I have a function of the (very simplified) form below.
doThing <- function(df){
result <- c() #Sets up dataframe to store lists
totals <- rowSums(df) #Creates rowsum list from df column
result[1] <- totals #intended to make totals a column in result. does not work
}
How do I assign the list created in this function to a column in my result dataset? I've also tried the following use of the assign function, to no avail
assign(result[1], totals)
Thank you all!
You could assign a row-wise sum as a new column to dataframe.
doThing <- function(df){
transform(df, total= rowSums(df))
}
doThing(mtcars)
# mpg cyl disp hp drat wt qsec vs am gear carb total
#Mazda RX4 21.0 6 160.0 110 3.90 2.62 16.5 0 1 4 4 329
#Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.88 17.0 0 1 4 4 330
#Datsun 710 22.8 4 108.0 93 3.85 2.32 18.6 1 1 4 1 260
#Hornet 4 Drive 21.4 6 258.0 110 3.08 3.21 19.4 1 0 3 1 426
#Hornet Sportabout 18.7 8 360.0 175 3.15 3.44 17.0 0 0 3 2 590
#...
#...
We can use
doThing <- function(df) {
df[["total"]] <- rowSums(df)
df
}
doThing(mtcars)
Related
Hey I'm sure I'm missing something simple with mapping, but I can't get it to work. I want to use a loop to do the same calculation for multiple dataframes that have the same name. Basically, I want this loop to not throw an error:
mtcars1 <- mtcars
mtcars2 <- mtcars
for(x in c(mtcars1, mtcars2)){
x$new <- x$mpg * x$cyl
}
So that at the end, both mtcars1 and mtcars2 have a new variable called new, that is mpg * cyl.
If you first put your dataframes into a list, you can index into each using seq_along():
dfs <- list(mtcars1 = mtcars, mtcars2 = mtcars)
for (i in seq_along(dfs)) {
dfs[[i]]$new <- dfs[[i]]$mpg * dfs[[i]]$cyl
}
Or, using lapply():
dfs <- lapply(dfs, \(x) {
x$new <- x$mpg * x$cyl
x
})
Result from either approach:
#> head(dfs$mtcars1)
mpg cyl disp hp drat wt qsec vs am gear carb new
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 126.0
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 126.0
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 91.2
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 128.4
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 149.6
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 108.6
If you really want to leave your dataframes loose in the environment, you could do something like
for (nm in c("mtcars1", "mtcars2")) {
x <- get(nm)
x$new <- x$mpg * x$cyl
assign(nm, x)
}
Result:
#> head(mtcars1)
mpg cyl disp hp drat wt qsec vs am gear carb new
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 126.0
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 126.0
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 91.2
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 128.4
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 149.6
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 108.6
The for loop does not work as you think, and more important, the effect of c(mtcars1, mtcars2) is not what you think, to see so do
test <- c(mtcars1, mtcars2)
length(test)
str(test)
You need to replace the c with list. Below is one solution:
mtcars1 <- mtcars
mtcars2 <- mtcars
test <- list(mtcars1, mtcars2)
newtest <- lapply(test, FUN=function(x)
within(x, new <- mpg * cyl))
We could use transform with lapply
lst1 <- lapply(lst1, transform, new = mpg * cyl)
data
lst1 <- list(mtcars1, mtcars2)
I have downloaded an .ods file from this website (UK office for national statistics). Because of the way the sheet is structured, I import it as two separate dataframes:
library(readODS)
income_pretax <- read_ods('/Users/c.robin/Downloads/NS_Table_3_1a_1819.ods', range = "A4:U103")
income_posttax <- read_ods('/Users/c.robin/Downloads/NS_Table_3_1a_1819.ods', range = "A104:U203")
I want to do some cleaning on both dataframes: changing the name of the two of the variables and recasting one of the variables as numeric. This is what I have for this, which works on a single df:
income_pretax <- income_pretax %>%
rename(pp_tot_income_pretax = 'Percentile point\nTotal income before tax',
'2008-09' = '2008-09(a)')
income_pretax['2008-09'] <- as.numeric(income_pretax$'2008-09')
I'm struggling to get the above into a function though. I think it should be something like the below, but honestly I have no idea how to tell R i'm passing multiple dataframes to the function, nor how to handle multiple variables. Can anyone advise on this?
##Attempting a function
cleanvars <- function(data, varlist){
data <- data %>%
rename(pp_tot_income_pretax = {{varlist}})
data['2008-09'] <- as.numeric(data$'2008-09')
}
You can pass a named vector to the function.
library(dplyr)
cleanvars <- function(data, varlist){
data %>% rename(varlist)
}
cleanvars(mtcars %>% head, c('new_mpg' = 'mpg', 'new_cyl' = 'cyl'))
# new_mpg new_cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
We can do this in base R
nm1 <- c('mpg', 'cyl')
nm2 <- paste0("new_", nm1)
i1 <- match(nm1, names(mtcars))
names(mtcars)[i1] <- nm2
I need to scale some columns out of my dataframe and I found a function which did it perfectly. So my question is how to replace the scaled columns in my dataframe? Is there a specific function for that or am I supposed to do it in a different way?
