Zählen Sie die Anzahl der Male (Frequenz) eine Zeichenfolge auftritt

Question

ich eine Spalte in meiner Datenrahmen haben wie folgt

Col1 
    ---------------------------------------------------------------------------- 
    Center for Animal Control, Division of Hypertension, Department of Medicine 
    Department of Surgery, Division of Primary Care, Center for Animal Control 
    Department of Internal Medicine, Division of Hypertension, Center for Animal Control 

Wie kann ich die Anzahl der Strings zählen kann, die auftreten, dass durch ein Komma getrennt ist, mit anderen Worten was ich versuche zu erreichen, ist etwas wie das unter

Affiliation       Freq 
    ------------------------------------------ 
    Center for Animal Control   3 
    Division of Hypertension   2 
    Department of Medicine    1 
    Department of Surgery    1 
    Division of Primary Care   1 
    Department of Internal Medicine  1 

Könnte jemand mir helfen, dies herauszufinden?

Answer 1

Hier ist ein Ansatz. Ersetzen Sie auch '\n' durch ein Komma, da Sie einige Zeilen in Ihrem Text haben.

df <- data.frame(col1 = rep("Center for Animal Control, Division of Hypertension, Department of Medicine, Department of Surgery, Division of Primary Care, Center for Animal Control, Department of Internal Medicine, Division of Hypertension, Center for Animal Control", 1), stringsAsFactors = FALSE) 
df$col1 <- gsub('\\n', ', ', df$col1) 
as.data.frame(table(unlist(strsplit(df$col1, ', ')))) 

Ausgabe als (auf Originaldaten) folgt:

       Var1 Freq 
1  Center for Animal Control 3 
2 Department of Internal Medicine 1 
3   Department of Medicine 1 
4   Department of Surgery 1 
5  Division of Hypertension 2 
6  Division of Primary Care 1 

Answer 2

Annahme: Center for Animal Control, Division of Hypertension, Department of Medicine ist Wert für Zeile 1, Department of Surgery, Division of Primary Care, Center for Animal Control für Zeile 2 und so weiter.

df ist der Datenrahmen.

aff_val <- trimws(unlist(strsplit(df$col1,","))) 

ans <- data.frame(table(aff_val)) 

colnames(ans)[1] <- 'Affiliation' 

Answer 3

library(stringr) 
a<-"Center for Animal Control, Division of Hypertension, Department of Medicine 
Department of Surgery, Division of Primary Care, Center for Animal Control 
Department of Internal Medicine, Division of Hypertension, Center for Animal Control" 
con<-textConnection(a) 
tbl<-read.table(con,sep=",") 
vec<-str_trim(unlist(tbl)) 
as.data.frame(table(vec)) 

Die Antwort ist

1  Center for Animal Control 3 
2 Department of Internal Medicine 1 
3   Department of Medicine 1 
4   Department of Surgery 1 
5  Division of Hypertension 2 
6  Division of Primary Care 1 

Answer 4

text = "Center for Animal Control, Division of Hypertension, Department of Medicine 
Department of Surgery, Division of Primary Care, Center for Animal Control 
Department of Internal Medicine, Division of Hypertension, Center for Animal Control" 

library(stringi) 
library(dplyr) 
library(tidyr) 

data_frame(text = text) %>% 
    mutate(line = text %>% stri_split_fixed("\n")) %>% 
    unnest(line) %>% 
    mutate(phrase = line %>% stri_split_fixed(", ")) %>% 
    unnest(phrase) %>% 
    count(phrase) 

Answer 5

Ich benutze scan und trimws für diese Textverarbeitungsaufgaben.

inp <- " Center for Animal Control, Division of Hypertension, Department of Medicine 
    Department of Surgery, Division of Primary Care, Center for Animal Control 
    Department of Internal Medicine, Division of Hypertension, Center for Animal Control" 

> table(trimws(scan(text=inp, what="", sep=","))) 
Read 9 items 

     Center for Animal Control Department of Internal Medicine 
           3        1 
     Department of Medicine   Department of Surgery 
           1        1 
     Division of Hypertension  Division of Primary Care 
           2        1 

auch wickeln as.data.frame Kann das Ergebnis um:

> as.data.frame(table( trimws(scan(text=inp, what="", sep=",")))) 
Read 9 items 
          Var1 Freq 
1  Center for Animal Control 3 
2 Department of Internal Medicine 1 
3   Department of Medicine 1 
4   Department of Surgery 1 
5  Division of Hypertension 2 
6  Division of Primary Care 1