I have a data set with about 600 animals with this structure:
anim <- c(1,1,1,1,1,2,2,2,2)
point <- c(1,2,3,4,6,3,4,5,6)
temp <- c(38.8,38.5,33.2,33.5,37.2,36.2,36.5,36.0,37.8)
mydf <- data.frame(anim,point,temp)
anim point temp
1 1 38.8
1 2 38.5
1 3 33.2
1 4 33.5
1 6 37.2
2 3 36.2
2 4 36.5
2 5 36.0
2 6 37.8
The variable "point" contains different measuring points (temperature) on an animal and I need to have these measuring points (1,2,3,4,5,6) as new variables such that 1=bel,2=ber,3=le,4=re,5=ey,6=cr. Note, that some points may not appear or measured and therefore NA should be put in.
mynewdf should look like this:
anim bel ber le re ey cr
1 38.8 38.5 33.2 33.5 NA 37.2
2 NA NA 36.2 36.5 36.0 37.8
I hope that my question is clear enough and any help would be very much appreciated.
EDITED:
This is an extract from my actual data set:
head(irpig,n=25)
head(irpig,n=25)
dam anim point temp
1 1A0331 20584 1 37.9
2 1A0331 20584 2 37.7
3 1A0331 20584 3 34.3
4 1A0331 20584 4 35.8
5 1A0331 20584 6 37.6
6 1A0331 20585 2 38.7
7 1A0331 20585 4 36.4
8 1A0331 20585 6 38.0
9 1A0331 20586 1 39.0
10 1A0331 20586 2 39.8
11 1A0331 20586 3 37.9
12 1A0331 20586 4 38.0
13 1A0331 20586 6 38.5
14 1A0331 20587 1 39.3
15 1A0331 20587 2 38.9
16 1A0331 20587 3 39.4
17 1A0331 20587 4 38.6
18 1A0331 20587 6 39.4
19 1A0331 20588 1 39.6
20 1A0331 20588 2 39.2
21 1A0331 20588 3 38.9
22 1A0331 20588 4 38.0
23 1A0331 20588 6 39.6
24 1A0331 20589 1 38.1
25 1A0331 20589 2 38.7
Baz
Here is one solution. It uses the dcast function from reshape2 library by Hadley Wickham
mydf$point = as.factor(mydf$point)
levels(mydf$point) = c("bel", "ber", "le", "re", "ey", "cr")
library(reshape2)
dcast(mydf, anim ~ point)
anim bel ber le re ey cr
1 1 38.8 38.5 33.2 33.5 NA 37.2
2 2 NA NA 36.2 36.5 36 37.8
Related
I have weather dataset my data is date-dependent
I want to predict the temperature from 07 May 2008 until 18 May 2008 (which is maybe a total of 10-15 observations) my data size is around 200
I will be using decision tree/RF and SVM & NN to make my prediction
I've never handled data like this so I'm not sure how to sample non random data
I want to sample data 80% train data and 30% test data but I want to sample the data in the original order not randomly. Is that possible ?
