I have the following data frame:
1 8.03 0.37 0.55 1.03 1.58 2.03 15.08 2.69 1.63 3.84 1.26 1.9692516
2 4.76 0.70 NA 0.12 1.62 3.30 3.24 2.92 0.35 0.49 0.42 NA
3 6.18 3.47 3.00 0.02 0.19 16.70 2.32 69.78 3.72 5.51 1.62 2.4812459
4 1.06 45.22 0.81 1.07 8.30 196.23 0.62 118.51 13.79 22.80 9.77 8.4296220
5 0.15 0.10 0.07 1.52 1.02 0.50 0.91 1.75 0.02 0.20 0.48 0.3094169
7 0.27 0.68 0.09 0.15 0.26 1.54 0.01 0.21 0.04 0.28 0.31 0.1819510
I want to calculate the geometric mean for each row. My codes is
dat <- read.csv("MXreport.csv")
if(any(dat$X18S > 25)){ print("Fail!") } else { print("Pass!")}
datpass <- subset(dat, dat$X18S <= 25)
gene <- datpass[, 42:52]
gm_mean <- function(x){ prod(x)^(1/length(x))}
gene$score <- apply(gene, 1, gm_mean)
head(gene)
I got this output after typing this code:
1 8.03 0.37 0.55 1.03 1.58 2.03 15.08 2.69 1.63 3.84 1.26 1.9692516
2 4.76 0.70 NA 0.12 1.62 3.30 3.24 2.92 0.35 0.49 0.42 NA
3 6.18 3.47 3.00 0.02 0.19 16.70 2.32 69.78 3.72 5.51 1.62 2.4812459
4 1.06 45.22 0.81 1.07 8.30 196.23 0.62 118.51 13.79 22.80 9.77 8.4296220
5 0.15 0.10 0.07 1.52 1.02 0.50 0.91 1.75 0.02 0.20 0.48 0.3094169
7 0.27 0.68 0.09 0.15 0.26 1.54 0.01 0.21 0.04 0.28 0.31 0.1819510
The problem is I got NA after applying the geometric mean function to the row that has NA. How do I skip NA and calculate the geometric mean for the row that has NA
When I used gene<- na.exclude(datpass[, 42:52]). It skipped the row that has NA and not calculate the geometric mean at all. That is now what I want. I want to also calculate the geometric mean for the row that has NA also. How do I do this?
Related
I would expect gsub and stringr::str_replace_all to return the same result in the following, but only gsub returns the intended result. I am developing a lesson to demonstrate str_replace_all so I would like to know why it returns a different result here.
txt <- ".72 2.51\n2015** 2.45 2.30 2.00 1.44 1.20 1.54 1.84 1.56 1.94 1.47 0.86 1.01\n2016** 1.53 1.75 2.40 2.62 2.35 2.03 1.25 0.52 0.45 0.56 1.88 1.17\n2017** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n2018** 0.70 0"
gsub(".*2017|2018.*", "", txt)
stringr::str_replace_all(txt, ".*2017|2018.*", "")
gsub returns the intended output (everything before and including 2017, and after and including 2018, has been removed).
output of gsub (intended)
[1] "** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
However str_replace_all only replaces the 2017 and 2018 but leaves the rest, even though the same pattern is used for both.
output of str_replace_all (not intended)
[1] ".72 2.51\n2015** 2.45 2.30 2.00 1.44 1.20 1.54 1.84 1.56 1.94 1.47 0.86 1.01\n2016** 1.53 1.75 2.40 2.62 2.35 2.03 1.25 0.52 0.45 0.56 1.88 1.17\n** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
Why is this the case?
Base R relies on two regex libraries.
As default R uses TRE.
We can specify perl = TRUE to use PCRE (perl like regular expressions).
The {stringr} package uses ICU (Java like regular expressions).
