Convert a small dataset written in SPSS to CSV - r
I have a small dataset written in SPSS syntax which comes from Table 5.3 p. 189 of this book (type 210 in the page slot to see the table).
I was wondering if there might be a way to convert this data to .csv file? (I want to use the data in R afterwards)
# SPSS Code:
DATA LIST FREE/gpid anx socskls assert.
BEGIN DATA.
1 5 3 3 1 5 4 3 1 4 5 4 1 4 5 4
1 3 5 5 1 4 5 4 1 4 5 5 1 4 4 4
1 5 4 3 1 5 4 3 1 4 4 4
2 6 2 1 2 6 2 2 2 5 2 3 2 6 2 2
2 4 4 4 2 7 1 1 2 5 4 3 2 5 2 3
2 5 3 3 2 5 4 3 2 6 2 3
3 4 4 4 3 4 3 3 3 4 4 4 3 4 5 5
3 4 5 5 3 4 4 4 3 4 5 4 3 4 6 5
3 4 4 4 3 5 3 3 3 4 4 4
END DATA.
EDIT - in order to check answers I am adding here the actual way the data looks after reading it in SPSS :
gpid anx socskls assert
1 5 3 3
1 5 4 3
1 4 5 4
1 4 5 4
1 3 5 5
1 4 5 4
1 4 5 5
1 4 4 4
1 5 4 3
1 5 4 3
1 4 4 4
2 6 2 1
2 6 2 2
2 5 2 3
2 6 2 2
2 4 4 4
2 7 1 1
2 5 4 3
2 5 2 3
2 5 3 3
2 5 4 3
2 6 2 3
3 4 4 4
3 4 3 3
3 4 4 4
3 4 5 5
3 4 5 5
3 4 4 4
3 4 5 4
3 4 6 5
3 4 4 4
3 5 3 3
3 4 4 4
If I understand correctly, the 1st, 5th, 9th, and 13th column of the dataset belong to variable gpid, the 2nd, 6th, 10th, and 14th column belong to variable anx, and so on. So, we need to
reshape from wide to long format
with multiple measure variables
where each measure variable spans several columns
and where some values are missing.
Many roads lead to Rome.
This is what I would do using my favourite tools. In particular, this approach uses the feature of data.table::melt() to reshape multiple measure columns simultaneously. There is no manual cleanup of the data section in a text editor required.
The resulting dataset result can be used directly afterwards in any subsequent R code as requested by the OP. There is no need to take a detour using a .csv file (However, feel free to save result as a .csv file).
library(data.table)
library(magrittr)
cols <- c("gpid", "anx", "socskls", "assert")
raw <- fread(text = "
1 5 3 3 1 5 4 3 1 4 5 4 1 4 5 4
1 3 5 5 1 4 5 4 1 4 5 5 1 4 4 4
1 5 4 3 1 5 4 3 1 4 4 4
2 6 2 1 2 6 2 2 2 5 2 3 2 6 2 2
2 4 4 4 2 7 1 1 2 5 4 3 2 5 2 3
2 5 3 3 2 5 4 3 2 6 2 3
3 4 4 4 3 4 3 3 3 4 4 4 3 4 5 5
3 4 5 5 3 4 4 4 3 4 5 4 3 4 6 5
3 4 4 4 3 5 3 3 3 4 4 4",
fill = TRUE)
mv <- colnames(raw) %>%
matrix(ncol = 4L, byrow = TRUE) %>%
as.data.table() %>%
setnames(new = cols)
result <- melt(raw, measure.vars = mv, na.rm = TRUE)[
order(rowid(variable))][
, variable := NULL]
result
gpid anx socskls assert
1: 1 5 3 3
2: 1 5 4 3
3: 1 4 5 4
4: 1 4 5 4
5: 1 3 5 5
6: 1 4 5 4
7: 1 4 5 5
8: 1 4 4 4
9: 1 5 4 3
10: 1 5 4 3
11: 1 4 4 4
12: 2 6 2 1
13: 2 6 2 2
14: 2 5 2 3
15: 2 6 2 2
16: 2 4 4 4
17: 2 7 1 1
18: 2 5 4 3
19: 2 5 2 3
20: 2 5 3 3
21: 2 5 4 3
22: 2 6 2 3
23: 3 4 4 4
24: 3 4 3 3
25: 3 4 4 4
26: 3 4 5 5
27: 3 4 5 5
28: 3 4 4 4
29: 3 4 5 4
30: 3 4 6 5
31: 3 4 4 4
32: 3 5 3 3
33: 3 4 4 4
gpid anx socskls assert
Some explanations
fread() returns a data.table raw with default column names V1, V2, ... V16 and with missing values filled with NA
mv is a data.table which indicates which columns of raw belong to each target variable:
mv
gpid anx socskls assert
1: V1 V2 V3 V4
2: V5 V6 V7 V8
3: V9 V10 V11 V12
4: V13 V14 V15 V16
This informations is used by melt(). melt() also removes rows with missing values from the resulting long format.
