I'm a bit new to R and wanting to remove a column from a matrix by the name of that column. I know that X[,2] gives the second column and X[,-2] gives every column except the second one. What I really want to know is if there's a similar command using column names. I've got a matrix and want to remove the "sales" column, but X[,-"sales"] doesn't seem to work for this. How should I do this? I would use the column number only I want to be able to use it for other matrices later, which have different dimensions. Any help would be much appreciated.
I'm not sure why all the answers are solutions for data frames and not matrices.
Per #Sotos's and #Moody_Mudskipper's comments, here is an example with the builtin state.x77 data matrix.
dat <- head(state.x77)
dat
#> Population Income Illiteracy Life Exp Murder HS Grad Frost Area
#> Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
#> Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
#> Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
#> Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
#> California 21198 5114 1.1 71.71 10.3 62.6 20 156361
#> Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
# for removing one column
dat[, colnames(dat) != "Area"]
#> Population Income Illiteracy Life Exp Murder HS Grad Frost
#> Alabama 3615 3624 2.1 69.05 15.1 41.3 20
#> Alaska 365 6315 1.5 69.31 11.3 66.7 152
#> Arizona 2212 4530 1.8 70.55 7.8 58.1 15
#> Arkansas 2110 3378 1.9 70.66 10.1 39.9 65
#> California 21198 5114 1.1 71.71 10.3 62.6 20
#> Colorado 2541 4884 0.7 72.06 6.8 63.9 166
# for removing more than one column
dat[, !colnames(dat) %in% c("Area", "Life Exp")]
#> Population Income Illiteracy Murder HS Grad Frost
#> Alabama 3615 3624 2.1 15.1 41.3 20
#> Alaska 365 6315 1.5 11.3 66.7 152
#> Arizona 2212 4530 1.8 7.8 58.1 15
#> Arkansas 2110 3378 1.9 10.1 39.9 65
#> California 21198 5114 1.1 10.3 62.6 20
#> Colorado 2541 4884 0.7 6.8 63.9 166
#be sure to use `colnames` and not `names`
names(state.x77)
#> NULL
Created on 2020-06-27 by the reprex package (v0.3.0)
my favorite way:
# create data
df <- data.frame(x = runif(100),
y = runif(100),
remove_me = runif(100),
remove_me_too = runif(100))
# remove column
df <- df[,!names(df) %in% c("remove_me", "remove_me_too")]
so this dataframe:
> df
x y remove_me remove_me_too
1 0.731124508 0.535219259 0.33209113 0.736142042
2 0.612017350 0.404128030 0.84923974 0.624543223
3 0.415403559 0.369818154 0.53817387 0.661263087
4 0.199780006 0.679946936 0.58782429 0.085624708
5 0.343304259 0.892128112 0.02827132 0.038203599
becomes this:
> df
x y
1 0.731124508 0.535219259
2 0.612017350 0.404128030
3 0.415403559 0.369818154
4 0.199780006 0.679946936
5 0.343304259 0.892128112
As always in R there are many potential solutions. You can use the package dplyr and select() to easily remove or select columns in a data frame.
df <- data.frame(x = runif(100),
y = runif(100),
remove_me = runif(100),
remove_me_too = runif(100))
library(dplyr)
select(df, -remove_me, -remove_me_too) %>% head()
#> x y
#> 1 0.35113636 0.134590652
#> 2 0.72545356 0.165608839
#> 3 0.81000067 0.090696049
#> 4 0.29882204 0.004602398
#> 5 0.93492918 0.256870750
#> 6 0.03007377 0.395614901
You can read more about dplyr and its verbs here.
As a general case, if you remove so many columns that only one column remains, R will convert it to a numeric vector. You can prevent it by setting drop = FALSE.