In your case the problem is that the dot . doesn’t match line breaks in PCRE and ICU, while it does match line breaks in TRE:
library(stringr)
txt <- ".72 2.51\n2015** 2.45 2.30 2.00 1.44 1.20 1.54 1.84 1.56 1.94 1.47 0.86 1.01\n2016** 1.53 1.75 2.40 2.62 2.35 2.03 1.25 0.52 0.45 0.56 1.88 1.17\n2017** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n2018** 0.70 0"
(base_tre <- gsub(".*2017|2018.*", "", txt))
#> [1] "** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
(base_perl <- gsub(".*2017|2018.*", "", txt, perl = TRUE))
#> [1] ".72 2.51\n2015** 2.45 2.30 2.00 1.44 1.20 1.54 1.84 1.56 1.94 1.47 0.86 1.01\n2016** 1.53 1.75 2.40 2.62 2.35 2.03 1.25 0.52 0.45 0.56 1.88 1.17\n** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
(string_r <- str_replace_all(txt, ".*2017|2018.*", ""))
#> [1] ".72 2.51\n2015** 2.45 2.30 2.00 1.44 1.20 1.54 1.84 1.56 1.94 1.47 0.86 1.01\n2016** 1.53 1.75 2.40 2.62 2.35 2.03 1.25 0.52 0.45 0.56 1.88 1.17\n** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
identical(base_perl, string_r)
#> [1] TRUE
We can use modifiers
to change the behavior of PCRE and ICU regex so that line breaks are matched
by .. This will produce the same output as with base R TRE:
(base_perl <- gsub("(?s).*2017|2018(?s).*", "", txt, perl = TRUE))
#> [1] "** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
(string_r <- str_replace_all(txt, "(?s).*2017|2018(?s).*", ""))
#> [1] "** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50\n"
identical(base_perl, string_r)
#> [1] TRUE
Finally, unlike TRE, PCRE and ICU allow us to use look arounds which are also
an option to solve the problem
str_match(txt, "(?<=2017).*.(?=\\n2018)")
#> [,1]
#> [1,] "** 0.77 0.70 0.74 1.12 0.88 0.79 0.10 0.09 0.32 0.05 0.15 0.50"
Created on 2021-08-10 by the reprex package (v0.3.0)
i want to plot a stacked column using ggplot2 with R1, R2, R3 as the y variables while the varieties names remain in the x variable.
i have tried it on excel it worked but i decided importing the dataset in csv format to R for a more captivating outlook as this is part of my final year project.
varieties R1 R2 R3 Relative.yield SD
1 bd 0.40 2.65 1.45 1.50 1.13
2 bdj1 4.60 NA 2.80 3.70 1.27
3 bdj2 2.40 1.90 0.50 1.60 0.98
4 bdj3 2.40 1.65 5.20 3.08 1.87
5 challenge 2.10 5.15 1.35 2.87 2.01
6 doris 4.20 2.50 2.55 3.08 0.97
7 fel 0.80 2.40 0.75 1.32 0.94
8 fel2 NA 0.70 1.90 1.30 0.85
9 felbv 0.10 2.95 2.05 1.70 1.46
10 felnn 1.50 4.05 1.25 2.27 1.55
11 lad1 0.55 2.20 0.20 0.98 1.07
12 lad2 0.50 NA 0.50 0.50 0.00
13 lad3 1.10 3.90 1.00 2.00 1.65
14 lad4 1.50 1.65 0.50 1.22 0.63
15 molete1 2.60 1.80 2.75 2.38 0.51
16 molete2 1.70 4.70 4.20 3.53 1.61
17 mother's delight 0.10 4.00 1.90 2.00 1.95
18 ojaoba1a 1.90 3.45 2.75 2.70 0.78
19 ojaoba1b 4.20 2.75 4.30 3.75 0.87
20 ojoo 2.80 NA 3.60 3.20 0.57
21 omini 0.20 0.30 0.25 0.25 0.05
22 papa1 2.20 6.40 3.55 4.05 2.14
23 pk5 1.00 2.75 1.10 1.62 0.98
24 pk6 2.30 1.30 3.10 2.23 0.90
25 sango1a 0.40 0.90 1.55 0.95 0.58
26 sango1b 2.60 5.10 3.15 3.62 1.31
27 sango2a 0.50 0.55 0.75 0.60 0.13
28 sango2b 2.95 NA 2.60 2.78 0.25
29 usman 0.60 3.50 1.20 1.77 1.53
30 yau 0.05 0.85 0.20 0.37 0.43
> barplot(yield$R1)
> barplot(yield$Relative.yield)
> barplot(yield$Relative.yield, names.arg = varieties)
Error in barplot.default(yield$Relative.yield, names.arg = varieties) :
object 'varieties' not found
> ggplot(data = yield, mapping = aes(x = varieties, y = yield[,2:4])) + geom_()
Error in geom_() : could not find function "geom_"
> ggplot(data = yield, mapping = aes(x = varieties, y = yield[,2:4])) + geom()
Error in geom() : could not find function "geom"
You should put it in long format first, with tidyr::gather provides this functionality:
library(tidyverse)
gather(df[1:4],R, value, R1:R3) %>%
ggplot(aes(varieties,value, fill = R)) + geom_col()
#> Warning: Removed 5 rows containing missing values (position_stack).