After reshaping, the rows are ordered by the variable number but need to be reordered in the original row order by using rowid(variable). Finally, the variable column is removed.
EDIT: Improved version
Giving a second thought, here is a streamlined version of the code which skips the creation of mv and uses data.table chaining:
library(data.table)
cols <- c("gpid", "anx", "socskls", "assert")
result <- fread(
text = "
1 5 3 3 1 5 4 3 1 4 5 4 1 4 5 4
1 3 5 5 1 4 5 4 1 4 5 5 1 4 4 4
1 5 4 3 1 5 4 3 1 4 4 4
2 6 2 1 2 6 2 2 2 5 2 3 2 6 2 2
2 4 4 4 2 7 1 1 2 5 4 3 2 5 2 3
2 5 3 3 2 5 4 3 2 6 2 3
3 4 4 4 3 4 3 3 3 4 4 4 3 4 5 5
3 4 5 5 3 4 4 4 3 4 5 4 3 4 6 5
3 4 4 4 3 5 3 3 3 4 4 4",
fill = TRUE, col.names = rep(cols, 4L))[
, melt(.SD, measure.vars = patterns(cols), value.name = cols, na.rm = TRUE)][
order(rowid(variable))][
, variable := NULL][]
result
Here, the columns are renamed within the call to fread(). In this case, duplicated column names are desirable (as opposed to the usual use case) because the patterns() function in the subsequent call to melt() use the duplicated column names to combine the columns which belong to one measure variable.
This requires some manual clean-up in Notepad or similar to place the data in the right format. But essentially, this could be imported using the following
df <- data.frame(
gpid = c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,
2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3),
anx = c(5,5,4,4,3,4,4,4,5,5,4,6,6,5,6,
4,7,5,5,5,5,6,4,4,4,4,4,4,4,4,4,5,4),
socskls = c(3,4,5,5,5,5,5,4,4,4,4,2,2,2,2,
4,1,4,2,3,4,2,4,3,4,5,5,4,5,6,4,3,4),
assert = c(3,3,4,4,5,4,5,4,3,3,4,1,2,3,2,
4,1,3,3,3,3,3,4,3,4,5,5,4,4,5,4,3,4)
)
write.csv(df, "df.csv", row.names = F)
Note that the first 4 values (1, 5, 3, 3) are the gpid, anx, socskls, and assert values for row 1. Whereas the values 1, 5, 4, 3 which appear to be in the next column of the pasted data in SPSS syntax (i.e. the next 4 values reading the syntax left to right) are actually the values for participant 10.
Note: I'm assuming you don't have SPSS installed. If you did the easiest option would using SPSS syntax to create the dataset in SPSS and then just export to R.
