I have this admission_table containing ADMIT, GRE, GPA and RANK.
> head(admission_table)
ADMIT GRE GPA RANK
1 0 380 3.61 3
2 1 660 3.67 3
3 1 800 4.00 1
4 1 640 3.19 4
5 0 520 2.93 4
6 1 760 3.00 2
I'm trying to convert the summary of this table into data.frame. I want to have ADMIT, GRE, GPA and RANK as my column headers.
> summary(admission_table)
ADMIT GRE GPA RANK
Min. :0.0000 Min. :220.0 Min. :2.260 Min. :1.000
1st Qu.:0.0000 1st Qu.:520.0 1st Qu.:3.130 1st Qu.:2.000
Median :0.0000 Median :580.0 Median :3.395 Median :2.000
Mean :0.3175 Mean :587.7 Mean :3.390 Mean :2.485
3rd Qu.:1.0000 3rd Qu.:660.0 3rd Qu.:3.670 3rd Qu.:3.000
Max. :1.0000 Max. :800.0 Max. :4.000 Max. :4.000
> as.data.frame(summary(admission_table))
Var1 Var2 Freq
1 ADMIT Min. :0.0000
2 ADMIT 1st Qu.:0.0000
3 ADMIT Median :0.0000
4 ADMIT Mean :0.3175
5 ADMIT 3rd Qu.:1.0000
6 ADMIT Max. :1.0000
7 GRE Min. :220.0
8 GRE 1st Qu.:520.0
9 GRE Median :580.0
10 GRE Mean :587.7
11 GRE 3rd Qu.:660.0
12 GRE Max. :800.0
13 GPA Min. :2.260
14 GPA 1st Qu.:3.130
15 GPA Median :3.395
16 GPA Mean :3.390
17 GPA 3rd Qu.:3.670
18 GPA Max. :4.000
19 RANK Min. :1.000
20 RANK 1st Qu.:2.000
21 RANK Median :2.000
22 RANK Mean :2.485
23 RANK 3rd Qu.:3.000
24 RANK Max. :4.000
As I'm trying to convert into data.frame, this is the only result I get. I want the data frame have the exact output just like the summary table because after that I want to insert that into Oracle database using this line of code:
dbWriteTable(connection,name="SUM_ADMISSION_TABLE",value=as.data.frame(summary(admission_table)),row.names = FALSE, overwrite = TRUE ,append = FALSE)
Is the any way to do so?
You can consider unclass, I suppose:
data.frame(unclass(summary(mydf)), check.names = FALSE, stringsAsFactors = FALSE)
# ADMIT GRE GPA RANK
# 1 Min. :0.0000 Min. :380.0 Min. :2.930 Min. :1.000
# 2 1st Qu.:0.2500 1st Qu.:550.0 1st Qu.:3.047 1st Qu.:2.250
# 3 Median :1.0000 Median :650.0 Median :3.400 Median :3.000
# 4 Mean :0.6667 Mean :626.7 Mean :3.400 Mean :2.833
# 5 3rd Qu.:1.0000 3rd Qu.:735.0 3rd Qu.:3.655 3rd Qu.:3.750
# 6 Max. :1.0000 Max. :800.0 Max. :4.000 Max. :4.000
str(.Last.value)
# 'data.frame': 6 obs. of 4 variables:
# $ ADMIT: chr "Min. :0.0000 " "1st Qu.:0.2500 " "Median :1.0000 " "Mean :0.6667 " ...
# $ GRE : chr "Min. :380.0 " "1st Qu.:550.0 " "Median :650.0 " "Mean :626.7 " ...
# $ GPA : chr "Min. :2.930 " "1st Qu.:3.047 " "Median :3.400 " "Mean :3.400 " ...
# $ RANK: chr "Min. :1.000 " "1st Qu.:2.250 " "Median :3.000 " "Mean :2.833 " ...
Note that there is a lot of excessive whitespace there, in both the names and the values.
