Wie konvertiert man meine Daten in R

Question

Ich habe eine Daten, die das Format wie folgt hat, um die Daten visualisieren zu können, die ich brauche, um die Formatierung zu etwas wie das zweite Beispiel zu ändern, wissen Sie, wie ich ändern kann die Formatierung so?

The first row is Age range 
0–14 15–24 25–34 35–44 45–54 55–64 65 years and over 
1,873.4 1,088.4 1,296.4 1,157.2 1,207.5 1,177.5 1,498.7 
513.0 351.8 339.1 419.1 485.0 624.1 925.7 
1,049.9 666.4 594.2 682.9 645.7 650.2 727.1 
422.6 287.7 354.1 344.9 400.6 411.5 528.3 
2,069.1 1,234.7 1,429.0 1,310.3 1,323.1 1,229.6 1,514.9 
178.0 306.8 253.8 248.9 178.5 75.2 42.1  
2,247.2 1,541.5 1,682.9 1,559.2 1,501.5 1,304.8 1,557.0 

Wie kann ich meine Daten so aussehen konvertieren:

Age  Count 
0-14 1,873.4 
15-24 1,088.4 
25-34 1,296.4 
35-44 1,157.2 
45-54 1,207.5 
55-64 1,177.5 
65+  1,498.7 
0-14 513.0 
15-24 351.8 
25-34 339.1 
35-44 419.1 
45-54 485.0 
55-64 624.1 
65+  925.7 
0-14 1,049.0 
15-24 666.4 
25-34 594.2 
35-44 682.9 
45-54 645.7 
55-64 650.2 
65+  727.1 
0-14 422.6 
15-24 287.7 
25-34 354.1 
35-44 344.9 
45-54 400.6 
55-64 411.5 
65+  528.3 

Answer 1

Wenn Ihre Tabelle tab benannt ist, ich tun würde:

# Load library 
    library(reshape) 

# Use melt() function 
    melt.tab <- melt(tab) 

Was Sie suchen das ist zweite und dritte Reihe von melt.tab; das heißt:

melt.tab[,-1] 

Answer 2

Ich würde alos reshape2::melt aber anders verwenden. Zuerst die Daten im Format dput.

Tamra <- 
structure(list(`0–14` = c("1,873.4", "513.0", "1,049.9", "422.6", 
"2,069.1", "178.0", "2,247.2"), `15–24` = c("1,088.4", "351.8", 
"666.4", "287.7", "1,234.7", "306.8", "1,541.5"), `25–34` = c("1,296.4", 
"339.1", "594.2", "354.1", "1,429.0", "253.8", "1,682.9"), `35–44` = c("1,157.2", 
"419.1", "682.9", "344.9", "1,310.3", "248.9", "1,559.2"), `45–54` = c("1,207.5", 
"485.0", "645.7", "400.6", "1,323.1", "178.5", "1,501.5"), `55–64` = c("1,177.5", 
"624.1", "650.2", "411.5", "1,229.6", "75.2", "1,304.8"), `65+` = c("1,498.7", 
"925.7", "727.1", "528.3", "1,514.9", "42.1", "1,557.0")), .Names = c("0–14", 
"15–24", "25–34", "35–44", "45–54", "55–64", "65+"), class = "data.frame", row.names = c(NA, 
-7L)) 

Jetzt der Code.

molten <- reshape2::melt(Tamra, measure.vars = names(Tamra), 
         variable.name = "Age", value.name = "Count") 
#head(molten) 
# Age Count 
#1 0–14 1,873.4 
#2 0–14 513.0 
#3 0–14 1,049.9 
#4 0–14 422.6 
#5 0–14 2,069.1 
#6 0–14 178.0 

Answer 3

Lösung mit dplyr + tidyr:

library(dplyr) 
library(tidyr) 
df %>% 
    gather(AgeRange, count) %>% 
    mutate(count = as.numeric(gsub(",", "", count))) %>% 
    arrange(rep(1:nrow(df), ncol(df))) 

Ergebnis:

AgeRange count 
1  0–14 1873.4 
2  15–24 1088.4 
3  25–34 1296.4 
4  35–44 1157.2 
5  45–54 1207.5 
6  55–64 1177.5 
7  65+ 1498.7 
8  0–14 513.0 
9  15–24 351.8 
10 25–34 339.1 
11 35–44 419.1 
12 45–54 485.0 
13 55–64 624.1 
14  65+ 925.7 
15  0–14 1049.9 
16 15–24 666.4 
17 25–34 594.2 
18 35–44 682.9 
19 45–54 645.7 
20 55–64 650.2 
... 

Hinweis:

Ich habe auch hinzugefügt mutate Schritt die counts Spalte numeric

Daten zu konvertieren:

df = structure(list(`0–14` = c("1,873.4", "513.0", "1,049.9", "422.6", 
"2,069.1", "178.0", "2,247.2"), `15–24` = c("1,088.4", "351.8", 
"666.4", "287.7", "1,234.7", "306.8", "1,541.5"), `25–34` = c("1,296.4", 
"339.1", "594.2", "354.1", "1,429.0", "253.8", "1,682.9"), `35–44` = c("1,157.2", 
"419.1", "682.9", "344.9", "1,310.3", "248.9", "1,559.2"), `45–54` = c("1,207.5", 
"485.0", "645.7", "400.6", "1,323.1", "178.5", "1,501.5"), `55–64` = c("1,177.5", 
"624.1", "650.2", "411.5", "1,229.6", "75.2", "1,304.8"), `65+` = c("1,498.7", 
"925.7", "727.1", "528.3", "1,514.9", "42.1", "1,557.0")), .Names = c("0–14", 
"15–24", "25–34", "35–44", "45–54", "55–64", "65+"), class = "data.frame", row.names = c(NA, 
-7L))