Using readLines and some string manipulating tools.
tmp <- readLines("spss1.txt") ## read from .txt
tmp <- trimws(gsub("[A-Z/.]", "", tmp)) ## remove caps and specials
nm <- strsplit(tmp[[1]], " ")[[1]] ## split names
tmp <- unlist(strsplit(tmp[3:11], "\\s{2,}") ) ## split data blocks
Finally, splitting at the spaces gives the result.
dat <- setNames(
type.convert(do.call(rbind.data.frame, strsplit(tmp, "\\s"))),
nm)
Result
dat
# gpid anx socskls assert
# 1 1 5 3 3
# 2 1 5 4 3
# 3 1 4 5 4
# 4 1 4 5 4
# 5 1 3 5 5
# 6 1 4 5 4
# 7 1 4 5 5
# 8 1 4 4 4
# 9 1 5 4 3
# 10 1 5 4 3
# 11 1 4 4 4
# 12 2 6 2 1
# 13 2 6 2 2
# 14 2 5 2 3
# 15 2 6 2 2
# 16 2 4 4 4
# 17 2 7 1 1
# 18 2 5 4 3
# 19 2 5 2 3
# 20 2 5 3 3
# 21 2 5 4 3
# 22 2 6 2 3
# 23 3 4 4 4
# 24 3 4 3 3
# 25 3 4 4 4
# 26 3 4 5 5
# 27 3 4 5 5
# 28 3 4 4 4
# 29 3 4 5 4
# 30 3 4 6 5
# 31 3 4 4 4
# 32 3 5 3 3
# 33 3 4 4 4
Note: Results in the same Wilks' lambda as #emily-kothe's method. Maybe the authors used different data or your manova method is flawed?
Related
How to combine columns with the same ID using R?
I want to Combine V1 and V2 with the matching ID number using R. What's the simplest way to go about it? Below is an example how I want to combine my data. Hopefully this makes sense if not I can try to be more clear. I did try the group by but I dont know if thats the best way to go about it ID V1 V2 1 3 2 2 3 4 3 5 1 3 2 3 4 2 3 4 5 7 4 1 3 This is what I would like it to look like ID V3 1 3 1 2 2 3 2 4 3 5 3 1 3 2 3 3 4 2 4 3 4 5 4 7 4 1 4 3
Try using pivot_longer with names_to = NULL to remove the unwanted column. tidyr::pivot_longer(df, V1:V2, values_to = "V3", names_to = NULL) Output: # ID V3 # <int> <int> # 1 1 3 # 2 1 2 # 3 2 3 # 4 2 4 # 5 3 5 # 6 3 1 # 7 3 2 # 8 3 3 # 9 4 2 # 10 4 3 # 11 4 5 # 12 4 7 # 13 4 1 # 14 4 3
You may try library(dplyr) reshape2::melt(df, "ID") %>% select(ID, value) %>% arrange(ID) ID value 1 1 3 2 1 2 3 2 3 4 2 4 5 3 5 6 3 2 7 3 1 8 3 3 9 4 2 10 4 5 11 4 1 12 4 3 13 4 7 14 4 3
Frequency Table of Categorical Variables as a Data Frame in R
I would like to create a frequency Table of all Categorical Variables as a Data Frame in R. I would like to find the frequency and percentage of each survey response (grouped by condition, as well as the total frequency). I would like to generate this as a data frame.
An example of the desired frequency count out for just ONE variable ("q1"). I want a similar freq count for most of the variables in my data:
I have data such as this. The actual data has many more categorical variables.
