I have a dataframe called clusters with various counts and totals and want to perform a simple two-prop z-test over rows to create a new column called pvals. When I use this code, pvals ends up being zero. When I replace the z-test with a simple sum() function, pvals becomes a summation for all values. This should be quite trivial, and I'm aware I could just alternatively iterate through, but I can't seem to figure out why this doesn't work. Thanks for the help.
clusters<- read.csv(file="clusters.csv", header=TRUE, sep=",")
clusters <- clusters %>% mutate(
pvals = prop.test(x=c(Charter.Count,Other.Count),n=c(Charter.Total,Other.Total), alternative="two.sided")$p.value
)
I can't test this as I don't have your clusters dataset, but it sounds like you need to use rowwise():
clusters<- read.csv(file="clusters.csv", header=TRUE, sep=",")
clusters <- clusters %>%
rowwise() %>%
mutate(
pvals = prop.test(x=c(Charter.Count,Other.Count),n=c(Charter.Total,Other.Total), alternative="two.sided")$p.value
)
Related
At the moment I am trying to apply GLM predict on a dataframe. The dataframe is quite large therefore I want to apply predict by chunks.
I have found a solution but it is quite unhandy. I first create an empty dataframe and then use rbind. Is there a more efficient way of doing this?
df=data[c(),]
for (x in split(data, factor(sort(rank(row.names(data))%%10)))) {
x["prediction"]=predict(model, x, type="response")
df=rbind(df,x)
}
As the comments mention, an example of what you want your output dataframe to look like would be very helpful.
But I think you can achieve what you want by making a grouping variable first then using 'group_by', something like this:
df <- data %>%
mutate(group = rep(1:10, times = nrow(.)/10)) %>% # make an arbitrary grouping factor for this example
group_by(group) %>% # group by whatever your grouping factor is
summarise(predictions = predict(model, x, type = 'response')) # summarise could be replaced by mutate
I am trying to create a table which provides the weighted means of a list of variables by categories of another list of variables. I want to iterate over the second list of variables with each iteration appending the dataframe to the previous dataframe. I think this is supposed to involve imap_dfr from purrr but I can't quite get the code right. I want to use tidyverse for my code.
I'll use the illinois dataset from the pollster package for my example.
require(pollster)
# rv and voter dummy variables that I want to recode to 1
# and 0 so that I can get the percent of people who are 1s # in each variable. Here I recode them.
voter_vars <- c("rv", "voter")
df2 <- illinois %>%
mutate_at(
voter_vars, ~
recode(.x,
"1" = 0,
"2" = 1)) %>%
mutate_at(
voter_vars, ~
as.numeric(.x))
So those are the variables I want as the columns in my table. To get the weighted means for these two variables I write a function
news_summary <- function(var1){
var1 <- ensym(var1)
df3 <- df2 %>%
group_by(!!var1) %>%
summarise_at(vars(voter_vars),
funs(weighted.mean(., weight, na.rm=TRUE)))
return(df3)
}
This creates a data frame output if I run it for one variable in the dataset
news_summary(educ6)
But what I want to do is run it for three variables in the dataset, rowbinding each output to the previous output so I have a table with all of the weighted means together.
demographic_vars <- c("educ6", "raceethnic", "maritalstatus")
However, I don't quite understand how to put this into imap_dfr (which I think is what I am supposed to use to do this) to make it work. I tried this based on code I found elsewhere. But it doesn't work.
purrr::imap_dfr(demographic_vars ~ news_summary(!!.x))
I would like to create lagged values for multiple columns in R.
First, I used a function to create lead/lag like this:
mleadlag <- function(x, n, ts_id) {
pos <- match(as.numeric(ts_id) + n, as.numeric(ts_id))
x[pos]
}
Second, I would like to apply this function for several columns in R. firm.characteristics is list of columns I would like to compute lagged values.
library(dplyr)
firm.characteristics <- colnames(df)[4:6]
for(i in 1:length(firm.characteristics)){
df <- df %>%
group_by(company) %>%
mutate(!!paste0("lag_", i) := mleadlag(df[[i]] ,-1, fye)) %>%
ungroup()
}
However, I didn't get the correct values. The output for all companies in year t is the last row in year t-1. It didn't group by the company any compute the lagged values.
Can anyone help me which is wrong in the loop? Or what should I do to get the correct lagged values?
Thank you so much for your help.
