I want to use lists and mapply on user created function for sum coding contrasts in r. But when I try it does not work. Any help would be appreciated.
Specifically, I want to apply sum contrasts to am and vs to create the sum-coding variables am_c and vs_c in the mtcars data. I can do this the long way, but when I try to create a user-created function that produces a data frame with these results, called function_data_frame__sum_contrast() to complete this task, it does not work.
The function works when I put in individual inputs:
### applied function to individually inputted ivs and datasets
head(function_data_frame__sum_contrast(vs, mtcars_short_way_df))
mpg cyl disp hp drat wt qsec vs am gear carb vs_c
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 0
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 0
3 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0
4 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 0
5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0
6 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 0
It does not work when using the list form
### applied function to first value in list
function_data_frame__sum_contrast(IV_info_short_way$IV_analyses[1], IV_info_short_way$dataset_analyses[1])
Error in get(nm1) :
object 'IV_info_short_way$dataset_analyses[1]' not found
3.
get(nm1)
2.
data.frame(get(nm1))
1.
function_data_frame__sum_contrast(IV_info_short_way$IV_analyses[1],
IV_info_short_way$dataset_analyses[1])
It does not work with mapply() either.
### attempts to mapply for all parts of relevant lists
mtcars_short_way_df <-
mapply(function_data_frame__sum_contrast,
(IV_info$IV_original[IV_info$IV_nature == "nominal"]),
(IV_info$dataset_analyses[IV_info$IV_nature == "nominal"]),
SIMPLIFY = FALSE)
Error in mapply(function_data_frame__sum_contrast, (IV_info$IV_original[IV_info$IV_nature == :
zero-length inputs cannot be mixed with those of non-zero length
Please help, if possible.
Here is the code for the practice:
# practice script
## loads packages for analyses
# ---- NOTE: data wrangling
if(!require(tidyverse)){install.packages("tidyverse")}
## gives information about datasets
### mtcars
# ---- NOTE: displays head of data
head(mtcars)
# ---- NOTE: gives structure of data
str(mtcars, list.len=ncol(mtcars))
# ---- NOTE: gives colnames of data
colnames(mtcars)
## produces IV_info chart
IV_info <-
data.frame(
cbind(
IV = c("vs", "am"),
IV_analyses = c("vs", "am"),
IV_nature = c("nominal", "nominal"),
dataset_name = c("mtcars"),
dataset_analyses = c("mtcars")
))
## produces datasets for practice
# ---- NOTE: creates long way dataset
mtcars_long_way_df <- mtcars
# ---- NOTE: creates long way dataset
mtcars_short_way_df <- mtcars
## long way
### changes IV_info to IV_info_short_way
# ---- NOTE: creates dataset
IV_info_short_way <- IV_info
# ---- NOTE: changes dataset_analyses variable
IV_info_short_way$dataset_analyses <- paste(IV_info_short_way$dataset_analysis, "_short_way_df", sep="")
### creates individual contrast variables
# ---- NOTE: based on IV_info_long_way$IV_analyses list
#### variable: vs
# ---- NOTE: gives levels of variable
unique(mtcars_long_way_df$vs)
# ---- NOTE: gives length of levels of variable
length(unique(mtcars_long_way_df$vs))
# ---- NOTE: creates contrast variable
mtcars_long_way_df$vs_c <- mtcars_long_way_df$vs
# ---- NOTE: turns contrast variable to factor
mtcars_long_way_df$vs_c <- as.factor(as.character(mtcars_long_way_df$vs_c))
# ---- NOTE: creates contrast variable
contrasts(mtcars_long_way_df$vs_c) <-
contr.sum(as.numeric(as.character(length(unique(mtcars_long_way_df$vs)))))
#### variable: am
# ---- NOTE: gives levels of variable
unique(mtcars_long_way_df$am)
# ---- NOTE: gives length of levels of variable
length(unique(mtcars_long_way_df$am))
# ---- NOTE: creates contrast variable
mtcars_long_way_df$am_c <- mtcars_long_way_df$am
# ---- NOTE: turns contrast variable to factor
mtcars_long_way_df$am_c <- as.factor(as.character(mtcars_long_way_df$am_c))
# ---- NOTE: creates contrast variable
contrasts(mtcars_long_way_df$am_c) <-
contr.sum(as.numeric(as.character(length(unique(mtcars_long_way_df$am)))))
## short way way
### changes IV_info to IV_info_short_way
# ---- NOTE: creates dataset
IV_info_short_way <- IV_info
# ---- NOTE: changes dataset_analyses variable
IV_info_short_way$dataset_analyses <- paste(IV_info_short_way$dataset_analyses, "_short_way_df", sep="")
