Ángel
Reputation: 145
How to create a dataframe with the table data in R
I need to create a dataframe with the data of a table like:
> table
, , sex = Female
brain
age no yes
-30 9 1
+60 57 2
30-60 64 6
, , sex = Male
brain
age no yes
-30 12 1
+60 36 2
30-60 90 9
Manually I created the dataframe by hand with
> age <- c('-30', '-30','-30', '-30', '30-60', '30-60', '30-60', '30-60', '+60', '+60','+60', '+60')
> sex <- c('Male','Male', 'Female','Female', 'Male','Male', 'Female','Female','Male','Male', 'Female','Female')
> brain <- c('yes', 'no', 'yes', 'no', 'yes', 'no', 'yes', 'no', 'yes', 'no', 'yes', 'no')
> cases <- c(1,12,1,9,9,90,6,64,2,36,2,57)
> data <- data.frame(age, sex, brain, cases, stringsAsFactors = TRUE)
> data
age sex brain cases
1 -30 Male yes 1
2 -30 Male no 12
3 -30 Female yes 1
4 -30 Female no 9
5 30-60 Male yes 9
6 30-60 Male no 90
7 30-60 Female yes 6
8 30-60 Female no 64
9 +60 Male yes 2
10 +60 Male no 36
11 +60 Female yes 2
12 +60 Female no 57
What would be the best way to do that? Because this way is almost useless when the table change values and its easy to make mistakes...
Upvotes: 0
Views: 77
Answers (2)
akrun
Reputation: 887118
We could use tabyl from janitor
library(janitor)
origdata %>%
tabyl(age, sex, brain)
-output
#$no
# age Female Male
# -30 9 12
# +60 57 36
# 30-60 64 90
#$yes
# age Female Male
# -30 1 1
# +60 2 2
# 30-60 6 9
If we want a single data
library(dplyr)
origdata %>%
tabyl(age, sex, brain) %>%
bind_rows(.id = 'brain')
In base R, we can use ftable
ftable(origdata)
To convert to a data.frame
as.data.frame(ftable(origdata))
age sex brain Freq
1 -30 Female no 9
2 +60 Female no 57
3 30-60 Female no 64
4 -30 Male no 12
5 +60 Male no 36
6 30-60 Male no 90
7 -30 Female yes 1
8 +60 Female yes 2
9 30-60 Female yes 6
10 -30 Male yes 1
11 +60 Male yes 2
12 30-60 Male yes 9
data
origdata <- structure(list(age = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("-30", "+60", "30-60"), class = "factor"), sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Female", "Male"), class = "factor"), brain = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("no", "yes"), class = "factor")), row.names = c("4", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "4.7", "4.8", "12", "12.1", "12.2", "12.3", "12.4", "12.5", "12.6", "12.7", "12.8", "12.9", "12.10", "12.11", "12.12", "12.13", "12.14", "12.15", "12.16", "12.17", "12.18", "12.19", "12.20", "12.21", "12.22", "12.23", "12.24", "12.25", "12.26", "12.27", "12.28", "12.29", "12.30", "12.31", "12.32", "12.33", "12.34", "12.35", "12.36", "12.37", "12.38", "12.39", "12.40", "12.41", "12.42", "12.43", "12.44", "12.45", "12.46", "12.47", "12.48", "12.49", "12.50", "12.51", "12.52", "12.53", "12.54", "12.55", "12.56", "8", "8.1", "8.2", "8.3", "8.4", "8.5", "8.6", "8.7", "8.8", "8.9", "8.10", "8.11", "8.12", "8.13", "8.14", "8.15", "8.16", "8.17", "8.18", "8.19", "8.20", "8.21", "8.22", "8.23", "8.24", "8.25", "8.26", "8.27", "8.28", "8.29", "8.30", "8.31", "8.32", "8.33", "8.34", "8.35", "8.36", "8.37", "8.38", "8.39", "8.40", "8.41", "8.42", "8.43", "8.44", "8.45", "8.46", "8.47", "8.48", "8.49", "8.50", "8.51", "8.52", "8.53", "8.54", "8.55", "8.56", "8.57", "8.58", "8.59", "8.60", "8.61", "8.62", "8.63", "2", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7", "2.8", "2.9", "2.10", "2.11", "10", "10.1", "10.2", "10.3", "10.4", "10.5", "10.6", "10.7", "10.8", "10.9", "10.10", "10.11", "10.12", "10.13", "10.14", "10.15", "10.16", "10.17", "10.18", "10.19", "10.20", "10.21", "10.22", "10.23", "10.24", "10.25", "10.26", "10.27", "10.28", "10.29", "10.30", "10.31", "10.32", "10.33", "10.34", "10.35", "6", "6.1", "6.2", "6.3", "6.4", "6.5", "6.6", "6.7", "6.8", "6.9", "6.10", "6.11", "6.12", "6.13", "6.14", "6.15", "6.16", "6.17", "6.18", "6.19", "6.20", "6.21", "6.22", "6.23", "6.24", "6.25", "6.26", "6.27", "6.28", "6.29", "6.30", "6.31", "6.32", "6.33", "6.34", "6.35", "6.36", "6.37", "6.38", "6.39", "6.40", "6.41", "6.42", "6.43", "6.44", "6.45", "6.46", "6.47", "6.48", "6.49", "6.50", "6.51", "6.52", "6.53", "6.54", "6.55", "6.56", "6.57", "6.58", "6.59", "6.60", "6.61", "6.62", "6.63", "6.64", "6.65", "6.66", "6.67", "6.68", "6.69", "6.70", "6.71", "6.72", "6.73", "6.74", "6.75", "6.76", "6.77", "6.78", "6.79", "6.80", "6.81", "6.82", "6.83", "6.84", "6.85", "6.86", "6.87", "6.88", "6.89", "3", "11", "11.1", "7", "7.1", "7.2", "7.3", "7.4", "7.5", "1", "9", "9.1", "5", "5.1", "5.2", "5.3", "5.4", "5.5", "5.6", "5.7", "5.8"), class = "data.frame")
Upvotes: 0
r2evans
Reputation: 160447
If you get your table with:
tab <- xtabs(~ age + brain + sex, data = origdata)
tab
# , , sex = Female
# brain
# age no yes
# -30 9 1
# +60 57 2
# 30-60 64 6
# , , sex = Male
# brain
# age no yes
# -30 12 1
# +60 36 2
# 30-60 90 9
Then use as.data.frame:
as.data.frame(tab)
# age brain sex Freq
# 1 -30 no Female 9
# 2 +60 no Female 57
# 3 30-60 no Female 64
# 4 -30 yes Female 1
# 5 +60 yes Female 2
# 6 30-60 yes Female 6
# 7 -30 no Male 12
# 8 +60 no Male 36
# 9 30-60 no Male 90
# 10 -30 yes Male 1
# 11 +60 yes Male 2
# 12 30-60 yes Male 9
Data
origdata <- structure(list(age = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("-30", "+60", "30-60"), class = "factor"), sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Female", "Male"), class = "factor"), brain = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("no", "yes"), class = "factor")), row.names = c("4", "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "4.7", "4.8", "12", "12.1", "12.2", "12.3", "12.4", "12.5", "12.6", "12.7", "12.8", "12.9", "12.10", "12.11", "12.12", "12.13", "12.14", "12.15", "12.16", "12.17", "12.18", "12.19", "12.20", "12.21", "12.22", "12.23", "12.24", "12.25", "12.26", "12.27", "12.28", "12.29", "12.30", "12.31", "12.32", "12.33", "12.34", "12.35", "12.36", "12.37", "12.38", "12.39", "12.40", "12.41", "12.42", "12.43", "12.44", "12.45", "12.46", "12.47", "12.48", "12.49", "12.50", "12.51", "12.52", "12.53", "12.54", "12.55", "12.56", "8", "8.1", "8.2", "8.3", "8.4", "8.5", "8.6", "8.7", "8.8", "8.9", "8.10", "8.11", "8.12", "8.13", "8.14", "8.15", "8.16", "8.17", "8.18", "8.19", "8.20", "8.21", "8.22", "8.23", "8.24", "8.25", "8.26", "8.27", "8.28", "8.29", "8.30", "8.31", "8.32", "8.33", "8.34", "8.35", "8.36", "8.37", "8.38", "8.39", "8.40", "8.41", "8.42", "8.43", "8.44", "8.45", "8.46", "8.47", "8.48", "8.49", "8.50", "8.51", "8.52", "8.53", "8.54", "8.55", "8.56", "8.57", "8.58", "8.59", "8.60", "8.61", "8.62", "8.63", "2", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7", "2.8", "2.9", "2.10", "2.11", "10", "10.1", "10.2", "10.3", "10.4", "10.5", "10.6", "10.7", "10.8", "10.9", "10.10", "10.11", "10.12", "10.13", "10.14", "10.15", "10.16", "10.17", "10.18", "10.19", "10.20", "10.21", "10.22", "10.23", "10.24", "10.25", "10.26", "10.27", "10.28", "10.29", "10.30", "10.31", "10.32", "10.33", "10.34", "10.35", "6", "6.1", "6.2", "6.3", "6.4", "6.5", "6.6", "6.7", "6.8", "6.9", "6.10", "6.11", "6.12", "6.13", "6.14", "6.15", "6.16", "6.17", "6.18", "6.19", "6.20", "6.21", "6.22", "6.23", "6.24", "6.25", "6.26", "6.27", "6.28", "6.29", "6.30", "6.31", "6.32", "6.33", "6.34", "6.35", "6.36", "6.37", "6.38", "6.39", "6.40", "6.41", "6.42", "6.43", "6.44", "6.45", "6.46", "6.47", "6.48", "6.49", "6.50", "6.51", "6.52", "6.53", "6.54", "6.55", "6.56", "6.57", "6.58", "6.59", "6.60", "6.61", "6.62", "6.63", "6.64", "6.65", "6.66", "6.67", "6.68", "6.69", "6.70", "6.71", "6.72", "6.73", "6.74", "6.75", "6.76", "6.77", "6.78", "6.79", "6.80", "6.81", "6.82", "6.83", "6.84", "6.85", "6.86", "6.87", "6.88", "6.89", "3", "11", "11.1", "7", "7.1", "7.2", "7.3", "7.4", "7.5", "1", "9", "9.1", "5", "5.1", "5.2", "5.3", "5.4", "5.5", "5.6", "5.7", "5.8"), class = "data.frame")
Upvotes: 3
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