I'm kinda new to R and still looking for ways to make my code more elegant. I want to create multiple datasets in a more efficient way, each based on a particular value over different columns.
This is my dataset:
df<-data.frame(A=c(1,2,2,3,4,5,1,1,2,3),
B=c(4,4,2,3,4,2,1,5,2,2),
C=c(3,3,3,3,4,2,5,1,2,3),
D=c(1,2,5,5,5,4,5,5,2,3),
E=c(1,4,2,3,4,2,5,1,2,3),
dummy1=c("yes","yes","no","no","no","no","yes","no","yes","yes"),
dummy2=c("high","low","low","low","high","high","high","low","low","high"))
And I need each column to be a factor:
df[colnames(df)] <- lapply(df[colnames(df)], factor)
Now, what I want to obtain is one dataframe called "Likert_rank_yes" that contains all the observations that in the column "dummy1" have "yes", one dataframe called "Likert_rank_no" that contains all the observations that in the column "dummy1" have "no", one dataframe called "Likert_rank_high" that contains all the observations that in the column "dummy2" have "high" and so on for all my other dummies.
I want to loop or streamline the process in some way, so that there are few commands to run to get all the datasets I need.
The first two dataframes should look something like this:
Dataframe called "Likert_rank_yes" that contains all the observations that in the column "dummy1" have "yes"
Dataframe called "Likert_rank_no" that contains all the observations that in the column "dummy1" have "no"
I have to do this with several dummies with multiple levels and would like to automate/loop the process or make it more efficient, so that I don't have to subset and rename every dataframe for each dummy level. Ideally I would also need to drop the last column in each df created (the one containing the dummy considered).
I tried splitting like below but it seems it is not possible using multiple values, I just get 4 dfs (yes AND high observations, yes AND low obs, no AND high obs etc.) like so:
Splitting with a list of columns doesn't work
list_df <- split(df[c(1:5)], list(df$dummy1,df$dummy2), sep=".")
Can you help? Thanks in advance!
You need two lapplys:
vals <- colnames(df)[1:5]
dummies <- colnames(df)[-(1:5)]
step1 <- lapply(dummies, function(x) df[, c(vals, x)])
step2 <- lapply(step1, function(x) split(x, x[, 6]))
names(step2) <- dummies
step2
# $dummy1
# $dummy1$no
# A B C D E dummy1
# 3 2 2 3 5 2 no
# 4 3 3 3 5 3 no
# 5 4 4 4 5 4 no
# 6 5 2 2 4 2 no
# 8 1 5 1 5 1 no
#
# $dummy1$yes
# A B C D E dummy1
# 1 1 4 3 1 1 yes
# 2 2 4 3 2 4 yes
# 7 1 1 5 5 5 yes
# 9 2 2 2 2 2 yes
# 10 3 2 3 3 3 yes
#
#
# $dummy2
# $dummy2$high
# A B C D E dummy2
# 1 1 4 3 1 1 high
# 5 4 4 4 5 4 high
# 6 5 2 2 4 2 high
# 7 1 1 5 5 5 high
# 10 3 2 3 3 3 high
#
# $dummy2$low
# A B C D E dummy2
# 2 2 4 3 2 4 low
# 3 2 2 3 5 2 low
# 4 3 3 3 5 3 low
# 8 1 5 1 5 1 low
# 9 2 2 2 2 2 low
For the first data set ("dummy1" and "no") use step2$dummy1$no or step2[[1]][[1]] or step2[["dummy1"]][["no"]].
For programming purposes it is usually better to keep the list intact since it makes it simple to write code that processes all of the data frames in the list without having to specify them individually.
You are very close:
tbls <- unlist(step2, recursive=FALSE)
list2env(tbls, envir=.GlobalEnv)
ls()
# [1] "df" "dummies" "dummy1.no" "dummy1.yes" "dummy2.high" "dummy2.low" "step1" "step2" "tbls" "vals"
This will create the same set of tables.
