創建數據框的子集預測和追加到原始文件

Question

我，使用R 3.3.2。

我想預測機構的得分基於他們在往年的分數不同subrankings。然後，我需要將這些預測分數作爲新行添加到原始數據框中。我的輸入是一個CSV文件

我想用最小二乘線性模型，發現「LM」和「預測」不正是我需要的。

我知道這一個相當初級的問題，但希望有人能幫助我。請參閱下面的數據和代碼，我已經開始使用兩種解決方案。

score<-c(63.6, 60.3, 60.4, 53.4, 46.5, 65.8, 45.8, 65.9, 
44.9, 60, 83.5, 81.7, 81.2, 78.8, 83.3, 79.4, 83.2, 77.3, 
79.4) 

year<-c(2013, 2014, 2015, 2016, 2014, 2014, 2015, 2015, 
2016, 2016, 2011, 2012, 2013, 2014, 2014, 2015, 2015, 
2016, 2016) 

institution<-c(1422, 1422, 1422, 1422, 1384, 1422, 1384, 
1422, 1384, 1422, 1384, 1384, 1384, 1422, 1384, 1422, 
1384, 1422, 1384) 

subranking<-c('CMP', 'CMP', 'CMP', 'CMP', 'SSC', 'SSC', 'SSC', 
'SSC', 'SSC', 'SSC', 'ETC', 'ETC', 'ETC', 'ETC', 'ETC', 'ETC', 
'ETC', 'ETC', 'ETC') 

d <- data.frame(score, year, institution,subranking) 


#-----------SOLUTION 1 ------------------- 

p<- unique(d$institution) 
for (i in (1:length(p))){ 
    x<- d$score[d$institution==p[i]] 
    y<- d$year[d$institution==p[i]] 
    model<- lm(x~y) 
    result<-predict(model, data.frame(y = c(2017,2018,2019,2020))) 
    z<- cbind(result,data.frame(y = c(2017,2018,2019,2020))) 
    print(z) 
} 

##----------SOLUTION 2 ------------------- 

calculate_predicted_scores <- function(scores, years) {predicted_scores <-0 
mod = lm(scores ~ years) 
predicted_scores<-predict(mod, data.frame(years = c(2017,2018,2019,2020))) 
return(predicted_scores) 
} 

爲了說明這一點，這就是我想在最後 - 黃行是預言：

Answer 1

你可以嘗試dplyr用在這個非常有幫助answer描述掃帚

library(dplyr) 
library(broom) 
pred_per_group = d %>% group_by(subranking, institution) %>% 
    do(predicted_scores=predict(lm(score ~ year, data=.), data.frame(year = c(2017,2018,2019, 2020)))) 
pred_df = tidy(pred_per_group, predicted_scores) 

然後，用rbind添加帶有預測結果的數據幀給你。

pred_df <- data.frame(score=pred_df$x, year=rep(c(2017,2018,2019,2020), 5), institution=pred_df$institution, subranking=pred_df$subranking) 
result <- rbind(d, pred_df) 

在8月3日編輯：正如你想得出的編碼我會去了解它，如下所示的自己的追求：

p<- unique(d$institution) 
r <- unique(d$subranking) 
for (i in (1:length(p))){ 
    for(j in seq_along(r)){ 
    score<- d$score[d$institution==p[i] & d$subranking==r[j]] 
    year<- d$year[d$institution==p[i] & d$subranking==r[j]] 
    if(length(score)== 0){ 
    print(sprintf("No level for the following combination: Institution: %s and Subrank: %s", p[i], r[j])) 
    } else{ 
    model<- lm(score~year) 
    result<-predict(model, data.frame(year = c(2017,2018,2019,2020))) 
    z<- cbind(result,data.frame(year = c(2017,2018,2019,2020))) 
    print(sprintf("For Institution: %s and Subrank: %s the Score is:",p[i], r[j])) 
    print(z) 
    } 
    } 
} 

給

[1] "For Institution: 1422 and Subrank: CMP the Score is:" 
    result year 
1 51.80 2017 
2 48.75 2018 
3 45.70 2019 
4 42.65 2020 
[1] "For Institution: 1422 and Subrank: SSC the Score is:" 
    result year 
1 58.1 2017 
2 55.2 2018 
3 52.3 2019 
4 49.4 2020 
[1] "For Institution: 1422 and Subrank: ETC the Score is:" 
    result year 
1 77.00 2017 
2 76.25 2018 
3 75.50 2019 
4 74.75 2020 
[1] "No level for the following combination: Institution: 1384 and Subrank: CMP" 
[1] "For Institution: 1384 and Subrank: SSC the Score is:" 
    result year 
1 44.13333 2017 
2 43.33333 2018 
3 42.53333 2019 
4 41.73333 2020 
[1] "For Institution: 1384 and Subrank: ETC the Score is:" 
    result year 
1 80.66000 2017 
2 80.26286 2018 
3 79.86571 2019 
4 79.46857 2020