However, it might be sufficient to do something like:
do.call(cbind, lapply(mydf, summary))
# ADMIT GRE GPA RANK
# Min. 0.0000 380.0 2.930 1.000
# 1st Qu. 0.2500 550.0 3.048 2.250
# Median 1.0000 650.0 3.400 3.000
# Mean 0.6667 626.7 3.400 2.833
# 3rd Qu. 1.0000 735.0 3.655 3.750
# Max. 1.0000 800.0 4.000 4.000
Another way to output a dataframe is:
as.data.frame(apply(mydf, 2, summary))
Works if only numerical columns are selected.
And it may throw an Error in dimnames(x) if there are columns with NA's. It's worth checking for that without the as.data.frame() function first.
None of these solutions actually capture the output of the summary function. The tidy() function extracts the elements from a summary object and makes a bland data.frame, so it does not preserve other features or formatting.
If you want the exact output of the summary function in a data frame, you can do:
output<-capture.output(summary(thisModel), file=NULL,append=FALSE)
output_df <-as.data.frame(output)
This retains all of the new lines and is suitable for writing to XLSX, etc., which will result in the output appropriately spaced across rows.
If you want this output collapsed into a single cell, you can do:
output_collapsed <- paste0(output,sep="",collapse="\n")
output_df <-as.data.frame(output_collapsed)
Related
I have this admission_table containing ADMIT, GRE, GPA and RANK.
> head(admission_table)
ADMIT GRE GPA RANK
1 0 380 3.61 3
2 1 660 3.67 3
3 1 800 4.00 1
4 1 640 3.19 4
5 0 520 2.93 4
6 1 760 3.00 2
I'm trying to convert the summary of this table into data.frame. I want to have ADMIT, GRE, GPA and RANK as my column headers.
> summary(admission_table)
ADMIT GRE GPA RANK
Min. :0.0000 Min. :220.0 Min. :2.260 Min. :1.000
1st Qu.:0.0000 1st Qu.:520.0 1st Qu.:3.130 1st Qu.:2.000
Median :0.0000 Median :580.0 Median :3.395 Median :2.000
Mean :0.3175 Mean :587.7 Mean :3.390 Mean :2.485
3rd Qu.:1.0000 3rd Qu.:660.0 3rd Qu.:3.670 3rd Qu.:3.000
Max. :1.0000 Max. :800.0 Max. :4.000 Max. :4.000
> as.data.frame(summary(admission_table))
Var1 Var2 Freq
1 ADMIT Min. :0.0000
2 ADMIT 1st Qu.:0.0000
3 ADMIT Median :0.0000
4 ADMIT Mean :0.3175
5 ADMIT 3rd Qu.:1.0000
6 ADMIT Max. :1.0000
7 GRE Min. :220.0
8 GRE 1st Qu.:520.0
9 GRE Median :580.0
10 GRE Mean :587.7
11 GRE 3rd Qu.:660.0
12 GRE Max. :800.0
13 GPA Min. :2.260
14 GPA 1st Qu.:3.130
15 GPA Median :3.395
16 GPA Mean :3.390
17 GPA 3rd Qu.:3.670
18 GPA Max. :4.000
19 RANK Min. :1.000
20 RANK 1st Qu.:2.000
21 RANK Median :2.000
22 RANK Mean :2.485
23 RANK 3rd Qu.:3.000
24 RANK Max. :4.000
As I'm trying to convert into data.frame, this is the only result I get. I want the data frame have the exact output just like the summary table because after that I want to insert that into Oracle database using this line of code:
dbWriteTable(connection,name="SUM_ADMISSION_TABLE",value=as.data.frame(summary(admission_table)),row.names = FALSE, overwrite = TRUE ,append = FALSE)
Is the any way to do so?
