I have the following 2 dataframes:
> bvg1
Parameters X18.Oct.14 X19.Oct.14 X20.Oct.14 X21.Oct.14 X22.Oct.14 X23.Oct.14 X24.Oct.14
1 24K Equivalent Plan 29.00 29.60 33.80 36.60 35.30 31.90 29.00
2 24K Equivalent Act 28.80 31.00 35.40 35.90 34.70 33.40 31.90
3 Plan Rep WS 2463.00 2513.00 2869.00 3115.00 2999.00 2714.00 2468.00
4 Act Rep WS 2447.00 2633.00 3013.00 3054.00 2953.00 2842.00 2714.00
5 Rep WS Var -16.00 120.00 144.00 -61.00 -46.00 128.00 246.00
6 Plan Rep Intakes 568.00 461.00 1159.00 1146.00 1126.00 1124.00 1106.00
7 Act Rep Intakes 707.00 494.00 1106.00 1096.00 1274.00 1087.00 1101.00
8 Rep Intakes Var 139.00 33.00 -53.00 -50.00 148.00 -37.00 -5.00
9 Plan Rep Comps_DL 468.00 54.00 836.00 1190.00 1327.00 1286.00 1108.00
10 Act Rep Comps_DL 471.00 70.00 995.00 1137.00 1323.00 1150.00 1073.00
11 Rep Comps Var_DL 3.00 16.00 159.00 -53.00 -4.00 -136.00 -35.00
12 Plan Rep Mandays_DL 148.00 19.00 260.00 368.00 412.00 398.00 345.00
13 Act Rep Mandays_DL 147.00 19.00 303.00 359.00 423.00 374.00 348.00
14 Rep Mandays Var_DL -1.00 1.00 43.00 -9.00 12.00 -24.00 3.00
15 Plan FVR Mandays_DL 0.00 0.00 4.00 18.00 18.00 18.00 18.00
16 Act FVR Mandays_DL 0.00 0.00 4.00 7.00 8.00 8.00 7.00
17 FVR Mandays Var_DL 0.00 0.00 0.00 -11.00 -10.00 -10.00 -11.00
18 Plan Rep Prod_DL 3.16 2.88 3.21 3.23 3.22 3.23 3.21
19 Act Rep Prod_DL 3.21 3.62 3.28 3.16 3.12 3.07 3.08
20 Rep Prod Var_DL 0.05 0.74 0.07 -0.07 -0.10 -0.16 -0.13
> bvg2
Parameters X18.Oct X19.Oct X20.Oct X21.Oct X22.Oct X23.Oct X24.Oct
1 24K Equivalent Plan 30.50 31.30 35.10 36.10 33.60 28.80 25.50
2 24K Equivalent Act 31.40 33.40 36.60 38.10 36.80 34.40 32.10
3 Plan Rep WS 3419.00 3509.00 3933.00 4041.00 3764.00 3220.00 2859.00
4 Act Rep WS 3514.00 3734.00 4098.00 4271.00 4122.00 3852.00 3591.00
5 Rep WS Var 95.00 225.00 165.00 230.00 358.00 632.00 732.00
6 Plan Rep Intakes 813.00 613.00 1559.00 1560.00 1506.00 1454.00 1410.00
7 Act Rep Intakes 964.00 602.00 1629.00 1532.00 1657.00 1507.00 1439.00
8 Rep Intakes Var 151.00 -11.00 70.00 -28.00 151.00 53.00 29.00
9 Plan Rep Comps_DL 675.00 175.00 1331.00 1732.00 1938.00 1706.00 1493.00
10 Act Rep Comps_DL 718.00 224.00 1389.00 1609.00 1848.00 1698.00 1537.00
11 Rep Comps Var_DL 43.00 49.00 58.00 -123.00 -90.00 -8.00 44.00
12 Plan Rep Mandays_DL 203.00 58.00 428.00 541.00 605.00 536.00 475.00
13 Act Rep Mandays_DL 215.00 63.00 472.00 542.00 608.00 556.00 523.00
14 Rep Mandays Var_DL 12.00 5.00 44.00 2.00 3.00 20.00 48.00
15 Plan FVR Mandays_DL 0.00 0.00 1.00 12.00 2.00 32.00 57.00
16 Act FVR Mandays_DL 0.00 0.00 2.00 2.00 5.00 5.00 5.00
17 FVR Mandays Var_DL 0.00 0.00 1.00 -10.00 3.00 -27.00 -52.00
18 Plan Rep Prod_DL 3.33 3.03 3.11 3.20 3.20 3.18 3.14
19 Act Rep Prod_DL 3.34 3.56 2.94 2.97 3.04 3.05 2.94
20 Rep Prod Var_DL 0.01 0.53 -0.17 -0.23 -0.16 -0.13 -0.20
It is a time series data i.e. 24K Equivalent Plan was 29 on 18th Oct, 29.60 on 19th Oct and 33.80 on 20th Oct. First dataframe have data for one business unit and second dataframe have the data for a different business unit.