library(readr)
data_in <- read_table2("treatment_cur q13_3 q14_1 q14_2 q14_3 q14_4 q14_5 q14_6 q14_7 q14_8 q14_9 q14_10 q14_11 q14_12 q14_13 q14_14 q14_15
Control 3 2 3 6 5 6 6 6 4 5 5 5 4 6 6 5
Control 2 4 5 6 5 6 5 5 6 4 5 5 6 5 4 6
Treatment 3 1 2 6 4 6 5 4 6 4 6 1 5 6 4 6
Control 3 2 3 6 4 6 6 6 6 6 6 6 6 5 5 6
Control NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Control 4 6 5 6 5 6 5 6 6 5 1 1 6 5 5 6
Control 3 3 2 2 3 3 6 6 4 6 5 5 3 6 6 2
Treatment 2 3 2 3 1 3 1 1 1 3 3 3 3 3 3 1
Control 3 5 5 6 3 6 3 3 3 2 2 1 4 2 3 4
Control 2 1 1 1 1 1 4 4 1 1 1 1 1 4 4 2
Control 4 3 4 6 6 6 6 6 6 6 6 6 6 6 6 6
Control 4 2 6 6 4 6 5 6 6 5 6 5 6 6 6 6
Control 2 2 3 3 2 3 5 6 5 3 3 3 3 5 3 2
Control 3 2 4 3 4 5 4 4 5 3 3 5 4 5 5 4
Treatment 2 2 2 2 2 3 1 1 2 2 3 2 3 3 2 3
Control 4 3 3 3 5 6 6 6 6 6 6 6 6 6 6 6
Treatment 2 1 3 3 2 1 3 4 2 2 3 3 2 3 3 3
Treatment 4 2 6 4 4 2 3 5 4 5 1 1 5 4 4 5
Control 3 3 3 4 4 4 4 5 3 2 5 4 5 5 4 4
Control 4 6 6 6 6 6 6 6 6 6 6 6 5 6 6 5
Control 2 2 3 6 2 5 1 2 4 4 1 1 6 4 4 6
Treatment 4 3 3 6 6 6 6 6 6 6 6 6 6 6 6 6
Treatment 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Treatment 1 1 2 4 4 4 1 1 1 1 1 1 6 1 1 6
Treatment 3 2 3 3 2 6 6 6 6 3 3 2 4 5 5 6
Control 2 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1
Control 1 3 3 3 1 1 5 5 2 4 5 5 4 1 2 5
Treatment 3 4 4 5 5 4 4 4 3 5 3 4 4 6 6 5
Control NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Control 2 2 4 6 2 4 2 2 3 5 4 4 4 3 3 5
Treatment 1 1 2 1 1 1 1 1 6 1 1 1 6 2 3 6
Treatment 2 6 1 4 4 1 1 2 2 2 1 2 1 2 2 2
Treatment 3 3 4 4 4 6 6 5 4 6 3 5 5 6 6 4
Treatment 2 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Control 4 3 4 6 4 6 4 5 6 3 4 4 6 6 4 6
Control 4 4 3 6 2 5 2 2 4 3 1 6 5 5 5 5
Control NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Treatment 2 3 3 6 5 6 1 2 6 5 4 4 5 5 5 6
Control 4 6 6 6 6 6 5 5 5 5 5 6 5 5 5 5
Treatment 2 1 1 3 1 3 4 4 4 4 1 4 3 4 4 4
Treatment 2 1 3 3 3 3 4 6 5 4 5 5 4 6 6 5
Control 4 6 6 6 6 6 5 5 5 6 6 5 5 5 6 6
Control NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Control 4 2 2 4 2 4 6 6 6 6 4 6 5 6 6 5
Control 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Treatment 3 4 2 5 5 5 6 5 5 5 5 5 5 6 6 6
Control NA 2 4 4 4 4 4 3 4 6 4 5 4 6 4 4
Control 2 2 2 3 1 3 4 1 1 1 2 1 3 3 3 3
Treatment 2 2 2 3 2 2 3 3 2 2 2 2 2 2 2 2
Control 3 3 3 6 6 6 6 6 6 6 5 6 6 6 6 6
Treatment 2 1 2 2 2 1 2 2 1 1 2 1 2 2 1 3
Treatment 4 5 5 6 6 5 5 6 5 5 4 5 5 4 4 5
Control 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Treatment 3 3 4 4 4 6 3 2 5 3 2 2 5 6 5 6
Control 4 4 3 3 6 3 6 6 3 2 4 4 4 4 4 4
Treatment 4 1 3 4 4 4 5 6 6 6 6 6 6 6 6 6
Control 4 4 5 6 5 5 4 6 6 6 6 5 6 6 6 6
Treatment 3 3 4 6 6 6 6 6 5 6 6 5 4 6 6 4
Control 4 4 6 6 4 6 6 6 6 4 4 3 5 6 6 6
Control 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Treatment 4 5 5 6 6 6 6 6 5 5 6 6 5 5 6 6
Treatment 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Control 2 1 2 1 1 1 1 3 1 4 4 1 1 1 1 1
Treatment 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Treatment 4 6 5 5 5 5 5 6 5 4 5 4 4 5 5 4