### creates function function_data_frame__sum_contrast
# ---- NOTE: creates function
function_data_frame__sum_contrast <-
# ---- NOTE: turns variable into sum contrasted version of variable
# ---- NOTE: variable_name == variable to be turned to sum contrast
# ---- NOTE: dataset_name == dataset that contains variable name
# ---- NOTE: generally speaking, procedure is to create new variable with "_c" as suffix for corresponding sum contrasted variable
function(variable_name, dataset_name)
{
# ---- NOTE: # changes variable_name and dataset_name to strings
colnm <- deparse(substitute(variable_name))
nm1 <- deparse(substitute(dataset_name))
# ---- NOTE: # base data frame
dataset_funct_object_A <-
data.frame(get(nm1))
# ---- NOTE: adds merging column to base data frame
dataset_funct_object_A$merging_column <- dataset_funct_object_A[[colnm]]
# ---- NOTE: ## turns data into data frame
dataset_funct_object_A <- data.frame(dataset_funct_object_A)
# ---- NOTE: # sets up unique values part of data
# ---- NOTE: ## creates object with unique variable values
dataset_funct_object_B <- unique(dataset_funct_object_A[[colnm]])
# ---- NOTE: ### turns object to data frame
dataset_funct_object_B <- data.frame(dataset_funct_object_B)
# ---- NOTE: ### changes colnames
colnames(dataset_funct_object_B) <- c("variable_levels")
# ---- NOTE: ## gives info on whether a given variable_level has a value of NA
dataset_funct_object_B$isNA <- is.na(dataset_funct_object_B$variable_levels)
length(which(dataset_funct_object_B$isNA=="TRUE"))
# ---- NOTE: ## gives length of data frame column
dataset_funct_object_B$variable_level_number <- (as.numeric(as.character(length(dataset_funct_object_B$variable_levels))) - as.numeric(as.character(length(which(dataset_funct_object_B$isNA=="TRUE")))))
# ---- NOTE: ## displays distinct number of levels of variariable of interest, from variable_level_number
as.numeric(distinct(dataset_funct_object_B, variable_level_number))
# ---- NOTE: ## the contrast matrix for categorical variable with a given number of levels
contr.sum(as.numeric(distinct(dataset_funct_object_B, variable_level_number)))
# ---- NOTE: ## creates variable_levels_c variable, which will be used to hold contrast matrix
dataset_funct_object_B$variable_levels_c <- as.factor(as.character(dataset_funct_object_B$variable_levels))
# ---- NOTE: ## inserts the contrast matrix for categorical variable with a given number of levels transfers into appropriate variable
contrasts(dataset_funct_object_B$variable_levels_c) = contr.sum(as.numeric(distinct(dataset_funct_object_B, variable_level_number)))
# ---- NOTE: adds merging column to data frame
dataset_funct_object_B$merging_column <- dataset_funct_object_B$variable_levels
# ---- NOTE: merges original dataset with dataset of interest
dataset_funct_object_C <-
merge(dataset_funct_object_A,
dataset_funct_object_B,
by.x = "merging_column",
by.y = "merging_column",
all.x = TRUE,
all.y = FALSE,
no.dups = TRUE)
# ---- NOTE: # removes merging column from appropriate object
dataset_funct_object_D <-
dataset_funct_object_C %>%
select(
-c(merging_column,
variable_levels,
isNA,
variable_level_number)
)
# ---- NOTE: turns data into data frame
dataset_funct_object_D <- data.frame(dataset_funct_object_D)
# ---- NOTE: ## changes colname
names(dataset_funct_object_D)[names(dataset_funct_object_D) == "variable_levels_c"] <- paste(colnm, "_c", sep = "")
# ---- NOTE: turns data into data frame
dataset_funct_object_D <- data.frame(dataset_funct_object_D)
# ---- NOTE: # returns appropriate object/variable
return(dataset_funct_object_D)
}
### applied function to individually inputted ivs and datasets
head(function_data_frame__sum_contrast(vs, mtcars_short_way_df))
### applied function to first value in list
function_data_frame__sum_contrast(IV_info_short_way$IV_analyses[1], IV_info_short_way$dataset_analyses[1])
# ---- NOTE: does not work
### attempts to mapply for all parts of relevant lists
mtcars_short_way_df <-
mapply(function_data_frame__sum_contrast,
(IV_info$IV_original[IV_info$IV_nature == "nominal"]),
(IV_info$dataset_analyses[IV_info$IV_nature == "nominal"]),
SIMPLIFY = FALSE)
# ---- NOTE: does not work
The deparse/substitute works when the input argument is unquoted and want to retrieve as a string. In the loop, we are passing a string directly. So, we can change that line to
