Values))
Gene_name Values
<chr> <int>
1 2RSSE.1 13
2 B0222.5 4
3 B0222.6 16
4 B0228.7 14
5 B0350.2 47
6 R02F11.11 4
As someone has also suggested, I would get gene names first, and then search for them in the original data.frame
df <- data.frame(Gene_name = c("B0222.5", "B0222.6", "B0228.7.1", "B0228.7.2", "B0350.2h.1", "B0350.2h.2", "B0350.2i", "2RSSE.1a", "2RSSE.1b", "R02F11.11"),
Values = c(4, 16, 2, 12, 30, 2, 15, 3, 10, 4),
stringsAsFactors = F)
pat <- "(^[[:alnum:]]+\\.[[:digit:]]*)"
cap.pos <- regexpr(pat, df$Gene_name)
cap.gene <- unique(substr(df$Gene_name, cap.pos, (cap.pos + attributes(cap.pos)$match.length - 1)))
do.call(rbind, lapply(cap.gene, (function(nm){
sumval <- sum(df[grepl(nm, df$Gene_name, fixed = T),]$Values, na.rm = T)
data.frame(Gene_name = nm, Value = sumval)
})))
The result tracks with your request
Gene_name Value
1 B0222.5 4
2 B0222.6 16
3 B0228.7 14
4 B0350.2 47
5 2RSSE.1 13
6 R02F11.11 4
You can also create the Gene_name as a factor and change the levels.
# coerce the vector as a factor
Gene_name <- as.factor(Gene_name)
# view the levels
levels(Gene_name)
# to make B0228.7.1 into B0228.7
levels(Gene_name)[ *index for B0228.7.1* ] <- B0228.7
You can repeat this for the levels that need to change and then the values will automatically sum together and rows with similar levels will be treated as the same category.
This is the first time that I ask a question on stack overflow. I have tried searching for the answer but I cannot find exactly what I am looking for. I hope someone can help.
I have a huge data set of 20416 observation. Basically, I have 83 subjects and for each subject I have several observations. However, the number of observations per subject is not the same (e.g. subject 1 has 256 observations, while subject 2 has only 64 observations).
I want to add an extra column containing the mean of the observations for each subject (the observations are reading times (RT)).
I tried with the aggregate function:
aggregate (RT ~ su, data, mean)
This formula returns the correct mean per subject. But then I cannot simply do the following:
data$mean <- aggregate (RT ~ su, data, mean)
as R returns this error:
Error in $<-.data.frame(tmp, "mean", value = list(su = 1:83, RT
= c(378.1328125, : replacement has 83 rows, data has 20416
I understand that the formula lacks a command specifying that the mean for each subject has to be repeated for all the subject's rows (e.g. if subject 1 has 256 rows, the mean for subject 1 has to be repeated for 256 rows, if subject 2 has 64 rows, the mean for subject 2 has to be repeated for 64 rows and so forth).
How can I achieve this in R?
The data.table syntax lends itself well to this kind of problem:
Dt[, Mean := mean(Value), by = "ID"][]
# ID Value Mean
# 1: a 0.05881156 0.004426491
# 2: a -0.04995858 0.004426491
# 3: b 0.64054432 0.038809830
# 4: b -0.56292466 0.038809830
# 5: c 0.44254622 0.099747707
# 6: c -0.10771992 0.099747707
# 7: c -0.03558318 0.099747707
# 8: d 0.56727423 0.532377247
# 9: d -0.60962095 0.532377247
# 10: d 1.13808538 0.532377247
# 11: d 1.03377033 0.532377247
# 12: e 1.38789640 0.568760936
# 13: e -0.57420308 0.568760936
# 14: e 0.89258949 0.568760936
As we are applying a grouped operation (by = "ID"), data.table will automatically replicate each group's mean(Value) the appropriate number of times (avoiding the error you ran into above).
