I want to change a column from a dataframe from character to numeric.
My data frame was a .txt file with 12 columns and 1000 rows.
When I passed the .txt file to R, one of my columns is now character.
I tried to use
as.numeric(my_data$iw)
But I get a "Warning message:
NAs introduced by coercion
Here is the data frame structure:
data.frame': 1000 obs. of 12 variables:
$ im : num 0 15396 16537 20252 17967 ...
$ iw : chr "20064.97" "7397.191" "18380.77" "14042.25" ...
$ r : num 5984 0 0 0 0 ...
$ am : num 0 42 33 38 24 62 27 38 0 29 ...
$ af : num 38 30 28 38 39 42 18 33 24 35 ...
$ a1c: num 0 1 1 1 1 0 0 1 0 1 ...
$ a2c: num 0 0 0 1 0 0 0 1 0 1 ...
$ a3c: num 0 0 0 0 0 0 0 1 0 0 ...
$ a4c: num 0 0 0 0 0 0 0 0 0 0 ...
$ a5c: num 0 0 0 0 0 0 0 0 0 0 ...
$ a6c: num 0 0 0 0 0 0 0 0 0 0 ...
$ a7c: num 0 0 0 0 0 0 0 0 0 0 ...
May I change it with gsub?
structure(list(im = c(0, 15395.61, 16536.74, 20251.87, 17967.04,
12686.43, 16833.22, 16919.34, 0, 20515.88, 17991.9, 15528.29,
16683.96, 14485.19, 17957.98, 19923.31, 13526.9, 16516.68, 16337.52,
12904.97, 17418.99, 12419.21, 14561.9, 12309.77, 21138.87, 0,
17315.74, 17762.09, 12678.82, 13883.37, 11140.66, 16502.91, 18293.78,
12533.36, 16536.61, 4336.741, 22449.17, 16532.1, 0, 15905.14,
0, 8542.03, 12589.29, 15154.76, 15441.59, 18575.05, 15915.47,
0, 15085.51, 16597.42, 15358.47, 22480.95, 10555.28, 21771.2,
22863.56, 15937.55, 12230.58, 17814.67, 7972.471, 10286.75, 15335.8,
10762.59, 18583.2, 12167.99, 21723.37, 15670.79, 13045.83, 13305.73,
14305.99, 10353.15, 4504.009, 10157.7, 15967.28, 23640.21, 15053.78,
21404.11, 8509.353, 15693.39, 9009.99, 17249.29, 9115.844, 16057.39,
14069.93, 0, 0, 16840.09, 0, 15289.29, 12223.93, 13048.58, 18524.13,
14344.22, 20658.66, 0, 0, 13984.69, 21636.72, 13969.12, 12919.83,
13214.16, 17066.98, 20060.25, 11414.15, 12907.53, 11289.97, 17600.97,
14741.77, 12089.57, 13603.85, 9330.662, 0, 16191.81, 12029.75,
12666.29, 8138.166, 10636.2, 22570.1, 12833.66, 12585.56, 20197.42,
12621.56, 19021.65, 9948.49, 25772.41, 15102.54, 19225.57, 11188.96,
11707.66, 9766.824, 16082.82, 17693.2......
To read in the .txt file, I wrote:
my_data <- read.table("project.txt", header=TRUE);
As some comments already said, this is because either there are some rows which cannot be coerced to numeric. An unsuitable description of missing data or maybe a comma to denote decimals as in
expl <- read.table(text = "1.0 2.0 2,3
2.0 2.1 2.5
. 2.2 2.1")
str(expl)
which leads to
> str(expl)
'data.frame': 3 obs. of 3 variables:
$ V1: chr "1.0" "2.0" "."
$ V2: num 2 2.1 2.2
$ V3: chr "2,3" "2.5" "2.1"
for the reasons stated above.
It is not always easy to find the culprid in 1000 lines, but something like this may help:
> which(is.na(as.numeric(expl$V1)))
[1] 3
This will provide you with the row numbers that produce NA in conversion.
Related
Good evening everybody,
I'm stuck about the construction of the for loop, I don't have any problem, buit I'd like to understand how I can create dataframe "independents" (duplicite with some differences).
