是否可以将 lapply() （或其他命令）与 r 中的多个列表对象输入一起使用？
Is it possible to use lapply() (or some other command) with multiple list object inputs in r?
我想将 lapply() 用于多个列表输入。
具体来说，我想 运行 lm() 测试不同的 IV、DV 和数据集。
我从 ggplot2::diamonds 数据集创建了 2 个数据集，分别称为 diamonds_top300 和 diamonds_bottom300。我想对这些数据集进行 运行 lm() 测试，其中 IV 为 x 或 y，DV 为 price 或 carat。
我可以做很长的路要走，使用下面的代码：
## long way
# ---- NOTE: works
### DV is price, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_top300)
### DV is price, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_top300)
### DV is carat, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_top300)
### DV is carat, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_top300)
### DV is price, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_bottom300)
### DV is price, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_bottom300)
### DV is carat, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_bottom300)
### DV is carat, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_bottom300)
当我尝试使用下面的 lapply() 代码以较短的方式进行时，但这没有用。我想使用 3 个列表输入（即，一个用于使用的数据集，一个用于使用的 DV，一个用于使用的 IV）来做到这一点。
## short way, using lapply()
# ---- NOTE: does not work
### creates list object
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
lapply(
# ---- NOTE: tells dataset used in analysis
DV_info$dataset_analyses,
# ---- NOTE: tells DV used in analysis
DV_info$DV_original,
# ---- NOTE: tells IV used in analysis
IV_info$IV_original,
function(
# ---- NOTE: name of function object input for dataset used in lapply() object
dataset_list,
# ---- NOTE: name of function object input for DV used in lapply() object
DV_list,
# ---- NOTE: name of function object input for IV used in lapply() object
IV_list
) {
# ---- NOTE: creates _funct_object versions of function() inputs
IV_funct_object <-
IV_list
DV_funct_object <-
DV_list
dataset_funct_object <-
dataset_list
# ---- NOTE: creates
lm_funct_object <-
lm(DV_funct_object ~ IV_funct_object, data = dataset_funct_object)
# ---- NOTE: returns object
return(lm_funct_object)
}
)
### changes list object name
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
setNames(
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX,
paste(
"list_derived_object_",
"lm",
"_dataset_is",
DV_info$dataset_analyses,
"_DV_is",
DV_info$DV_original,
"_IV_is",
IV_info$IV_original,
sep = "_"
)
)
如果可能的话，我想使用 lapply() 来执行此操作，因为我可以修改与 lapply() 关联的代码，但我愿意接受其他选项（例如，使用mapply() 带有预制的用户生成函数）。
非常感谢任何建议。
仅供参考，我使用的是 2013 Macbook Pro，配备 2.4 GHz 双核英特尔芯片、8 GB 内存、macOS big sur 11.2.2、RStudio 版本 1.4.1106 和 R 基础包 4.04。
谢谢。
我使用的R脚本如下：
#### lapply() with multiple objects ####
# Loads packages
# ---- NOTE: making plots and diamonds dataset
if(!require(ggplot2)){install.packages("ggplot2")}
# ---- NOTE: for data wrangling
if(!require(dplyr)){install.packages("dplyr")}
# dataset creation
## for dataset with top 300 rows
# ---- NOTE: selects only the top 300 rows of the dataset
diamonds_top300 <- data.frame(dplyr::top_n(diamonds, 300, table))
# ---- NOTE: gives dataset info
head(diamonds_top300)
str(diamonds_top300)
colnames(diamonds_top300)
nrow(diamonds_top300)
# ---- NOTE: gives unique values of Fixed and Random effects, and dvs
unique(diamonds_top300$price)
unique(diamonds_top300$y)
unique(diamonds_top300$cut)
unique(diamonds_top300$color)
unique(diamonds_top300$carat)
unique(diamonds_top300$clarity)
unique(diamonds_top300$depth)
unique(diamonds_top300$table)
## for dataset with bottom 300 rows
### dataset
# ---- NOTE: selects only the bottom 300 rows of the dataset
diamonds_bottom300 <- data.frame(dplyr::top_n(diamonds, -300, table))
