I have several series, each one indicates the deflator for the GDP for each country. (Data attached down below)
So what I want to do is to divide every column for the 97th position.
I know this could be pretty simple for you, but I am struggling.
This is my code so far:
d_data <- d_data %>%
mutate_if(is.numeric, function(x) x/d_data[[97,x]])
So as you can see in the data, from columns 3 to 8 data are numeric.
I think the error is that argument x of the function refers to the column name, while in the d_data, the second argument refers to column position and that is the main issue.
How can I solve this? Thanks in advance!!
Data
Data was massive to put here (745 rows, 8 columns)
So I uploaded the dput(d_data) output here
Use mutate with across as _at/_all are deprecated. Also, to extract by position, use nth
library(dplyr)
d_data %>%
mutate(across(where(is.numeric), ~ .x/nth(.x, 97)))
In the OP's code, instead of d_data[[97,x]], it should be x[97] as x here is the column value itself
d_data %>%
mutate_if(is.numeric, function(x) x/x[97])
If we want to subset the original data column, have to pass either column index or column name. Here, x doesn't refer to column index or name. But with across, we can get the column name with cur_column() e.g. (mtcars %>% summarise(across(everything(), ~ cur_column()))) which is not needed for this case
I need to categorize numeric variable into the quartile and assign the median values for the quartile groups using loop (because my original dataset has lots of variable).
What I intend is doing the following manipulation over lots of variables:
data(iris)
iris%>%mutate(Sepal.Lengthq=as.factor(ntile(Sepal.Length,4)))%>%
group_by(Sepal.Lengthq)%>%
mutate(Sepal.Lengthq_median=median(Sepal.Length,na.rm=T))
I need loop, so I wrote codes like:
quartilization=c("Sepal.Length","Sepal.Width")
for (i in seq_along(quartilization)){
iris2=iris %>%
mutate(!!str_c(quartilization[i],"q"):=ntile(.[[quartilization[i]]],4)) %>%
group_by_at(vars(one_of(!!str_c(quartilization[i],"q")))) %>%
mutate(!!str_c(quartilization[i],"qn"):=median(.[[quartilization[i]]],na.rm=T)) %>%
ungroup()
}
However, 1) it does not return "Sepal.Lengthqn" and 2) "Sepal.Widthqn" is a same value over samples.
I feel like the syntax for the median function is wrong, but cannot fix it.
So appreciated if anyone could share me some input. Thank you.
When you are using ., you refer to entire dataframe, hence you get the same value for all the years. Use .data in median to get data in the group.
I use map_dfc instead of for loop because it is easier and shorter. I also use transmute instead of mutate because mutate returns all the column every time whereas transmute only returns the changed columns which can be binded to original dataframe.
library(dplyr)
library(purrr)
library(stringr)
quartilization=c("Sepal.Length","Sepal.Width")
bind_cols(iris, map_dfc(quartilization, ~{
iris %>%
group_by(!!str_c(.x,"q") := ntile(.[[.x]],4)) %>%
transmute(!!str_c(.x,"qn"):= median(.data[[.x]],na.rm=TRUE))
}))
library(tidyverse)
Using the sample data at the bottom, I want to find counts of the Gender and FP variables, then spread these variables using tidyr::spread(). I'm attempting to do this by creating a list of dataframes, one for the Gender counts, and one for FP counts. The reason I'm doing this is to eventually cbind both dataframes. However, I'm having trouble incorporating the tidyr::spread into my function.
The function below creates a list of two dataframes with counts for Gender and FP, but the counts are not "spread."
group_by_quo=quos(Gender,FP)
DF2<-map(group_by_quo,~DF%>%
group_by(Code,!!.x)%>%
summarise(n=n()))
If I add tidyr::spread, it doesn't work. I'm not sure how to incorporate this since each dataframe in the list has a different variable.
group_by_quo=quos(Gender,FP)
DF2<-map(group_by_quo,~DF%>%
group_by(Code,!!.x)%>%
summarise(n=n()))%>%
spread(!!.x,n)
Any help would be appreciated!