I want to merge dataframes into 1 and want to analyse the variance i.e. where they differ in values. Draw ggplots like 2 histograms showing the difference, timeseries plots etc.
I have tried the following:
I can merge the two dataframes by:
joined = rbind(bvg1, bvg2)
however, i can't identify the record whether it belongs to bvg1 or bvg2 df.
if i add an additional column i.e.
bvg1$id = "bvg1"
bvg2$id = "bvg2"
then merge command doesn't work and gives the following error:
Error in match.names(clabs, names(xi)) :
names do not match previous names
Any sample code would be highly appreciated.
You can match the column names of the two datasets by stripping the . followed by the digits in the bvg1. This can be done using regex. In the below code, a lookbehind regex is used. It matches the lookbehind (?<=[A-Za-]) i.e. an alphabet followed by . followed by one or more elements .* to the end of string $ and remove those "".
colnames(bvg1) <-gsub("(?<=[A-Za-z])\\..*$", "", colnames(bvg1), perl=TRUE)
res <- rbind(bvg1, bvg2)
dim(res)
#[1] 40 9
head(res,3)
# Parameters X18.Oct X19.Oct X20.Oct X21.Oct X22.Oct X23.Oct X24.Oct
#1 24K Equivalent Plan 29.0 29.6 33.8 36.6 35.3 31.9 29.0
#2 24K Equivalent Act 28.8 31.0 35.4 35.9 34.7 33.4 31.9
#3 Plan Rep WS 2463.0 2513.0 2869.0 3115.0 2999.0 2714.0 2468.0
# id
#1 bvg1
#2 bvg1
#3 bvg1
Related
everyone. I am using DLNM in R to analyze to lag-effect of climatic conditions on the prevalence of the disease.
I followed somebody else's program strictly
, and it worked in avg.temp and max.speed, but showed err "coef/vcov not consistent with basis matrix" in avg.ap and avg.hum. However, i just changed the variables set in code, and never changed other code.
I have a hypothesis that maybe DLNM doesn't like wet weather. T T
I don't know what to do, can you help me?
Part 1 was the Successfully run code, part 2 was the code that showed err, and part 3 was the data I used.
