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I have two dataframes with each two columns c("price", "size") with different lengths.
Each price must be linked to its size. It's two lists of trade orders. I have to discover the differences between the two dataframes knowing that the two databases can have orders that the other doesn't have and vice versa. I would like an output with the differences or two outputs, it doesn't matter. But I need the row number in the output to find where are the differences in the series.
Here is sample data :
> out
price size
1: 36024.86 0.01431022
2: 36272.00 0.00138692
3: 36272.00 0.00277305
4: 36292.57 0.05420000
5: 36292.07 0.00403948
---
923598: 35053.89 0.30904890
923599: 35072.76 0.00232000
923600: 35065.60 0.00273000
923601: 35049.36 0.01760000
923602: 35037.23 0.00100000
>bit
price size
1: 37279.89 0.01340020
2: 37250.84 0.00930000
3: 37250.32 0.44284049
4: 37240.00 0.00056491
5: 37215.03 0.99891906
---
923806: 35053.89 0.30904890
923807: 35072.76 0.00232000
923808: 35065.60 0.00273000
923809: 35049.36 0.01760000
923810: 35037.23 0.00100000
For example, I need to know if the first row of the database out is in the database bit.
I've tried many functions : comparedf()
summary(comparedf(bit, out, by = c("price","size"))
but I've got error:
Error in vecseq(f__, len__, if (allow.cartesian || notjoin ||
!anyDuplicated(f__, :
I've tried compare_df() :
compareout=compare_df(out,bit,c("price","size"))
But I know the results are wrong, I've only 23 results and I know that there are more than 200 differences minimum.
I've tried match(), which() functions but it doesn't get the results I search.
If you have any other methods, I will take them.
Perhaps you could just do inner_join on out and bit by price and size? But first make id variable for both data.frame's
library(dplyr)
out$id <- 1:nrow(out)
bit$id <- 1:nrow(bit)
joined <- inner_join(bit, out, by = c("price", "size"))
Now we can check which id from out and bit are not present in joined table:
id_from_bit_not_included_in_out <- bit$id[!bit$id %in% joined$id.x]
id_from_out_not_included_in_bit <- out$id[!out$id %in% joined$id.y]
And these ids are the rows not included in out or bit, i.e. variable id_from_bit_not_included_in_out contains rows present in bit, but not in out and variable id_from_out_not_included_in_bit contains rows present in out, but not in bit
First attempt here. It will be difficult to do a very clean job with this data tho.
The data I used:
out <- read.table(text = "price size
36024.86 0.01431022
36272.00 0.00138692
36272.00 0.00277305
36292.57 0.05420000
36292.07 0.00403948
35053.89 0.30904890
35072.76 0.00232000
35065.60 0.00273000
35049.36 0.01760000
35037.23 0.00100000", header = T)
bit <- read.table(text = "price size
37279.89 0.01340020
37250.84 0.00930000
37250.32 0.44284049
37240.00 0.00056491
37215.03 0.99891906
37240.00 0.00056491
37215.03 0.99891906
35053.89 0.30904890
35072.76 0.00232000
35065.60 0.00273000
35049.36 0.01760000
35037.23 0.00100000", header = T)
Assuming purely that row 1 of out should match with row 1 of bit a simple solution could be:
df <- cbind(distinct(out), distinct(bit))
names(df) <- make.unique(names(df))
However judging from the data you have provided I am not sure if this is the way to go (big differences in the first few rows) so maybe try sorting the data first?:
df <- cbind(distinct(out[order(out$price, out$size),]), distinct(bit[order(bit$price, bit$size),]))
names(df) <- make.unique(names(df))
Very new to R, trying to make a table as so:
"range","number"
"0.000->0.510",863
"0.510->1.020",21
"1.020->1.530",2
"1.530->2.040",2
"2.040->2.550",0
"2.550->3.059",2
"3.059->3.596",0
"3.596->4.079",3
"4.079->4.589",1
"4.589->5.099",1
which is a csv file output. My mission was to identify each line of an .data file (which can be seen as a one-column data frame where looks like this but much longer.