# ---- NOTE: gives dataset info
head(diamonds_bottom300)
str(diamonds_bottom300)
colnames(diamonds_bottom300)
nrow(diamonds_bottom300)
# ---- NOTE: gives unique values of Fixed and Random effects, and dvs
unique(diamonds_bottom300$price)
unique(diamonds_bottom300$y)
unique(diamonds_bottom300$cut)
unique(diamonds_bottom300$color)
unique(diamonds_bottom300$carat)
unique(diamonds_bottom300$clarity)
unique(diamonds_bottom300$depth)
unique(diamonds_bottom300$table)
## creates DV_info object
# ---- NOTE: length of data needs to match up with the maximum number of tests being done (i.e., 8 tests total means this list based data frame needs to be 8 rows in length)
DV_info <-
data.frame(
DV_original = c("price", "carat", "price", "carat", "price", "carat", "price", "carat"),
dataset_analyses = c("diamonds_top300", "diamonds_top300", "diamonds_top300", "diamonds_top300", "diamonds_bottom300", "diamonds_bottom300", "diamonds_bottom300")
)
## creates IV_info object
# ---- NOTE: length of data needs to match up with the maximum number of tests being done (i.e., 8 tests total means this list based data frame needs to be 8 rows in length)
IV_info <-
data.frame(
IV_original = c("x", "y", "x", "y", "x", "y", "x", "y")
)
# creating lm() objects
## long way
# ---- NOTE: works
### DV is price, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_top300)
### DV is price, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_top300)
### DV is carat, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_top300)
### DV is carat, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_top300)
### DV is price, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_bottom300)
### DV is price, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_bottom300)
### DV is carat, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_bottom300)
### DV is carat, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_bottom300)
### lists created file(s)
# ---- NOTE: list of object, out right
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y
# ---- NOTE: apropos() command list
apropos("lm__dataset_is_")
## short way, using lapply()
# ---- NOTE: does not work
### creates list object
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
lapply(
# ---- NOTE: tells dataset used in analysis
DV_info$dataset_analyses,
# ---- NOTE: tells DV used in analysis
DV_info$DV_original,
# ---- NOTE: tells IV used in analysis
IV_info$IV_original,
function(
# ---- NOTE: name of function object input for dataset used in lapply() object
dataset_list,
# ---- NOTE: name of function object input for DV used in lapply() object
DV_list,
# ---- NOTE: name of function object input for IV used in lapply() object
IV_list
) {
# ---- NOTE: creates _funct_object versions of function() inputs
IV_funct_object <-
IV_list
DV_funct_object <-
DV_list
dataset_funct_object <-
dataset_list
# ---- NOTE: creates
lm_funct_object <-
lm(DV_funct_object ~ IV_funct_object, data = dataset_funct_object)
# ---- NOTE: returns object
return(lm_funct_object)
}
)
### changes list object name
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
setNames(
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX,
paste(
"list_derived_object_",
"lm",
"_dataset_is",
DV_info$dataset_analyses,
"_DV_is",
DV_info$DV_original,
"_IV_is",
IV_info$IV_original,
sep = "_"
)
)
你可以利用Map-
Map(function(x, y, z) lm(reformulate(x, y), data = z),
IV_info$IV_original, DV_info$DV_original, mget(DV_info$dataset_analyses))
考虑使用 expand.grid 构建输入的所有组合，然后使用 reformulate 调用 Map 以从字符串动态构建公式，并调用 get 以按字符名称检索对象.
