I have searched a lot for this simple question, but have not found a solution. It looks really simple. I have a dataframe with a column like this:
Value
0.13
0.35
0.62
0.97
0.24
0.59
0.92
0.16
0.29
0.62
0.98
All values have a range between 0 and 1. What I want is that when the value starts to drop, I assign a new group to it. Within each group, the value is increasing. So the ideal outcome will look like this:
Value Group
0.13 1
0.35 1
0.62 1
0.97 1
0.24 2
0.59 2
0.92 2
0.16 3
0.29 3
0.62 3
0.98 3
Does anyone have a suggestion for how to address this?
This should do the trick, and uses only vectorised base functions. You may want to exchange the < for <=, if thats the behaviour you wanted.
vec <- c(0.13, 0.35, 0.62, 0.97, 0.24, 0.59, 0.92, 0.16, 0.29, 0.62, 0.98)
cumsum(c(1, diff(vec) < 0))
This isn't the most elegant solution, but it works:
value <- c(0.13, 0.35, 0.62, 0.97, 0.24, 0.59, 0.92, 0.16, 0.29, 0.62, 0.98)
foo <- data.frame(value, group = 1)
current_group <- 1
for(i in 2:nrow(foo)){
if(foo$value[i] >= foo$value[i-1]){
foo$group[i] <- current_group
}else{
current_group <- current_group + 1
foo$group[i] <- current_group
}
}
df <- data.frame( x = c(0.13, 0.35, 0.62, 0.97, 0.24, 0.59, 0.92, 0.16, 0.29, 0.62, 0.98))
df$y <- c(df$x[-1], NA) # lag column
df$chgdir <- as.numeric(df$y - df$x < 0) # test for change in direction
df$chgdir[is.na(df$chgdir)] <- 0 # deal with NA
df$group <- cumsum(df$chgdir) + 1 # determine group number
df[,c("x", "group")]
#> x group
#> 1 0.13 1
#> 2 0.35 1
#> 3 0.62 1
#> 4 0.97 2
#> 5 0.24 2
#> 6 0.59 2
#> 7 0.92 3
#> 8 0.16 3
#> 9 0.29 3
#> 10 0.62 3
#> 11 0.98 3
Related
I am struggling to write an R code for the following problem:
df1 and df2 are two dataframes.
> df1 <- read.csv(file = 'Indx.csv')
> df1
St_Name I1 I2 I3 I4
1 TN 0.10 0.15 0.20 0.25
2 AZ 0.30 0.35 0.40 0.45
3 TX 0.50 0.55 0.60 0.65
4 KS 0.70 0.75 0.80 0.85
5 KY 0.90 0.95 0.11 0.12
6 MN 0.13 0.14 0.16 0.17
> df2 <- as.data.frame(fromJSON(file = "NewIndx.json"))
> df2
St_Name I1 I3
1 KS 100 200
# The output should be
> df1
St_Name I1 I2 I3 I4
1 TN 0.10 0.15 0.20 0.25
2 AZ 0.30 0.35 0.40 0.45
3 TX 0.50 0.55 0.60 0.65
4 KS 100 0.75 200 0.85
5 KY 0.90 0.95 0.11 0.12
6 MN 0.13 0.14 0.16 0.17
>
what is the optimal code to achieve this?
We could use this slightly modified function coalesce_join provided by Edward Visel:
library(tidyverse)
# the function:
coalesce_join <- function(x, y,
by = NULL, suffix = c(".y", ".x"),
join = dplyr::full_join, ...) {
joined <- join(y, x, by = by, suffix = suffix, ...)
# names of desired output
cols <- union(names(y), names(x))
to_coalesce <- names(joined)[!names(joined) %in% cols]
suffix_used <- suffix[ifelse(endsWith(to_coalesce, suffix[1]), 1, 2)]
# remove suffixes and deduplicate
to_coalesce <- unique(substr(
to_coalesce,
1,
nchar(to_coalesce) - nchar(suffix_used)
))
coalesced <- purrr::map_dfc(to_coalesce, ~dplyr::coalesce(
joined[[paste0(.x, suffix[1])]],
joined[[paste0(.x, suffix[2])]]
))
names(coalesced) <- to_coalesce
dplyr::bind_cols(joined, coalesced)[cols]
}
# apply
coalesce_join(df1, df2, by = 'St_Name')
St_Name I1 I3 I2 I4
1 KS 100.00 200.00 0.75 0.85
2 TN 0.10 0.20 0.15 0.25
3 AZ 0.30 0.40 0.35 0.45
4 TX 0.50 0.60 0.55 0.65
5 KY 0.90 0.11 0.95 0.12
6 MN 0.13 0.16 0.14 0.17
Kindly let me know if this is what you were anticipating.