(df <- data.frame(x = runif(6),
y = runif(6),
remove_me = runif(6),
remove_me_too = runif(6)))
# x y remove_me remove_me_too
# 1 0.4839869 0.18672217 0.0973506 0.72310641
# 2 0.2467426 0.37950878 0.2472324 0.80133920
# 3 0.4449471 0.58542547 0.8185943 0.57900456
# 4 0.9119014 0.12089776 0.2153147 0.05584816
# 5 0.4979701 0.04890334 0.7420666 0.44906667
# 6 0.3266374 0.37110822 0.6809380 0.29091746
df[, -c(3, 4)]
# x y
# 1 0.4839869 0.18672217
# 2 0.2467426 0.37950878
# 3 0.4449471 0.58542547
# 4 0.9119014 0.12089776
# 5 0.4979701 0.04890334
# 6 0.3266374 0.37110822
# Result is a numeric vector
df[, -c(2, 3, 4)]
# [1] 0.4839869 0.2467426 0.4449471 0.9119014 0.4979701 0.3266374
# Keep the matrix type
df[, -c(2, 3, 4), drop = FALSE]
# x
# 1 0.4839869
# 2 0.2467426
# 3 0.4449471
# 4 0.9119014
# 5 0.4979701
# 6 0.3266374
Related
I'm trying to carry out the following action on the columns of a dataframe (df1):
term1+term2+term3*req_no
req_no is a range of numbers: 20:24
df1:
ID term1 term2 term3
X299 1.2 2.3 0.12
X300 1.4 0.6 2.4
X301 0.3 1.6 1.2
X302 0.9 0.6 0.4
X303 0.3 1.8 0.3
X304 1.3 0.3 2.1
I need help t get this output and here's my attempt:
Required output:
ID 20 21 22 23 24
X299 5.9 6.02 6.14 6.26 6.38
X300 50 52.4 54.8 57.2 59.6
X301 25.9 27.1 28.3 29.5 30.7
X302 9.5 9.9 10.3 10.7 11.1
X303 8.1 8.4 8.7 9 9.3
X304 43.6 45.7 47.8 49.9 52
Here's:
results <- list()
req_no <- 20:25
for(i in 1:nrow(df1){
for(j in rq_no){
res <- term1+term2+term3*j
results[j] <- res
}
results[[i]]
}
results2 <- do.call("rbind",result)
Help will be appreciated.
Here are a couple different approaches, though neither as succinct as Parfait's. Sample data:
df <- data.frame(ID=c("X299", "X300"),
term1=c(1.2, 1.4),
term2=c(2.3, 0.6),
term3=c(0.12, 2.4))
req_no <- 20:25
Loop approach
Your initial approach is headed in the right direction, but in the future, it would help to specify exactly what your error or problem is. For an iterated and perhaps easier-to-read approach, here's one answer:
results <- matrix(data=NA, nrow=nrow(df), ncol=length(req_no)) # Empty matrix to store our results
colnames(results) <- req_no # Optional; name columns based off of req_no values
for(i in 1:nrow(df)) {
# Do the calculation we want; returns a vector length 6
res <- df[i,]$term1 + df[i,]$term2 + (df[i,]$term3 * req_no)
# Save results for row i of df into row i of results matrix
results[i,] <- res
}
# Now bind the columns (named 20 through 25) to the respective rows of df
output <- cbind(df, results)
output
From your initial attempt, note:
We only do one loop, since it is easy to multiply by a vector in R
There are a few ways to subset data from a data frame in R. In this case, df[i,] gets everything in the i-th row, while $termX gets value in the column named termX
Using a results matrix instead of a list makes it very easy to copy the temporary computations (for each row) into rows of the matrix
Rather than rbind() (row bind), we want cbind() (column bind) to bind those results to new columns of the original rows.
Output:
ID term1 term2 term3 20 21 22 23 24 25
1 X299 1.2 2.3 0.12 5.9 6.02 6.14 6.26 6.38 6.5
2 X300 1.4 0.6 2.40 50.0 52.40 54.80 57.20 59.60 62.0
Dplyr/purrr functions
This could also be solved using tidy functions. In essence it's a pretty similar approach to Parfait's answer, but I've made the steps a bit more verbose to see what's going on.
