基于另一列的每个因子水平的比例数据框

Question

I would like to summarize a data frame by month where each column is the proportion of each factor level based on the Records column in the data frame below. I have been attempting to use dplyr but haven't quite figured it out.

library(dplyr)
set.seed(100)
df=data.frame(Month=rep(c("1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017",

                          "8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017"),10),

              Records=round(runif(120,6000,10000),0),

              V1=as.factor(sample(c("T","F"),120,replace=TRUE)),

              V2=as.factor(sample(c("A","B","C"),120,replace=TRUE)),

              V3=as.factor(sample(c("X","Y","Z","W"),120,replace=TRUE)),

              V4=as.factor(sample(c("YES","NO","Maybe"),120,replace=TRUE)))

Here is what I would like the output to be

> dput((resultsdf))
structure(list(Month = c("1/1/2017", "2/1/2017", "3/1/2017", 
"4/1/2017", "5/1/2017", "6/1/2017", "7/1/2017", "8/1/2017", "9/1/2017", 
"10/1/2017", "11/1/2017", "12/1/2017"), V1.F = c(0.4, 0.71, 0.63, 
0.35, 0.37, 0.41, 0.37, 0.61, 0.29, 0.5, 0.38, 0.82), V2.T = c(0.6, 
0.29, 0.37, 0.65, 0.63, 0.59, 0.63, 0.39, 0.71, 0.5, 0.62, 0.18
), V2.A = c(0.2, 0.28, 0.3, 0.31, 0.29, 0.3, 0.32, 0.45, 0.1, 
0.41, 0.3, 0.11), V2.B = c(0.59, 0.33, 0.19, 0.5, 0.51, 0.19, 
0.59, 0.22, 0.77, 0.2, 0.41, 0.16), V2.C = c(0.22, 0.38, 0.51, 
0.19, 0.21, 0.51, 0.09, 0.32, 0.12, 0.39, 0.29, 0.73), V3.W = c(0.42, 
0.11, 0, 0.21, 0.23, 0.3, 0.12, 0.45, 0.32, 0.28, 0.19, 0.19), 
    V3.X = c(0.19, 0.32, 0.18, 0.19, 0.19, 0.11, 0.19, 0, 0.27, 
    0.11, 0.23, 0.19), V3.Y = c(0.3, 0.29, 0.39, 0.4, 0.18, 0.4, 
    0.62, 0.34, 0.21, 0.33, 0.21, 0.1), V3.Z = c(0.09, 0.28, 
    0.43, 0.2, 0.4, 0.19, 0.07, 0.2, 0.2, 0.29, 0.38, 0.53), 
    V4.Maybe = c(0.4, 0.23, 0.39, 0.38, 0.62, 0.5, 0.2, 0.4, 
    0.4, 0.32, 0.3, 0.49), V4.NO = c(0.32, 0.5, 0.39, 0.31, 0.18, 
    0.29, 0.22, 0.42, 0.29, 0.3, 0.44, 0.3), V4.YES = c(0.28, 
    0.27, 0.22, 0.31, 0.2, 0.21, 0.58, 0.18, 0.3, 0.39, 0.26, 
    0.22)), row.names = c(NA, -12L), class = c("tbl_df", "tbl", 
"data.frame"), spec = structure(list(cols = list(Month = structure(list(), class = c("collector_character", 
"collector")), V1.F = structure(list(), class = c("collector_double", 
"collector")), V2.T = structure(list(), class = c("collector_double", 
"collector")), V2.A = structure(list(), class = c("collector_double", 
"collector")), V2.B = structure(list(), class = c("collector_double", 
"collector")), V2.C = structure(list(), class = c("collector_double", 
"collector")), V3.W = structure(list(), class = c("collector_double", 
"collector")), V3.X = structure(list(), class = c("collector_double", 
"collector")), V3.Y = structure(list(), class = c("collector_double", 
"collector")), V3.Z = structure(list(), class = c("collector_double", 
"collector")), V4.Maybe = structure(list(), class = c("collector_double", 
"collector")), V4.NO = structure(list(), class = c("collector_double", 
"collector")), V4.YES = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector"))), class = "col_spec"))

Answer 1

Please check your expected output. I believe there are some mistakes.