0.5172000000000001
3.796199999999999
0.6227940000000004
1.3956000000000004
5.098802999999999
0.6755999999999986
1.6382092558672117
I've used a line like this rangeInterval <- seq(0, maxTime, maxTime/10) to get a new vector(where maxTime here is 5.098803), but I have no idea how should I convert to the table presents at front. Am I suppose to make a loop and try to build a vector with each element is rangeInterval's [index]->[index+1] or what? Also how am I suppose to make it 3 decimals long?
edit: The solution after I figured it
rangeInterval <- seq(0,maxTime-maxTime/10, maxTime/10)
range1 <- subset(ssdata,0<V1 & V1<(maxTime/10))
range2 <- subset(ssdata,V1>(maxTime/10)*1 & V1<=(maxTime/10)*2)
range3 <- subset(ssdata,V1>(maxTime/10)*2 & V1<=(maxTime/10)*3)
range4 <- subset(ssdata,V1>(maxTime/10)*3 & V1<=(maxTime/10)*4)
range5 <- subset(ssdata,V1>(maxTime/10)*4 & V1<=(maxTime/10)*5)
range6 <- subset(ssdata,V1>(maxTime/10)*5 & V1<=(maxTime/10)*6)
range7 <- subset(ssdata,V1>(maxTime/10)*6 & V1<=(maxTime/10)*7)
range8 <- subset(ssdata,V1>(maxTime/10)*7 & V1<=(maxTime/10)*8)
range9 <- subset(ssdata,V1>(maxTime/10)*8 & V1<=(maxTime/10)*9)
range10 <- subset(ssdata,V1>(maxTime/10)*9& V1<=(maxTime/10)*10)
#I'm sure there's an easier to do this...
rangeList <- c(range1, range2, range3, range4, range5, range6, range7, range8, range9, range10)
#The part to make the range column
newc = lapply(rangeInterval, doNothing)
newc2 = lapply(newc, plusInterval)
newc = lapply(newc, threeDecimal)
newc2 = lapply(newc2, threeDecimal)
#The part to make the dataframe basic
df <- data.frame(range = rangeInterval)
df$range <- paste0(newc,"->",newc2)
Where rangeInterval was the one-column dataframe I present before.
I am trying to find Cronbach's Alpha for survey data containing a series of multi-item measures. Rather than have to manually write out every single multi-item measure, it looks like something a loop should be able to manage far more effectively, but it needs to change only part of the column name, according to the question number.
The basic idea as it currently sits in my head would be...
for (N in 4:22) {
ytqN <- data.frame(YT_Data$QNa, YT_Data$QNb, YT_Data$QNc)
alpha(ytqN)
}
The loop would then create new data frames for each multi item measure and run Cronbach's Alpha as it goes.
This doesn't work though. :(
ytq4 <- data.frame(YT_Data$Q4a, YT_Data$Q4b, YT_Data$Q4c)
alpha(ytq4)
ytq5 <- data.frame(YT_Data$Q5a, YT_Data$Q5b, YT_Data$Q5c)
alpha(ytq5)
ytq6 <- data.frame(YT_Data$Q6a, YT_Data$Q6b, YT_Data$Q6c)
alpha(ytq6)
ytq7 <- data.frame(YT_Data$Q7a, YT_Data$Q7b, YT_Data$Q7c)
alpha(ytq7)
ytq8 <- data.frame(YT_Data$Q8a, YT_Data$Q8b, YT_Data$Q8c)
alpha(ytq8)
ytq9 <- data.frame(YT_Data$Q9a, YT_Data$Q9b, YT_Data$Q9c)
alpha(ytq9)
ytq10 <- data.frame(YT_Data$Q10a, YT_Data$Q10b, YT_Data$Q10c)
alpha(ytq10)
ytq11 <- data.frame(YT_Data$Q11a, YT_Data$Q11b, YT_Data$Q11c)
alpha(ytq11)
ytq12 <- data.frame(YT_Data$Q12a, YT_Data$Q12b, YT_Data$Q12c)
alpha(ytq12)
ytq13 <- data.frame(YT_Data$Q13a, YT_Data$Q13b, YT_Data$Q13c)
alpha(ytq13)
ytq14 <- data.frame(YT_Data$Q14a, YT_Data$Q14b, YT_Data$Q14c)
alpha(ytq14)
ytq15 <- data.frame(YT_Data$Q15a, YT_Data$Q15b, YT_Data$Q15c)
alpha(ytq15)
ytq16 <- data.frame(YT_Data$Q16a, YT_Data$Q16b, YT_Data$Q16c)
alpha(ytq16)
ytq17 <- data.frame(YT_Data$Q17a, YT_Data$Q17b, YT_Data$Q17c)
alpha(ytq17)
ytq18 <- data.frame(YT_Data$Q18a, YT_Data$Q18b, YT_Data$Q18c)
alpha(ytq18)
ytq19 <- data.frame(8 - YT_Data$Q19a, YT_Data$Q19b, YT_Data$Q19c)
# Reverse code Q19a
alpha(ytq19)
ytq20 <- data.frame(YT_Data$Q20a, YT_Data$Q20b, YT_Data$Q20c)
alpha(ytq20)
ytq21 <- data.frame(YT_Data$Q21a, YT_Data$Q21b, YT_Data$Q21c)
alpha(ytq21)
ytq22 <- data.frame(YT_Data$Q22a, YT_Data$Q22b, YT_Data$Q22c)
alpha(ytq22)
The desired results would be a single output containing all the Cronbach's Alphas for the multi item measures for questions 4-22 in the data set I am currently working on executed via a single piece of code, rather than have to go question by question.