I wrote the code step by step (it works), but I think that, maybe, there is a way to compact the code with the for.
x is my original data.frame
str(x)
Classes ‘data.table’ and 'data.frame': 13500 obs. of 6 variables:
$ a: int 1 56 1058 567 987 574 1001...
$ b: int 10 5 10 5 5 10 10 5 10 10 ...
$ c: int NA NA NA NA NA NA NA NA NA NA ...
$ d: int 0 0 0 0 0 0 0 0 0 0 ...
$ e: int 0 0 0 0 0 0 0 0 0 0 ...
$ f: int 22 22 22 22 22 22 22 22 22 22 ...
My first goal is to delete per every column the eventualy NA and "" elements. I do this by these codes of rows.
x_b<- x[!(!is.na(x$b) & x$b==""), ]
x_c<- x[!(!is.na(x$c) & x$c==""), ]
x_d<- x[!(!is.na(x$d) & x$d==""), ]
x_e<- x[!(!is.na(x$e) & x$e==""), ]
x_f<- x[!(!is.na(x$f) & x$f==""), ]
After this the second goal is to create per each new data.frame a id code that I create using the function paste0(x_b$a, x_b$f).
x_b$ID_1<-paste0(x_b$a, x_b$b)
x_c$ID_2<-paste0(x_c$a, x_c$c)
x_d$ID_3<-paste0(x_c$a, x_c$d)
x_e$ID_4<-paste0(x_c$a, x_c$e)
x_f$ID_5<-paste0(x_c$a, x_c$f)
I created this for loop to try to minimize the rows that I use, and to create a good code visualization.
z<-data.frame("a", "b","c","d","e","f")
zy<-data.frame("x_b", "x_c", "x_d", "x_e", "x_f")
for(i in z) {
for (j in zy ) {
target <- paste("_",i)
x[[i]]<-(!is.na(x[[i]]) & x[[i]]=="") #with this I able to create a column on the x data.frame,
#but if I put a new dataframe the for doesn't work
#the name, but I don't want this. I'd like to create a
#data.base per each transformation.
#at this point of the script, I should have a new
#different dataframe, as x_b, x_c, x_d, x_e, x_f but I
#don't know
#How to create them?
#If I have these data frame I will do this anther function
#in the for loop:
zy[[ID]]<-paste0(x_b$a, "_23X")
}
}
I'd like to have as output this:
str(x_b)
Classes ‘data.table’ and 'data.frame': 13500 obs. of 6 variables:
$ a: int 1 56 1058 567 987 574 1001...
$ b: int 10 5 10 5 5 10 10 5 10 10 ...
$ c: int NA NA NA NA NA NA NA NA NA NA ...
$ d: int 0 0 0 0 0 0 0 0 0 0 ...
$ e: int 0 0 0 0 0 0 0 0 0 0 ...
$ f: int 22 22 22 22 22 22 22 22 22 22 ...
$ ID: int 1_23X 56_23X 1058_23X 567_23X 987_23X 574_23X 1001_23X...
and so on.
I think that there is some important concept about the dataframe that I miss.
Where I wrong?
Thank you so much in advance for the support.
There is simple way to do this with the tidyverse package(s):
First goal:
drop.na(df)
You can also use na_if if you want convert "" to NA.
Second goal: use mutate to create a new variable:
df <- df %>%
mutate(id = paste0(x_b$a, "_23X"))
I have been looking for a way of having a stacked bar plot in an upsetR graph.
I downloaded the movies data set (from here) and added a column having only two values "M" and "C".
Below, information on how I loaded the data and added the "x" column.
Edit:
m <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"),
header = T, sep = ";")
nrow(m)
[1] 3883
x<-c(rep("M", 3000), rep("C", 883))
m<-cbind(m, x)
unique(m$x)
[1] M C
This is the structure of the data frame:
str(m)
'data.frame': 3883 obs. of 22 variables:
$ Name : Factor w/ 3883 levels "$1,000,000 Duck (1971)",..: 3577 1858 1483 3718 1175 1559 3010 3548 3363 1420 ...
$ ReleaseDate: int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
$ Action : int 0 0 0 0 0 1 0 0 1 1 ...
$ Adventure : int 0 1 0 0 0 0 0 1 0 1 ...
$ Children : int 1 1 0 0 0 0 0 1 0 0 ...