data
df <- read.table(h=T,strin=F,text=
" varieties R1 R2 R3 Relative.yield SD
1 bd 0.40 2.65 1.45 1.50 1.13
2 bdj1 4.60 NA 2.80 3.70 1.27
3 bdj2 2.40 1.90 0.50 1.60 0.98
4 bdj3 2.40 1.65 5.20 3.08 1.87
5 challenge 2.10 5.15 1.35 2.87 2.01
6 doris 4.20 2.50 2.55 3.08 0.97
7 fel 0.80 2.40 0.75 1.32 0.94
8 fel2 NA 0.70 1.90 1.30 0.85
9 felbv 0.10 2.95 2.05 1.70 1.46
10 felnn 1.50 4.05 1.25 2.27 1.55
11 lad1 0.55 2.20 0.20 0.98 1.07
12 lad2 0.50 NA 0.50 0.50 0.00
13 lad3 1.10 3.90 1.00 2.00 1.65
14 lad4 1.50 1.65 0.50 1.22 0.63
15 molete1 2.60 1.80 2.75 2.38 0.51
16 molete2 1.70 4.70 4.20 3.53 1.61
17 'mother\\'s delight' 0.10 4.00 1.90 2.00 1.95
18 ojaoba1a 1.90 3.45 2.75 2.70 0.78
19 ojaoba1b 4.20 2.75 4.30 3.75 0.87
20 ojoo 2.80 NA 3.60 3.20 0.57
21 omini 0.20 0.30 0.25 0.25 0.05
22 papa1 2.20 6.40 3.55 4.05 2.14
23 pk5 1.00 2.75 1.10 1.62 0.98
24 pk6 2.30 1.30 3.10 2.23 0.90
25 sango1a 0.40 0.90 1.55 0.95 0.58
26 sango1b 2.60 5.10 3.15 3.62 1.31
27 sango2a 0.50 0.55 0.75 0.60 0.13
28 sango2b 2.95 NA 2.60 2.78 0.25
29 usman 0.60 3.50 1.20 1.77 1.53
30 yau 0.05 0.85 0.20 0.37 0.43"
)
I have a dataframe like this:
head(Betula, 10)
year start Start_DayOfYear end End_DayOfYear duration DateMax Max_DayOfYear BetulaPollenMax SPI Jan.NAO Jan.AO
1 1997 <NA> NA <NA> NA NA <NA> NA NA NA -0.49 -0.46
2 1998 <NA> 143 <NA> 184 41 <NA> 146 42 361 0.39 -2.08
3 1999 <NA> 148 <NA> 188 40 <NA> 158 32 149 0.77 0.11
4 2000 <NA> 135 <NA> 197 62 <NA> 156 173 917 0.60 1.27
5 2001 <NA> 143 <NA> 175 32 <NA> 154 113 457 0.25 -0.96
Jan.SO Feb.NAO Feb.AO Feb.SO Mar.NAO Mar.AO Mar.SO Apr.NAO Apr.AO Apr.SO DecJanFebMarApr.NAO DecJanFebMar.NAO
1 0.5 1.70 1.89 1.7 1.46 1.09 -0.4 -1.02 0.32 -0.6 0.14 0.43
2 -2.7 -0.11 -0.18 -2.0 0.87 -0.25 -2.4 -0.68 -0.04 -1.4 0.27 0.51
3 1.8 0.29 0.48 1.0 0.23 -1.49 1.3 -0.95 0.28 1.4 0.39 0.73
4 0.7 1.70 1.08 1.7 0.77 -0.45 1.3 -0.03 -0.28 1.2 0.49 0.62
5 1.0 0.45 -0.62 1.7 -1.26 -1.69 0.9 0.00 0.91 0.2 -0.28 -0.35
DecJanFeb.NAO DecJan.NAO JanFebMarApr.NAO JanFebMar.NAO JanFeb.NAO FebMarApr.NAO FebMar.NAO MarApr.NAO
1 0.08 -0.73 0.41 0.89 0.61 0.71 1.58 0.22
2 0.38 0.63 0.12 0.38 0.14 0.03 0.38 0.10
3 0.89 1.19 0.09 0.43 0.53 -0.14 0.26 -0.36
4 0.57 0.01 0.76 1.02 1.15 0.81 1.24 0.37
5 -0.04 -0.29 -0.14 -0.19 0.35 -0.27 -0.41 -0.63
DecJanFebMarApr.AO DecJanFebMar.AO DecJanFeb.AO DecJan.AO JanFebMarApr.AO JanFebMar.AO JanFeb.AO FebMarApr.AO
1 0.55 0.61 0.45 -0.27 0.71 0.84 0.72 1.10
2 -0.24 -0.29 -0.30 -0.37 -0.64 -0.84 -1.13 -0.16