Thank you very much. I hope you can help me
Part 1. Successfully run code
attach(cpdlnm)
cb.temp = crossbasis(avg.temp, lag=1 ,
argvar=list(fun="ns",
knots= c(10)),
arglag=list(fun="lin"))
modeltemp = glm(pre1 ~ cb.temp +
ns(no,1*1),
family=quasipoisson(), cpdlnm)
pred1.temp = crosspred(cb.temp,
modelhum,
cen=round(median(avg.temp)),
bylag=1)
Part 2. Error code
attach(cpdlnm)
cb.hum = crossbasis(avg.hum, lag=1 ,
argvar=list(fun="ns",
knots= c(10)),
arglag=list(fun="lin"))
modelhum = glm(pre1 ~ cb.hum +
ns(no,1*1),
family=quasipoisson(), cpdlnm)
pred1.hum = crosspred(cb.hum, # This step shows "coef/vcov not consistent with basis matrix"
modelhum,
cen=round(median(avg.hum)),
bylag=0.1)
Part 3. the data are as following:
no pre1 date year month avg.ap avg.temp avg.hum max.speed
1 3.23 12-Jan 2012 1 996.60 9.00 81.60 5.30
2 6.04 12-Feb 2012 2 993.20 10.90 80.80 6.20
3 5.18 12-Mar 2012 3 991.00 16.40 78.70 7.60
4 4.07 12-Apr 2012 4 985.40 23.50 73.50 7.40
5 4.88 12-May 2012 5 982.60 26.30 77.20 7.00
6 5.11 12-Jun 2012 6 978.10 27.00 81.30 6.20
7 6.18 12-Jul 2012 7 979.50 28.10 77.70 6.40
8 6.17 12-Aug 2012 8 980.40 28.00 75.60 7.90
9 5.18 12-Sep 2012 9 987.60 25.30 73.60 6.30
10 5.16 12-Oct 2012 10 990.70 23.60 72.20 6.20
11 4.61 12-Nov 2012 11 991.70 18.00 79.70 6.90
12 5.26 12-Dec 2012 12 995.00 13.20 74.90 6.50
13 3.79 13-Jan 2013 1 997.10 11.20 78.40 5.70
14 3.87 13-Feb 2013 2 993.50 15.30 82.20 6.50
15 3.37 13-Mar 2013 3 989.90 20.20 74.20 8.00
16 2.85 13-Apr 2013 4 987.00 21.50 78.50 7.70
17 4.38 13-May 2013 5 983.30 25.60 79.20 6.80
18 5.67 13-Jun 2013 6 980.60 27.40 76.90 6.60
19 6.45 13-Jul 2013 7 981.30 28.00 77.50 7.10
20 6.95 13-Aug 2013 8 980.50 27.90 78.20 7.90
21 6.51 13-Sep 2013 9 985.90 25.40 77.60 6.00
22 8.16 13-Oct 2013 10 992.20 22.10 68.80 5.30
23 5.34 13-Nov 2013 11 994.50 18.70 72.30 6.20
24 6.18 13-Dec 2013 12 997.30 11.70 67.20 5.30
25 5.69 14-Jan 2014 1 996.70 12.70 70.30 6.00
26 6.44 14-Feb 2014 2 993.00 12.10 76.90 6.40
27 4.16 14-Mar 2014 3 991.60 16.50 83.90 7.30
28 4.13 14-Apr 2014 4 987.60 22.60 82.40 6.70
29 3.96 14-May 2014 5 983.60 25.70 78.80 7.70
30 4.72 14-Jun 2014 6 979.20 27.70 81.40 7.90
31 5.21 14-Jul 2014 7 980.70 28.30 80.20 9.40
32 5.29 14-Aug 2014 8 982.40 27.50 81.30 7.50
33 6.74 14-Sep 2014 9 984.70 27.10 77.70 8.50
34 4.80 14-Oct 2014 10 991.20 23.90 73.10 5.90
35 4.31 14-Nov 2014 11 993.30 18.60 79.60 6.20
36 4.35 14-Dec 2014 12 998.70 12.30 67.30 5.90
37 2.95 15-Jan 2015 1 996.70 13.30 76.30 6.20
38 4.63 15-Feb 2015 2 993.50 15.50 78.30 6.50
39 4.00 15-Mar 2015 3 991.70 17.70 83.40 6.30
40 4.16 15-Apr 2015 4 988.40 22.80 70.20 7.30