You can consider unclass, I suppose:
data.frame(unclass(summary(mydf)), check.names = FALSE, stringsAsFactors = FALSE)
# ADMIT GRE GPA RANK
# 1 Min. :0.0000 Min. :380.0 Min. :2.930 Min. :1.000
# 2 1st Qu.:0.2500 1st Qu.:550.0 1st Qu.:3.047 1st Qu.:2.250
# 3 Median :1.0000 Median :650.0 Median :3.400 Median :3.000
# 4 Mean :0.6667 Mean :626.7 Mean :3.400 Mean :2.833
# 5 3rd Qu.:1.0000 3rd Qu.:735.0 3rd Qu.:3.655 3rd Qu.:3.750
# 6 Max. :1.0000 Max. :800.0 Max. :4.000 Max. :4.000
str(.Last.value)
# 'data.frame': 6 obs. of 4 variables:
# $ ADMIT: chr "Min. :0.0000 " "1st Qu.:0.2500 " "Median :1.0000 " "Mean :0.6667 " ...
# $ GRE : chr "Min. :380.0 " "1st Qu.:550.0 " "Median :650.0 " "Mean :626.7 " ...
# $ GPA : chr "Min. :2.930 " "1st Qu.:3.047 " "Median :3.400 " "Mean :3.400 " ...
# $ RANK: chr "Min. :1.000 " "1st Qu.:2.250 " "Median :3.000 " "Mean :2.833 " ...
Note that there is a lot of excessive whitespace there, in both the names and the values.
However, it might be sufficient to do something like:
do.call(cbind, lapply(mydf, summary))
# ADMIT GRE GPA RANK
# Min. 0.0000 380.0 2.930 1.000
# 1st Qu. 0.2500 550.0 3.048 2.250
# Median 1.0000 650.0 3.400 3.000
# Mean 0.6667 626.7 3.400 2.833
# 3rd Qu. 1.0000 735.0 3.655 3.750
# Max. 1.0000 800.0 4.000 4.000
Another way to output a dataframe is:
as.data.frame(apply(mydf, 2, summary))
Works if only numerical columns are selected.
And it may throw an Error in dimnames(x) if there are columns with NA's. It's worth checking for that without the as.data.frame() function first.
None of these solutions actually capture the output of the summary function. The tidy() function extracts the elements from a summary object and makes a bland data.frame, so it does not preserve other features or formatting.
If you want the exact output of the summary function in a data frame, you can do:
output<-capture.output(summary(thisModel), file=NULL,append=FALSE)
output_df <-as.data.frame(output)
This retains all of the new lines and is suitable for writing to XLSX, etc., which will result in the output appropriately spaced across rows.
If you want this output collapsed into a single cell, you can do:
output_collapsed <- paste0(output,sep="",collapse="\n")
output_df <-as.data.frame(output_collapsed)
My record has 821050 rows and 18 columns. The rows represent different online users, the columns the browsing behavior of the users in an online shop. The column variables include shopping cart cancellations, number of items in the shopping cart, detailed view of items, product list/multi-item view, detailed search view, etc... Half of the variables are discrete, half are continuous. 8 of the variables are dummy variables. Based on the data set, I want to apply different hard and soft clustering methods and analyze the shopping cart abondonnement of my data set more precisely. With the help of descriptive statistics I have analyzed my data set and obtained the following results.