It's easier to help if you include your data, but I guess this should work:
alpha_list = list()
for(N in 4:22){
ytq = data.frame(YT_Data[paste0("Q",N,"a")],
YT_Data[paste0("Q",N,"b")],
YT_Data[paste0("Q",N,"c")])
alpha_list[[N]] = alpha(ytq)
}
We are using paste0() to create the column names while looping on N. alpha_list will be a list with the results given by alpha()
i've gone through several answers and tried the following but each either yields an error or an un-wanted result:
here's the data:
Network Campaign
Moburst_Chartboost Test Campaign
Moburst_Chartboost Test Campaign
Moburst_Appnext unknown
Moburst_Appnext 1065
i'd like to replace "Test Campaign" with "1055" whenever "Network" == "Moburst_Chartboost". i realize this should be very simple but trying out these:
dataset = read.csv('C:/Users/User/Downloads/example.csv')
for( i in 1:nrow(dataset)){
if(dataset$Network == 'Moburst_Chartboost') dataset$Campaign <- '1055'
}
this yields an error: Warning messages:
1: In if (dataset$Network == "Moburst_Chartboost") dataset$Campaign <- "1055" :
the condition has length > 1 and only the first element will be used
2: In if (dataset$Network == "Moburst_Chartboost") dataset$Campaign <- "1055" :
the condition has length > 1 and only the first element will be used
etc.
then i tried:
within(dataset, {
dataset$Campaign <- ifelse(dataset$Network == 'Moburst_Chartboost', '1055', dataset$Campaign)
})
this turned ALL 4 values in row "Campaign" into "1055" over running what was there even when condition isn't met
also this:
dataset$Campaign[which(dataset$Network == 'Moburst_Chartboost')] <- 1055
yields this error, and replaced the values in the two first rows of "Campaign" with NA:
Warning message:
In `[<-.factor`(`*tmp*`, which(dataset$Network == "Moburst_Chartboost"), :
invalid factor level, NA generated
scratching my head here. new to R but this shouldn't be so hard :(
In your first attempt, you're trying to iterate over all the columns, when you only want to change the 2nd column.
In your second, you're trying to assign the value "1055" to all of the 2nd column.
The way to think about it is as an if else, where if the condition in col 1 is met, col 2 is changed, otherwise it remains the same.
dataset <- data.frame(Network = c("Moburst_Chartboost", "Moburst_Chartboost",
"Moburst_Appnext", "Moburst_Appnext"),
Campaign = c("Test Campaign", "Test Campaign",
"unknown", "1065"))
dataset$Campaign <- ifelse(dataset$Network == "Moburst_Chartboost",
"1055",
dataset$Campaign)
head(dataset)
Network Campaign
1 Moburst_Chartboost 1055
2 Moburst_Chartboost 1055
3 Moburst_Appnext unknown
4 Moburst_Appnext 1065
You may also try dataset$Campaign[dataset$Campaign=="Test Campaign"]<-1055 to avoid the use of loops and ifelse statements.
Where dataset
dataset <- data.frame(Network = c("Moburst_Chartboost", "Moburst_Chartboost",
"Moburst_Appnext", "Moburst_Appnext"),
Campaign = c("Test Campaign", "Test Campaign",
"unknown", 1065))
Try the following
dataset = read.csv('C:/Users/User/Downloads/example.csv', stringsAsFactors = F)
for( i in 1:nrow(dataset)){
if(dataset$Network[i] == 'Moburst_Chartboost') dataset$Campaign[i] <- '1055'
}
It seems your forgot the index variable. Without [i] you work on the whole vector of the data frame, resulting in the error/warning you mentioned.