df <- raw_data %>%
select(CLIENTNUM:Avg_Utilization_Ratio) %>%
rename(Customer_Nr = CLIENTNUM,
Customer_Act = Attrition_Flag,
Total_Product_Count = Total_Relationship_Count)
scaled <- scale(select(df, Customer_Nr, Customer_Age, Dependent_count, Months_on_book,
Total_Product_Count, Months_Inactive_12_mon, Contacts_Count_12_mon, Credit_Limit,
Total_Revolving_Bal, Avg_Open_To_Buy, Total_Amt_Chng_Q4_Q1, Total_Trans_Amt,
Total_Trans_Ct)
, center = T, scale = T)
Create a character vector of column name and apply scale only on those columns.
cols <- c('Customer_Nr', 'Customer_Age', 'Dependent_count' .....)
df[cols] <- scale(df[cols])
Using an example of mtcars dataset :
df <- mtcars
cols <- c('mpg', 'disp')
df[cols] <- scale(df[cols])
df
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 0.1509 6 -0.5706 110 3.90 2.62 16.5 0 1 4 4
#Mazda RX4 Wag 0.1509 6 -0.5706 110 3.90 2.88 17.0 0 1 4 4
#Datsun 710 0.4495 4 -0.9902 93 3.85 2.32 18.6 1 1 4 1
#Hornet 4 Drive 0.2173 6 0.2201 110 3.08 3.21 19.4 1 0 3 1
#Hornet Sportabout -0.2307 8 1.0431 175 3.15 3.44 17.0 0 0 3 2
#Valiant -0.3303 6 -0.0462 105 2.76 3.46 20.2 1 0 3 1
#...
#...
I would like to log transform several variables in a dataframe and to then add the transformed variables to the dataframe as new variables named using 'logoldname'. What are the best ways of doing these in R efficiently? Thank you!
data("mtcars")
head(mtcars)
#Log transform - maunally
mtcars$logdisp <- log(mtcars$disp)
mtcars$loghp <- log(mtcars$hp)
mtcars$logwt <- log(mtcars$wt)
mtcars$logqsec <- log(mtcars$qsec)
Not sure why the downvotes; I think the question is perfectly fine, and a comment with an explanation how OP could've improved his question would have helped.
That aside, here is a tidyverse solution:
# These are the columns with entries you'd like to log-transform
ss <- c("disp", "hp", "wt", "qsec")
mtcars %>%
mutate_at(vars(one_of(ss)), funs(log = log(.))) %>%
rename_at(vars(contains("_log")), funs(paste0("log_", gsub("_log", "", .)))) %>%
select(contains("log_"))
# log_disp log_hp log_wt log_qsec
#1 5.075174 4.700480 0.9631743 2.800933
#2 5.075174 4.700480 1.0560527 2.834389
#3 4.682131 4.532599 0.8415672 2.923699
#4 5.552960 4.700480 1.1678274 2.967333
#5 5.886104 5.164786 1.2354715 2.834389
#6 5.416100 4.653960 1.2412686 3.006672
Explanation: mutate_at selects columns that match ss and applies a log transformation. This generates new columns, named e.g. "disp_log", "hp_log" and so on. We then rename those columns into log_disp, log_hp, etc., and select only the log-transformed columns in the final step.
This solution uses base R only, and I believe is simpler than the tidyverse solution. I will use the vector ss in that solution, by #Maurits Evers.
data("mtcars")
ss <- c("disp", "hp", "wt", "qsec")
logs <- sapply(mtcars[ss], log)
colnames(logs) <- paste("log", ss, sep = "_")
result <- cbind(mtcars, logs)
head(result)
# mpg cyl disp hp drat wt qsec vs am gear carb log_disp
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 5.075174
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 5.075174
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 4.682131
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 5.552960
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5.886104
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 5.416100
# log_hp log_wt log_qsec
#Mazda RX4 4.700480 0.9631743 2.800933
#Mazda RX4 Wag 4.700480 1.0560527 2.834389
#Datsun 710 4.532599 0.8415672 2.923699
#Hornet 4 Drive 4.700480 1.1678274 2.967333
#Hornet Sportabout 5.164786 1.2354715 2.834389
#Valiant 4.653960 1.2412686 3.006672
If you don't want to cbind the logs with the original dataframe, you can coerce the matrix produced by sapply to data.frame:
result <- as.data.frame(logs)
And maybe a final clean up, rm(logs).
I have a set of data frames - let us say called report_001, report_002, report_003 and so on - I have the names of them in a character vector such as:
n <- c('report_001', 'report_002', 'report_003')
I need to turn this into a list of data frames as follows:
dfList <- list(report_001 = report_001, report_002 = report_002, report_003 = report_003)
So that I can index like this:
dfList[['report_002']]
However, since I have a large number of data frames, I don't want to do this manually. Trying to do something like this, has not worked:
dfList <- sapply(n, function(x) assign(x, as.name(x)))
For this question, what those data frames are is not important. To keep things simple, I can have:
report_001 <- mtcars
report_002 <- mtcars
report_003 <- mtcars
How can I achieve auto conversion of my names of data frames into a list of data frames of same name indices?
report_001 <- mtcars
report_002 <- mtcars
report_003 <- mtcars
n <- c('report_001', 'report_002', 'report_003')
dfList <- mget(n)
head(dfList[['report_001']])
# mpg cyl disp hp drat wt qsec vs am
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1
# Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0
# Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0
# Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0