Treatment 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Control 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Treatment 4 5 6 6 6 5 6 6 6 5 6 6 6 6 6 6
Control 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Treatment 3 3 2 5 4 4 5 6 6 4 5 5 4 5 4 6
Treatment 4 5 4 4 4 5 5 6 4 5 4 3 6 6 6 6
Control 1 2 3 2 1 4 1 1 3 1 3 3 3 3 4 4
Control 3 6 6 6 6 6 5 1 5 6 5 6 6 6 6 6
Control 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Control 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
")
My current solution is too complicated. If I wanted to know the frequency of variables from q13_3:q14_9, I know that I can do something like this to find it:
library(tables)
varList <- 2:11
data_in[varList] <- lapply(data_in[varList], factor,exclude = NULL)
lapply(varList,function(x,df,byVar){
tabular((Factor(df[[x]],paste(colnames(df)[x])) + 1) ~ ((Factor(df[[byVar]],paste(byVar)))*((n=1) + Percent("col"))),
data= df)
},data_in,"treatment_cur")
Below is a snippet of what my current output looks like. The problem is that the output is a list of a list which cannot be exported into a single excel sheet. I have to manually copy everything from the console onto an excel file.
treatment_cur
Control Treatment
q14_8 n Percent n Percent
1 6 13.953 4 12.50
2 4 9.302 4 12.50
3 5 11.628 2 6.25
4 6 13.953 4 12.50
5 5 11.628 7 21.88
6 13 30.233 11 34.38
NA 4 9.302 0 0.00
All 43 100.000 32 100.00
[[10]]
treatment_cur
Control Treatment
q14_9 n Percent n Percent
1 6 13.953 4 12.50
2 6 13.953 4 12.50
3 4 9.302 4 12.50
4 6 13.953 5 15.62
5 5 11.628 8 25.00
6 12 27.907 7 21.88
NA 4 9.302 0 0.00
All 43 100.000 32 10
This works alright, but I want to:
Find the total frequency of each variable value as well (treatment + condition) as an additional column (as seen in the image above);
I do not like the function I am using to produce this output. I want to export this into an excel file, but since this output is actually a list of lists (it cannot be exported to excel), and I am finding it quite cumbersome to copy and paste these values from the console into excel. I would like an easier way of finding these frequencies! Surely R has a better way of doing this...
Any help is MUCH appreciated!!
One way to do this would be to explore using the gtsummary package.
using your code above you can produce a table quite easily with counts and percentages:
library(gtsummary)
library(readr)
library(flextable)
tbl_summary(data_in, by = "treatment_cur") %>%
add_overall() %>%
as_flex_table() %>%
flextable::save_as_docx(., path = "G:/test.docx")
If you just run:
tbl_summary(data_in, by = "treatment_cur") %>%
add_overall()
you will see the table it generates for you. The extra code after that makes it so that it is able to be exported to a docx file. From there you can copy that into excel. This generates the counts you requested and you can determine if it is a simpler implementation.
Another alternative is to write directly to a csv file:
tbl_summary(data_in, by = "treatment_cur") %>%
add_overall() %>%
as_tibble() %>%
readr::write_csv( .,path = "G:/test.csv")
OR
if you really need everything in separate columns you can separate the n and percents into two tables, merge them and then write to csv.