41 4.67 15-May 2015 5 982.40 26.70 80.50 8.00
42 5.62 15-Jun 2015 6 980.90 28.20 81.00 7.40
43 5.04 15-Jul 2015 7 980.20 27.30 79.40 6.70
44 5.79 15-Aug 2015 8 982.40 27.60 80.10 6.50
45 5.28 15-Sep 2015 9 986.30 26.00 84.60 6.50
46 4.39 15-Oct 2015 10 991.20 23.00 78.30 6.90
47 4.13 15-Nov 2015 11 993.50 19.40 85.30 6.90
48 3.30 15-Dec 2015 12 997.80 13.00 80.90 5.70
49 5.30 16-Jan 2016 1 996.00 11.80 82.30 6.40
50 4.57 16-Feb 2016 2 997.80 12.20 68.90 7.00
51 4.66 16-Mar 2016 3 991.70 17.00 78.90 7.00
52 4.01 16-Apr 2016 4 984.60 23.40 80.90 9.80
53 4.90 16-May 2016 5 983.80 25.50 78.70 8.30
54 3.75 16-Jun 2016 6 981.70 28.20 78.80 7.70
55 3.13 16-Jul 2016 7 981.10 28.90 77.60 7.60
56 3.25 16-Aug 2016 8 979.00 28.00 79.80 8.70
57 2.93 16-Sep 2016 9 984.30 26.60 75.20 6.40
58 2.93 16-Oct 2016 10 987.90 24.40 72.90 7.00
59 3.08 16-Nov 2016 11 993.40 18.10 79.60 6.70
60 2.99 16-Dec 2016 12 995.70 15.40 71.70 6.80
61 3.10 17-Jan 2017 1 994.70 14.50 79.20 6.50
62 3.75 17-Feb 2017 2 994.80 14.70 71.50 8.30
63 3.49 17-Mar 2017 3 990.20 16.50 83.60 8.50
64 3.36 17-Apr 2017 4 986.80 21.90 76.70 7.80
65 3.69 17-May 2017 5 985.00 24.80 77.50 10.00
66 3.76 17-Jun 2017 6 980.20 26.90 84.80 8.50
67 2.69 17-Jul 2017 7 981.00 27.50 83.60 9.80
68 3.05 17-Aug 2017 8 980.50 27.70 83.40 9.00
69 3.05 17-Sep 2017 9 984.20 27.60 81.50 7.10
70 2.46 17-Oct 2017 10 990.00 22.80 75.90 7.90
71 2.08 17-Nov 2017 11 993.00 17.80 79.50 7.00
72 2.32 17-Dec 2017 12 996.90 13.30 69.30 6.90
73 2.53 18-Jan 2018 1 992.10 12.00 78.40 8.10
74 3.29 18-Feb 2018 2 992.90 13.40 68.70 7.20
75 3.03 18-Mar 2018 3 988.30 19.20 78.20 9.10
76 2.30 18-Apr 2018 4 986.50 21.80 77.30 8.70
77 1.75 18-May 2018 5 982.60 26.70 79.40 8.90
78 2.03 18-Jun 2018 6 978.30 26.90 81.60 9.00
79 2.79 18-Jul 2018 7 976.80 27.90 82.10 9.20
80 2.32 18-Aug 2018 8 976.40 27.50 83.40 9.60
81 1.88 18-Sep 2018 9 983.50 26.10 80.10 8.90
82 2.76 18-Oct 2018 10 990.50 21.10 78.70 7.10
83 2.14 18-Nov 2018 11 991.50 18.20 80.30 7.10
84 1.78 18-Dec 2018 12 994.50 13.00 84.00 7.80
85 2.77 19-Jan 2019 1 995.20 11.70 84.50 7.30
86 4.60 19-Feb 2019 2 990.50 13.70 84.80 8.10
87 2.32 19-Mar 2019 3 987.70 17.30 85.90 9.90
88 2.07 19-Apr 2019 4 983.60 23.10 84.80 9.80
89 2.97 19-May 2019 5 981.80 24.30 83.20 7.70
90 2.48 19-Jun 2019 6 977.80 27.50 84.80 9.00
91 2.32 19-Jul 2019 7 977.20 27.80 85.00 8.90
92 2.06 19-Aug 2019 8 977.20 28.30 81.20 10.30
93 2.10 19-Sep 2019 9 984.60 26.40 72.70 8.20
94 2.89 19-Oct 2019 10 989.10 22.70 78.00 7.00
My guess is that when you specify "knots= c(10)", 10 is within the range of temperature but not the same for humidity (if the min>10, then the lag can't be defined).