# Use purrr's map functions to do the computation we want
nested_df <- df %>%
# Make new column holding term3 * req_no (stores a vector in each new cell)
mutate(term3r = map(term3, ~ .x * req_no)) %>%
# Make new column which sums the three columns of interest (stores a vector in each new cell)
mutate(sum = pmap(list(term1, term2, term3r), ~ ..1 + ..2 + ..3))
# "Unnest" those vectors which store our sums, and keep only those and ID
output <- nested_df %>%
# Creates six new columns (named ...1 to ...6) with the elements of each sum
unnest_wider(sum) %>%
# Keeps only the output data and IDs
select(ID, ...1:...6)
output
Output:
# A tibble: 2 x 7
ID ...1 ...2 ...3 ...4 ...5 ...6
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 X299 5.9 6.02 6.14 6.26 6.38 6.5
2 X300 50 52.4 54.8 57.2 59.6 62
Consider directly assigning new columns with sapply using your formula:
df[paste0(req_no)] <- sapply(req_no, function(r) with(df, term1 + term2 + term3 * r))
df
# ID term1 term2 term3 20 21 22 23 24
# 1 X299 1.2 2.3 0.12 5.9 6.02 6.14 6.26 6.38
# 2 X300 1.4 0.6 2.40 50.0 52.40 54.80 57.20 59.60
# 3 X301 0.3 1.6 1.20 25.9 27.10 28.30 29.50 30.70
# 4 X302 0.9 0.6 0.40 9.5 9.90 10.30 10.70 11.10
# 5 X303 0.3 1.8 0.30 8.1 8.40 8.70 9.00 9.30
# 6 X304 1.3 0.3 2.10 43.6 45.70 47.80 49.90 52.00
I have a dataframe with values for multiple macro variables. When i compute log of the values and then the log differences it changes the variables into lists, causing problems with my script later on.
Example code:
#Compute log of relevant macrovariables
macro[,c("hp", "unem", "m1", "inc")] <- log(macro[,c("hp", "unem", "m1", "inc")])
colnames(macro)[2:5] <- paste(colnames(macro)[2:5], "log", sep = "_")
#Computing log differences
macro$ldiff_hp <- c(-diff(macro$hp_log), na.omit)
Im trying to unlist the columns and convert them to numeric with either of the following:
#Alternative 1
macro[,15:19]<- unlist(as.numeric(macro[,15:19]))
#Alternative 2
macro[,15:19] <- sapply(macro[,15:19],as.numeric)
It gives me the following error output:
> macro[,15:19]<- unlist(as.numeric(macro[,15:19]))
Error in unlist(as.numeric(macro[, 15:19])) :
(list) object cannot be coerced to type 'double'
Using the economics dataset from ggplot2 as example data and making use of dplyrs lag function the log differenced vars can be computed like so:
library(ggplot2)
library(dplyr)
macro <- ggplot2::economics
vars <- c("uempmed", "psavert")
vars_log <- paste(vars, "log", sep = "_")
vars_ldiff <- paste(vars, "ldiff", sep = "_")
#Compute log of relevant macrovariables
macro[, vars_log] <- sapply(macro[, vars], log)
# Lag values
macro[, vars_ldiff] <- sapply(macro[, vars_log], dplyr::lag)
# First Difference of logs
macro[, vars_ldiff] <- macro[, vars_log] - macro[, vars_ldiff]
macro
#> # A tibble: 574 x 10
#> date pce pop psavert uempmed unemploy uempmed_log psavert_log
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1967-07-01 507. 198712 12.6 4.5 2944 1.50 2.53
#> 2 1967-08-01 510. 198911 12.6 4.7 2945 1.55 2.53
#> 3 1967-09-01 516. 199113 11.9 4.6 2958 1.53 2.48
#> 4 1967-10-01 512. 199311 12.9 4.9 3143 1.59 2.56
#> 5 1967-11-01 517. 199498 12.8 4.7 3066 1.55 2.55
#> 6 1967-12-01 525. 199657 11.8 4.8 3018 1.57 2.47
#> 7 1968-01-01 531. 199808 11.7 5.1 2878 1.63 2.46
#> 8 1968-02-01 534. 199920 12.3 4.5 3001 1.50 2.51
#> 9 1968-03-01 544. 200056 11.7 4.1 2877 1.41 2.46
#> 10 1968-04-01 544 200208 12.3 4.6 2709 1.53 2.51
#> # ... with 564 more rows, and 2 more variables: uempmed_ldiff <dbl>,
#> # psavert_ldiff <dbl>
Created on 2020-03-23 by the reprex package (v0.3.0)
How can I randomly add missing values to some or each column (say random ~5% missing in each) in a simulated dataframe, plus, is there a more efficient way of simulating a dataframe with both continuous and factor columns?
