[英]Apply Simple Linear Regression to Multiple Data Frames in R
I have a dataset that I split into multiple data frames and need to apply simple linear regression to each of the split-out data frames.我有一个数据集,我将其拆分为多个数据框,并且需要对每个拆分出的数据框应用简单的线性回归。 My code is as follows:我的代码如下:
library(dplyr)
library(readr)
library(magrittr)
library(lubridate)
library(stats)
c_data <- read_csv("D:/projects/sloper_tool/data_2013_to_2017.csv")
C_data_out <-
c_data %>%
group_by(SAMP_SITE_NAME, STD_CON_LONG_NAME, FILTERED_FLAG) %>%
mutate(MED_V = median(STD_VALUE_RPTD)) %>%
mutate(MIN_V = min(STD_VALUE_RPTD)) %>%
mutate(MAX_V = max(STD_VALUE_RPTD)) %>%
ungroup() %>%
select(SAMP_SITE_NAME, STD_CON_LONG_NAME, SAMP_DATE, STD_VALUE_RPTD, STD_ANAL_UNITS_RPTD, FILTERED_FLAG, LAB_QUALIFIER, MED_V, MIN_V, MAX_V) %>%
rename(Well = SAMP_SITE_NAME, Constit = STD_CON_LONG_NAME, Date = SAMP_DATE, Value = STD_VALUE_RPTD, Unit = STD_ANAL_UNITS_RPTD, Filtered = FILTERED_FLAG, Flag = LAB_QUALIFIER, Median = MED_V, Min = MIN_V, Max = MAX_V) %>%
mutate(Date = mdy(Date))
dfs <- split(C_data_out, with(C_data_out, interaction(Well, Constit, Filtered)), drop = TRUE)
dfs[2]
This splits out data frames from original inputs that look like the following:这从原始输入中分离出数据帧,如下所示:
$`299-E13-14.Gross alpha.N`
# A tibble: 4 x 10
Well Constit Date Value Unit Filtered Flag Median Min Max
<chr> <chr> <date> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 299-E13-14 Gross alpha 2014-04-11 3.40 pCi/L N <NA> 2.745 1.86 3.89
2 299-E13-14 Gross alpha 2015-04-08 2.09 pCi/L N <NA> 2.745 1.86 3.89
3 299-E13-14 Gross alpha 2016-04-25 3.89 pCi/L N <NA> 2.745 1.86 3.89
4 299-E13-14 Gross alpha 2017-04-06 1.86 pCi/L N <NA> 2.745 1.86 3.89
Next I need to apply a simple linear regression model to each of the split out data frames.接下来,我需要将一个简单的线性回归模型应用于每个拆分出的数据框。 I tried using various permutations of the following to no avail.我尝试使用以下各种排列无济于事。
fit <-
dfs %>%
lm(Value ~ Date)
# Get slope by:
slope <- fit$coefficients[[2]]
slope
The output from this gives:这个输出给出:
fit <-
dfs %>%
lm(Value ~ Date, data = dfs)
Error in formula.default(object, env = baseenv()) : invalid formula
slope = fit$coefficients[[2]]
Error: object 'fit' not found
slope
(Intercept) Date
109778.966473 -5.093003
This appears to be being applied to the entire original dataset and not to the individual split out data frames.这似乎应用于整个原始数据集,而不是应用于单个拆分的数据框。 I would like to output the slopes of the individual data frames to a file or better yet have the slopes appended as a vector to the data frames in dfs.我想将单个数据帧的斜率输出到一个文件,或者更好的是将斜率作为向量附加到 dfs 中的数据帧。
Any and all help would be greatly appreciated!任何和所有的帮助将不胜感激!
Something like this might work.像这样的事情可能会奏效。 I don't have your data though, so can't test.我没有你的数据,所以无法测试。
# calculate the fit models per data frame
fits <- lapply( dfs, function(x) {
lm( formula = Value ~ Date, data = x )
} )
# extract the slope from all models
slopes <- sapply( fits, function(x) x$coefficients )
# print one of the results to see it
slopes[1]
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