3 0.08 0.04 0.54 0.58 -0.16 -0.30 0.30 -0.24
4 -0.15 -0.11 0.00 -0.54 0.41 0.63 1.18 0.12
5 -0.74 -1.15 -0.97 -1.14 -0.59 -1.09 -0.79 -0.47
FebMar.AO MarApr.AO DecJanFebMarApr.SO DecJanFebMar.SO DecJanFeb.SO DecJan.SO JanFebMarApr.SO JanFebMar.SO
1 1.49 0.71 0.04 0.20 0.40 -0.25 0.30 0.60
2 -0.22 -0.15 -1.42 -1.43 -1.10 -0.65 -2.13 -2.37
3 -0.51 -0.61 1.38 1.38 1.40 1.60 1.38 1.37
4 0.32 -0.37 1.14 1.13 1.07 0.75 1.23 1.23
5 -1.16 -0.39 0.60 0.70 0.63 0.10 0.95 1.20
JanFeb.SO FebMarApr.SO FebMar.SO MarApr.SO TmaxAprI TminAprI TmeanAprI RainfallAprI HumidityAprI SunshineAprI
1 1.10 0.23 0.65 -0.50 3.27 -3.86 -0.44 0.82 76.3 3.45
2 -2.35 -1.93 -2.20 -1.90 4.52 -3.28 -0.15 0.12 73.5 7.12
3 1.40 1.23 1.15 1.35 4.11 -3.86 -0.34 1.32 78.4 4.85
4 1.20 1.40 1.50 1.25 6.11 -1.31 1.93 0.80 71.9 4.20
5 1.35 0.93 1.30 0.55 1.46 -2.37 -1.04 2.83 84.4 1.21
CloudAprI WindAprI SeeLevelPressureAprI TmaxAprII TminAprII TmeanAprII RainfallAprII HumidityAprII
1 6.30 5.26 1008.63 12.12 2.11 6.17 0.23 76.5
2 3.93 3.86 1022.39 5.57 -0.44 1.82 0.83 77.9
3 5.02 3.23 1007.09 0.20 -6.36 -3.23 2.63 82.5
4 6.15 5.13 1012.21 2.74 -4.88 -2.35 0.34 76.0
5 7.50 3.90 1009.50 6.75 -3.22 1.16 0.32 71.5
SunshineAprII CloudAprII WindAprII SeeLevelPressureAprII TmaxAprIII TminAprIII TmeanAprIII RainfallAprIII
1 3.12 6.53 5.19 1024.31 7.35 0.33 3.37 0.33
2 2.41 6.85 3.70 1012.01 6.34 0.76 2.69 2.01
3 4.99 5.87 6.23 1019.66 8.65 0.73 4.23 0.70
4 6.63 5.17 5.84 1022.62 5.84 -1.81 2.02 0.00
5 6.11 4.82 3.92 1018.81 8.47 1.02 4.17 1.09
HumidityAprIII SunshineAprIII CloudAprIII WindAprIII SeeLevelPressureAprIII TmaxDecI TminDecI TmeanDecI
1 75.0 3.73 6.40 4.08 1009.91 -0.90 -5.88 -3.67
2 83.5 1.52 7.31 4.66 1008.33 5.33 0.01 2.46
3 73.4 6.62 5.12 3.16 1017.01 -0.24 -6.93 -3.64
4 69.0 8.80 4.80 4.99 1021.18 4.67 1.86 2.79
5 72.7 5.33 5.41 4.27 1005.48 3.69 -1.43 1.65
RainfallDecI HumidityDecI SunshineDecI CloudDecI WindDecI SeeLevelPressureDecI TmaxDecII TminDecII TmeanDecII
1 0.12 77.3 0.22 5.08 3.49 1003.15 7.99 0.77 4.10
2 1.10 73.5 0.04 6.29 5.21 999.94 0.24 -4.74 -2.67
3 2.41 82.3 0.00 6.70 4.92 998.64 1.22 -5.90 -2.05
4 3.13 88.1 0.00 7.97 4.00 997.82 2.76 -3.89 -0.54
5 1.60 79.1 0.07 5.44 5.76 996.35 10.82 4.36 6.90
RainfallDecII HumidityDecII SunshineDecII CloudDecII WindDecII SeeLevelPressureDecII TmaxDecIII TminDecIII
1 1.90 71.3 0 4.96 5.55 1007.16 4.78 -2.12
2 4.34 82.2 0 7.03 6.06 998.02 2.07 -4.60
3 1.94 78.6 0 6.53 5.82 1008.33 2.09 -2.48