install.packages("rattle")
install.packages("RGtk2")
library("rattle")
seed <- 42
set.seed(seed)
fname <- system.file("csv", "weather.csv", package = "rattle")
dataset <- read.csv(fname, encoding = "UTF-8")
dataset <- dataset[1:200,]
dataset <- dataset[order(dataset$Date),]
set.seed(321)
sample_data = sample(nrow(dataset), nrow(dataset)*.8)
test<-dataset[sample_data,] # 30%
train<-dataset[-sample_data,] # 80%
output
> head(dataset)
Date Location MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir WindGustSpeed
1 2007-11-01 Canberra 8.0 24.3 0.0 3.4 6.3 NW 30
2 2007-11-02 Canberra 14.0 26.9 3.6 4.4 9.7 ENE 39
3 2007-11-03 Canberra 13.7 23.4 3.6 5.8 3.3 NW 85
4 2007-11-04 Canberra 13.3 15.5 39.8 7.2 9.1 NW 54
5 2007-11-05 Canberra 7.6 16.1 2.8 5.6 10.6 SSE 50
6 2007-11-06 Canberra 6.2 16.9 0.0 5.8 8.2 SE 44
WindDir9am WindDir3pm WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am
1 SW NW 6 20 68 29 1019.7
2 E W 4 17 80 36 1012.4
3 N NNE 6 6 82 69 1009.5
4 WNW W 30 24 62 56 1005.5
5 SSE ESE 20 28 68 49 1018.3
6 SE E 20 24 70 57 1023.8
Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM RainTomorrow
1 1015.0 7 7 14.4 23.6 No 3.6 Yes
2 1008.4 5 3 17.5 25.7 Yes 3.6 Yes
3 1007.2 8 7 15.4 20.2 Yes 39.8 Yes
4 1007.0 2 7 13.5 14.1 Yes 2.8 Yes
5 1018.5 7 7 11.1 15.4 Yes 0.0 No
6 1021.7 7 5 10.9 14.8 No 0.2 No
> head(test)
Date Location MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir WindGustSpeed
182 2008-04-30 Canberra -1.8 14.8 0.0 1.4 7.0 N 28
77 2008-01-16 Canberra 17.9 33.2 0.0 10.4 8.4 N 59
88 2008-01-27 Canberra 13.2 31.3 0.0 6.6 11.6 WSW 46
58 2007-12-28 Canberra 15.1 28.3 14.4 8.8 13.2 NNW 28
96 2008-02-04 Canberra 18.2 22.6 1.8 8.0 0.0 ENE 33
126 2008-03-05 Canberra 12.0 27.6 0.0 6.0 11.0 E 46
WindDir9am WindDir3pm WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am
182 E N 2 19 80 40 1024.2
77 N NNE 15 20 58 62 1008.5
88 N WNW 4 26 71 28 1013.1
58 NNW NW 6 13 73 44 1016.8
96 SSE ENE 7 13 92 76 1014.4
126 SSE WSW 7 6 69 35 1025.5
Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM RainTomorrow
182 1020.5 1 7 5.3 13.9 No 0.0 No
77 1006.1 6 7 24.5 23.5 No 4.8 Yes
88 1009.5 1 4 19.7 30.7 No 0.0 No
58 1013.4 1 5 18.3 27.4 Yes 0.0 No
96 1011.5 8 8 18.5 22.1 Yes 9.0 Yes
126 1022.2 1 1 15.7 26.2 No 0.0 No
> head(train)
Date Location MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir WindGustSpeed
7 2007-11-07 Canberra 6.1 18.2 0.2 4.2 8.4 SE 43
9 2007-11-09 Canberra 8.8 19.5 0.0 4.0 4.1 S 48
11 2007-11-11 Canberra 9.1 25.2 0.0 4.2 11.9 N 30
16 2007-11-16 Canberra 12.4 32.1 0.0 8.4 11.1 E 46
22 2007-11-22 Canberra 16.4 19.4 0.4 9.2 0.0 E 26
25 2007-11-25 Canberra 15.4 28.4 0.0 4.4 8.1 ENE 33
WindDir9am WindDir3pm WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am
7 SE ESE 19 26 63 47 1024.6
9 E ENE 19 17 70 48 1026.1
11 SE NW 6 9 74 34 1024.4
16 SE WSW 7 9 70 22 1017.9
22 ENE E 6 11 88 72 1010.7
25 SSE NE 9 15 85 31 1022.4
Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM RainTomorrow
7 1022.2 4 6 12.4 17.3 No 0.0 No
9 1022.7 7 7 14.1 18.9 No 16.2 Yes
11 1021.1 1 2 14.6 24.0 No 0.2 No
16 1012.8 0 3 19.1 30.7 No 0.0 No
22 1008.9 8 8 16.5 18.3 No 25.8 Yes
25 1018.6 8 2 16.8 27.3 No 0.0 No
I use mtcars as an example. An option to non-randomly split your data in train and test is to first create a sample size based on the number of rows in your data. After that you can use split to split the data exact at the 80% of your data. You using the following code:
smp_size <- floor(0.80 * nrow(mtcars))
split <- split(mtcars, rep(1:2, each = smp_size))
With the following code you can turn the split in train and test:
train <- split$`1`
test <- split$`2`
Let's check the number of rows:
> nrow(train)
[1] 25
> nrow(test)
[1] 7
Now the data is split in train and test without losing their order.
using this function I calculate the variance of some 3d points.
