gruppenweise Zusammenfassungen/Subsets dplyr

Question

Ich habe ein Datensatz von zwei Kurse in 2 verschiedenen Semestern, die die folgende Form annimmt:

set.seed(200) 
sem <- sample(c("1", "2"), 200, replace = T) 
course <- sample(c("1", "2"), 200, replace = T) 
d.gender = sample(c(0, 1), 200, replace = T, prob = c(0.6, 0.4)) 
d.pass = sample(c(0, 1), 200, replace = T, prob = c(0.7, 0.3)) 
df <- data.frame(sem, course, d.gender, d.pass) 

Ich bin zu versuchen effizient eine Tabl der 4 verschiedenen erstellen sem, Kurs Kombinationen zusammen mit ihrer gesamten Erfolgsquote, der Prozentsatz von d.gender = 1, und schließlich die Erfolgsquoten innerhalb dieser beiden Geschlechterkategorien. Ich kann eine Tabelle erstellen, die alle Werte enthält, die ich berechnen muss, aber ich weiß, dass es einen effizienteren Weg gibt, um zu berechnen, was ich brauche, ohne eine Menge verschiedener group_by- und summary-Funktionen zu verschachteln oder eine ganze Reihe von verschiedenen tbls zu machen Spalten, die ich will. Ich kann das bekommen, was ich mit Indexen und Subset-Funktionen loswerden muss, aber ich hoffe, dass es eine bessere Möglichkeit gibt, eine vierreihige Matrix mit allem zu bekommen, was ich brauche, aber es ist hässlich und dauert ewig, und es ist leicht, Fehler zu machen der Code:

df1 <- df %>% group_by(sem, course, d.gender, d.pass) %>% summarize(total = n()) 
df1$total_pass <- rep(NA, dim(df1)[1]) 
df1$total_pass[1:4] <- sum(subset(df1, sem == "1" & course == "1" & d.pass == "1", 
    select = total)) 
df1$total_pass[5:8] <- sum(subset(df1, sem == "1" & course == "2" & d.pass == "1", 
    select = total)) 
df1$total_pass[9:12] <- sum(subset(df1, sem == "2" & course == "1" & d.pass == "1", 
    select = total)) 
df1$total_pass[13:16] <- sum(subset(df1, sem == "2" & course == "2" & d.pass == "1", 
    select = total)) 

df1$n_male <- rep(NA, dim(df1)[1]) 
df1$n_male[1:4] <- sum(subset(df1, sem == "1" & course == "1" & d.gender == "1", 
    select = total)) 
df1$n_male[5:8] <- sum(subset(df1, sem == "1" & course == "2" & d.gender == "1", 
    select = total)) 
df1$n_male[9:12] <- sum(subset(df1, sem == "2" & course == "1" & d.gender == "1", 
    select = total)) 
df1$n_male[13:16] <- sum(subset(df1, sem == "2" & course == "2" & d.gender == "1", 
    select = total)) 

df1$n_fem <- rep(NA, dim(df1)[1]) 
df1$n_fem[1:4] <- sum(subset(df1, sem == "1" & course == "1" & d.gender == "0", select = total)) 
df1$n_fem[5:8] <- sum(subset(df1, sem == "1" & course == "2" & d.gender == "0", select = total)) 
df1$n_fem[9:12] <- sum(subset(df1, sem == "2" & course == "1" & d.gender == "0", 
    select = total)) 
df1$n_fem[13:16] <- sum(subset(df1, sem == "2" & course == "2" & d.gender == "0", 
    select = total)) 

df1$pct_male <- rep(NA, dim(df1)[1]) 
df1$pct_male[1:4] <- df1$n_male[1:4]/sum(subset(df1, sem == "1" & course == "1", 
    select = total)) 
df1$pct_male[5:8] <- df1$n_male[5:8]/sum(subset(df1, sem == "1" & course == "2", 
    select = total)) 
df1$pct_male[9:12] <- df1$n_male[9:12]/sum(subset(df1, sem == "2" & course == "1", 
    select = total)) 
df1$pct_male[13:16] <- df1$n_male[13:16]/sum(subset(df1, sem == "2" & course == "2", 
    select = total)) 