Since my data has summarized counts, I am using the freq function from summmarytools with weights.
With weights, the freq function works fine for summarizing a column when
The column is numeric or integer
The columns is a factor with NA or NaN values
But when
The column is a factor without NA or NaN values then the summary take a level away from the column and displays it in NA!!
I came across this issue in a live case and have reproduced a sample.
library(data.table)
library(summarytools)
dt <- data.table(A= as.integer( c(5,3,4,5,6,1,2,NA,3,NaN)),
B= c(5,3,4,5,6,1,2,NA,3,NaN),
C=as.factor( c(5,3,4,5,6,1,2,NA,3,NA)),
D=as.factor( c(5,3,4,5,6,1,2,NaN,3,NaN)),
E=as.factor( c(5,3,4,5,6,1,2,5,3,3)),
Frequency=c(10,20,30,40,5,60,7,80,99,10)
)
str(dt)
frequency being an integer or numeric does not matter
if we have a factor without Nan or NA values it makes a difference
writeLines("\n\n\n Without weights: No errors")
#summarytools::freq(dt[,1:5]) #Commented to minimize clutter
writeLines("\n\n\n With weights, Column E shows incorrect values but not C and D")
summarytools::freq(dt[,1:5],weights=dt$Frequency)
Without weights: No errors
With weights, Column E shows incorrect values but not C and D
1 NaN value(s) converted to NA
0 NaN value(s) converted to NA
Weighted Frequencies
dt$A
Weights: weights
Freq % Valid % Valid Cum. % Total % Total Cum.
1 60.00 22.14 22.14 16.62 16.62
2 7.00 2.58 24.72 1.94 18.56
3 119.00 43.91 68.63 32.96 51.52
4 30.00 11.07 79.70 8.31 59.83
5 50.00 18.45 98.15 13.85 73.68
6 5.00 1.85 100.00 1.39 75.07
<NA> 90.00 24.93 100.00
Total 361.00 100.00 100.00 100.00 100.00
dt$B
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
1 60.00 22.14 22.14 16.62 16.62
2 7.00 2.58 24.72 1.94 18.56
3 119.00 43.91 68.63 32.96 51.52
4 30.00 11.07 79.70 8.31 59.83
5 50.00 18.45 98.15 13.85 73.68
6 5.00 1.85 100.00 1.39 75.07
<NA> 90.00 24.93 100.00
Total 361.00 100.00 100.00 100.00 100.00
dt$C
Type: Factor
Freq % Valid % Valid Cum. % Total % Total Cum.
1 60.00 22.14 22.14 16.62 16.62
2 7.00 2.58 24.72 1.94 18.56
3 119.00 43.91 68.63 32.96 51.52
4 30.00 11.07 79.70 8.31 59.83
5 50.00 18.45 98.15 13.85 73.68
6 5.00 1.85 100.00 1.39 75.07
<NA> 90.00 24.93 100.00
Total 361.00 100.00 100.00 100.00 100.00
dt$D
Type: Factor
Freq % Valid % Valid Cum. % Total % Total Cum.
1 60.00 22.14 22.14 16.62 16.62
2 7.00 2.58 24.72 1.94 18.56
3 119.00 43.91 68.63 32.96 51.52
4 30.00 11.07 79.70 8.31 59.83
5 50.00 18.45 98.15 13.85 73.68
6 5.00 1.85 100.00 1.39 75.07
<NA> 90.00 24.93 100.00
Total 361.00 100.00 100.00 100.00 100.00
dt$E
Type: Factor
Freq % Valid % Valid Cum. % Total % Total Cum.