centroid_3d_sq_dist <- function(
point_matrix
) {
if (nrow(point_matrix) == 1) {
return(0)
}
mean_point <- apply(point_matrix, 2, mean)
point_sq_distances <- apply(
point_matrix,
1,
function(row_point) {
sum((row_point - mean_point) ** 2)
}
)
sum_sq_distances <- sum(point_sq_distances)
return(sum_sq_distances)
}
point_3d_variance <- function(
point_matrix
) {
if (nrow(point_matrix) == 1) {
return(0)
}
dist_var <- centroid_3d_sq_dist(point_matrix) /
(nrow(point_matrix) - 1)
return(dist_var)
}
The argument of this function is a matrix (x,y,z).
Now I have a dataset with two 3D points.
ID Trial Size PP PA FkA ciccioX ciccioY ciccioZ pinoX pinoY pinoZ
1 Gigi 1 40 39.6 1050. 31.5 521. 293. 10.6 516. 323. 6.41
2 Gigi 2 20.0 30.7 944. 9.35 525. 300. 12.6 520. 305. 7.09
3 Gigi 3 30 29.5 1056. 24.1 521. 298. 12.3 519. 321. 5.89
4 Gigi 5 60 53.0 1190. 53.0 680. 287. 64.4 699. 336. 68.6
5 Bibi 1 40 38.3 1038. 31.4 524. 289. 10.9 519. 319. 6.17
6 Bibi 2 60 64.7 1293. 47.8 516. 282. 10.4 519. 330. 6.32
7 Bibi 3 20.0 33.8 1092. 17.5 523. 300. 12.8 518. 315. 6.22
8 Bibi 4 30 35.0 1108. 26.4 525. 295. 11.7 517. 320. 5.78
9 Bibi 5 50 46.5 1199. 34.2 515. 289. 11.2 517. 323. 6.27
10 Bibi 6 30 28.7 1016. 17.1 528. 298. 12.7 524. 314. 6.36
The 3D points are:
ciccio: ciccioX ciccioY ciccioZ
pino: pinoX pinoY pinoZ
I want to calculate the variance of ciccio and the variance of pino grouped by ID and SIZE.
I tried to do:
data %>%
group_by(SubjectID, Size) %>%
summarize(as.data.frame(matrix(f4(dd[7:9],dd[10:12]), nr = 1)))
But it doesn't work.
Do you have any advice?
Your shown dataset is too small to calculate (meaningful) variations. But you could use:
library(dplyr)
df %>%
group_by(ID, Size) %>%
summarise(var_ciccio = point_3d_variance(as.matrix(across(ciccioX:ciccioZ))),
var_pino = point_3d_variance(as.matrix(across(pinoX:pinoZ))),
.groups = "drop")
This returns
# A tibble: 9 x 4
ID Size var_ciccio var_pinoo
<chr> <dbl> <dbl> <dbl>
1 Bibi 20 0 0
2 Bibi 30 9.5 42.7
3 Bibi 40 0 0
4 Bibi 50 0 0
5 Bibi 60 0 0
6 Gigi 20 0 0
7 Gigi 30 0 0
8 Gigi 40 0 0
9 Gigi 60 0 0
This question already has answers here:
Categorize numeric variable into group/ bins/ breaks
(4 answers)
Closed 1 year ago.
I am attempting to add a new column to the state sample data frame in R. I am hoping for this column to cluster the ID of states into broader categories (1-4). My code is close to what I am looking for but I am not getting it quite right.. I know I could enter each state ID line by line but is there a a quicker way? Thank you!
library(tidyverse)
#Add column to denote each state
States=state.x77
States=data.frame(States)
States <- tibble::rowid_to_column(States, "ID")
States
#Create new variable for state buckets
States <- States %>%
mutate(WAGE_BUCKET=case_when(ID <= c(1,12) ~ '1',
ID <= c(13,24) ~ '2',
ID <= c(25,37) ~ '3',
ID <= c(38,50) ~ '4',
TRUE ~ 'NA'))
View(States) #It is not grouping the states in the way I want/I am still getting some NA values but unsure why!