df1$pct_fem <- rep(NA, dim(df1)[1]) 
df1$pct_fem <- 1 - df1$pct_male 

df1$pct_pass <- rep(NA, dim(df1)[1]) 
df1$pct_pass[1:4] <- df1$total_pass[1:4]/sum(subset(df1, sem == "1" & course == "1", 
    select = total)) 
df1$pct_pass[5:8] <- df1$total_pass[5:8]/sum(subset(df1, sem == "1" & course == "2", 
    select = total)) 
df1$pct_pass[9:12] <- df1$total_pass[9:12]/sum(subset(df1, sem == "2" & course == 
    "1", select = total)) 
df1$pct_pass[13:16] <- df1$total_pass[13:16]/sum(subset(df1, sem == "2" & course == 
    "2", select = total)) 

df1$male_pass_pct <- rep(NA, dim(df1)[1]) 
df1$male_pass_pct[1:4] <- subset(df1, sem == "1" & course == "1" & d.gender == "1" & 
    d.pass == "1", select = total)/df1$n_male[1:4] 
df1$male_pass_pct[5:8] <- subset(df1, sem == "1" & course == "2" & d.gender == "1" & 
    d.pass == "1", select = total)/df1$n_male[5:8] 
df1$male_pass_pct[9:12] <- subset(df1, sem == "2" & course == "1" & d.gender == "1" & 
    d.pass == "1", select = total)/df1$n_male[9:12] 
df1$male_pass_pct[13:16] <- subset(df1, sem == "2" & course == "2" & d.gender == 
    "1" & d.pass == "1", select = total)/df1$n_male[13:16] 

df1$fem_pass_pct <- rep(NA, dim(df1)[1]) 
df1$fem_pass_pct[1:4] <- subset(df1, sem == "1" & course == "1" & d.gender == "0" & 
    d.pass == "1", select = total)/df1$n_fem[1:4] 
df1$fem_pass_pct[5:8] <- subset(df1, sem == "1" & course == "2" & d.gender == "0" & 
    d.pass == "1", select = total)/df1$n_fem[5:8] 
df1$fem_pass_pct[9:12] <- subset(df1, sem == "2" & course == "1" & d.gender == "0" & 
    d.pass == "1", select = total)/df1$n_fem[9:12] 
df1$fem_pass_pct[13:16] <- subset(df1, sem == "2" & course == "2" & d.gender == "0" & 
    d.pass == "1", select = total)/df1$n_fem[13:16] 


df2 <- df1 %>% 
    group_by(sem, course) %>% 
    summarize(total_pass = first(total_pass), 
       pct_pass = first(pct_pass), 
       n_male = first(n_male), 
       n_fem = first(n_fem), 
       pct_male = first(pct_male), 
       pct_fem = first(pct_fem), 
       male_pass_pct = first(male_pass_pct), 
       fem_pass_pct = first(fem_pass_pct)) 

df2 <- unique(df1[, c(1, 2, 6, 11, 7:10, 12, 13)]) 
df2[, c(9, 10)] <- lapply(df2[, c(9, 10)], as.numeric) 

, die nur um Maßnahmen für 4 Reihen wirklich mühsam, aber ich kann es nicht anders für diese Aggregation zu arbeiten ... Jede Hilfe wäre

Answer 1

Gerade Gruppe und dann genial summarise das Original. Sie können n() verwenden, um die Anzahl der Zeilen in einer Gruppe zu verweisen, und Variablen verweisen können, die in summarise zuvor erstellt wurden, die Sie

df %>% group_by(sem, course) %>% 
    summarise(total_pass = sum(d.pass), 
       n_male = sum(d.gender), 
       n_fem = sum(d.gender == 0), 
       pct_male = n_male/n(), 
       pct_fem = n_fem/n(), 
       pct_pass = total_pass/n(), 
       male_pass_pct = sum(d.gender & d.pass)/n_male, 
       fem_pass_pct = sum(d.gender == 0 & d.pass)/n_fem) 

## Source: local data frame [4 x 10] 
## Groups: sem [?] 
## 
##  sem course total_pass n_male n_fem pct_male pct_fem pct_pass male_pass_pct fem_pass_pct 
## <fctr> <fctr>  <dbl> <dbl> <int>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl> 
## 1  1  1   14  20 30 0.4000000 0.6000000 0.2800000 0.25000000 0.3000000 
## 2  1  2   7  19 26 0.4222222 0.5777778 0.1555556 0.05263158 0.2307692 
## 3  2  1   12  23 23 0.5000000 0.5000000 0.2608696 0.30434783 0.2173913 
## 4  2  2   16  25 34 0.4237288 0.5762712 0.2711864 0.20000000 0.3235294 

umformen Ihre Daten tun können Geschlechts aus Spaltenüberschriften zu einer tatsächlichen Variable bewegen macht Ihre Daten aufgeräumt und erfordert weniger Operationen, wenn Sie möchten.