# 1. WKA_ohneJB <- read.csv("WKA_ohneJB_PCA.csv", header=TRUE, sep = ";", stringsAsFactors = FALSE)
# 2. summary(WKA_ohneJB)
X BASKETS_NZ LOGONS PIS PIS_AP PIS_DV
Min. : 1 Min. : 0.000 Min. :0.0000 Min. : 1.00 Min. : 0.000 Min. : 0.000
1st Qu.:205263 1st Qu.: 1.000 1st Qu.:1.0000 1st Qu.: 9.00 1st Qu.: 0.000 1st Qu.: 0.000
Median :410525 Median : 1.000 Median :1.0000 Median : 20.00 Median : 1.000 Median : 1.000
Mean :410525 Mean : 1.023 Mean :0.9471 Mean : 31.11 Mean : 1.783 Mean : 4.554
3rd Qu.:615786 3rd Qu.: 1.000 3rd Qu.:1.0000 3rd Qu.: 41.00 3rd Qu.: 2.000 3rd Qu.: 5.000
Max. :821048 Max. :49.000 Max. :1.0000 Max. :593.00 Max. :71.000 Max. :203.000
PIS_PL PIS_SDV PIS_SHOPS PIS_SR QUANTITY WKA
Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. : 1.00 Min. :0.0000
1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 1.00 1st Qu.:0.0000
Median : 0.000 Median : 0.00 Median : 2.00 Median : 0.000 Median : 2.00 Median :1.0000
Mean : 5.729 Mean : 2.03 Mean : 10.67 Mean : 3.873 Mean : 3.14 Mean :0.6341
3rd Qu.: 4.000 3rd Qu.: 2.00 3rd Qu.: 11.00 3rd Qu.: 4.000 3rd Qu.: 4.00 3rd Qu.:1.0000
Max. :315.000 Max. :142.00 Max. :405.00 Max. :222.000 Max. :143.00 Max. :1.0000
NEW_CUST EXIST_CUST WEB_CUST MOBILE_CUST TABLET_CUST LOGON_CUST_STEP2
Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.00000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.00000 Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
Mean :0.07822 Mean :0.9218 Mean :0.4704 Mean :0.3935 Mean :0.1361 Mean :0.1743
3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
With the non-dummy variables it is noticeable that they have a right-skewed distribution. For the dummy variables, 5 have a right-skewed distribution and 3 have a left-skewed distribution.
I have also listed range and quantiles for the 9 non-dummies
# BASKETS_NZ
range(WKA_ohneJB$BASKETS_NZ) # 0 49
quantile(WKA_ohneJB$BASKETS_NZ, 0.5) # 1
quantile(WKA_ohneJB$BASKETS_NZ, 0.25) # 1
quantile(WKA_ohneJB$BASKETS_NZ, 0.75) # 1
# PIS
range(WKA_ohneJB$PIS) # 1 593
quantile(WKA_ohneJB$PIS, 0.25) # 9
quantile(WKA_ohneJB$PIS, 0.5) # 20
quantile(WKA_ohneJB$PIS, 0.75) # 41
# PIS_AP
range(WKA_ohneJB$PIS_AP) # 0 71
quantile(WKA_ohneJB$PIS_AP, 0.25) # 0
quantile(WKA_ohneJB$PIS_AP, 0.5) # 1
quantile(WKA_ohneJB$PIS_AP, 0.75) # 2
# PIS_DV
range(WKA_ohneJB$PIS_DV) # 0 203
quantile(WKA_ohneJB$PIS_DV, 0.25) # 0
quantile(WKA_ohneJB$PIS_DV, 0.5) # 1
quantile(WKA_ohneJB$PIS_DV, 0.75) # 5
#PIS_PL
range(WKA_ohneJB$PIS_PL) # 0 315
quantile(WKA_ohneJB$PIS_PL, 0.25) # 0
quantile(WKA_ohneJB$PIS_PL, 0.5) # 0
quantile(WKA_ohneJB$PIS_PL, 0.75) # 4
#PIS_SDV
range(WKA_ohneJB$PIS_SDV) # 0 142
quantile(WKA_ohneJB$PIS_SDV, 0.25) # 0
quantile(WKA_ohneJB$PIS_SDV, 0.5) # 0
quantile(WKA_ohneJB$PIS_SDV, 0.75) # 2
# PIS_SHOPS
range(WKA_ohneJB$PIS_SHOPS) # 0 405
quantile(WKA_ohneJB$PIS_SHOPS, 0.25) # 0
quantile(WKA_ohneJB$PIS_SHOPS, 0.5) # 2
quantile(WKA_ohneJB$PIS_SHOPS, 0.75) # 11
# PIS_SR
range(WKA_ohneJB$PIS_SR) # 0 222
quantile(WKA_ohneJB$PIS_SR, 0.25) # 0
quantile(WKA_ohneJB$PIS_SR, 0.5) # 0
quantile(WKA_ohneJB$PIS_SR, 0.75) # 4
# QUANTITY
range(WKA_ohneJB$QUANTITY) # 1 143
quantile(WKA_ohneJB$QUANTITY, 0.25) # 1
quantile(WKA_ohneJB$QUANTITY, 0.5) # 2
quantile(WKA_ohneJB$QUANTITY, 0.75) # 4
How can I recognize from the distribution of my data which cluster methods are suitable for mixed type clickstream data?