You can use cut or findInterval if all of your groups will be using contiguous ID values:
findInterval(States$ID, c(0, 12, 24, 37, 51))
# [1] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4
If you want to make it a bit more verbose, you can use dplyr::between in your case_when:
States %>%
mutate(
WAGE_BUCKET = case_when(
between(ID, 1, 12) ~ "1",
between(ID, 13, 24) ~ "2",
between(ID, 25, 37) ~ "3",
between(ID, 38, 50) ~ "4",
TRUE ~ NA_character_)
)
# ID Population Income Illiteracy Life Exp Murder HS Grad Frost Area WAGE_BUCKET
# 1 1 3615 3624 2.1 69.05 15.1 41.3 20 50708 1
# 2 2 365 6315 1.5 69.31 11.3 66.7 152 566432 1
# 3 3 2212 4530 1.8 70.55 7.8 58.1 15 113417 1
# 4 4 2110 3378 1.9 70.66 10.1 39.9 65 51945 1
# 5 5 21198 5114 1.1 71.71 10.3 62.6 20 156361 1
# 6 6 2541 4884 0.7 72.06 6.8 63.9 166 103766 1
# 7 7 3100 5348 1.1 72.48 3.1 56.0 139 4862 1
# 8 8 579 4809 0.9 70.06 6.2 54.6 103 1982 1
# 9 9 8277 4815 1.3 70.66 10.7 52.6 11 54090 1
# 10 10 4931 4091 2.0 68.54 13.9 40.6 60 58073 1
# 11 11 868 4963 1.9 73.60 6.2 61.9 0 6425 1
# 12 12 813 4119 0.6 71.87 5.3 59.5 126 82677 1
# 13 13 11197 5107 0.9 70.14 10.3 52.6 127 55748 2
# 14 14 5313 4458 0.7 70.88 7.1 52.9 122 36097 2
# 15 15 2861 4628 0.5 72.56 2.3 59.0 140 55941 2
# 16 16 2280 4669 0.6 72.58 4.5 59.9 114 81787 2
# 17 17 3387 3712 1.6 70.10 10.6 38.5 95 39650 2
# 18 18 3806 3545 2.8 68.76 13.2 42.2 12 44930 2
# 19 19 1058 3694 0.7 70.39 2.7 54.7 161 30920 2
# 20 20 4122 5299 0.9 70.22 8.5 52.3 101 9891 2
# 21 21 5814 4755 1.1 71.83 3.3 58.5 103 7826 2
# 22 22 9111 4751 0.9 70.63 11.1 52.8 125 56817 2
# 23 23 3921 4675 0.6 72.96 2.3 57.6 160 79289 2
# 24 24 2341 3098 2.4 68.09 12.5 41.0 50 47296 2
# 25 25 4767 4254 0.8 70.69 9.3 48.8 108 68995 3
# 26 26 746 4347 0.6 70.56 5.0 59.2 155 145587 3
# 27 27 1544 4508 0.6 72.60 2.9 59.3 139 76483 3
# 28 28 590 5149 0.5 69.03 11.5 65.2 188 109889 3
# 29 29 812 4281 0.7 71.23 3.3 57.6 174 9027 3
# 30 30 7333 5237 1.1 70.93 5.2 52.5 115 7521 3
# 31 31 1144 3601 2.2 70.32 9.7 55.2 120 121412 3
# 32 32 18076 4903 1.4 70.55 10.9 52.7 82 47831 3
# 33 33 5441 3875 1.8 69.21 11.1 38.5 80 48798 3
# 34 34 637 5087 0.8 72.78 1.4 50.3 186 69273 3
# 35 35 10735 4561 0.8 70.82 7.4 53.2 124 40975 3
# 36 36 2715 3983 1.1 71.42 6.4 51.6 82 68782 3
# 37 37 2284 4660 0.6 72.13 4.2 60.0 44 96184 3
# 38 38 11860 4449 1.0 70.43 6.1 50.2 126 44966 4
# 39 39 931 4558 1.3 71.90 2.4 46.4 127 1049 4
# 40 40 2816 3635 2.3 67.96 11.6 37.8 65 30225 4
# 41 41 