4 1.45 77.2 0 6.57 5.26 1005.11 -1.49 -8.37
5 1.15 66.6 0 5.74 5.47 1030.02 1.40 -7.34
TmeanDecIII RainfallDecIII HumidityDecIII SunshineDecIII CloudDecIII WindDecIII SeeLevelPressureDecIII TmaxFebI
1 1.15 3.96 82.36 0 6.01 4.02 991.60 -0.23
2 -0.51 4.10 81.18 0 6.67 3.91 986.52 0.79
3 -0.61 1.97 81.27 0 6.21 5.53 982.13 2.19
4 -5.28 1.26 79.64 0 6.11 4.22 1019.63 3.27
5 -3.45 1.19 82.18 0 6.20 4.77 1015.53 2.42
TminFebI TmeanFebI RainfallFebI HumidityFebI SunshineFebI CloudFebI WindFebI SeeLevelPressureFebI TmaxFebII
1 -6.67 -3.57 0.84 84.3 1.11 6.81 5.35 990.51 2.97
2 -7.79 -4.49 2.31 72.2 1.88 4.73 4.53 990.39 3.31
3 -4.14 -1.77 0.42 73.3 1.29 6.02 5.57 1007.67 1.55
4 -2.48 0.04 2.28 77.0 0.46 6.84 4.29 982.97 -1.24
5 -3.52 -0.74 1.98 81.5 0.76 5.78 4.93 1008.29 6.71
TminFebII TmeanFebII RainfallFebII HumidityFebII SunshineFebII CloudFebII WindFebII SeeLevelPressureFebII
1 -2.31 -0.10 1.44 82.2 1.07 6.45 4.42 980.59
2 -4.85 -0.99 3.84 75.0 2.54 5.91 5.05 999.98
3 -5.76 -2.44 2.89 75.3 0.40 6.95 5.82 990.44
4 -8.47 -4.65 3.33 83.1 0.63 6.55 4.95 1000.10
5 -0.25 3.01 1.38 66.1 1.16 6.18 6.28 1001.46
TmaxFebIII TminFebIII TmeanFebIII RainfallFebIII HumidityFebIII SunshineFebIII CloudFebIII WindFebIII
1 0.05 -6.01 -3.35 4.60 83.50 1.29 6.58 4.71
2 -0.45 -7.43 -4.51 2.93 78.38 1.00 6.91 5.99
3 2.13 -4.51 -1.21 2.90 79.38 2.51 5.76 5.46
4 0.59 -3.79 -1.92 5.94 88.33 1.40 6.86 6.70
5 -2.68 -7.23 -5.05 1.39 83.88 1.13 7.41 5.69
SeeLevelPressureFebIII TmaxJanI TminJanI TmeanJanI RainfallJanI HumidityJanI SunshineJanI CloudJanI WindJanI
1 980.25 0.38 -5.57 -3.36 0.01 82.9 0.27 3.45 2.97
2 997.71 4.29 -0.03 2.08 3.70 82.9 0.00 7.39 5.01
3 988.45 1.02 -4.47 -1.87 2.22 82.3 0.00 6.94 4.29
4 987.21 0.04 -6.28 -3.03 4.99 85.8 0.00 5.84 4.75
5 1023.84 -0.33 -5.11 -3.17 0.66 81.2 0.00 7.08 3.88
SeeLevelPressureJanI TmaxJanII TminJanII TmeanJanII RainfallJanII HumidityJanII SunshineJanII CloudJanII
1 1023.71 0.09 -6.48 -2.50 4.29 86.5 0.01 7.23
2 984.57 -0.34 -6.49 -3.61 2.74 80.2 0.23 6.99
3 1004.06 0.32 -5.59 -3.03 5.28 83.3 0.00 6.68
4 983.42 8.38 1.46 4.97 0.64 69.3 0.10 6.13
5 1010.31 7.35 3.00 5.09 1.27 66.3 0.03 6.19
WindJanII SeeLevelPressureJanII TmaxJanIII TminJanIII TmeanJanIII RainfallJanIII HumidityJanIII SunshineJanIII
1 5.42 998.88 5.66 -2.39 1.97 1.03 74.27 0.65
2 6.38 1011.44 3.84 -3.32 -0.37 0.70 73.55 0.55
3 6.24 980.15 4.33 -5.19 -0.59 2.23 76.64 0.69
4 6.44 1019.41 4.09 -2.67 0.05 2.18 71.73 0.42
