Using R Package dplyr - r
I am trying to create a table using dplyr. When I try I get an error that says "Error: Can't subset with [ using an object of class quoted." Nothing is quoted in my script. Ideally I would like to create a table that is grouped by ScoutGrade, and shows the count of players for each designated ScoutGrade. I attached my script below:
PlayerSalariesProject %>%
count(PlayerSalariesProject$WAR) %>%
group_by(PlayerSalariesProject, PlayerSalariesProject$GradingScale)
We can use the unquoted column name inside the tidyverse function
library(dplyr)
PlayerSalariesProject %>%
group_by(WAR) %>%
mutate(n = n()) %>%
group_by(GradingScale, n) %>%
summarise(meanWAR = mean(WAR))
As summarise only returns the columns summarised along with the grouping variables, we can use mutate and then do another group_by
Related
Using the R syntax sequence operator ":" within the the sum command with more then 50 columns
i would like to index by column name within the sum command using the sequence operator.
library(dbplyr)
library(tidyverse)
df=data.frame(
X=c("A","B","C"),
X.1=c(1,2,3),X.2=c(1,2,3),X.3=c(1,2,3),X.4=c(1,2,3),X.5=c(1,2,3),X.6=c(1,2,3),X.7=c(1,2,3),X.8=c(1,2,3),X.9=c(1,2,3),X.10=c(1,2,3),
X.11=c(1,2,3),X.12=c(1,2,3),X.13=c(1,2,3),X.14=c(1,2,3),X.15=c(1,2,3),X.16=c(1,2,3),X.17=c(1,2,3),X.18=c(1,2,3),X.19=c(1,2,3),X.20=c(1,2,3),
X.21=c(1,2,3),X.22=c(1,2,3),X.23=c(1,2,3),X.24=c(1,2,3),X.25=c(1,2,3),X.26=c(1,2,3),X.27=c(1,2,3),X.28=c(1,2,3),X.29=c(1,2,3),X.30=c(1,2,3),
X.31=c(1,2,3),X.32=c(1,2,3),X.33=c(1,2,3),X.34=c(1,2,3),X.35=c(1,2,3),X.36=c(1,2,3),X.37=c(1,2,3),X.38=c(1,2,3),X.39=c(1,2,3),X.40=c(1,2,3),
X.41=c(1,2,3),X.42=c(1,2,3),X.43=c(1,2,3),X.44=c(1,2,3),X.45=c(1,2,3),X.46=c(1,2,3),X.47=c(1,2,3),X.48=c(1,2,3),X.49=c(1,2,3),X.50=c(1,2,3),
X.51=c(1,2,3),X.52=c(1,2,3),X.53=c(1,2,3),X.54=c(1,2,3),X.55=c(1,2,3),X.56=c(1,2,3))
Is there a quicker way todo this. The following provides the correct result. However, for large datasets (larger than this one ) it becomes vary laborious to deal with especially when pivot_wider is used and the columns are not created before hand (like above)
df %>% rowwise() %>% mutate(
Result_column=case_when(
X=="A"~ sum(c(X.1,X.2,X.3,X.4,X.5)),
X=="B"~ sum(c(X.4,X.5)),
X=="C" ~ sum(c( X.3, X.4, X.5, X.6, X.7, X.8, X.9, X.10, X.11, X.12, X.13, X.14, X.15, X.16,
X.17, X.18, X.19, X.20, X.21, X.22, X.23, X.24, X.25, X.26, X.27, X.28, X.29, X.30,
X.31, X.32, X.33, X.34, X.35, X.36, X.37, X.38, X.39, X.40, X.41, X.42,X.43, X.44,
X.45, X.46, X.47, X.48, X.49, X.50, X.51, X.52, X.53, X.54, X.55, X.56)))) %>% dplyr::select(Result_column)
The following is the how it would be used when using "select" syntax, which is that i would like to use. However, does not provide correct numerical solution. One can shorter the code by ~50 entries, by using a sequence operator ":".
df %>% rowwise() %>% mutate(
Result_column=case_when(
X=="A"~ sum(c(X.1:X.5)),
X=="B"~ sum(c(X.4:X.5)),
X=="C" ~ sum(c(X.3:X.56)))) %>% dplyr::select(Result_column)
below is a related question, however, not the same because what is needed is not a column that starts with "X" but rather a sequence.