681 4167 0.5 72.08 1.7 53.3 172 75955 4
# 42 42 4173 3821 1.7 70.11 11.0 41.8 70 41328 4
# 43 43 12237 4188 2.2 70.90 12.2 47.4 35 262134 4
# 44 44 1203 4022 0.6 72.90 4.5 67.3 137 82096 4
# 45 45 472 3907 0.6 71.64 5.5 57.1 168 9267 4
# 46 46 4981 4701 1.4 70.08 9.5 47.8 85 39780 4
# 47 47 3559 4864 0.6 71.72 4.3 63.5 32 66570 4
# 48 48 1799 3617 1.4 69.48 6.7 41.6 100 24070 4
# 49 49 4589 4468 0.7 72.48 3.0 54.5 149 54464 4
# 50 50 376 4566 0.6 70.29 6.9 62.9 173 97203 4
It is a vector of length > 1. The comparison operators works on a single vector. We could use between
library(dplyr)
States <- States %>%
mutate(WAGE_BUCKET=case_when(between(ID, 1, 12) ~ '1',
between(ID, 13,24) ~ '2',
between(ID, 25,37) ~ '3',
between(ID, 38,50) ~ '4',
TRUE ~ NA_character_))
Or another option is to use & with > and <=
States %>%
mutate(WAGE_BUCKET=case_when(ID >= 1 & ID <=12 ~ '1',
ID >= 13 & ID <= 24) ~ '2',
ID >= 25 & ID <= 37 ~ '3',
ID >= 38 & ID <= 50 ~ '4',
TRUE ~ NA_character))
Or may be the OP meant to use %in%
States %>%
mutate(WAGE_BUCKET=case_when(ID %in% c(1,12) ~ '1',
ID %in% c(13,24) ~ '2',
ID %in% c(25,37) ~ '3',
ID %in% c(38,50) ~ '4',
TRUE ~ NA_character_))
I am trying to reshape my data to long instead of wide format using the same code provided earlier link , however it doesn't work even after several trials to modify names_pattern = "(.*)_(pre|post.*)",
My data sample is
data1<-read.table(text="
Serial_ID pre_EDV pre_ESV pre_LVEF post_EDV post_ESV post_LVEF
1 76.2 32.9 56.8 86.3 36.6 57.6
2 65.4 35.9 45.1 60.1 26.1 56.7
3 64.4 35.1 45.5 72.5 41.1 43.3
4 50 13.9 72.1 46.4 18.4 60.4
5 89.6 32 64.3 70.9 19.3 72.8
6 62 20.6 66.7 55.9 17.8 68.2
7 91.2 37.7 58.6 61.9 23.8 61.6
8 62 24 61.3 69.3 34.9 49.6
9 104.1 22.7 78.8 38.6 11.5 70.1
10 90.6 31.2 65.6 48 16.1 66.4", sep="", header=T)
I want to reshape my data to
put identical column headings below each other eg post_EDV below
pre_EDV
Create new column Pre vs. post
Fix column heading (remove "pre_" and "post_" to be "EDV" only (as shown in the screenshot below)).
This is the used code:
library(dplyr);library(tidyr);library(stringr)
out <- data %>% pivot_longer(cols = -Serial_ID,
names_to = c(".value", "prevspost"),
names_pattern = "(.*)_(pre|post.*)",
names_sep="_") #%>% as.data.frame
Also I tried names_prefix = c("pre_","post_") instead of names_pattern = "(.*)_(pre|post.*)", but it doesn't work.
Any advice will be greatly appreciated.
Edit I recommend using #Dave2e's superior approach.
