Each row in the dataset has an id number, and each row lists a single product with the net price. Some deals have multiple items sold, hence the multiple rows with the same id number. How do I create groups of the rows so that they are grouped by product combination?
For example, IDs 1 and 4 show be seen as the same since they have the exact same products in the deals.
The end goal is to be able to create matching groups, then do a statistical analysis on these groups.
Goal output:
I tried:
data_wide <- spread(dt, Product, Price)
Output:
'var' must evaluate to a single number or a column name, not a character vector
structure(list(`ID Number` = c(1, 1, 1, 2, 3, 3, 4, 4, 4), Product = c("A",
"B", "D", "A", "A", "B", "A", "B", "D"), Price = c(8, 7, 11,
4, 5, 9, 10, 3, 5)), row.names = c(NA, -9L), class = c("tbl_df",
"tbl", "data.frame"))
Related
Is it possible to use the gtsummary R package to make a pre-post summary table with 2 columns that summarize multiple variables at 2 different time points?
I know the arsenal R package supports this, but I would prefer to use gtsummary if possible since it supports the tidyverse.
For example, is it possible to make a pre-post summary table using gtsummary that is similar to the table in this example? Here is a simpler version of the dataset from their example:
dat <- data.frame(
tp = paste0("Time Point ", c(1, 2, 1, 2, 1, 2, 1, 2, 1, 2)),
id = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 6),
Cat = c("A", "A", "A", "B", "B", "B", "B", "A", NA, "B"),
Fac = factor(c("A", "B", "C", "A", "B", "C", "A", "B", "C", "A")),
Num = c(1, 2, 3, 4, 4, 3, 3, 4, 0, NA),
stringsAsFactors = FALSE)
Note the dataset is in "long format": tp is the 2 pre-post time points, and id is the subject ID for the 2 repeated measures. To make the table, Cat and Fac are categorical variables that would be summarized as count(%) at each time point, and use McNemar's test to compare if they change over time. Num is a numeric variable that would be summarized as mean(standard deviation) at each time point, and use paired t-test to assess change over time.
Yes, as of gtsummary v1.3.6, there is a function called add_difference() for this express purpose. The function supports both paired (e.g. pre- and post-responses), and unpaired data. The method is specified in the test= argument.
Worked example here: http://www.danieldsjoberg.com/gtsummary/articles/gallery.html#paired-test
Here's an unpaired example:
trial %>%
select(trt, age, marker, response, death) %>%
tbl_summary(
by = trt,
statistic =
list(
all_continuous() ~ "{mean} ({sd})",
all_dichotomous() ~ "{p}%"
),
missing = "no"
) %>%
add_n() %>%
add_difference()
I am working with a long-format longitudinal dataset where each person has 1, 2 or 3 time points. In order to perform certain analyses I need to make sure that each person has the same number of rows even if it consists of NAs because they did not complete the certain time point.
Here is a sample of the data before adding the rows:
structure(list(Values = c(23, 24, 45, 12, 34, 23), P_ID = c(1,
1, 2, 2, 2, 3), Event_code = c(1, 2, 1, 2, 3, 1), Site_code = c(1,
1, 3, 3, 3, 1)), class = "data.frame", row.names = c(NA, -6L))
This is the data I aim to get after adding the relevant rows:
structure(list(Values = c(23, 24, NA, 45, 12, 34, 23, NA, NA),
P_ID = c(1, 1, 1, 2, 2, 2, 3, 3, 3), Event_code = c(1, 2,
3, 1, 2, 3, 1, 2, 3), Site_code = c(1, 1, 1, 3, 3, 3, 1,
1, 1)), class = "data.frame", row.names = c(NA, -9L))
I want to come up with code that would automatically add rows to the dataset conditionally on whether the participant has had 1, 2 or 3 visits. Ideally it would make rest of data all NAs while copying Participant_ID and site_code but if not possible I would be satisfied just with creating the right number of rows.
We could use fill after doing a complete
library(dplyr)
library(tidyr)
ExpandedDataset %>%
complete(P_ID, Event_code) %>%
fill(Site_code)
I came with quite a long code, but you could group it in a function and make it easier:
Here's your dataframe:
df <- data.frame(ID = c(rep("P1", 2), rep("P2", 3), "P3"),
Event = c("baseline", "visit 2", "baseline", "visit 2", "visit 3", "baseline"),
Event_code = c(1, 2, 1, 2, 3, 1),
Site_code = c(1, 1, 2, 2, 2, 1))
How many records you have per ID?
values <- summary(df$ID)
What is the maximum number of records for a single patient?
target <- max(values)
Which specific patients have less records than the maximum?
uncompliant <- names(which(values<target))
And how many records do you have for those patients who have missing information?