I have voter and party-data from several datasets that I further separated into different dataframes and lists to make it comparable. I could just use the summary command on each of them individually then compare manually, but I was wondering whether there was a way to get them all together and into one table?
Here's a sample of what I have:
> summary(eco$rilenew)
Min. 1st Qu. Median Mean 3rd Qu. Max.
3 4 4 4 4 5
> summary(ecovoters)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 3.000 4.000 3.744 5.000 10.000 26
> summary(lef$rilenew)
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 3.000 3.000 3.692 4.000 7.000
> summary(lefvoters)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.000 3.000 3.612 5.000 10.000 332
> summary(soc$rilenew)
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 4.000 4.000 4.143 5.000 6.000
> summary(socvoters)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 3.000 4.000 3.674 5.000 10.000 346
Is there a way I can summarize these lists (ecovoters, lefvoters, socvoters etc) and the dataframe variables (eco$rilenew, lef$rilenew, soc$rilenew etc) together and have them in one table?
You could put everything into a list and summarize with a small custom function.
L <- list(eco$rilenew, ecovoters, lef$rilenew,
lefvoters, soc$rilenew, socvoters)
t(sapply(L, function(x) {
s <- summary(x)
length(s) <- 7
names(s)[7] <- "NA's"
s[7] <- ifelse(!any(is.na(x)), 0, s[7])
return(s)
}))
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
[1,] 0.9820673 3.3320662 3.958665 3.949512 4.625109 7.229069 0
[2,] -4.8259384 0.5028293 3.220546 3.301452 6.229384 9.585749 26
[3,] -0.3717391 2.3280366 3.009360 3.013908 3.702156 6.584659 0
[4,] -2.6569493 1.6674330 3.069440 3.015325 4.281100 8.808432 332
[5,] -2.3625651 2.4964361 3.886673 3.912009 5.327401 10.349040 0
[6,] -2.4719404 1.3635785 2.790523 2.854812 4.154936 8.491347 346
Data
set.seed(42)
eco <- data.frame(rilenew=rnorm(800, 4, 1))
ecovoters <- rnorm(75, 4, 4)
ecovoters[sample(length(ecovoters), 26)] <- NA
lef <- data.frame(rilenew=rnorm(900, 3, 1))
lefvoters <- rnorm(700, 3, 2)
lefvoters[sample(length(lefvoters), 332)] <- NA
soc <- data.frame(rilenew=rnorm(900, 4, 2))
socvoters <- rnorm(700, 3, 2)
socvoters[sample(length(socvoters), 346)] <- NA
Can use map from tidyverse to get the summary list, then if you want the result as dataframe, then plyr::ldply can help to convert list to dataframe:
ll = map(L, summary)
ll
plyr::ldply(ll, rbind)
> ll = map(L, summary)
> ll
[[1]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.9821 3.3321 3.9587 3.9495 4.6251 7.2291
[[2]]
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-4.331 1.347 3.726 3.793 6.653 16.845 26
[[3]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.3717 2.3360 3.0125 3.0174 3.7022 6.5847
[[4]]
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-2.657 1.795 3.039 3.013 4.395 9.942 332
[[5]]
Min. 1st Qu. Median Mean 3rd Qu. Max.