$ Comedy : int 1 0 1 1 1 0 1 0 0 0 ...
$ Crime : int 0 0 0 0 0 1 0 0 0 0 ...
$ Documentary: int 0 0 0 0 0 0 0 0 0 0 ...
$ Drama : int 0 0 0 1 0 0 0 0 0 0 ...
$ Fantasy : int 0 1 0 0 0 0 0 0 0 0 ...
$ Noir : int 0 0 0 0 0 0 0 0 0 0 ...
$ Horror : int 0 0 0 0 0 0 0 0 0 0 ...
$ Musical : int 0 0 0 0 0 0 0 0 0 0 ...
$ Mystery : int 0 0 0 0 0 0 0 0 0 0 ...
$ Romance : int 0 0 1 0 0 0 1 0 0 0 ...
$ SciFi : int 0 0 0 0 0 0 0 0 0 0 ...
$ Thriller : int 0 0 0 0 0 1 0 0 0 1 ...
$ War : int 0 0 0 0 0 0 0 0 0 0 ...
$ Western : int 0 0 0 0 0 0 0 0 0 0 ...
$ AvgRating : num 4.15 3.2 3.02 2.73 3.01 3.88 3.41 3.01 2.66 3.54 ...
$ Watches : int 2077 701 478 170 296 940 458 68 102 888 ...
$ x : Factor w/ 2 levels "M","C": 1 1 1 1 1 1 1 1 1 1 ...
Now I tried to implement the stacked bar plot as follow:
upset(m,
queries = list(
list(query = elements,
params = list("x", "M"), color = "#e69f00", active = T),
list(query = elements,
params = list("x", "C"), color = "#cc79a7", active = T)))
The result looks like this:
As you can see the proportions are wrong as there should be in each bar only two colors (factor) either "M" or "C".
This issue seems to be not a trivial one, as also pointed out here.
Does anyone have an idea on how to implement this in UpsetR?
Thanks a lot
Here is a way to create an upset plot with stacked barplot, but using my ComplexUpset rather than UpSetR:
library(ComplexUpset)
movies = as.data.frame(ggplot2movies::movies)
genres = colnames(movies)[18:24]
# for simplicity of examples, only use the complete data points
movies[movies$mpaa == '', 'mpaa'] = NA
movies = na.omit(movies)
upset(
movies,
genres,
base_annotations=list(
'Intersection size'=intersection_size(
counts=FALSE,
mapping=aes(fill=mpaa)
)
),
width_ratio=0.1
)
Please see more examples in the documentation.
The Installation instructions are available on GitHub: krassowski/complex-upset (there is also a comparison to UpSetR and other packages).
I had a similar problem and found this workaround:
library("UpSetR")
m <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"),
header = T, sep = ";")
x<-c(rep("M", 2000), rep("Q", 1000), rep("C", 883))
m<-cbind(m, x)
upset(m,
queries = list(
list(query = elements,
params = list("x", c("M","Q", "C")), color = "#e69f00", active = T),
list(query = elements,
params = list("x", c("Q","C")), color = "#cc79a7", active = T),
list(query = elements,
params = list("x", "C"), color = grey(0.7), active = T)))
The problem in the original example is that every query overlays over the total bar separately and starts at y=0. Thus, the remaining black part of the bar always has the exact same height as the purple part at the bottom. The workaround is to systematically add queries of combinations of the different values the variable can take:
Start with a query and a respective color for the combination of all possible values (here c("M","Q","C") as the second parameter to params = list()).
Successively leave out one of the possible values (e.g. c("Q","C") in the first step here). The value left out will be represented by the color of the query, the last one that still included it ("M" in this example).
Continue adding queries until you have only one value left for the second parameter to params = list().
It should be possible do this programmatically for larger numbers of possible values and providing some color palette. But this remains a workaround and a native implementation of stacking the queries would be nice to have--so if you would like to see this functionality, you might consider bumping up the respective issue over at the Github repo.