Sample Code:
Subject<-c("Subject1","Subject2","Subject1","Subject3","Subject3","Subject4","Subject2","Subject1","Subject2","Subject4","Subject3","Subject4")
Code<-c("AAA","BBB","AAA","CCC","CCC","DDD","BBB","AAA","BBB","DDD","CCC","DDD")
Code2<-c("AAA2","BBB2","AAA2","CCC2","CCC2","DDD2","BBB2","AAA2","BBB2","DDD2","CCC2","DDD2")
Gender<-c("Male","Male","Female","Male","Female","Female","Female","Male","Male","Male","Male","Male")
FP<-c("F","P","P","P","F","F","F","F","F","F","F","F")
DF<-data_frame(Subject,Code,Code2,Gender,FP)
I think you misplaced the closing parenthesis. This code works for me:
library(tidyverse)
Subject<-c("Subject1","Subject2","Subject1","Subject3","Subject3","Subject4","Subject2","Subject1","Subject2","Subject4","Subject3","Subject4")
Code<-c("AAA","BBB","AAA","CCC","CCC","DDD","BBB","AAA","BBB","DDD","CCC","DDD")
Code2<-c("AAA2","BBB2","AAA2","CCC2","CCC2","DDD2","BBB2","AAA2","BBB2","DDD2","CCC2","DDD2")
Gender<-c("Male","Male","Female","Male","Female","Female","Female","Male","Male","Male","Male","Male")
FP<-c("F","P","P","P","F","F","F","F","F","F","F","F")
DF<-data_frame(Subject,Code,Code2,Gender,FP)
group_by_quo <- quos(Gender, FP)
DF2 <- map(group_by_quo,
~DF %>%
group_by(Code,!!.x) %>%
summarise(n=n()) %>%
spread(!!.x,n))
This last part is a bit more concise using count:
DF2 <- map(group_by_quo,
~DF %>%
count(Code,!!.x) %>%
spread(!!.x,n))
And by using count the unnecessary grouping information is removed as well.
library(tidyverse)
library(ggmosaic) for "happy" dataset.
I feel like this should be a somewhat simple thing to achieve, but I'm having difficulty with percentages when using purrr::map together with table(). Using the "happy" dataset, I want to create a list of frequency tables for each factor variable. I would also like to have rounded percentages instead of counts, or both if possible.
I can create frequency precentages for each factor variable separately with the code below.
with(happy,round(prop.table(table(marital)),2))
However I can't seem to get the percentages to work correctly when using table() with purrr::map. The code below doesn't work...
happy%>%select_if(is.factor)%>%map(round(prop.table(table)),2)
The second method I tried was using tidyr::gather, and calculating the percentage with dplyr::mutate and then splitting the data and spreading with tidyr::spread.
TABLE<-happy%>%select_if(is.factor)%>%gather()%>%group_by(key,value)%>%summarise(count=n())%>%mutate(perc=count/sum(count))
However, since there are different factor variables, I would have to split the data by "key" before spreading using purrr::map and tidyr::spread, which came close to producing some useful output except for the repeating "key" values in the rows and the NA's.
TABLE%>%split(TABLE$key)%>%map(~spread(.x,value,perc))
So any help on how to make both of the above methods work would be greatly appreciated...
You can use an anonymous function or a formula to get your first option to work. Here's the formula option.
happy %>%
select_if(is.factor) %>%
map(~round(prop.table(table(.x)), 2))
In your second option, removing the NA values and then removing the count variable prior to spreading helps. The order in the result has changed, however.
TABLE = happy %>%
select_if(is.factor) %>%
gather() %>%
filter(!is.na(value)) %>%
group_by(key, value) %>%
summarise(count = n()) %>%
mutate(perc = round(count/sum(count), 2), count = NULL)
TABLE %>%
split(.$key) %>%
map(~spread(.x, value, perc))
I am using the below code to extract the summary of data with respect to column x by counting the values in column x from the dataset unique_data and arranging the count values in descending order.
unique_data %>%
group_by(x) %>%
arrange(desc(count(x)))
But, when I execute the above code i am getting the error message as below,
Error: no applicable method for 'group_by_' applied to an object of class "character"
Kindly, let me know as what is going wrong in my code. For your information the column x is of character data type.
Regards,
Mohan
The reason is the wrapping of arrange on count. We need to do this separately. If we use the same code as in the OP's post, just split up the count and arrange step in two separate pipes. The output of count is a frequency column 'n' (by default), which we arrange in descending (desc) order.
unique_data %>%
group_by(x) %>%
count(x) %>%
arrange(desc(n))
also the group_by is not needed. According to the ?count documentation
tally is a convenient wrapper for summarise that will either call n or
sum(n) depending on whether you're tallying for the first time, or
re-tallying. count() is similar, but also does the group_by for you.
So based on that, we can just do
count(unique_data, x) %>%
arrange(desc(n))