-2.363 2.503 3.909 3.920 5.327 10.349
[[6]]
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-3.278 1.449 2.732 2.761 4.062 8.171 346
> plyr::ldply(ll, rbind)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 0.9820673 3.332066 3.958665 3.949512 4.625109 7.229069 NA
2 -4.3312551 1.346532 3.725708 3.793431 6.652917 16.844796 26
3 -0.3717391 2.335959 3.012507 3.017438 3.702156 6.584659 NA
4 -2.6569493 1.795307 3.038905 3.012928 4.395338 9.941819 332
5 -2.3625651 2.503324 3.908727 3.920050 5.327401 10.349040 NA
6 -3.2779863 1.448814 2.732515 2.760569 4.061854 8.170793 346
Is there an easy way to change the output format for R's summary function so that the results print in a column instead of row? R does this automatically when you pass summary a data frame. I'd like to print summary statistics in a column when I pass it a single vector. So instead of this:
>summary(vector)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 2.000 6.699 6.000 559.000
It would look something like this:
>summary(vector)
Min. 1.000
1st Qu. 1.000
Median 2.000
Mean 6.699
3rd Qu. 6.000
Max. 559.000
Sure. Treat it as a data.frame:
set.seed(1)
x <- sample(30, 100, TRUE)
summary(x)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 10.00 15.00 16.03 23.25 30.00
summary(data.frame(x))
# x
# Min. : 1.00
# 1st Qu.:10.00
# Median :15.00
# Mean :16.03
# 3rd Qu.:23.25
# Max. :30.00
For slightly more usable output, you can use data.frame(unclass(.)):
data.frame(val = unclass(summary(x)))
# val
# Min. 1.00
# 1st Qu. 10.00
# Median 15.00
# Mean 16.03
# 3rd Qu. 23.25
# Max. 30.00
Or you can use stack:
stack(summary(x))
# values ind
# 1 1.00 Min.
# 2 10.00 1st Qu.
# 3 15.00 Median
# 4 16.03 Mean
# 5 23.25 3rd Qu.
# 6 30.00 Max.
I am trying to run a GEE model with a logit outcome, using the following code.
mod.gee <- geeglm(general_elec~activity_outside_home+econ_scale_6_pt,
data=D_work_small, id="pidlink",
family=binomial(link="logit"), corstr="ar1")
But I keep getting this error:
Error in model.frame.default(formula = general_elec ~ activity_outside_home+ :
variable lengths differ (found for '(id)')
My data is longitudinal data from two waves of a survey. I have tried omitting responses with NAs, and changing the data types of the variables, but nothing has worked. Any suggestions, or has anyone run into this problem before?
My data is structured as follows:
> head(D_work_small)
pidlink econ_scale_6_pt activity_outside_home general_elec wave age female java educ_level pid_unit
1 001220001 3 1 1 3 48 0 0 1 0012200013
10 001220002 3 1 1 3 47 1 0 1 0012200023
19 001220003 2 1 1 3 27 0 0 4 0012200033
77 001250003 2 1 1 3 27 0 0 1 0012500033
79 001290001 2 1 1 3 52 0 0 1 0012900013
88 001290002 2 1 1 3 49 1 0 1 0012900023
> summary(D_work_small)
pidlink econ_scale_6_pt activity_outside_home general_elec wave
Length:44106 Min. :1.000 Min. :0.0000 Min. :0.0000 Min. :3.000
Class :character 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:3.000
Mode :character Median :3.000 Median :1.0000 Median :1.0000 Median :4.000
Mean :2.894 Mean :0.7048 Mean :0.8304 Mean :3.608
3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000
Max. :6.000 Max. :1.0000 Max. :1.0000 Max. :4.000
age female java educ_level pid_unit
Min. : 14.00 Min. :0.0000 Min. :0.0000 Min. :1.0 Length:44106
1st Qu.: 26.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0 Class :character
Median : 35.00 Median :1.0000 Median :0.0000 Median :2.0 Mode :character
Mean : 37.35 Mean :0.5118 Mean :0.4171 Mean :2.2
3rd Qu.: 47.00 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:3.0
Max. :999.00 Max. :1.0000 Max. :1.0000 Max. :5.0