run_model <- function(dv, iv, data)
lm(reformulate(iv, dv), data=get(data))
inputs <- expand.grid(
dv = c("price", "carat"),
iv = c("x", "y"),
data = c("diamonds_bottom300", "diamonds_top300")
)
lm_results <- Map(run_model, inputs$dv, inputs$iv, inputs$data)
# EQUIVALENTLY:
lm_results <- mapply(run_model, inputs$dv, inputs$iv, inputs$data, SIMPLIFY = FALSE)
我想将 lapply() 用于多个列表输入。
具体来说，我想 运行 lm() 测试不同的 IV、DV 和数据集。
我从 ggplot2::diamonds 数据集创建了 2 个数据集，分别称为 diamonds_top300 和 diamonds_bottom300。我想对这些数据集进行 运行 lm() 测试，其中 IV 为 x 或 y，DV 为 price 或 carat。
我可以做很长的路要走，使用下面的代码：
## long way
# ---- NOTE: works
### DV is price, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_top300)
### DV is price, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_top300)
### DV is carat, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_top300)
### DV is carat, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_top300)
### DV is price, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_bottom300)
### DV is price, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_bottom300)
### DV is carat, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_bottom300)
### DV is carat, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_bottom300)
当我尝试使用下面的 lapply() 代码以较短的方式进行时，但这没有用。我想使用 3 个列表输入（即，一个用于使用的数据集，一个用于使用的 DV，一个用于使用的 IV）来做到这一点。
## short way, using lapply()
# ---- NOTE: does not work
### creates list object
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
lapply(
# ---- NOTE: tells dataset used in analysis
DV_info$dataset_analyses,
# ---- NOTE: tells DV used in analysis
DV_info$DV_original,
# ---- NOTE: tells IV used in analysis
IV_info$IV_original,
function(
# ---- NOTE: name of function object input for dataset used in lapply() object
dataset_list,
# ---- NOTE: name of function object input for DV used in lapply() object
DV_list,
# ---- NOTE: name of function object input for IV used in lapply() object
IV_list
) {
# ---- NOTE: creates _funct_object versions of function() inputs
IV_funct_object <-
IV_list
DV_funct_object <-
DV_list
dataset_funct_object <-
dataset_list
# ---- NOTE: creates
lm_funct_object <-
lm(DV_funct_object ~ IV_funct_object, data = dataset_funct_object)
# ---- NOTE: returns object
return(lm_funct_object)
}
)
### changes list object name
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
setNames(
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX,
paste(
"list_derived_object_",
"lm",
"_dataset_is",
DV_info$dataset_analyses,
"_DV_is",
DV_info$DV_original,
"_IV_is",
IV_info$IV_original,
sep = "_"
)
)
如果可能的话，我想使用 lapply() 来执行此操作，因为我可以修改与 lapply() 关联的代码，但我愿意接受其他选项（例如，使用mapply() 带有预制的用户生成函数）。
非常感谢任何建议。
仅供参考，我使用的是 2013 Macbook Pro，配备 2.4 GHz 双核英特尔芯片、8 GB 内存、macOS big sur 11.2.2、RStudio 版本 1.4.1106 和 R 基础包 4.04。
谢谢。
我使用的R脚本如下：
#### lapply() with multiple objects ####
# Loads packages
# ---- NOTE: making plots and diamonds dataset
if(!require(ggplot2)){install.packages("ggplot2")}
# ---- NOTE: for data wrangling
if(!require(dplyr)){install.packages("dplyr")}
# dataset creation
## for dataset with top 300 rows
# ---- NOTE: selects only the top 300 rows of the dataset
diamonds_top300 <- data.frame(dplyr::top_n(diamonds, 300, table))
# ---- NOTE: gives dataset info
head(diamonds_top300)
str(diamonds_top300)
colnames(diamonds_top300)
nrow(diamonds_top300)
# ---- NOTE: gives unique values of Fixed and Random effects, and dvs
unique(diamonds_top300$price)
unique(diamonds_top300$y)
unique(diamonds_top300$cut)
unique(diamonds_top300$color)
unique(diamonds_top300$carat)
unique(diamonds_top300$clarity)
unique(diamonds_top300$depth)
unique(diamonds_top300$table)
## for dataset with bottom 300 rows
### dataset
# ---- NOTE: selects only the bottom 300 rows of the dataset
diamonds_bottom300 <- data.frame(dplyr::top_n(diamonds, -300, table))
# ---- NOTE: gives dataset info
head(diamonds_bottom300)
str(diamonds_bottom300)
colnames(diamonds_bottom300)
nrow(diamonds_bottom300)
# ---- NOTE: gives unique values of Fixed and Random effects, and dvs
unique(diamonds_bottom300$price)
unique(diamonds_bottom300$y)
unique(diamonds_bottom300$cut)
unique(diamonds_bottom300$color)
unique(diamonds_bottom300$carat)