#Simulate some data
N <- 2000
data <- data.frame(id = 1:2000,age = rnorm(N,18:90),bmi = rnorm(N,15:40),
chol = rnorm(N,50:350), insulin = rnorm(N,2:40),sbp = rnorm(N, 50:200),
dbp = rnorm(N, 30:150), sex = c(rep(1, 1000), rep(2, 1000)),
smoke = rep(c(1, 2), 1000), educ = sample(LETTERS[1:4]))
#Manually add some missing values
data <- data %>%
mutate(age = "is.na<-"(age, age <19 | age >88),
bmi = "is.na<-"(bmi, bmi >38 | bmi <16),
insulin = "is.na<-"(insulin, insulin >38),
educ = "is.na<-"(educ, bmi >35))
Best solution in my opinion would be using the mice package for this. This is a R package dedicated to imputation. It also has a function called amputate for introducing missing data into a data.frame.
ampute - Generate Missing Data For Simulation Purposes
This function generates multivariate missing data in a MCAR, MAR or MNAR manner.
The advantage of this solution is you can set multiple parameters for the simulation of your missing data.
ampute(data, prop = 0.5, patterns = NULL, freq = NULL, mech = "MAR",
weights = NULL, cont = TRUE, type = NULL, odds = NULL,
bycases = TRUE, run = TRUE)
As you can see you can set the percentage of missing values, the missing data mechanism (MCAR would be your choice for missing completely at random) and several other parameters. This solution would also be quite clean since it is only 1 line of code.
Here's a tidyverse approach that will remove roughly 20% of your data for each column you specify:
set.seed(1)
# example data
N <- 20
data <- data.frame(id = 1:N,
age = rnorm(N,18:90),
bmi = rnorm(N,15:40),
chol = rnorm(N,50:350))
library(tidyverse)
# specify which variables should have missing data and prc of missing data
c_names = c("age","bmi")
prc_missing = 0.20
data %>%
gather(var, value, -id) %>% # reshape data
mutate(r = runif(nrow(.)), # simulate a random number from 0 to 1 for each row
value = ifelse(var %in% c_names & r <= prc_missing, NA, value)) %>% # if it's one of the variables you specified and the random number is less than your threshold update to NA
select(-r) %>% # remove random number
spread(var, value) # reshape back to original format
# id age bmi chol
# 1 1 17.37355 15.91898 49.83548
# 2 2 19.18364 16.78214 50.74664
# 3 3 19.16437 17.07456 52.69696
# 4 4 NA 16.01065 53.55666
# 5 5 22.32951 19.61983 53.31124
# 6 6 22.17953 19.94387 54.29250
# 7 7 24.48743 NA 56.36458
# 8 8 25.73832 20.52925 57.76853
# 9 9 26.57578 NA 57.88765
# 10 10 26.69461 24.41794 59.88111
# 11 11 29.51178 26.35868 60.39811
# 12 12 NA 25.89721 60.38797
# 13 13 NA 27.38767 62.34112
# 14 14 28.78530 27.94619 61.87064
# 15 15 33.12493 27.62294 65.43302
# 16 16 32.95507 NA 66.98040
# 17 17 33.98381 30.60571 65.63278
# 18 18 35.94384 NA 65.95587
# 19 19 36.82122 34.10003 68.56972
# 20 20 37.59390 34.76318 68.86495
And this is an alternative that will remove exactly 20% of data for the columns you specify:
set.seed(1)
# example data
N <- 20
data <- data.frame(id = 1:N,
age = rnorm(N,18:90),
bmi = rnorm(N,15:40),