1 60.00 16.85 16.85 16.62 16.62
2 7.00 1.97 18.82 1.94 18.56
3 129.00 36.24 55.06 35.73 54.29
4 30.00 8.43 63.48 8.31 62.60
5 130.00 36.52 100.00 36.01 98.61
<NA> 5.00 1.39 100.00
Total 361.00 100.00 100.00 100.00 100.00
A fix was issued for this. You can install the latest version from GitHub with:
devtools::install_github("dcomtois/summarytools")
or, to get the latest development version:
devtools::install_github("dcomtois/summarytools", ref = "dev-current)
I'm trying to create a summary data frame which has averages, mins and maxes (see original post here). I can print the output I want, but whenever I try to save it to a data frame, I only get the averages, not the mins and maxes. I've tried updating R and tidyr, and I can't think of anything else that would cause this. I've tried using as.data.frame() and that doesn't help.
#example df
df <- read.table(header=TRUE, text="shop tables chairs beds
jim-1 2 63 31
jim-2a 10 4 16
jim-2b 32 34 43
jen-1 32 90 32
jen-2 73 91 6
jen-3 35 85 65
sam-a 72 57 72
sam-b 18 48 11
sam-c 34 49 79
paul-1 43 49 23
paul-2 76 20 23
paul-2a 34 20 8")
#create a grouping to allow me to average out group values
shop_group = sub("-.*", "", df$shop)
#print a summary table (works fine)
aggregate(df[,2:4], list(shop_group),
FUN = function(x) summary(x)[c(4,1,6)])
#generate a summary data frame (doesn't work, only gives me the averages, not the mins and maxes)
summ_df= aggregate(df[,2:4], list(shop_group),
FUN = function(x) summary(x)[c(4,1,6)])
it works fine..
> aggregate(df[,2:4], list(shop_group),
+ FUN = function(x) summary(x)[c(4,1,6)])
Group.1 tables.Mean tables.Min. tables.Max. chairs.Mean chairs.Min. chairs.Max.
1 jen 46.67 32.00 73.00 88.67 85.00 91.00
2 jim 14.67 2.00 32.00 33.67 4.00 63.00
3 paul 51.00 34.00 76.00 29.67 20.00 49.00
4 sam 41.33 18.00 72.00 51.33 48.00 57.00
beds.Mean beds.Min. beds.Max.
1 34.33 6.00 65.00
2 30.00 16.00 43.00
3 18.00 8.00 23.00
4 54.00 11.00 79.00
> summ_df= aggregate(df[,2:4], list(shop_group),
+ FUN = function(x) summary(x)[c(4,1,6)])
> summ_df
Group.1 tables.Mean tables.Min. tables.Max. chairs.Mean chairs.Min. chairs.Max.
1 jen 46.67 32.00 73.00 88.67 85.00 91.00
2 jim 14.67 2.00 32.00 33.67 4.00 63.00
3 paul 51.00 34.00 76.00 29.67 20.00 49.00
4 sam 41.33 18.00 72.00 51.33 48.00 57.00
beds.Mean beds.Min. beds.Max.
1 34.33 6.00 65.00
2 30.00 16.00 43.00
3 18.00 8.00 23.00
4 54.00 11.00 79.00
>
I have problem with my code. I can't trace the error. I have coor data (40 by 2 matrix) as below and a rainfall data (14610 by 40 matrix).