5 6.74 1006.10 4.43 -0.86 1.58 1.91 80.09 0.20
CloudJanIII WindJanIII SeeLevelPressureJanIII TmaxMarI TminMarI TmeanMarI RainfallMarI HumidityMarI
1 6.47 7.59 1004.59 2.83 -3.60 -0.72 2.14 79.9
2 5.25 4.72 1019.95 -5.31 -12.52 -9.52 2.28 72.6
3 5.34 4.65 1001.66 -0.70 -6.67 -4.47 1.39 81.0
4 5.85 4.83 1007.23 0.10 -7.91 -3.98 2.36 80.2
5 6.53 3.63 992.53 -0.38 -4.59 -2.27 3.00 86.4
SunshineMarI CloudMarI WindMarI SeeLevelPressureMarI TmaxMarII TminMarII TmeanMarII RainfallMarII HumidityMarII
1 0.85 6.77 6.64 986.96 -1.48 -8.43 -5.58 1.09 81.0
2 2.92 5.91 4.68 1013.17 6.53 -1.81 2.56 0.43 65.5
3 2.40 5.71 4.02 1014.62 0.53 -5.17 -2.90 5.20 82.8
4 0.91 7.02 5.87 1006.64 5.32 -0.94 1.23 1.11 74.4
5 0.19 7.82 4.49 999.35 1.60 -4.29 -1.89 0.95 79.3
SunshineMarII CloudMarII WindMarII SeeLevelPressureMarII TmaxMarIII TminMarIII TmeanMarIII RainfallMarIII
1 2.12 5.51 3.93 1021.57 3.88 -1.95 0.55 1.42
2 2.25 6.29 6.11 1008.31 3.95 -2.46 -0.15 1.30
3 1.00 6.61 5.77 1006.63 -0.68 -6.60 -4.07 0.70
4 2.16 6.61 6.45 1003.23 5.49 -0.68 1.65 1.58
5 4.07 5.21 3.14 1017.24 -0.66 -7.21 -4.00 1.37
HumidityMarIII SunshineMarIII CloudMarIII WindMarIII SeeLevelPressureMarIII
1 80.45 2.80 6.13 4.03 995.31
2 72.09 3.98 5.99 5.14 1000.32
3 78.73 2.34 6.46 3.81 1005.67
4 74.64 2.85 6.54 6.34 1013.45
5 79.45 4.71 5.65 4.95 1010.47
[ reached 'max' / getOption("max.print") -- omitted 5 rows ]
And I would like to do the normality test for all column in once. I tried
apply(x, shapiro.test)
Betula_shapiro <- apply(Betula, shapiro.test)
Error in FUN(X[[i]], ...) : is.numeric(x) is not TRUE
and it didn´t work. I also tried this:
Betula <- apply(Betula[which(sapply(Betula, is.numeric))], 2, shapiro.test)
Error in FUN(newX[, i], ...) : all 'x' values are identical
f<-function(x){if(diff(range(x))==0)list()else shapiro.test(x)}
Betula <- apply(Betula[which(sapply(Betula, is.numeric))], 2, f)
Error in if (diff(range(x)) == 0) list() else shapiro.test(x) :
missing value where TRUE/FALSE needed
So I did:
Betula_numerics_only <- Betula[which(sapply(Betula, is.numeric))]
selecting columns with at least 3 not missing values and applying shapiro.test on them
Betula_numerics_only_filled_columns <- Betula_numerics_only[which(apply(Betula_numerics_only, 2, function(f) sum(!is.na(f))>=3 ))]
Betula_shapiro<-apply(Betula_numerics_only_filled_columns, 2, shapiro.test)
Error in FUN(newX[, i], ...) : all 'x' values are identical
Could you please help me with this problem?