chol = rnorm(N,50:350))
library(tidyverse)
# specify which variables should have missing data and prc of missing data
c_names = c("age","bmi")
prc_missing = 0.20
n_remove = prc_missing * nrow(data)
data %>%
gather(var, value, -id) %>% # reshape data
sample_frac(1) %>% # shuffle rows
group_by(var) %>% # for each variables
mutate(value = ifelse(var %in% c_names & row_number() <= n_remove, NA, value)) %>% # update to NA top x number of rows if it's one of the variables you specified
spread(var, value) # reshape to original format
# # A tibble: 20 x 4
# id age bmi chol
# <int> <dbl> <dbl> <dbl>
# 1 1 17.4 15.9 49.8
# 2 2 19.2 16.8 50.7
# 3 3 19.2 17.1 52.7
# 4 4 NA 16.0 53.6
# 5 5 22.3 NA 53.3
# 6 6 22.2 19.9 54.3
# 7 7 24.5 20.8 56.4
# 8 8 25.7 NA 57.8
# 9 9 26.6 NA 57.9
# 10 10 NA NA 59.9
# 11 11 NA 26.4 60.4
# 12 12 NA 25.9 60.4
# 13 13 29.4 27.4 62.3
# 14 14 28.8 27.9 61.9
# 15 15 33.1 27.6 65.4
# 16 16 33.0 29.6 67.0
# 17 17 34.0 30.6 65.6
# 18 18 35.9 31.9 66.0
# 19 19 36.8 34.1 68.6
# 20 20 37.6 34.8 68.9
Would this work?
n_rows <- nrow(data)
perc_missing <- 5 # percentage missing data
row_missing <- sample(1:n_rows, sample(1:n_rows, round(perc_missing/100 * n_rows,0))) # sample randomly x% of rows
col_missing <- 1 # define column
data[row_missing, col_missing] <- NA # assign missing values
I need to generate bins from a data.frame based on the values of one column. I have tried the function "cut".
For example: I want to create bins of air temperature values in the column "AirTDay" in a data frame:
AirTDay (oC)
8.16
10.88
5.28
19.82
23.62
13.14
28.84
32.21
17.44
31.21
I need the bin intervals to include all values in a range of 2 degrees centigrade from that initial value (i.e. 8-9.99, 10-11.99, 12-13.99...), to be labelled with the average value of the range (i.e. 9.5, 10.5, 12.5...), and to respect blank cells, returning "NA" in the bins column.
The output should look as:
Air_T (oC) TBins
8.16 8.5
10.88 10.5
5.28 NA
NA
19.82 20.5
23.62 24.5
13.14 14.5
NA
NA
28.84 28.5
32.21 32.5
17.44 18.5
31.21 32.5
I've gotten as far as:
setwd('C:/Users/xxx')
temp_data <- read.csv("temperature.csv", sep = ",", header = TRUE)
TAir <- temp_data$AirTDay
Tmin <- round(min(TAir, na.rm = FALSE), digits = 0) # is start at minimum value
Tmax <- round(max(TAir, na.rm = FALSE), digits = 0)
int <- 2 # bin ranges 2 degrees
mean_int <- int/2
int_range <- seq(Tmin, Tmax + int, int) # generate bin sequence
bin_label <- seq(Tmin + mean_int, Tmax + mean_int, int) # generate labels
temp_data$TBins <- cut(TAir, breaks = int_range, ordered_result = FALSE, labels = bin_label)
The output table looks correct, but for some reason it shows a sequential additional column, shifts column names, and collapse all values eliminating blank cells. Something like this:
Air_T (oC) TBins
1 8.16 8.5
2 10.88 10.5
3 5.28 NA
4 19.82 20.5
5 23.62 24.5
6 13.14 14.5
7 28.84 28.5
8 32.21 32.5
9 17.44 18.5
10 31.21 32.5
Any ideas on where am I failing and how to solve it?