Below the nice answer by #dlaehnemann but a little bit modified in order to create that list of list using a loop as well as linking wanted colors to it.
m <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"), header = T, sep = ";")
x<-c(rep("M", 2000), rep("Q", 1000), rep("C", 883))
m<-cbind(m, x)
i<-0
mylist<-list()
vectorUniqueValue <- unique(m$x)
colors = colorRampPalette(c("#332288",'#fdff00','#FF0000',"#CC6677","#88CCEE",'#36870c','#b786d2','#7c3c06',"#DDCC77",'#192194','#52cff4','#4f9c8b',"#4477AA",'#808080'))(length(vectorUniqueValue))
while ( length(vectorUniqueValue)>0 ){
i<-i+1
mylist[[i]]<-list(query = elements, params = list("x",as.character(vectorUniqueValue)), color = colors[i], active = T)
vectorUniqueValue<-vectorUniqueValue[-1]
}
upset(m, queries = mylist)
Hope it helps a bit until maybe one day someone works on the issue on github !
I apparently have successively used a newer R package called milr, multiple instance logistic regression. Admittedly, I do not make any claims regarding the goodness of the model. However, when I try to use the model to predict I get the error
Error in logit(cbind(1, newdata), .) : not compatible with requested type
when I call predict as follows:
miltp <- predict(milt, SQFM.te, SQFM.teb, type="bag") and
miltp <- predict(milt, SQFM.te, SQFM.teb)
However I get a NULL return when I call it as:
miltp <- predict(milt, SQFM.te, SQFM.teb, type="response") and
miltp <- predict(milt, SQFM.te, SQFM.teb, type="class")
I have tried using factors, integers and numerics, I am perplexed. My online search only yielded
Rcpp: Error: not compatible with requested type
which is not helpful for me as R and C++ is over my head. All comments are appreciated, some input info is given below I have tried some conversions
str(SQFM.te)
'data.frame': 100369 obs. of 5 variables:
$ arstmade: int 0 0 0 0 0 0 0 0 0 0 ...
$ perstop : int 0 0 0 0 0 0 0 0 0 0 ...
$ trhsloc : int 0 0 0 0 0 0 0 0 0 0 ...
$ acrept : int 0 0 0 0 0 0 0 0 0 0 ...
$ radio : int 1 1 1 1 1 1 1 1 1 1 ...
str(SQFM.teb)
int [1:100369] 3 3 3 3 3 3 3 3 3 3 ...
print(milt)
Coefficients:
intercept arstmade perstop trhsloc acrept radio
-1.69306 -0.09544 -7.95369 -0.53375 0.16506 -0.61778
Residual Deviance: Inf
BIC: Inf
I'm working with R and I have a problem with rbinding dataframe.
My data come from a Json file and the first think I have done is to split it accordingly to Chromosome number
#Input
Control <- fromJSON(file=O5)
RNAi <- fromJSON(file=s25p5)
#Loop throug each chromosome
Control.1 <- lapply(Control, function(I)
{
data.frame(matrix(unlist(I),ncol = 1, byrow = TRUE))
})
The problem is that now I have a list of 6 data.frame but on a random order
str(Control.1)
List of 6
$ II :'data.frame': 1771887 obs. of 1 variable:
..$ matrix.unlist.I...ncol...1..byrow...TRUE.: num [1:1771887] 0 0 0 0 0 0 0 0 0 0 ...
$ I :'data.frame': 1507243 obs. of 1 variable:
..$ matrix.unlist.I...ncol...1..byrow...TRUE.: num [1:1507243] 0 0 0 0 0 0 0 0 0 0 ...
$ III :'data.frame': 1378370 obs. of 1 variable:
..$ matrix.unlist.I...ncol...1..byrow...TRUE.: num [1:1378370] 0 0 0 0 0 0 0 0 0 0 ...
etc.
I would like to reorder them in order to have $I as the first data.frame, then $II etc.
my aim is to use rbind after
Control.2 <-do.call(rbind,Control.1)
in order to have one data frame containing all the data frame but in the correct oder.
Does anybody have any idea how it could be done?
Thank you!
for alphabetical order you can use :
Control.2 <-do.call(rbind,Control.1[order(names(Control.1)))
or you can use any other function than order to sort the names vector.
I'm struggling with using readLines() and read.Table() to get a well formatted data frame in R.
I want to read files like this which are Hockey stats. I'd like to get a nicely formatted data frame, however, specifying the concrete amount of lines to read is difficult because in other files like this the number of players is different. Also, non-players, signed as #.AC, #.HC and so on, should not be read in.