Data:
Dt <- data.table::data.table(
ID = sample(letters[1:5], size = 14, replace = TRUE),
Value = rnorm(14))[order(ID)]
Staying in Base R, ave is intended for this use:
data$mean = with(data, ave(x = RT, su, FUN = mean))
Simply merge your aggregated means data with full dataframe joined by the subject:
aggdf <- aggregate (RT ~ su, data, mean)
names(aggdf)[2] <- "MeanOfRT"
df <- merge(df, aggdf, by="su")
Another compelling way of handling this without generating extra data objects is by using group_by of dplyr package:
# Generating some data
data <- data.table::data.table(
su = sample(letters[1:5], size = 14, replace = TRUE),
RT = rnorm(14))[order(su)]
# Performing
> data %>% group_by(su) %>%
+ mutate(Mean = mean(RT)) %>%
+ ungroup()
Source: local data table [14 x 3]
su RT Mean
1 a -1.62841746 0.2096967
2 a 0.07286149 0.2096967
3 a 0.02429030 0.2096967
4 a 0.98882343 0.2096967
5 a 0.95407214 0.2096967
6 a 1.18823435 0.2096967
7 a -0.13198711 0.2096967
8 b -0.34897914 0.1469982
9 b 0.64297557 0.1469982
10 c -0.58995261 -0.5899526
11 d -0.95995198 0.3067978
12 d 1.57354754 0.3067978
13 e 0.43071258 0.2462978
14 e 0.06188307 0.2462978
I am trying to create a new column conditional on another column, a bit like a moving average or moving window but based on distance between points. Take for example row 2 with a CO2 of 399.935. I would like to have the mean of all the points within 100 m (traveled) of that point. In my example (looking at column CumDist), rows 1, 3, 4, 5 would be selected to calculate the mean. The column CumDist (*100,000 to have the units in meters) consists of cumulative distance traveled. I have 5000 points and obviously the width (or the number of rows) of the moving window will vary.
I tested over() from the sp package, but it's problematic if the same road is taken more than once. I looked on the web for other solutions and I did not find anything that could help me.
dput(DF)
structure(list(CO2 = c(399.9350305, 399.9350305, 399.9350305,
400.0320031, 400.0320031, 400.0320031, 399.7718229, 399.7718229,
399.7718229, 399.3855075, 399.3855075, 399.3855075, 399.4708139,
399.4708139, 399.4708139, 400.0362474, 400.0362474, 400.0362474,
399.7556753, 399.7556753), lon = c(-103.7093538, -103.709352,
-103.7093492, -103.7093467, -103.7093455, -103.7093465, -103.7093482,
-103.7093596, -103.7094074, -103.7094625, -103.7094966, -103.709593,
-103.709649, -103.7096717, -103.7097349, -103.7097795, -103.709827,
-103.7099007, -103.709924, -103.7099887), lat = c(49.46972027,
49.46972153, 49.46971675, 49.46971533, 49.46971307, 49.4697124,
49.46970636, 49.46968214, 49.46960921, 49.46955984, 49.46953621,
49.46945809, 49.46938994, 49.46935281, 49.46924309, 49.46918635,
49.46914762, 49.46912566, 49.46912407, 49.46913321),distDiff = c(0.000342016147509882,
0.000191466419697602, 0.000569046320857002, 0.000240367540492089,
0.000265977754839834, 0.000103953049523505, 0.000682968856240796,
0.0028176007969857, 0.00882013898948418, 0.00678966015562509,
0.00360774024245839, 0.011149423290729, 0.00859796340323456,
0.00444526066124642, 0.0130344010874029, 0.00709037369666853,
0.00551435348701512, 0.00587377717110946, 0.00169806309901329,
0.00479849401022625), CumDist = c(0.000342016147509882, 0.000533482567207484,
0.00110252888806449, 0.00134289642855657, 0.00160887418339641,
0.00171282723291991, 0.00239579608916071, 0.00521339688614641,
0.0140335358756306, 0.0208231960312557, 0.0244309362737141, 0.0355803595644431,
0.0441783229676777, 0.0486235836289241, 0.0616579847163269, 0.0687483584129955,
0.0742627119000106, 0.08013648907112, 0.0818345521701333, 0.0866330461803596
)), .Names = c("X12CO2_dry", "coords.x1", "coords.x2", "V1",
"CumDist"), row.names = 2:21, class = "data.frame")
thanks, Martin
Man you beat me to it with a cleaner solution mra68.