Since i was talking about readability in my comment i felt i should provide something more readable too as an answer.
Lets make some dummy-data:
data_test <- data.frame(matrix(rnorm(100, 10, 1), ncol = 5, byrow = T), stringsAsFactors = F)
Lets apply shapiro.test to each column
apply(data_test, 2, shapiro.test)
In case there are non numeric columns:
Lets add a dummy-char column for testing-purposes
data_test$non_numeric <- sample(c("hello", "hi", "good morning"), NROW(data_test), replace = T)
and try to apply the test again
apply(data_test, 2, shapiro.test)
which results in:
> apply(data_test, 2, shapiro.test)
Error: is.numeric(x) is not TRUE
To solve this we select only numeric colums by using sapply:
data_test[which(sapply(data_test, is.numeric))]
and combine it with the apply:
apply(data_test[which(sapply(data_test, is.numeric))], 2, shapiro.test)
Removing colums, that are all NA:
data_test_numerics_only <- data_test[which(sapply(data_test, is.numeric))]
Selecting colums with at least 3 not missing values and applying shapiro.test on them:
data_test_numerics_only_filled_colums = data_test_numerics_only[which(apply(data_test_numerics_only, 2, function(f) sum(!is.na(f)) >= 3))]
apply(data_test_numerics_only_filled_colums, 2, shapiro.test)
We will get this running, lets try once more :)
remove non numeric columns
Betula_numerics <- Betula[which(sapply(Betula, is.numeric))]
Remove columns with less than 3 values
Betula_numerics_filled <- Betula_numerics[which(apply(Betula_numerics, 2, function(f) sum(!is.na(f)) >= 3))]
Remove columns with zero variance
Betula_numerics_filled_not_constant <- Betula_numerics_filled [apply(Betula_numerics_filled , 2, function(f) var(f, na.rm = T) != 0)]
Shapiro.test and hope for the best :)
apply(Betula_numerics_filled_not_constant, 2, shapiro.test)
Monthly rainfall data is in a time series from 1983 Jan. to 2012 Dec.
One.Month.RainfallSJ.inch <- window(TS.RainfallSJ_inch, start=c(1983, 1), end=c(2012, 12))
One.Month.RainfallSJ.inch
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1983 7.41 4.87 5.92 3.90 0.15 0.00 0.00 0.02 1.08 0.19 5.26 3.82
1984 0.17 1.44 0.90 0.54 0.00 0.01 0.00 0.00 0.02 1.75 3.94 1.73
1985 0.74 0.76 2.98 0.48 0.23 0.00 0.13 0.00 0.35 0.98 2.47 1.40
1986 2.41 6.05 3.99 0.66 0.16 0.00 0.00 0.00 1.02 0.08 0.17 0.85
1987 1.60 2.10 1.87 0.14 0.00 0.00 0.00 0.00 0.00 0.93 1.65 3.31
1988 2.08 0.62 0.06 1.82 0.66 0.00 0.00 0.00 0.00 0.06 1.42 2.14
1989 1.06 1.07 1.91 0.57 0.09 0.00 0.00 0.00 0.83 1.33 0.80 0.04
1990 1.93 1.61 0.89 0.22 2.38 0.00 0.15 0.00 0.24 0.25 0.24 2.03
1991 0.18 2.22 6.17 0.18 0.15 0.06 0.00 0.04 0.12 0.85 0.43 2.43
1992 1.73 6.59 3.37 0.42 0.00 0.25 0.00 0.00 0.00 0.66 0.05 4.51