I tried something like this
LINES <- 19
stats <- read.table(file=Datei, skip=11, header=FALSE, stringsAsFactors=FALSE,
encoding="UTF-8", nrows=LINES)
but as mentioned above, the value for LINES is different each time.
I also tried readLines as in this post, but had no luck with it.
Is there a way to integrate a condition in read.table, like (pseudo code)
if (first character == "AC") {
break read.table
}
Sorry if this looks strange, I don't have that much experience in scripting or coding.
Any help is appreciated, thanks a lot!
Greetz!
Your data show a couple of difficulties which should be handled in a sequence, which means you should not try to read the entire file with one command:
Read plain lines and find start and stop row
Depending on the specification of the files you read in my suggestion is to first find the the first row you actually want to read in by any indicator. So this can be a lone number which is always the same or as in my example two lines after the line "TEAM STATS". Finding the last line is then simple again by just looking for the first line containing only whitespaces after the start line:
lines <- readLines( Datei )
start <- which(lines == "TEAM STATS") + 2
end <- start + min( grep( "^\\s+$", lines[ start:length(lines) ] ) ) -2
lines <- lines[start:end]
Read the data to data.frame
In your case you meet a couple of complications:
Your header line starts with an # which is on default recognized as a comment character, ignoring the line. But even if you switch this behavior off (comment.char = "") it's not a valid column name.
If we tell read.table to split the columns along whitespaces you end up with one more column in the data, than in the header since the Player column contains white spaces in the cells. So the best is at the moment to just ignore the header line and let read.table do this with it's default behavior (comment.char = "#"). Also we let the PLAYER column be split into two and will fix this later.
You won't be able to use the first column as row.names since they are not unique.
The rows have unequal length, since the POS column is not filled everywhere.
:
tab <- read.table( text = lines[ start:end ], fill = TRUE, stringsAsFactors=FALSE )
# fix the PLAYER column
tab$V2 <- paste( tab$V2, tab$V3 )
tab <- tab[-3]
Fix the header
Just split the start line at multiple whitespaces and reset the first entry (#) by a valid column name:
colns <- strsplit( lines[start], "\\s+" )[[1]]
colns[1] <- "code"
colnames(tab) <- colns
Fix cases were "POS" was empty
This is done by finding the rows which last cell contains NAs and shift them by one cell to the right:
colsToFix <- which( is.na(tab[, "SHO%"]) )
tab[ colsToFix, 4:ncol(tab) ] <- tab[ colsToFix, 3:(ncol(tab)-1) ]
tab[ colsToFix, 3 ] <- NA
> str(tab)
'data.frame': 25 obs. of 20 variables:
$ code : chr "93" "91" "61" "88" ...
$ PLAYER: chr "Eichelkraut, Flori" "Müller, Lars" "Alt, Sebastian" "Gross, Arthur" ...
$ POS : chr "F" "F" "D" "F" ...
$ GP : chr "8" "6" "7" "8" ...
$ G : int 10 1 4 3 4 2 0 2 1 0 ...
$ A : int 5 11 5 5 3 4 6 3 3 4 ...
$ PTS : int 15 12 9 8 7 6 6 5 4 4 ...
$ PIM : int 12 10 12 6 2 36 37 29 6 0 ...
$ PPG : int 3 0 1 1 1 1 0 0 1 0 ...
$ PPA : int 1 5 2 2 1 2 4 2 1 1 ...
$ SHG : int 0 1 0 1 1 0 0 0 0 0 ...
$ SHA : int 0 0 1 0 1 0 0 1 0 0 ...
$ GWG : int 2 0 1 0 0 0 0 0 0 0 ...
$ FG : int 1 0 1 1 1 0 0 0 0 0 ...
$ OTG : int 0 0 0 0 0 0 0 0 0 0 ...
$ UAG : int 1 0 1 0 0 0 0 0 0 0 ...
$ ENG : int 0 0 0 0 0 0 0 0 0 0 ...
$ SHOG : int 0 0 0 0 0 0 0 0 0 0 ...
$ SHOA : num 0 0 0 0 0 0 0 0 0 0 ...
$ SHO% : num 0 0 0 0 0 0 0 0 0 0 ...