library(tidyr)
id<- "St_Name"
df_1<- melt(df_1, id.vars = id, measure.vars = setdiff(colnames(df_1),id))
df_2 <- melt(df_2, id.vars = id, measure.vars = setdiff(colnames(df_2),id))
result <- merge(df_1,df_2, by=c("St_Name","variable"),no.dups = TRUE,all.x = TRUE)
result$value.x[which(!is.na(result$value.y))]<- result$value.y[which(!is.na(result$value.y))]
result <- result[,-4]
result <-spread(result, variable, value.x)
We could use {powerjoin} and use the conflict argument. coalesce_xy will pick in priority data from the right side table.
data
df1 <- tibble::tribble(
~St_Name, ~I1, ~I2, ~I3, ~I4,
"TN", 0.10, 0.15, 0.20, 0.25,
"AZ", 0.30, 0.35, 0.40, 0.45,
"TX", 0.50, 0.55, 0.60, 0.65,
"KS", 0.70, 0.75, 0.80, 0.85,
"KY", 0.90, 0.95, 0.11, 0.12,
"MN", 0.13, 0.14, 0.16, 0.17)
df2 <- tibble::tribble(
~St_Name, ~I1, ~I3,
"KS", 100, 200
)
solution
library(powerjoin)
power_left_join(df1, df2, by = "St_Name", conflict = coalesce_yx)
#> # A tibble: 6 × 5
#> St_Name I2 I4 I1 I3
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 TN 0.15 0.25 0.1 0.2
#> 2 AZ 0.35 0.45 0.3 0.4
#> 3 TX 0.55 0.65 0.5 0.6
#> 4 KS 0.75 0.85 100 200
#> 5 KY 0.95 0.12 0.9 0.11
#> 6 MN 0.14 0.17 0.13 0.16
I have this data frame (df) and I need to get a vector of the values in the column "amountN" when the "ID" column matches the values of a given vector (idvector), so I've created a for loop that I tested by printing the values of df$amountN to check they actually are accomplishing the df$ID condition, and they do. This is the for loop:
df
amountS amountN ID
64693 0.440 0.55 028
64702 0.360 0.52 028
64708 0.220 0.33 028
64714 0.500 0.27 028
64720 0.280 0.51 028
64726 0.520 0.47 028
64732 0.410 0.25 028
64735 0.090 0.11 028
64741 0.220 0.17 028
64750 0.630 0.48 028
64756 0.430 0.35 028
64762 1.200 0.40 028
65150 4.425 14.95 029
65156 5.035 23.60 029
65163 5.810 26.20 029
idvector <- c("010","025","028")
for(i in seq_len(nrow(df))){
for (j in seq_len(length(idvector))){
if(df$ID[i] == idvector[j]){
print(df$amountN[i])
}
}
}
So far, so good. However, I've tried many things to get those values into a vector by changing the function print() in the last line, but nothing seems to work:
for(i in seq_len(nrow(df))){
for (j in seq_len(length(idvector))){
if(df$ID[i] == idvector[j]){
x <- c(df$amountN[i])
}
x
}
}
I also tried to get the subset of df$amounN to then convert the data to vector using subset and select from dplyr library, but also don't get that, instead I get NULL:
for(i in seq_len(nrow(df))){
for (j in seq_len(length(idvector))){
if(df$ID[i] == idvector[j]){
x <- subset(df$amountN[i,])
}
x
}
}
I looked into the data and notice that the values look like this:
[1] 0.55
[1] 0.52
[1] 0.33
[1] 0.27
[1] 0.51
[1] 0.47
[1] 0.25
[1] 0.11
[1] 0.17
[1] 0.48
[1] 0.35
[1] 0.4
And they should look like this:
[1] 0.55 0.52 0.33 0.27 0.51 0.47 0.25 0.11 0.17 0.48
[11] 0.35 0.40
I really need a vector or a subset so I can apply summary statistics and others to the data, but have not figure it out.