v<-ceiling(max(dat$V1,na.rm=T))
breaks<-seq(8,v,2)
labels=seq(8.5,length.out=length(s)-1,by=2)
transform(dat,Tbins=cut(V1,breaks,labels))
V1 Tbins
1 8.16 8.5
2 10.88 10.5
3 5.28 <NA>
4 NA <NA>
5 19.82 18.5
6 23.62 22.5
7 13.14 12.5
8 NA <NA>
9 NA <NA>
10 28.84 28.5
11 32.21 <NA>
12 17.44 16.5
13 31.21 30.5
This result follows the logic given: we have
paste(seq(8,v,2),seq(9.99,v,by=2),sep="-")
[1] "8-9.99" "10-11.99" "12-13.99" "14-15.99" "16-17.99" "18-19.99" "20-21.99"
[8] "22-23.99" "24-25.99" "26-27.99" "28-29.99" "30-31.99"
From this we can tell that 19.82 will lie between 18 and 20 thus given the value 18.5, similar to 10.88 being between 10-11.99 thus assigned the value 10.5
I'm trying to reshape my data from a long format into a wide format based on multiple groupings, without success. with this data:
id <- 1:20
month <- rep(4:7, 50)
name <- rep(c("sam", "mike", "tim", "jill", "max"), 40)
cost <- sample(1:100, 200, replace=TRUE)
df <- data.frame(id, month, name, cost)
df.mo.mean <- aggregate(df$cost ~ df$name + df$month, FUN="mean")
df.mo.sd <- aggregate(df$cost ~ df$name + df$month, FUN="sd")
df.mo <- data.frame(df.mo.mean, df.mo.sd)
df.mo <- df.mo[,-c(4,5)]
df.mo[3:4] <- round(df.mo[3:4],2)
head(df)
id month name cost
1 1 4 sam 29
2 2 5 mike 93
3 3 6 tim 27
4 4 7 jill 67
5 5 4 max 28
6 6 5 sam 69
I'm trying to get my data to look like something below, and try to generalize it for an unknown number of names (but <15 max)
month name1.cost.mean name1.cost.sd name2.cost.mean name2.cost.sd
1 45 4 40 6
2 ...
I've tried reshape and do.call with rbind without success. The only other way I can think of doing it is with a loop, which means I'm doing something wrong. I dont have any experience with plyr and would prefer to solve this problem with base packages (for learning purposes), but if its not possible any other suggestions would be very helpful
set.seed(1)
library(plyr)
kk<-ddply(df,.(month,name),summarize,mean=mean(cost),sd=sd(cost))
reshape(kk,timevar="name",idvar="month",direction="wide")
month mean.jill sd.jill mean.max sd.max mean.mike sd.mike mean.sam sd.sam mean.tim sd.tim
1 4 55.3 34.62834 63.3 23.35261 57.6 22.91627 63.4 28.89906 43.3 25.42112
6 5 49.3 25.00689 51.1 27.85059 48.4 23.16223 43.0 24.33562 47.6 32.13928
11 6 60.4 23.61826 52.1 29.74503 38.6 34.39703 53.0 23.28567 52.4 20.88700
16 7 50.0 30.76073 62.7 23.98634 51.7 32.10763 52.8 32.27589 49.5 23.00845
> means <- with( df, tapply(cost, list(month, name), FUN=mean) )
> sds <- with( df, tapply(cost, list(month, name), FUN=sd) )
> colnames(means) <- paste0(colnames(means), ".mean")
> colnames(sds) <- paste0(colnames(sds), ".sd")
> comb.df <- as.data.frame( cbind(means, sds) )
> comb.df <- comb.df[order(names(comb.df))]