#keep counts only
ncount <- tbl_summary(data_in, by = "treatment_cur",
statistic = all_categorical()~ "{n}") %>%
add_overall()
#keep pcts only
pctdata <- tbl_summary(data_in, by = "treatment_cur",
statistic = all_categorical()~ "{p}%") %>%
add_overall()
#combine and output
tbl_merge(list(ncount, pctdata)) %>%
as_tibble() %>%
readr::write_csv(., "G:/test2.csv")
Edit:
Another way to approach this is with the janitor package. You can adorn counts and percentages pretty easily and merge the datasets together. After that it is easy to export to a csv/Excel. One downside here is you have to loop through your variables to get a table for each and then combine them together, however the code below is a good start to create it:
library(janitor)
datatry <- data_in %>%
janitor::tabyl( q13_3,treatment_cur) %>%
adorn_totals("col") %>%
adorn_totals("row")
datatry2 <- data_in %>%
janitor::tabyl( q13_3,treatment_cur) %>%
janitor::adorn_percentages(denominator = 'col') %>%
adorn_totals("row") %>%
adorn_totals("col") %>%
mutate(Total = ifelse(is.na(q13_3), Total, ifelse(q13_3 == 'Total',1, Total)))
datatry3 <- inner_join(datatry, datatry2, by = 'q13_3') %>%
mutate(variable ='q13_3')
Assuming that you constructed data_in as above:
library(dplyr)
library(purrr)
# reformat
tt <- data_in$treatment_cur
data_in$treatment_cur <- NULL
data_in %>% map(function(a)
{
ret <- data.frame(Treatment.n=rep(0, 6), Control.n=rep(0, 6))
b <- table(a[tt=="Treatment"])
ret[names(b), "Treatment.n"] <- b
b <- table(a[tt=="Control"])
ret[names(b), "Control.n"] <- b
ret$Treatment.percent <- ret$Treatment.n / sum(ret$Treatment.n)
ret$Control.percent <- ret$Control.n / sum(ret$Control.n)
ret
}) %>% do.call(what=cbind)
It assumes answers data is \in 1..6 and NA are ignored.
Imputing based on specific columns
I'm about to do imputation for missing values and I use the mice-package. I need to do imputation based on specific column content. So basically, I have 24 columns that are used to measure 4 Latent Variables (using the plspm-package). I wish to impute N/A's based on specific column content. So for cols 1-6 I wish to impute NAs in those specific columns based only on the content within these 6. (and so forth for cols 7-12, 13-18 and 19-24). I hope it makes sense for you guys. My data structure is: p1 p2 p3 p4 p5 p6 l1 l2 l3 l4 l5 l6 4 3 5 4 5 N/A 2 1 4 5 1 N/A 4 4 1 3 1 2 1 1 1 1 1 1 5 4 5 4 4 4 4 4 5 5 4 4 5 4 5 5 4 5 4 4 N/A 5 4 4 5 5 5 5 5 5 3 2 5 5 2 2 4 3 4 3 3 3 3 2 3 4 3 2 5 4 5 5 3 4 4 1 5 5 5 4 5 5 5 5 5 5 5 3 4 5 3 4 4 4 4 4 3 N/A 4 4 5 4 3 3 5 4 4 4 3 2 1 3 2 5 1 1 4 4 4 4 5 5 3 4 5 5 3 3 4 3 2 N/A 1 2 N/A 1 2 N/A 1 N/A 3 3 4 4 3 2 1 3 3 3 1 3 5 3 4 4 4 2 3 4 4 4 3 3 4 4 4 5 2 2 2 2 2 2 3 3 5 4 4 4 4 4 4 4 5 5 4 3 4 3 3 3 5 2 2 2 4 4 1 1 5 4 5 4 5 3 1 1 5 5 2 3 4 3 1 3 4 4 2 1 4 3 2 3 4 3 1 4 3 1 2 1 4 4 3 2 3 3 5 4 5 1 2 2 4 5 3 2 4 4 5 3 5 5 2 2 3 4 2 3 4 4 2 3 2 3 2 2 3 4 2 2 5 5 5 5 5 5 4 3 3 3 3 3 5 5 5 5 5 4 4 N/A 5 5 N/A N/A So I guess it's essentially splitting data into 4 blocks and then imputing. I read about the blocks()-function in the help(mice), but I'm not sure I can actually use that for this specific task. The code i've been using so far is: temp_pmm <- mice(data_predict, m = 3, maxit = 10, method = "pmm", seed = 2374) But the way I understand the package, it imputes based on entire row content (so my latent variable constructs overlap, which I am trying to mitigate). Hope you can help me out and I appreciate any help. Thanks in advance! Tobias