I'm using R version 4.0.3
In base R, we can do this more easily with subset
subset(df, ID %in% idvector, select = amountN)$amountN
#[1] 0.55 0.52 0.33 0.27 0.51 0.47 0.25 0.11 0.17 0.48 0.35 0.40
Regarding the OP's code, we can fix by defining 'x' as a NULL vector and then concatenate 'x' within each loop, and assign it back to 'x'. Also, make sure that the 'idvector' is of the same type i.e. assuming it is numeric as well
x <- c()
for(i in seq_len(nrow(df))){
for (j in seq_len(length(idvector))){
if(df$ID[i] == idvector[j]){
x <- c(x, df$amountN[i])
}
x
}
}
-output
x
#[1] 0.55 0.52 0.33 0.27 0.51 0.47 0.25 0.11 0.17 0.48 0.35 0.40
NOTE: The issue in the OP's code was that 'x' was getting updated on each iteration while removing the previous output. It needs concatenation
data
df <- structure(list(amountS = c(0.44, 0.36, 0.22, 0.5, 0.28, 0.52,
0.41, 0.09, 0.22, 0.63, 0.43, 1.2, 4.425, 5.035, 5.81), amountN = c(0.55,
0.52, 0.33, 0.27, 0.51, 0.47, 0.25, 0.11, 0.17, 0.48, 0.35, 0.4,
14.95, 23.6, 26.2), ID = c(28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L)), class = "data.frame",
row.names = c("64693",
"64702", "64708", "64714", "64720", "64726", "64732", "64735",
"64741", "64750", "64756", "64762", "65150", "65156", "65163"
))
idvector <- c(10, 25, 28)
You could directly avoid the loop ans using %in% over df and indexing:
#Code
vec <- df$amountN[df$ID %in% idvector]
Output:
[1] 0.55 0.52 0.33 0.27 0.51 0.47 0.25 0.11 0.17 0.48 0.35 0.40
I have a dataframe called "new_dat" containing the time (days) in column t, and temperature data (and occaisionally NA) in columns A - C (please see the example in the code below):
> new_dat
t A B C
1 0.00 0.82 0.88 0.46
2 0.01 0.87 0.94 0.52
3 0.02 NA NA NA
4 0.03 0.95 1.03 0.62
5 0.04 0.98 1.06 0.67
6 0.05 1.01 1.09 0.71
7 0.06 2.00 1.13 2.00
8 0.07 1.06 1.16 0.78
9 0.08 1.07 1.18 0.81
10 0.09 1.09 1.20 0.84
11 0.10 1.10 1.21 0.86
12 0.11 2.00 1.22 0.87
Here is a dput() of the dataframe:
structure(list(t = c(0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07,
0.08, 0.09, 0.1, 0.11), A = c(0.82, 0.870000000000001, NA,
0.949999999999999,
0.979999999999997, 1.01, 2, 1.06, 1.07, 1.09, 1.1, 2), B =
c(0.879999999999999,
0.940000000000001, NA, 1.03, 1.06, 1.09, 1.13, 1.16, 1.18, 1.2,
1.21, 1.22), C = c(0.460000000000001, 0.520000000000003, NA,
0.619999999999997, 0.669999999999998, 0.709999999999997, 2,
0.780000000000001,
0.809999999999999, 0.84, 0.859999999999999, 0.87)), .Names = c("t",
"A", "B", "C"), row.names = c(NA, 12L), class = "data.frame")
As output, I want a vector (list?) of the values of column t where the temperature reading from columns A-C >= 2 for the first time (and only the first time), or - if the temperature is never >= 2 - return the last time reading in column t (0.11 in my example). So 'A' would return the value 0.06 (and not 0.11), 'B' would have the value 0.11 and 'C' 0.06. I intended to use the vector generated to create a new dataframe something like this:
A B C
0.06 0.11 0.06
I'm inexperienced with R (and code in general) so, despite reading that looping can be ineficient (but not really understanding how to accomplish what i want without it), I tried to solve this by looping first by column and then by row as follows:
#create blank vector to add my results to
aer <- c()
#loop by column, then by row, adding values according to the if statement
for (c in 2:ncol(new_dat)){
c <- c
for (r in 1:nrow(new_dat)){
r <- r
if ((!is.na(new_dat[r,c] )) & (new_dat[r,c] >= 2)){
aer <- c(aer, new_dat$t[r])
}
}
}
This returns my vector, aer, as:
> aer
[1] 0.06 0.11 0.06
So it's returning both instances where 'A' is 2, and the one from column 'C'.
I dont know how to instruct the loop to stop and move to the next column after finding one instance where my 'if' statement is true. I also tried adding an 'else' to cover the situation where temperature doesnt exceed 2:
else {
aer <- c(aer, new_dat$t[nrow(new_dat)])
But this did not work.