Using mutate rowwise over a subset of columns
EDIT:
the provided code (below) from cnbrowlie is correct.
df %>% mutate(
Result_column=case_when(
X=="A"~ sum(c(X.1:X.5)),
X=="B"~ sum(c(X.4:X.5)),
X=="C" ~ sum(c(X.3:X.56)))) %>% dplyr::select(Result_column)
This can be done with dplyr>=1.0.0 using rowSums() (which computes the sum for a row across multiple columns) and across() (which superceded vars() as a method for specifying columns in a dataframe, allowing the use of : to select sequences of columns): df %>% rowwise() %>% mutate( Result_column=case_when( X=="A"~ rowSums(across(X.1:X.5)), X=="B"~ rowSums(across(X.4:X.5)), X=="C" ~ rowSums(across(X.3:X.56)) ) ) %>% dplyr::select(Result_column)
R version 3.6.3 (2020-02-29) | Using package dplyr_1.0.0 | Unable to perform summarise() function
Trying to perform the basic Summarise() function but getting the same error again and again! I have a large number of csv files having 4 columns. I am reading them into R using lapply and rbinding them. Next I need to see the number of complete observations present for each ID. Error: *Problem with `summarise()` input `complete_cases`. x unused argument (Date) i Input `complete_cases` is `n(Date)`. i The error occured in group 1: ID = 1.* Code: library(dplyr) merged <-do.call(rbind,lapply(list.files(),read.csv)) merged <- as.data.frame(merged) remove_na <- merged[complete.cases(merged),] new_data <- remove_na %>% group_by(ID) %>% summarise(complete_cases = n(Date)) Here is what the data looks like
The problem is not coming from summarise but from n. If you look at the help ?n, you will see that n is used without any argument, like this: new_data_count <- remove_na %>% group_by(ID) %>% summarise(complete_cases = n()) This will count the number of rows for each ID group and is independent from the Date column. You could also use the shortcut function count: new_data_count <- remove_na %>% count(ID) If you want to count the different Date values, you might want to use n_distinct: new_data_count_dates <- remove_na %>% group_by(ID) %>% summarise(complete_cases = n_distinct(Date)) Of note, you could have written your code with purrr::map, which has better functions than _apply as you can specify the return type with the suffix. This could look like this: library(purrr) remove_na = map_dfr(list.files(), read.csv) %>% na.omit() Here, map_dfr returns a data.frame with binding rows, but you could have used map_dfc which returns a data.frame with binding columns.
Store specific index value of output in R
I'm basically looking for the equivalent of the following python code in R:
df.groupby('Categorical')['Count'].count()[0]
The following is what I'm doing in R:
by(df$count,df$Categorical,sum)
This accomplishes the same thing as the first code but I'd like to know how to store an index value to a variable in R (new to R) .
Based on the by code, it seems like we can use (assuming that 'count' is a columns of 1s) library(dplyr) out <- df %>% group_by(Categorical) %>% summarise(Sum = sum(count)) If the columns 'count' have other values as well, the python function is taking the frequency count of 'Categorical' column. So, a similar option would be out <- df %>% count(Categorical) %>% slice(1) %>% pull(n)
With dplyr and enquo my code works but not when I pass to purrr::map
I want to create a plot for each column in a vector called dates. My data frame contains only these columns and I want to group on it, count the occurrences and then plot it.
Below code works, except for map which I want to use to go across a previously unknown number of columns. I think I'm using map correctly, I've had success with it before. I'm new to using quosures but given that my function call works I'm not sure what is wrong. I've looked at several other posts that appear to be set up this way.
df <- data.frame(
date1 = c("2018-01-01","2018-01-01","2018-01-01","2018-01-02","2018-01-02","2018-01-02"),
date2 = c("2018-01-01","2018-01-01","2018-01-01","2018-01-02","2018-01-02","2018-01-02"),
stringsAsFactors = FALSE
)
dates<-names(df)
library(tidyverse)
dates.count<-function(.x){
group_by<-enquo(.x)
df %>% group_by(!!group_by) %>% summarise(count=n()) %>% ungroup() %>% ggplot() + geom_point(aes(y=count,x=!!group_by))
}
dates.count(date1)
map(dates,~dates.count(.x))
I get this error: Error in grouped_df_impl(data, unname(vars), drop) : Column .x is unknown
When you pass the variable names to map() you are using strings, which indicates you need ensym() instead of enquo().
So your function would look like
dates.count <- function(.x){
group_by = ensym(.x)
df %>%
group_by(!!group_by) %>%
summarise(count=n()) %>%
ungroup() %>%
ggplot() +
geom_point(aes(y=count,x=!!group_by))
}
And you would use the variable names as strings for the argument.
dates.count("date2")
Note that tidyeval doesn't always play nicely with the formula interface of map() (I think I'm remembering that correctly). You can always do an anonymous function instead, but in your case where you want to map the column names to a function with a single argument you can just do
map(dates, dates.count)
Using the formula interface in map() I needed an extra !!:
map(dates, ~dates.count(!!.x))
summarise vs. summarise_each function in dplyr package
I am trying to summarise the value for one variable after splitting the data with group_by using dplyr package, the following code works fine and the output is listed below, but I can not substitute summarise_each with summriase even only one column need to be calculated, I wonder why?
iris %>% group_by(Species) %>% select(one_of('Sepal.Length')) %>%
summarise_each(funs(mean(.)))
or I will get the output like "S3:lazy".
summarize and summarize_each work quite differently. summarize is in fact simpler — just specify the expression directly: iris %>% group_by(Species) %>% select(Sepal.Length) %>% summarize(Sepal.Length = mean(Sepal.Length)) You can choose any name for the output column, it doesn’t need to be the same as the input.