Note that I added stringsAsFactors = F to the read.csv() function to make sure the strings are indeed interpreted as strings and not factors. Using factors this would result in an error like this
In `[<-.factor`(`*tmp*`, i, value = c(NA, 2L, 3L, 1L)) :
invalid factor level, NA generated
Alternatively you can do the following without using a for loop:
idx <- which(dataset$Network == 'Moburst_Chartboost')
dataset$Campaign[idx] <- '1055'
Here, idx is a vector containing the positions where Network has the value 'Moburst_Chartboost'
thank you for the help! not elegant, but since this lingered with me when going to sleep last night i decided to try to bludgeon this with some ugly code but it worked too - just as a workaround...separated to two data frames, replaced all values and then binded back...
# subsetting only chartboost
chartboost <- subset(dataset, dataset$Network=='Moburst_Chartboost')
# replace all values in Campaign
chartboost$Campaign <-sub("^.*", "1055",chartboost$Campaign)
#subsetting only "not chartboost"
notChartboost <-subset(dataset, dataset$Network!='Moburst_Chartboost')
# binding back to single dataframe
newSet <- rbind(chartboost, notChartboost)
Ugly as a duckling but worked :)
I have a factor column with football position abbreviations, around 17 unique values with 220 observations. I want to have only three factor levels which encompass the 17 unique values.
levels(nfldraft$Pos) <- list(Linemen = c("C","OG","OT","TE","DT","DE"),
Small_Backs = c("CB","WR","FS"),
Big_Backs = c("FB","ILB","OLB","P","QB",
"RB","SS","WR"))
is what I tried, printing nfldraft$Pos to the console shows 3 factor levels but all the values are either "Linemen" or "Small_Backs" and all the other ones are NA. Where am I going wrong?
I made up an example character vector with all of the abbreviations:
my_example <- c("C","OG","OT","TE","DT","DE","CB","WR","FS",
"FB","ILB","OLB","P","QB","RB","SS","WR")
class(my_example)
[1] "character"
Then I substituted the desired levels for their abbreviations (you could also use gsub here or any of many, many different approaches):
my_example[my_example %in% c("C","OG","OT","TE","DT","DE")] <- "Linemen"
my_example[my_example %in% c("CB","WR","FS")] <- "Small Backs"
my_example[my_example %in% c("FB","ILB","OLB","P",
"QB","RB","SS","WR")] <- "Big Backs"
Then I made it into a factor:
my_example <- as.factor(my_example)
head(my_example)
[1] Linemen Linemen Linemen Linemen Linemen Linemen
Levels: Big Backs Linemen Small Backs
tail(my_example)
[1] Big Backs Big Backs Big Backs Big Backs Big Backs Small Backs
Levels: Big Backs Linemen Small Backs
class(my_example)
[1] "factor"
This is a good example of needing a fully reproducible example. Actually OP's code looks like it should work. Taking from #Hack-R's sample input:
my_example <- c("C","OG","OT","TE","DT","DE","CB","WR","FS",
"FB","ILB","OLB","P","QB","RB","SS","WR")
OP's original code works as-is:
nfldraft = list(Pos = factor(my_example))
levels(nfldraft$Pos) <- list(
Linemen = c("C","OG","OT","TE","DT","DE"),
Small_Backs = c("CB","WR","FS"),
Big_Backs = c("FB","ILB","OLB","P","QB","RB","SS","WR")
)
table(nfldraft$Pos)
# Linemen Small_Backs Big_Backs
# 6 2 9
This is exactly in line with the documentation for how to use levels<-:
levels(x) <- value
value A valid value for levels(x)... For the factor method, a vector of character strings with length at least the number of levels of x, or a named list specifying how to rename the levels.
So it seems there's something else wrong with OP's input
You can also use the mapvalues() function from dplyr package.
In your example it would be:
Linemen_levels = c("C","OG","OT","TE","DT","DE")
Small_Backs_levels = c("CB","WR","FS")
Big_Backs_levels = c("FB","ILB","OLB","P","QB","RB","SS","WR")
nfldraft <- nfldraft %>% mutate(Pos=mapvalues(Pos,
from = c(Linemen_levels, Small_Backs_levels, Big_Backs_levels),
to = c(rep('Linemen', length(Linemen_levels), rep('Small_Backs', length(Small_Backs_levels), rep('Big_Backs', length(Big_Backs_levels))))))