Here's mine using a few loops.
####################
for (j in 1:nrow(DF)){#Loop through all rows of your dataset
CO2list<-NULL ##Need to make a variable before storing to it in the loop
for(i in 1:nrow(DF)){##Loop through all distances in the table
if ((abs(DF$CumDist[i]-DF$CumDist[j]))<=0.001) {
##Check to see if difference in CumDist<=100/100000 for all entries
#CumDist[j] is point with the 100 meter window around it
CO2list<-c(CO2list,DF$X12CO2_dry[i])
##Store your CO2 entries that are within the 100 meter window to a vector
}
}
DF$CO2AVG[j]<-mean(CO2list)
#Get the mean of your list and store it to column named CO2AVG
}
The window that belongs to the i-th row starts at n[i] and ends at m[i]-1. Hence the sum of the CO2-values in the i-th window is CumCO2[m[i]]-CumCO2[n[i]]. (Notice that the indices in CumCO2 are shifted by 1, because of the leading 0.) Dividing this CO2-sum by the window size m[i]-n[i] gives the values meanCO2 for the new column:
n <- sapply( df$CumDist,
function(x){
which.max( df$CumDist >= x-0.001 )
}
)
m <- sapply( df$CumDist,
function(x){
which.max( c(df$CumDist,Inf) > x+0.001 )
}
)
CumCO2 <- c( 0, cumsum(df$X12CO2) )
meanCO2 <- ( CumCO2[m] - CumCO2[n] ) / (m-n)
.
> n
[1] 1 1 1 2 3 3 5 8 9 10 11 12 13 14 15 16 17 18 19 20
> m
[1] 4 5 7 7 8 8 8 9 10 11 12 13 14 15 16 17 18 19 20 21
> meanCO2
[1] 399.9350 399.9593 399.9835 399.9932 399.9606 399.9606 399.9453 399.7718 399.7718 399.3855 399.3855 399.3855 399.4708 399.4708 399.4708 400.0362
[17] 400.0362 400.0362 399.7557 399.7557
>
GIVEN DATA
I have 6 columns of data of vehicle trajectory (observation of vehicles' change in position, velocity, etc over time) a part of which is shown below:
Vehicle ID Frame ID Global X Vehicle class Vehicle velocity Lane
1 177 6451181 2 24.99 5
1 178 6451182 2 24.95 5
1 179 6451184 2 24.91 5
1 180 6451186 2 24.90 5
1 181 6451187 2 24.96 5
1 182 6451189 2 25.08 5
Vehicle ID is the identification of individual vehicles e.g. vehicle 1, vehicle 2, etc. It is repeated in the column for each frame in which it was observed. Please note that each frame is 0.1 seconds long so 10 frames make 1 second. The IDs of frames is in Frame ID column. Vehicle class is the type of vehicle (1=motorcycle, 2=car, 3=truck). Vehicle velocity column represents instantaneous speed of vehicle in that instant of time i.e. in a frame. Lane represents the number or ID of the lane in which vehicle is present in a particular frame.
WHAT I NEED TO FIND
The data I have is for 15 minutes period. The minimum frame ID is 5 and maximum frame ID is 9952. I need to find the total number of vehicles in every 30 seconds time period. This means that starting from the first 30 seconds (frame ID 5 to frame ID 305), I need to know the unique vehicle IDs observed. Also, for these 30 seconds period, I need to find the average velocity of each vehicle class. This means that e.g. for cars I need to find the average of all velocities of those vehicles whose vehicle class is 2.
I need to find this for all 30 seconds time period i.e. 5-305, 305-605, 605-905,..., 9605-9905. The ouput should tables for cars, trucks and motorcycles like this:
Time Slots Total Cars Average Velocity
5-305 xx xx
305-605 xx xx
. . .