rowcount <- values[which(values<target)]
So now, let's create the vectors of the data frame we will add to your original one. First, IDs:
IDs <- vector()
for(i in 1:length(rowcount)){
y <- rep(uncompliant[i], target - rowcount[i])
IDs <- c(IDs, y)
}
And now, the sitecodes:
SC <- vector()
for(i in 1:length(rowcount)){
y <- rep(unique(df$Site_code[which(df$ID == uncompliant[i])]), target - rowcount[i])
SC <- c(SC, y)
}
Finally, a data frame with the values we will introduce:
introduce <- data.frame(ID = IDs, Event = rep(NA, length(IDs)),
Event_code = rep(NA, length(IDs)),
Site_code = SC)
Combine the original dataframe with the new values to be added and sort it so it looks nice:
final <- as.data.frame(rbind(df, introduce))
final <- final[order(v$ID), ]
This question already has answers here:
Numbering rows within groups in a data frame
(10 answers)
Closed 2 years ago.
I have a set of data from children, recorded across a number of sessions. The number of sessions and age of each child in each session is different for each participant, so it looks something like this:
library(tibble)
mydf <- tribble(~subj, ~age,
"A", 16,
"A", 17,
"A", 19,
"B", 10,
"B", 11,
"B", 12,
"B", 13)
What I don't currently have in the data is a variable for Session number, and I'd like to add this to my dataframe. Basically I want to create a numeric variable that is ordinal from 1-n for each child, something like this:
mydf2 <- tribble(~subj, ~age, ~session,
"A", 16, 1,
"A", 17, 2,
"A", 19, 3,
"B", 10, 1,
"B", 11, 2,
"B", 12, 3
"B", 13, 4)
Ideally I'd like to do this in dplyr().
You simply need to group by subj and use row_number():
mydf %>%
group_by(subj) %>%
mutate(session = row_number())
Sorry in advance because I am new at asking questions here and don't know how to input this table properly.
Say I have a data frame in R constructed like:
team = c("A", "A", "A", "B", "B", "B", "C", "C", "C")
value = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
m = cbind(team, value)
I want to create a plot that will give me 3 lines graphing the values for teams A, B, and C. I believe I can do this inputting the matrix m into matplot somehow, but I'm not sure how.
EDIT: I've gotten a lot closer to solving my problem. However I've realized that for some reason, with the code I have, "Value" is a list of 745 which matches the number of rows in my dataframe m. However when I unlist(Value) it turns into a numeric of length 894. Any ideas on why this would happen?
You can try something like this:
team = c("A", "A", "A", "B", "B", "B", "C", "C", "C")
value = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
m = cbind.data.frame(team, value)
library(ggplot2)
ggplot(m, aes(x=as.factor(1:nrow(m)), y=value, group=team, col=team)) +
geom_line(lwd=2) + xlab('index')
if you have same number of ordered values for each team, you could use matplot to visualize them. but the data should be converted to matrix first;
m = cbind.data.frame(team, value, index = rep(1:3, 3))
m <- reshape(m, v.names = 'value', idvar = 'team', direction = 'wide', timevar = 'index')
matplot(t(m[, 2:4]), type = 'l', lty = 1)
legend('top', legend = m[, 1], lty = 1, col = 1:3)
I am trying to split a dataset in 80/20 - training and testing sets. I am trying to split by location, which is a factor with 4 levels, however each level has not been sampled equally. Out of 1892 samples -
Location1: 172
Location2: 615
Location3: 603
Location4: 502
I am trying to split the whole dataset 80/20, as mentioned above, but I also want each location to be split 80/20 so that I get an even proportion from each location in the training and testing set. I've seen one post about this using stratified function from the splitstackshape package but it doesn't seem to want to split my factors up.
Here is a simplified reproducible example -
x <- c(1, 2, 3, 4, 1, 3, 7, 4, 5, 7, 8, 9, 4, 6, 7, 9, 7, 1, 5, 6)
xx <- c("A", "A", "B", "B", "B", "B", "B", "B", "B", "C", "C", "C", "C", "C", "C", "D", "D", "D", "D", "D")
df <- data.frame(x, xx)
validIndex <- stratified(df, "xx", size=16/nrow(df))
valid <- df[-validIndex,]
train <- df[validIndex,]
where A, B, C, D correspond to the factors in the approximate proportions as the actual dataset (~ 10, 32, 32, and 26%, respectively)
Using bothSets should return you a list containing the split of the original data frame into validation and training set (whose union should be the original data frame):
splt <- stratified(df, "xx", size=16/nrow(df), replace=FALSE, bothSets=TRUE)
valid <- splt[[1]]
train <- splt[[2]]
## check
df2 <- as.data.frame(do.call("rbind",splt))
all.equal(df[with(df, order(xx, x)), ],
df2[with(df2, order(xx, x)), ],
check.names=FALSE)