unique(diamonds_bottom300$clarity)
unique(diamonds_bottom300$depth)
unique(diamonds_bottom300$table)
## creates DV_info object
# ---- NOTE: length of data needs to match up with the maximum number of tests being done (i.e., 8 tests total means this list based data frame needs to be 8 rows in length)
DV_info <-
data.frame(
DV_original = c("price", "carat", "price", "carat", "price", "carat", "price", "carat"),
dataset_analyses = c("diamonds_top300", "diamonds_top300", "diamonds_top300", "diamonds_top300", "diamonds_bottom300", "diamonds_bottom300", "diamonds_bottom300")
)
## creates IV_info object
# ---- NOTE: length of data needs to match up with the maximum number of tests being done (i.e., 8 tests total means this list based data frame needs to be 8 rows in length)
IV_info <-
data.frame(
IV_original = c("x", "y", "x", "y", "x", "y", "x", "y")
)
# creating lm() objects
## long way
# ---- NOTE: works
### DV is price, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_top300)
### DV is price, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_top300)
### DV is carat, IV is x, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_top300)
### DV is carat, IV is y, and dataset is diamonds_top300
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_top300)
### DV is price, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x <-
lm(price ~ x, data = diamonds_bottom300)
### DV is price, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y <-
lm(price ~ y, data = diamonds_bottom300)
### DV is carat, IV is x, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x <-
lm(carat ~ x, data = diamonds_bottom300)
### DV is carat, IV is y, and dataset is diamonds_bottom300
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y <-
lm(carat ~ y, data = diamonds_bottom300)
### lists created file(s)
# ---- NOTE: list of object, out right
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_x
lm__dataset_is_diamonds_top300__DV_is_price__IV_is_y
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_x
lm__dataset_is_diamonds_top300__DV_is_carat__IV_is_y
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_x
lm__dataset_is_diamonds_bottom300__DV_is_price__IV_is_y
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_x
lm__dataset_is_diamonds_bottom300__DV_is_carat__IV_is_y
# ---- NOTE: apropos() command list
apropos("lm__dataset_is_")
## short way, using lapply()
# ---- NOTE: does not work
### creates list object
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
lapply(
# ---- NOTE: tells dataset used in analysis
DV_info$dataset_analyses,
# ---- NOTE: tells DV used in analysis
DV_info$DV_original,
# ---- NOTE: tells IV used in analysis
IV_info$IV_original,
function(
# ---- NOTE: name of function object input for dataset used in lapply() object
dataset_list,
# ---- NOTE: name of function object input for DV used in lapply() object
DV_list,
# ---- NOTE: name of function object input for IV used in lapply() object
IV_list
) {
# ---- NOTE: creates _funct_object versions of function() inputs
IV_funct_object <-
IV_list
DV_funct_object <-
DV_list
dataset_funct_object <-
dataset_list
# ---- NOTE: creates
lm_funct_object <-
lm(DV_funct_object ~ IV_funct_object, data = dataset_funct_object)
# ---- NOTE: returns object
return(lm_funct_object)
}
)
### changes list object name
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX <-
setNames(
lm__dataset_is_XXXX__DV_is_XXXX__IV_is_XXXX,
paste(
"list_derived_object_",
"lm",
"_dataset_is",
DV_info$dataset_analyses,
"_DV_is",
DV_info$DV_original,
"_IV_is",
IV_info$IV_original,
sep = "_"
)
)
你可以利用Map-
Map(function(x, y, z) lm(reformulate(x, y), data = z),
IV_info$IV_original, DV_info$DV_original, mget(DV_info$dataset_analyses))
考虑使用 expand.grid 构建输入的所有组合，然后使用 reformulate 调用 Map 以从字符串动态构建公式，并调用 get 以按字符名称检索对象.
run_model <- function(dv, iv, data)
lm(reformulate(iv, dv), data=get(data))
inputs <- expand.grid(
dv = c("price", "carat"),
iv = c("x", "y"),
data = c("diamonds_bottom300", "diamonds_top300")
)
lm_results <- Map(run_model, inputs$dv, inputs$iv, inputs$data)
# EQUIVALENTLY:
lm_results <- mapply(run_model, inputs$dv, inputs$iv, inputs$data, SIMPLIFY = FALSE)