colnm <- variable_name
nm1 <- dataset_name
-full function
function_data_frame__sum_contrast <-
# ---- NOTE: turns variable into sum contrasted version of variable
# ---- NOTE: variable_name == variable to be turned to sum contrast
# ---- NOTE: dataset_name == dataset that contains variable name
# ---- NOTE: generally speaking, procedure is to create new variable with "_c" as suffix for corresponding sum contrasted variable
function(variable_name, dataset_name)
{
# ---- NOTE: # changes variable_name and dataset_name to strings
#colnm <- deparse(substitute(variable_name))
colnm <- variable_name
nm1 <- dataset_name
# ---- NOTE: # base data frame
dataset_funct_object_A <-
data.frame(get(nm1))
# ---- NOTE: adds merging column to base data frame
dataset_funct_object_A$merging_column <- dataset_funct_object_A[[colnm]]
# ---- NOTE: ## turns data into data frame
dataset_funct_object_A <- data.frame(dataset_funct_object_A)
# ---- NOTE: # sets up unique values part of data
# ---- NOTE: ## creates object with unique variable values
dataset_funct_object_B <- unique(dataset_funct_object_A[[colnm]])
# ---- NOTE: ### turns object to data frame
dataset_funct_object_B <- data.frame(dataset_funct_object_B)
# ---- NOTE: ### changes colnames
colnames(dataset_funct_object_B) <- c("variable_levels")
# ---- NOTE: ## gives info on whether a given variable_level has a value of NA
dataset_funct_object_B$isNA <- is.na(dataset_funct_object_B$variable_levels)
length(which(dataset_funct_object_B$isNA=="TRUE"))
# ---- NOTE: ## gives length of data frame column
dataset_funct_object_B$variable_level_number <- (as.numeric(as.character(length(dataset_funct_object_B$variable_levels))) - as.numeric(as.character(length(which(dataset_funct_object_B$isNA=="TRUE")))))
# ---- NOTE: ## displays distinct number of levels of variariable of interest, from variable_level_number
as.numeric(distinct(dataset_funct_object_B, variable_level_number))
# ---- NOTE: ## the contrast matrix for categorical variable with a given number of levels
contr.sum(as.numeric(distinct(dataset_funct_object_B, variable_level_number)))
# ---- NOTE: ## creates variable_levels_c variable, which will be used to hold contrast matrix
dataset_funct_object_B$variable_levels_c <- as.factor(as.character(dataset_funct_object_B$variable_levels))
# ---- NOTE: ## inserts the contrast matrix for categorical variable with a given number of levels transfers into appropriate variable
contrasts(dataset_funct_object_B$variable_levels_c) = contr.sum(as.numeric(distinct(dataset_funct_object_B, variable_level_number)))
# ---- NOTE: adds merging column to data frame
dataset_funct_object_B$merging_column <- dataset_funct_object_B$variable_levels
# ---- NOTE: merges original dataset with dataset of interest
dataset_funct_object_C <-
merge(dataset_funct_object_A,
dataset_funct_object_B,
by.x = "merging_column",
by.y = "merging_column",
all.x = TRUE,
all.y = FALSE,
no.dups = TRUE)
# ---- NOTE: # removes merging column from appropriate object
dataset_funct_object_D <-
dataset_funct_object_C %>%
select(
-c(merging_column,
variable_levels,
isNA,
variable_level_number)
)
# ---- NOTE: turns data into data frame
dataset_funct_object_D <- data.frame(dataset_funct_object_D)
# ---- NOTE: ## changes colname
names(dataset_funct_object_D)[names(dataset_funct_object_D) == "variable_levels_c"] <- paste(colnm, "_c", sep = "")
# ---- NOTE: turns data into data frame
dataset_funct_object_D <- data.frame(dataset_funct_object_D)
# ---- NOTE: # returns appropriate object/variable
return(dataset_funct_object_D)
}
-testing
Assuming IV_original is IV (as the column was not found in the OP's input example)
mtcars_short_way_df <-
mapply(function_data_frame__sum_contrast,
(IV_info$IV[IV_info$IV_nature == "nominal"]),
(IV_info$dataset_analyses[IV_info$IV_nature == "nominal"]),
SIMPLIFY = FALSE)
lapply(mtcars_short_way_df, head, 3)
$vs
mpg cyl disp hp drat wt qsec vs am gear carb vs_c
1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 0
2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 0
3 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4 0
$am
mpg cyl disp hp drat wt qsec vs am gear carb am_c
1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0
2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 0
3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 0