No Longitude Latitude
1 100.69 6.34
2 100.77 6.24
3 100.39 6.11
4 100.43 5.53
5 100.39 5.38
6 101.00 5.71
7 101.06 5.30
8 100.80 4.98
9 101.17 4.48
10 102.26 6.11
11 102.22 5.79
12 102.28 5.31
13 102.02 5.38
14 101.97 4.88
15 102.95 5.53
16 103.13 5.32
17 103.06 4.94
18 103.42 4.76
19 103.42 4.23
20 102.38 4.24
21 101.94 4.23
22 103.04 3.92
23 103.36 3.56
24 102.66 3.03
25 103.19 2.89
26 101.35 3.70
27 101.41 3.37
28 101.75 3.16
29 101.39 2.93
30 102.07 3.09
31 102.51 2.72
32 102.26 2.76
33 101.96 2.74
34 102.19 2.36
35 102.49 2.29
36 103.02 2.38
37 103.74 2.26
38 103.97 1.85
39 103.72 1.76
40 103.75 1.47
rainfall= 14610 by 40 matrix;
coor= 40 by 2 matrix
my_prog=function(rainrain,coordinat,misss,distance)
{
rain3<-rainrain # target station i**
# neighboring stations for target station i
a=coordinat # target station i**
diss=as.matrix(distHaversine(a,coor,r=6371))
mmdis=sort(diss,decreasing=F,index.return=T)
mdis=as.matrix(mmdis$x)
mdis1=as.matrix(mmdis$ix)
dist=cbind(mdis,mdis1)
# NA creation
# create missing values in rainfall data
set.seed(100)
b=sample(1:nrow(rain3),(misss*nrow(rain3)),replace=F)
k=replace(rain3,b,NA)
# pick i closest stations
neig=mdis1[distance] # neighbouring selection distance
# target (with NA) and their neighbors
rainB=rainfal00[,neig]
rainA0=rainB[,2:ncol(rainB)]
rainA<-as.matrix(cbind(k,rainA0))
rain2=na.omit(rainA)
x=as.matrix(rain2[,1]) # used to calculate the correlation
n1=ncol(rainA)-1
#1) normal ratio(nr)
jum=as.matrix(apply(rain2,2,mean))
nr0=(jum[1]/jum)
nr=as.matrix(nr0[2:nrow(nr0),])
m01=as.matrix(rainA[is.na(k),])
m1=m01[,2:ncol(m01)]
out1=as.matrix(sapply(seq_len(nrow(m1)),
function(i) sum(nr*m1[i,],na.rm=T)/n1))
print(out1)
}
impute=my_prog(rainrain=rainfall[,1],coordinat=coor[1,],misss=0.05,distance=mdis<200)
I have run this code and and the output obtained is:
Error in my_prog(rainrain = rainfal00[, 1], misss = 0.05, coordinat = coor[1, :
object 'mdis' not found
I have checked the program, but cannot trace the problem. I would really appreciate if someone could help me.
I would like to round the output to one decimal place in mean table
aov_two <- aov(Mass ~ Distance + Colony + Distance:Colony, data = seed_ant)
summary(aov_two)
B <- model.tables(aov_two, "means")
model table of mean result is,
B
Tables of means
Grand mean
54.2461
Distance
0 5 10
56.53 53.19 52.25
rep 221.00 217.00 139.00
Colony
101 2 23 25 28 3 4 X
64.91 41.84 51.44 60.55 50.83 45.32 54.25 60.85
rep 82.00 71.00 52.00 76.00 59.00 77.00 75.00 85.00
Distance:Colony
Colony
Distance 101 2 23 25 28 3 4 X
0 61.79 41.04 51.97 74.52 53.45 41.33 53.26 72.04
rep 29.00 24.00 29.00 29.00 29.00 27.00 27.00 27.00
5 70.33 40.45 52.61 55.20 49.47 47.75 54.18 57.15
rep 27.00 29.00 23.00 25.00 30.00 28.00 28.00 27.00
10 62.23 44.50 48.05 46.59 55.30 53.42
rep 26.00 18.00 0.00 22.00 0.00 22.00 20.00 31.00
how can I round all those numbers to one decimal place?
It's rather difficult to read the data.frames in your question, but try:
df <- round(df, 1)