library(purrr)

finalResult = purrr::map_df(unique(LE_GDP_C$year), function(modelYear){

myModel = lm(sqrt(LE) ~log_gdp, data = LE_GDP_C %>% filter(year == modelYear) %>% select(-year))

data.frame(year = modelYear, RSquared = summary(myModel)$r.squared)

}

)

We need same sample data and more of your code. Please look at

FAQ: How to do a minimal reproducible example ( reprex ) for beginners for some suggestions.

```
require(readxl)
require(dplyr)
require(tidyverse)
require(ggplot2)
getwd()
LE_A = read_xls("LE.xls")
LE_A = as.data.frame(LE_A)
#Selecting the columns necessary for Linear Regression evaluation
LE_GDP = select(LE_A, LE, GDP, Inc_resources, Year, Country, Status, Population)
LE_GDP_C = na.omit(LE_GDP)# Omit the N/A values
LE_GDP_C$log_gdp = log(LE_GDP_C$GDP) # added new variable log_gdp instead of mutating GDP
str(LE_GDP_C)
datapasts::df_paste(head(LE_GDP_C,6))[,c('LE' , 'log_gdp' , 'Year')]
# I want to do linear regression over years
finalResult = purrr::map_df(unique(LE_GDP_C$year), function(modelYear){
myModel = lm(sqrt(LE) ~log_gdp, data = LE_GDP_C %>% filter(year == modelYear) %>% select(-year))
data.frame(year = modelYear, RSquared = summary(myModel)$r.squared)
}
)
```

I don't know how to upload the sample data

That link should suggest some ways. However, a very simple and very effective way to supply some data is to use the dput() command.

```
dput(mydata)
```

where mydata is the name of your dataframe or tibble,

and then simply copy the output and paste it here.

If you have a very large data set then a sample should be fine. To supply us with 100 rows of your data set do

```
dput(head(mydata , 100))
```
copy and paste.
```

system
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