1993 6.98 4.71 2.81 0.54 0.47 0.54 0.00 0.00 0.00 0.67 2.17 1.99
1994 1.33 3.03 0.44 1.47 1.21 0.01 0.00 0.00 0.07 0.27 2.37 1.76
1995 8.66 0.53 6.85 1.06 1.27 0.84 0.01 0.00 0.00 0.00 0.05 4.71
1996 3.03 4.85 2.62 0.75 1.42 0.00 0.00 0.00 0.01 1.08 1.65 4.78
1997 6.80 0.14 0.17 0.11 0.55 0.21 0.00 0.51 0.00 0.69 5.01 1.85
1998 4.81 10.23 2.40 1.46 1.93 0.00 0.00 0.00 0.05 0.60 1.77 0.72
1999 3.25 2.88 2.69 1.56 0.02 0.14 0.14 0.00 0.00 0.00 0.50 0.55
2000 3.57 4.56 1.69 0.74 0.40 0.30 0.00 0.01 0.12 2.16 0.44 0.31
2001 2.87 4.44 1.71 1.48 0.00 0.13 0.00 0.00 0.13 0.12 2.12 4.47
2002 0.75 0.81 1.80 0.35 0.68 0.00 0.00 0.00 0.00 0.00 1.99 6.60
2003 0.65 1.65 0.77 2.95 0.72 0.00 0.00 0.03 0.03 0.00 1.91 4.91
2004 1.61 4.28 0.49 0.40 0.08 0.00 0.00 0.00 0.15 3.04 0.73 4.32
2005 3.47 5.31 3.55 2.52 0.00 0.00 0.01 0.00 0.00 0.10 0.45 5.47
2006 2.94 2.39 6.55 4.55 0.45 0.00 0.00 0.00 0.00 0.39 1.38 1.77
2007 0.93 3.49 0.46 0.96 0.08 0.00 0.01 0.00 0.26 1.13 0.55 1.18
2008 5.81 1.81 0.15 0.03 0.00 0.00 0.00 0.00 0.00 0.19 1.33 1.53
2009 1.30 5.16 1.89 0.30 0.09 0.01 0.00 0.02 0.19 2.41 0.41 2.16
2010 4.58 2.12 2.05 3.03 0.35 0.00 0.00 0.00 0.00 0.25 1.76 2.53
2011 0.96 3.15 4.32 0.20 0.40 1.51 0.00 0.00 0.00 0.77 0.08 0.08
2012 0.90 0.63 1.98 1.88 0.00 0.15 0.00 0.00 0.01 0.35 2.59 4.24
How can I code Jan. average value from 1983 to 2012 and so on?
Thanks,
Nahm
Try maybe colMeans
colMeans(One.Month.RainfallSJ.inch)
# Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
# 2.8170000 3.1166667 2.4483333 1.1756667 0.4646667 0.1386667 0.0150000 0.0210000 0.1560000 0.7100000 1.5230000
# Dec
# 2.6063333
I'm trying to plot a boxplot graph with my data, using 'ggplot' in R, but I just can't do it. Can anyone help me out?
The data is like the table below:
Paratio ShapeIdx FracD NNDis Core
-3.00 1.22 0.14 2.71 7.49
-1.80 0.96 0.16 0.00 7.04
-3.00 1.10 0.13 2.71 6.85
-1.80 0.83 0.16 0.00 6.74
-0.18 0.41 0.27 0.00 6.24
-1.66 0.12 0.11 2.37 6.19
-1.07 0.06 0.14 0.00 6.11
-0.32 0.18 0.23 0.00 5.93
-1.16 0.32 0.15 0.00 5.59
-0.94 0.14 0.15 1.96 5.44
-1.13 0.31 0.16 0.00 5.42
-1.35 0.40 0.15 0.00 5.38
-0.53 0.25 0.20 2.08 5.32
-1.96 0.36 0.12 0.00 5.27
-1.09 0.07 0.13 0.00 5.22
-1.35 0.27 0.14 0.00 5.21
-1.25 0.21 0.14 0.00 5.19
-1.02 0.25 0.16 0.00 5.19
-1.28 0.22 0.14 0.00 5.11
-1.44 0.32 0.14 0.00 5.00
And what I exactly want is a boxplot of each column, without any relation "column by column".
ggplot2 requires data in a specific format. Here, you need x= and y= where y will be the values and x will be the corresponding column ids. Use melt from reshape2 package to melt the data to get the data in this format and then plot.
require(reshape2)
ggplot(data = melt(dd), aes(x=variable, y=value)) + geom_boxplot(aes(fill=variable))