The reason your attempt didn't work is because the pattern has to match in order. You could try this:
library(tidyr)
library(dplyr)
data1 %>% pivot_longer(cols = -Serial_ID,
names_to = c("prevspost",".value"),
names_pattern = "(pre|post)_(\\w+)") %>%
dplyr::arrange(desc(prevspost),Serial_ID)
# A tibble: 20 x 5
Serial_ID prevspost EDV ESV LVEF
<int> <chr> <dbl> <dbl> <dbl>
1 1 pre 76.2 32.9 56.8
2 2 pre 65.4 35.9 45.1
3 3 pre 64.4 35.1 45.5
4 4 pre 50 13.9 72.1
5 5 pre 89.6 32 64.3
6 6 pre 62 20.6 66.7
7 7 pre 91.2 37.7 58.6
8 8 pre 62 24 61.3
9 9 pre 104. 22.7 78.8
10 10 pre 90.6 31.2 65.6
11 1 post 86.3 36.6 57.6
12 2 post 60.1 26.1 56.7
13 3 post 72.5 41.1 43.3
14 4 post 46.4 18.4 60.4
15 5 post 70.9 19.3 72.8
16 6 post 55.9 17.8 68.2
17 7 post 61.9 23.8 61.6
18 8 post 69.3 34.9 49.6
19 9 post 38.6 11.5 70.1
20 10 post 48 16.1 66.4
Your initial approach very close, it needed some simplification. Use only "names_sep" or "names_pattern"
library(tidyr)
library(dplyr)
data1 %>% pivot_longer(cols = -Serial_ID,
names_to = c("Pre vs. post", '.value'),
names_sep="_")
# A tibble: 20 x 5
Serial_ID `Pre vs. post` EDV ESV LVEF
<int> <chr> <dbl> <dbl> <dbl>
1 1 pre 76.2 32.9 56.8
2 1 post 86.3 36.6 57.6
3 2 pre 65.4 35.9 45.1
4 2 post 60.1 26.1 56.7
5 3 pre 64.4 35.1 45.5
6 3 post 72.5 41.1 43.3
7 4 pre 50 13.9 72.1
8 4 post 46.4 18.4 60.4
9 5 pre 89.6 32 64.3
10 5 post 70.9 19.3 72.8
11 6 pre 62 20.6 66.7
12 6 post 55.9 17.8 68.2
13 7 pre 91.2 37.7 58.6
14 7 post 61.9 23.8 61.6
15 8 pre 62 24 61.3
16 8 post 69.3 34.9 49.6
17 9 pre 104. 22.7 78.8
18 9 post 38.6 11.5 70.1
19 10 pre 90.6 31.2 65.6
20 10 post 48 16.1 66.4
try this:
library(dplyr);library(tidyr);library(stringr)
out <- data1 %>% pivot_longer(-Serial_ID,
names_to = c("measurement", "names"),
values_to = "values",
names_sep = "_")
out
# # A tibble: 60 x 4
# Serial_ID measurement names values
# <int> <chr> <chr> <dbl>
# 1 1 pre EDV 76.2
# 2 1 pre ESV 32.9
# 3 1 pre LVEF 56.8
# 4 1 post EDV 86.3
# 5 1 post ESV 36.6
# 6 1 post LVEF 57.6
# 7 2 pre EDV 65.4
# 8 2 pre ESV 35.9
# 9 2 pre LVEF 45.1
# 10 2 post EDV 60.1
# # ... with 50 more rows
Your code snipped passed the object "data" instead of "data1" into the pipe which produced an error:
"Error: No tidyselect variables were registered".
So I'm starting with a dataframe called max.mins that has 153 rows.
day Tx Hx Tn
1 1 10.0 7.83 2.1
2 2 7.7 6.19 2.5
3 3 7.1 4.86 0.0
4 4 9.8 7.37 2.7
5 5 13.4 12.68 0.4
6 6 17.5 17.47 3.5
7 7 16.5 15.58 6.5
8 8 21.5 20.30 6.2
9 9 21.7 21.41 9.7
10 10 24.4 28.18 8.0
I'm applying these statements to the dataframe to look for specific criteria
temp_warnings <- subset(max.mins, Tx >= 32 & Tn >=20)
humidex_warnings <- subset(max.mins, Hx >= 40)
Now when I open up humidex_warnings for example I have this dataframe
row.names day Tx Hx Tn
1 41 10 31.1 40.51 20.7
2 56 25 33.4 42.53 19.6
3 72 11 34.1 40.78 18.1
4 73 12 33.8 40.18 18.8
5 74 13 34.1 41.10 22.4
6 79 18 30.3 41.57 22.5
7 94 2 31.4 40.81 20.3
8 96 4 30.7 40.39 20.2
The next step is to search for 2 or 3 consective numbers in the column row.names and give me a total of how many times this occurs (I asked this in a previous question and have a function that should work once this problem is sorted out). The issue is that row.names is class NULL which is preventing me from applying further functions to this dataframe.
Help? :)
Thanks in advance,
Nick
If you need the row.names as a data as integer:
humidex_warnings$seq <- as.integer(row.names(humidex_warnings))
If you don't need row.names
row.names(humidex_warnings) <- NULL