Here is a tidyverse option:

library(tidyverse)
df %>%
    gather(key, value, -Month, -Records) %>%
    group_by(Month, key, value) %>%
    summarise(freq = n()) %>%
    mutate(freq = freq / sum(freq)) %>%
    unite(col, key, value, sep = ".") %>%
    spread(col, freq)
## A tibble: 12 x 13
## Groups:   Month [12]
#   Month  V1.F  V1.T  V2.A  V2.B  V2.C  V3.W  V3.X  V3.Y  V3.Z V4.Maybe V4.NO
#   <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl>
# 1 1/1/…   0.4   0.6   0.2   0.6   0.2   0.4   0.2   0.3   0.1      0.4   0.3
# 2 10/1…   0.5   0.5   0.4   0.2   0.4   0.3   0.1   0.3   0.3      0.3   0.3
# 3 11/1…   0.4   0.6   0.3   0.4   0.3   0.2   0.2   0.2   0.4      0.3   0.4
# 4 12/1…   0.8   0.2   0.1   0.2   0.7   0.2   0.2   0.1   0.5      0.5   0.3
# 5 2/1/…   0.7   0.3   0.3   0.3   0.4   0.1   0.3   0.3   0.3      0.2   0.5
# 6 3/1/…   0.6   0.4   0.3   0.2   0.5  NA     0.2   0.4   0.4      0.4   0.4
# 7 4/1/…   0.4   0.6   0.3   0.5   0.2   0.2   0.2   0.4   0.2      0.4   0.3
# 8 5/1/…   0.4   0.6   0.3   0.5   0.2   0.2   0.2   0.2   0.4      0.6   0.2
# 9 6/1/…   0.4   0.6   0.3   0.2   0.5   0.3   0.1   0.4   0.2      0.5   0.3
#10 7/1/…   0.4   0.6   0.3   0.6   0.1   0.1   0.2   0.6   0.1      0.2   0.2
#11 8/1/…   0.6   0.4   0.5   0.2   0.3   0.5  NA     0.3   0.2      0.4   0.4
#12 9/1/…   0.3   0.7   0.1   0.8   0.1   0.3   0.3   0.2   0.2      0.4   0.3
## ... with 1 more variable: V4.YES <dbl>

Answer 2

Here is an alternative approach which uses the table() and prop.table() functions from base R and dcast() for reshaping to wide format. Unfortunately, I am not fluently enough in dplyr so I resort to data.table for grouping.

library(data.table)
library(magrittr)
setDT(df)[, lapply(.SD, function(.x) table(.x) %>% prop.table %>% as.data.table) %>% 
    rbindlist(idcol = TRUE), .SDcols = V1:V4, by = Month] %>% 
  dcast(Month ~ .id + .x)

  Month V1_F V1_T V2_A V2_B V2_C V3_W V3_X V3_Y V3_Z V4_Maybe V4_NO V4_YES 1: 1/1/2017 0.4 0.6 0.2 0.6 0.2 0.4 0.2 0.3 0.1 0.4 0.3 0.3 2: 10/1/2017 0.5 0.5 0.4 0.2 0.4 0.3 0.1 0.3 0.3 0.3 0.3 0.4 3: 11/1/2017 0.4 0.6 0.3 0.4 0.3 0.2 0.2 0.2 0.4 0.3 0.4 0.3 4: 12/1/2017 0.8 0.2 0.1 0.2 0.7 0.2 0.2 0.1 0.5 0.5 0.3 0.2 5: 2/1/2017 0.7 0.3 0.3 0.3 0.4 0.1 0.3 0.3 0.3 0.2 0.5 0.3 6: 3/1/2017 0.6 0.4 0.3 0.2 0.5 0.0 0.2 0.4 0.4 0.4 0.4 0.2 7: 4/1/2017 0.4 0.6 0.3 0.5 0.2 0.2 0.2 0.4 0.2 0.4 0.3 0.3 8: 5/1/2017 0.4 0.6 0.3 0.5 0.2 0.2 0.2 0.2 0.4 0.6 0.2 0.2 9: 6/1/2017 0.4 0.6 0.3 0.2 0.5 0.3 0.1 0.4 0.2 0.5 0.3 0.2 10: 7/1/2017 0.4 0.6 0.3 0.6 0.1 0.1 0.2 0.6 0.1 0.2 0.2 0.6 11: 8/1/2017 0.6 0.4 0.5 0.2 0.3 0.5 0.0 0.3 0.2 0.4 0.4 0.2 12: 9/1/2017 0.3 0.7 0.1 0.8 0.1 0.3 0.3 0.2 0.2 0.4 0.3 0.3