I would appreciate any help in completing the code, or suggestions for a better solution.
library(tidyverse)
new_dat %>%
gather(col, temp, -t) %>% # reshape data
na.omit() %>% # remove rows with NAs
group_by(col) %>% # for each column value
summarise(v = ifelse(is.na(first(t[temp >= 2])), last(t), first(t[temp >= 2]))) %>% # return the last t value if there are no temp >=2 otherwise return the first t with temp >= 2
spread(col, v) # reshape again
# # A tibble: 1 x 3
# A B C
# <dbl> <dbl> <dbl>
# 1 0.06 0.11 0.06
This solution will create the dataframe for you automatically, instead of returning a vector for you to create the dataframe yourself.
Here is a two steps solution.
First get an index vector of the values you want, then use that index vector to subset the dataframe.
inx <- sapply(new_dat[-1], function(x) {
w <- which(x >= 2)
if(length(w)) min(w) else NROW(x)
})
new_dat[inx, 1]
#[1] 0.06 0.11 0.06
So I have a data frame of participant data, where I have participant IDs, for each of those a bunch of target values (continuous) and predicted values.
The target value is a continuous variable, but there is a finite number of possible values, and each participant will have made a prediction for a subset of these target values.
For example, take this data frame:
data.frame(
subjectID = c(rep("p001",4),rep("p002",4),rep("p003",4)),
target = c(0.1,0.2,0.3,0.4,0.2,0.3,0.4,0.5,0.1,0.3,0.4,0.5),
pred = c(0.12, 0.23, 0.31, 0.42, 0.18, 0.32, 0.44, 0.51, 0.09, 0.33, 0.41, 0.55)
)
There're 5 possible target values: 0.1, 0.2, 0.3, 0.4 and 0.5, but each participant only predicted 4 of these values each. I want to get the average prediction pred for each target value target. It's further complicated by each participant having a group, and I only want to average within each group.
I tried using summarise_at but it wasn't liking the continuous data, and whilst I'm pretty experienced in coding in R, it's been a long while since I've done data summary manipulations etc.
I could do this easily in a for loop, but I want to learn to do this properly and I wasn't able to find a solution after googling for a long time.
Thanks very much
H
Just add the second grouping variable in group_by as well:
df <- data.frame(
subjectID = c(rep("p001",4),rep("p002",4),rep("p003",4)),
group = c(rep("A", 8), rep("B", 4)),
target = c(0.1,0.2,0.3,0.4,0.2,0.3,0.4,0.5,0.1,0.3,0.4,0.5),
pred = c(0.12, 0.23, 0.31, 0.42, 0.18, 0.32, 0.44, 0.51, 0.09, 0.33, 0.41, 0.55)
)
df %>%
group_by(target, group) %>%
summarise(mean(pred))
Output:
# A tibble: 9 x 3
# Groups: target [?]
target group `mean(pred)`
<dbl> <chr> <dbl>
1 0.100 A 0.120
2 0.100 B 0.0900
3 0.200 A 0.205
4 0.300 A 0.315
5 0.300 B 0.330
6 0.400 A 0.430
7 0.400 B 0.410
8 0.500 A 0.510
9 0.500 B 0.550
I need to include some operations before summing the rows in my data frame. Here is an example:
df1 <- data.frame(
AC1Q = c(0.53, 0.57, 0.60, 0.51),
AC4Q = c(0.15, 0.12, 0.09,0.19),
AC2Q = c(0.09, 0.05, 0.07, 0.05),
AC3Q = c(0.23, 0.26, 0.23, 0.26)
)
df1
# AC1Q AC4Q AC2Q AC3Q
# 1 0.53 0.15 0.09 0.23
# 2 0.57 0.12 0.05 0.26
# 3 0.60 0.09 0.07 0.23
# 4 0.51 0.19 0.05 0.26
I want to get the row sums based on (sin(2*pi*(AC1Q-0.25)) + sin(2*pi*(-AC4Q+0.25)) - sin(2*pi*(AC2Q+0.25)) - sin(2*pi*(AC3Q-0.25)))/4) The result should be:
# 1 0.20
# 2 0.15
# 3 0.21
# 4 0.10
I am learning apply and tried apply(df1, 1, function(x) (sin(2*pi*(df1$AC1Q-0.25)) + sin(2*pi*(-df1$AC4Q+0.25)) - sin(2*pi*(-df1$AC2Q+0.25)) - sin(2*pi*(df1$AC3Q-0.25)))/4)but the result is wrong. I am not sure what I did wrong. I know I can always do the calculation for each column first, combine them into a data frame, and use rowsum But is there a more efficient way to do it?
apply(df1, 1, function(x) (sin(2*pi)*(x["AC1Q"]-0.25) +
sin(2*pi)*(-x["AC4Q"]+0.25) -
sin(2*pi)*(-x["AC2Q"]+0.25) -
sin(2*pi)*(x["AC3Q"]-0.25))/4)