> comb.df
jill.mean jill.mean.sd max.mean max.mean.sd mike.mean mike.mean.sd
4 62.1 22.29823 39.7 25.53016 39.6 30.11164
5 40.7 30.72838 44.4 29.12502 54.2 23.91095
6 47.3 31.54556 46.9 32.30910 65.3 30.05569
7 55.5 33.16038 45.9 28.13637 59.7 31.79815
sam.mean sam.mean.sd tim.mean tim.mean.sd
4 40.9 23.54877 58.5 21.69613
5 51.5 30.76163 34.2 32.16900
6 69.1 18.26016 55.2 32.99764
7 46.9 29.90150 55.8 27.17352
I'm not sure what you are asking for, but maybe something like this could be useful
> set.seed(1)
> df <- data.frame(id=1:20, month=rep(4:7, 50),
+ name=rep(c("sam", "mike", "tim", "jill", "max"), 40),
+ cost= sample(1:100, 200, replace=TRUE))
>
> DF.mean <- aggregate(cost ~ name + month, FUN=mean, data=df) ## mean
> DF.sd <- aggregate(cost ~ name + month, FUN=sd, data=df) ## sd
>
> x1 <- as.data.frame.matrix(xtabs(cost~month+name, data=DF.mean)) # reshaping mean
> colnames(x1) <- paste0(colnames(x1), ".mean")
> x2 <- as.data.frame.matrix(xtabs(cost~month+name, data=DF.sd)) # reshaping sd
> colnames(x2) <- paste0(colnames(x2), ".sd")
>
> cbind(x1, x2)
jill.mean max.mean mike.mean sam.mean tim.mean jill.sd max.sd mike.sd sam.sd tim.sd
4 55.3 63.3 57.6 63.4 43.3 34.62834 23.35261 22.91627 28.89906 25.42112
5 49.3 51.1 48.4 43.0 47.6 25.00689 27.85059 23.16223 24.33562 32.13928
6 60.4 52.1 38.6 53.0 52.4 23.61826 29.74503 34.39703 23.28567 20.88700
7 50.0 62.7 51.7 52.8 49.5 30.76073 23.98634 32.10763 32.27589 23.00845
Also, note that #Metrics approach can be done using R base functions without any extra packages:
> kk <- aggregate(cost ~ name + month, FUN=function(x) c(mean=mean(x), sd=sd(x)), data=df)
> reshape(kk,timevar="name",idvar="month",direction="wide")
month cost.jill.mean cost.jill.sd cost.max.mean cost.max.sd cost.mike.mean cost.mike.sd cost.sam.mean cost.sam.sd cost.tim.mean cost.tim.sd
1 4 55.30000 34.62834 63.30000 23.35261 57.60000 22.91627 63.40000 28.89906 43.30000 25.42112
6 5 49.30000 25.00689 51.10000 27.85059 48.40000 23.16223 43.00000 24.33562 47.60000 32.13928
11 6 60.40000 23.61826 52.10000 29.74503 38.60000 34.39703 53.00000 23.28567 52.40000 20.88700
16 7 50.00000 30.76073 62.70000 23.98634 51.70000 32.10763 52.80000 32.27589 49.50000 23.00845
You can use two reshape and then merge the results
library(reshape2)
> dcast(df, month ~ name, mean, value.var="cost")
month jill max mike sam tim
1 4 39.5 54.6 45.6 48.4 57.4
2 5 45.1 61.7 45.4 54.5 50.8
3 6 41.9 45.7 56.4 43.1 52.1
4 7 51.6 38.6 43.6 65.1 51.5
> dcast(df, month ~ name, sd, value.var="cost")
month jill max mike sam tim
1 4 29.31154 25.25954 28.96051 31.32695 29.82989
2 5 31.02848 27.96049 34.32589 30.08599 23.95273
3 6 32.09517 32.50316 37.16988 27.03681 30.42094
4 7 19.56300 31.50026 28.65969 36.53750 26.73429