. . .
9605-9905 xx xx
WHAT I HAVE TRIED SO FAR
# Finding the minimum and maximum Frame ID for creating 30-seconds time slots
minfid <- min(data$'Frame ID') # this was 5
maxfid <- max(data$'Frame ID') # this was 9952
for (i in 'Frame ID'==5:Frame ID'==305) {
table ('Vehicle ID')
mean('Vehicle Velocity', 'Vehicle class'==2)
} #For cars in first 30 seconds
I can't generate the required output and I don't know how can I do this for all 30 second periods. Please help.
It's a bit tough to make sure code is completely correct with your data since there is only one vehicle in the sample you show. That said, this is a typical split-apply-combine type analysis you can execute easily with the data.table package:
library(data.table)
dt <- data.table(df) # I just did a `read.table` on the text you posted
dt[, frame.group:=cut(Frame_ID, seq(5, 9905, by=300), include.lowest=T)]
Here, I just converted your data into a data.table (df was a direct import of your data posted above), and then created 300 frame buckets using cut. Then, you just let data.table do the work. In the first expression we calculate total unique vehicles per frame.group
dt[, list(tot.vehic=length(unique(Vehicle_ID))), by=frame.group]
# frame.group tot.vehic
# 1: [5,305] 1
Now we group by frame.group and Vehicle_class to get average speed and count for those combinations:
dt[, list(tot.vehic=length(unique(Vehicle_ID)), mean.speed=mean(Vehicle_velocity)), by=list(frame.group, Vehicle_class)]
# frame.group Vehicle_class tot.vehic mean.speed
# 1: [5,305] 2 1 24.965
Again, a bit silly when we only have one vehicle, but this should work for your data set.
EDIT: to show that it works:
library(data.table)
set.seed(101)
dt <- data.table(
Frame_ID=sample(5:9905, 50000, rep=T),
Vehicle_ID=sample(1:400, 50000, rep=T),
Vehicle_velocity=runif(50000, 25, 100)
)
dt[, frame.group:=cut(Frame_ID, seq(5, 9905, by=300), include.lowest=T)]
dt[, Vehicle_class:=Vehicle_ID %% 3]
head(
dt[order(frame.group, Vehicle_class), list(tot.vehic=length(unique(Vehicle_ID)), mean.speed=mean(Vehicle_velocity)), by=list(frame.group, Vehicle_class)]
)
# frame.group Vehicle_class tot.vehic mean.speed
# 1: [5,305] 0 130 63.34589
# 2: [5,305] 1 131 61.84366
# 3: [5,305] 2 129 64.13968
# 4: (305,605] 0 132 61.85548
# 5: (305,605] 1 132 64.76820
# 6: (305,605] 2 133 61.57129
Maybe it's your data?
Here is a plyr version:
data$timeSlot <- cut(data$FrameID,
breaks = seq(5, 9905, by=300),
dig.lab=5,
include.lowest=TRUE)
# split & combine
library(plyr)
data.sum1 <- ddply(.data = data,
.variables = c("timeSlot"),
.fun = summarise,
totalCars = length(unique(VehicleID)),
AverageVelocity = mean(velocity)
)
# include VehicleClass
data.sum2 <- ddply(.data = data,
.variables = c("timeSlot", "VehicleClass"),
.fun = summarise,
totalCars = length(unique(VehicleID)),
AverageVelocity = mean(velocity)
)
The column names like FrameID would have to be edited to match the ones you use:
data <- read.table(sep = "", header = TRUE, text = "
VehicleID FrameID GlobalX VehicleClass velocity Lane
1 177 6451181 2 24.99 5
1 178 6451182 2 24.95 5
1 179 6451184 2 24.91 5
1 180 6451186 2 24.90 5
1 181 6451187 2 24.96 5
1 182 6451189 2 25.08 5")
data.sum1
# timeSlot totalCars AverageVelocity
# 1 [5,305] 1 24.965
data.sum2
# timeSlot VehicleClass totalCars AverageVelocity
# 1 [5,305] 2 1 24.965