So Dominix' suggestion of simply running separate imputations seems to be the right way to go. Thanks a lot! For any future reference, this is how I worked it out: test_pmm_firstv <- mice(data_predict[,c(1:6)], m = 10, maxit = 20, method = "pmm", seed = 127493) test_pmm_secondv <- mice(data_predict[,c(7:12)], m = 10, maxit = 20, method = "pmm", seed = 1239754111) test_pmm_thirdv <- mice(data_predict[,c(13:18)], m = 10, maxit = 20, method = "pmm", seed = 1238603) test_pmm_fourthv <- mice(data_predict[,c(19:24)], m = 10, maxit = 20, method = "pmm", seed = 356811) data_pmm_firstv <- mice::complete(test_pmm_firstv, 1) data_pmm_secondv <- mice::complete(test_pmm_secondv, 1) data_pmm_thirdv <- mice::complete(test_pmm_thirdv, 1) data_pmm_fourthv <- mice::complete(test_pmm_fourthv, 1) data_fixed <- as.data.frame(cbind(data_pmm_firstv, data_pmm_secondv, data_pmm_thirdv, data_pmm_fourthv)) anyNA(data_fixed) [1] FALSE
Subsetting a dataframe in R including observations that satisfy condition
I would like to randomly subset dataframe with condition that if the observation with alpha=1 is included in a subset, then all observation which has alpha=1 must be included in the subset. I simplify data, so it looks like this. df alpha beta gamma 1 5 2 1 6 3 1 5 3 2 3 2 2 5 9 2 2 6 3 3 4 3 4 7 3 3 8 4 3 4 4 8 3 4 4 9 5 9 8 5 5 5 5 3 5 What command should I use to get subsets like the following? df1 alpha beta gamma 1 5 2 1 6 3 1 5 3 3 3 4 3 4 7 3 3 8 5 9 8 5 5 5 5 3 5 df2 alpha beta gamma 2 3 2 2 5 9 2 2 6 4 3 4 4 8 3 4 4 9 5 9 8 5 5 5 5 3 5 df3 alpha beta gamma 1 5 2 1 6 3 1 5 3 2 3 2 2 5 9 2 2 6 5 9 8 5 5 5 5 3 5 Specifically, the first observation in df with numbers (1,5,2) is randomly fell in subset df1 and df3. If so, it must follow that 2nd and 3d observations in df (1,6,3) and (1,5,3) are also included in subsets df1 and df2. I hope that my question is clear. Please help.
Try this str <- "alpha,beta,gamma 1,5,2 1,6,3 1,5,3 2,3,2 2,5,9 2,2,6 3,3,4 3,4,7 3,3,8 4,3,4 4,8,3 4,4,9 5,9,8 5,5,5 5,3,5" df <- read.csv(textConnection(str)) df[df$alpha %in% sample(unique(df$alpha), 3), ] Output alpha beta gamma 4 2 3 2 5 2 5 9 6 2 2 6 10 4 3 4 11 4 8 3 12 4 4 9 13 5 9 8 14 5 5 5 15 5 3 5
From table to data.frame
I have a table that looks like: dat = data.frame(expand.grid(x = 1:10, y = 1:10), z = sample(LETTERS[1:3], size = 100, replace = TRUE)) tabl <- with(dat, table(z, y)) tabl y z 1 2 3 4 5 6 7 8 9 10 A 5 3 1 1 3 6 3 7 2 4 B 4 5 3 6 5 1 3 1 4 4 C 1 2 6 3 2 3 4 2 4 2 Now how do I transform it into a data.frame that looks like 1 2 3 4 5 6 7 8 9 10 A 5 3 1 1 3 6 3 7 2 4 B 4 5 3 6 5 1 3 1 4 4 C 1 2 6 3 2 3 4 2 4 2
Here are a couple of options. The reason as.data.frame(tabl) doesn't work is that it dispatches to the S3 method as.data.frame.table() which does something useful but different from what you want. as.data.frame.matrix(tabl) # 1 2 3 4 5 6 7 8 9 10 # A 5 4 3 1 1 3 3 2 6 2 # B 1 4 3 4 5 3 4 4 3 3 # C 4 2 4 5 4 4 3 4 1 5 ## This will also work as.data.frame(unclass(tabl))