Reproducible sample could be like this:
set.seed(42) ## for sake of reproducibility
n <- 6
dat <- data.frame(company=1:n,
fye=2009,
x=rnorm(n),
y=rnorm(n),
z=rnorm(n),
k=rnorm(n),
m=rnorm(n))
dat2 <- data.frame(company=1:n,
fye=2010,
x=rnorm(n),
y=rnorm(n),
z=rnorm(n),
k=rnorm(n),
m=rnorm(n))
dat3 <- data.frame(company=1:n,
fye=2011,
x=rnorm(n),
y=rnorm(n),
z=rnorm(n),
k=rnorm(n),
m=rnorm(n))
df <- rbind(dat,dat2,dat3)
I would try to stay away from loops in the tidyverse. Many of the tidyverse applications that would traditionally require loops already exist and are very fast, which creates more efficient and intuitive code (the latter being my opinion). This is a great use case for dplyr's across() functionality. I first changed the df to a tibble.
df %>%
as_tibble() %>%
group_by(company) %>%
mutate(
across(firm.characteristics, ~lag(., 1L))
) %>%
ungroup()
This generates the required lagged values. For more information see dplyr's across documentation.
I have a data set similar to the example below, complex sample data. Thanks to SO user IRTFM, I was able to adapt the code and save results (i'm only interested in the total proportions, not the confidence intervals) as a reshaped object for further processing. What I would like to do is extend this sapply to generate results for 20 other variables. I would like to save the results as data frames in a list, ideally, since I think this is the most efficient way. My struggle is how to extend the sapply so that I can process multiple variables at once. I thought about a for loop over a list that holds the names of the variables and started to make this list, var_list below, but this seems not the way forward. I'd rather take advantage of the apply family since I would like the results to be stored in a list.
library(survey) # using the `dclus1` object that is standard in the examples.
library(reshape)
library(tidyverse)
data(api)
stype_t <- sapply( levels(dclus1$variables$stype),
function(x){
form <- as.formula( substitute( ~I(stype %in% x), list(x=x)))
z <- svyciprop(form, dclus1, method="me", df=degf(dclus1))
c( z, c(attr(z,"ci")) )} ) %>%
as.data.frame() %>% slice(1) %>% reshape::melt() %>% dplyr::mutate(value = round(value, digits = 4)*100)
Lets say you then wanted to repeat the above using the variable awards. You could copy the lines and do it that way but it would be better to be more efficient. So I started by making a list of the names of the two variables in this example data but I am stumped as to how to apply this list to the code above and retain the results in a list of dataframes. I tried wrapping the sapply with an lapply but this did not work because I'm betting that was wrong. Any advice or thoughts would be appreciated.
var_list <- list("stype", "awards")
Instead of $ to reference named elements, consider [[ extractor to reference names by string. Also, extend substitute for dynamic variable:
# DEFINED METHOD
df_build <- function(var) {
sapply(levels(dclus1$variables[[var]]), function(x) {
form <- as.formula(substitute(~I(var %in% x),
list(var=as.name(var), x=x)))
z <- svyciprop(form, dclus1, method="me", df=degf(dclus1))
c(z, c(attr(z,"ci")))
}) %>%
as.data.frame() %>%
slice(1) %>%
reshape::melt() %>%
dplyr::mutate(value = round(value, digits = 4)*100)
}
# ITERATE THROUGH CHARACTER VECTOR AND CALL METHOD
var_list <- list("stype", "awards")
df_list <- lapply(var_list, df_build)
This is my first stackoverflow question.
I'm trying to use dplyr to process and output a summary of data grouped by a categorical variable (inj_length_cat3) in my dataset. Actually, I generate this variable (from inj_length) on the fly using mutate(). I also want to output the same summary of the data without grouping. The only way I figured out how to do that is to do the analysis twice over, once with, once without grouping, and then combine the outputs. Ugh.
I'm sure there is a more elegant solution than this and it bugs me. I wonder if anyone would be able to help.
Thanks!
library(dplyr)
df<-data.frame(year=sample(c(2005,2006),20,replace=T),inj_length=sample(1:10,20,replace=T),hiv_status=sample(0:1,20,replace=T))
tmp <- df %>%
mutate(inj_length_cat3 = cut(inj_length, breaks=c(0,3,100), labels = c('<3 years','>3 years')))%>%
group_by(year,inj_length_cat3)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
) %>%
filter(inj_length_cat3%in%c('<3 years','>3 years'))
tmp_all <- df %>%
group_by(year)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
)
tmp_all$inj_length_cat3=as.factor('All')
tmp<-merge(tmp_all,tmp,all=T)
I'm not sure you consider this more elegant, but you can get a solution to work if you first create a dataframe that has all your data twice: once so that you can get the subgroups and once to get the overall summary:
df1 <- rbind(df,df)
df1$inj_length_cat3 <- cut(df$inj_length, breaks=c(0,3,100,Inf),
labels = c('<3 years','>3 years','All'))
df1$inj_length_cat3[-(1:nrow(df))] <- "All"
Now you just need to run your first analysis without mutate():
tmp <- df1 %>%
group_by(year,inj_length_cat3)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
) %>%
filter(inj_length_cat3%in%c('<3 years','>3 years','All'))