This question already has answers here:
Count number of occurences for each unique value
(14 answers)
Closed 2 years ago.
Using as input data frame:
df1 <- data.frame(num = c(1,1,1,2,2,2,3))
How is it possible to receive the sum of every number excited in the num column?
Example output:
num frequency
1 3
2 3
3 1
Using table and coerce it to a data frame.
as.data.frame(table(df1$num))
# Var1 Freq
# 1 1 3
# 2 2 3
# 3 3 1
or
with(df1, data.frame(num=unique(num), freq=tabulate(num)))
# num freq
# 1 1 3
# 2 2 3
# 3 3 1
I have been given a dataset that I am attempting to perform logistic regression on. However, to do so, I need to merge some columns in R.
For instance in the carevaluations data set, I am given (BuyingPrice_low, BuyingPrice_medium, BuyingPrice_high, BuyingPrice_vhigh, MaintenancePrice_low MaintenancePrice_medium MaintenancePrice_high MaintenancePrice_vhigh)
How would I combine the columns buying price_low, medium, etc. into one column called "BuyingPrice" with the order and their respective data in each column and the same with the maintenanceprice column?
library(dplyr)
df <- data.frame(Buy_low=rep(c(0,1), 10),
Buy_high=rep(c(0,1), 10))
one_column <- df %>%
gather(var, value)
head(one_column)
var value
1 Buy_low 0
2 Buy_low 1
3 Buy_low 0
4 Buy_low 1
5 Buy_low 0
6 Buy_low 1
It can be done with stack in base R :
df1 <- data.frame(a=1:3,b=4:6,c=7:9)
stack(df1)
# values ind
# 1 1 a
# 2 2 a
# 3 3 a
# 4 4 b
# 5 5 b
# 6 6 b
# 7 7 c
# 8 8 c
# 9 9 c
I am trying to adjust the formatting of a data set. My current set looks like this, in two columns. The first column is a "cluster" and the second column "name" contains values within each cluster:
Cluster Name
A 1
A 2
A 3
B 4
B 5
C 2
C 6
C 7
And I'd like a list that is, one column wherein all the values from column 2 are listed under the associated cluster from column 1 in a single column:
Cluster A
1
2
3
Cluster B
4
5
Cluster C
2
6
7
I've been trying in R and Excel with no luck for the last few hours. Any ideas?
Using a trick with tidyr::nest :
library(dplyr)
library(tidyr)
df %>% mutate(Cluster = paste0("Cluster_",Cluster)) %>% nest(Name) %>% t %>% unlist %>% as.data.frame
# .
# 1 Cluster_A
# 2 1
# 3 2
# 4 3
# 5 Cluster_B
# 6 4
# 7 5
# 8 Cluster_C
# 9 2
# 10 6
# 11 7
This question already has answers here:
Repeat each row of data.frame the number of times specified in a column
(10 answers)
Closed 7 years ago.
I have a data frame with names by type and their frequencies. I'd like to expand this data frame so that the names are repeated according to their name-type frequency.
For example, this:
> df = data.frame(name=c('a','b','c'),type=c(0,1,2),freq=c(2,3,2))
name type freq
1 a 0 2
2 b 1 3
3 c 2 2
would become this:
> df_exp
name type
1 a 0
2 a 0
3 b 1
4 b 1
5 b 1
6 c 2
7 c 2
Appreciate any suggestions on a easy way to do this.
You can just use rep to "expand" your data.frame rows:
df[rep(sequence(nrow(df)), df$freq), c("name", "type")]
# name type
# 1 a 0
# 1.1 a 0
# 2 b 1
# 2.1 b 1
# 2.2 b 1
# 3 c 2
# 3.1 c 2
And there's a function expandRows in the splitstackshape package that does exactly this. It also has the option to accept a vector specifying how many times to replicate each row, for example:
expandRows(df, "freq")