one. When assigning values, only variables with uniform data types can be assigned
Error: when conditionally assigning a value to a variable in the data frame, the assignment fails, and the error displayed is:
Error in x[...] <- m : invalid subscript type 'builtin' In addition: Warning message: In `[<-.factor`(`*tmp*`, is.na, value = 0) : invalid factor level, NA generated
Reason: when importing data from excel to data frame data.frame, when there are both numbers and characters or logical values in a column of variables, the imported variable data type will be automatically set as factor, and the variable of factor type cannot be assigned directly,
Solution: you must use functions such as as as.numeric(), as.character() to convert the data type of the variable to "numeric" or "string", and then perform the assignment operation
In addition, when batch converting the data format of multi column variable data in the data frame, batch operation can be carried out by combining the sapply and lapply functions.
The return value output from the sapply function is a vector type, which can be used as the index vector of the data frame to achieve the purpose of conditional filtering
The return value output by the happy function is the data frame data.frame or list type, which can be used as the nested function of the data type conversion function as.XX to output the result data frame
Example:
one. Import 20 lines of water quality monitoring data from the shear plate
wq <- read.table("clipboard",sep="\t",header=T)
View wq the data structure in the dataset
> str(wq) 'data.frame': 20 obs. of 37 variables: $ Station name: Factor w/ 20 levels "Bashang (Longwang Temple)",..: 2 one 7 5 4 one 5 one 3 9 one 8 one 4 one one ... $ Station code: int 99930 one 28 99930 one 33 99930 one 29 99930 one 30 999 one 0 one 36 99930 one 34 99930 one 3 one 999 one 0 one 24 999 one 0 one 26 999 one 0 one 25 ... $ Vertical line number: int one one one one one one one one one one ... $ Layer number: int one one one one one one 1 1 1 1 ... $ Water type: int 1 1 1 1 3 1 3 1 1 NA ... $ Sampling time: Factor w/ 1 level "2021/1/1 0:00": 1 1 1 1 1 1 1 1 1 1 ... $ WT : num 10.2 11.4 11.9 12 8.2 11 11.9 5.6 4.5 6.9 ... $ PH : num 8.2 7.7 7.7 7.7 8.1 7.7 7.6 8 8 8 ... $ DOX : num 9.4 9.5 9.6 9.7 11.7 10.1 9.2 14.1 12.8 13.2 ... $ CODMN : num 1.65 1.55 1.52 1.59 1.8 1.57 1.57 1.1 0.8 0.9 ... $ CODCR : Factor w/ 14 levels "<2.3","2.5","2.6",..: 7 13 5 13 3 11 6 10 1 1 ... $ BOD5 : Factor w/ 11 levels "<0.5","0.1","0.4",..: 1 1 1 1 8 3 2 8 7 6 ... $ NH3N : Factor w/ 14 levels "<0.025","0.022",..: 2 6 4 10 3 10 13 1 1 1 ... $ TP : Factor w/ 6 levels "<0.01","0.01",..: 1 1 1 2 3 1 1 5 4 3 ... $ TN : num 1.03 1.05 1.09 1.08 1.04 1.15 1.44 3.91 1.8 2.24 ... $ CU : Factor w/ 3 levels "<0.002","0.002",..: 1 1 1 1 1 1 1 3 2 2 ... $ ZN : Factor w/ 11 levels "<0.0006","0.0006",..: 1 1 1 10 1 1 1 3 2 6 ... $ F : num 0.15 0.21 0.13 0.19 0.13 0.19 0.2 0.19 0.13 0.12 ... $ SE : Factor w/ 1 level "<0.00041": 1 1 1 1 1 1 1 1 1 1 ... $ ARS : Factor w/ 13 levels "<0.00012","0.00016",..: 7 9 3 9 5 12 4 13 6 6 ... $ HG : Factor w/ 1 level "<0.00001": 1 1 1 1 1 1 1 1 1 1 ... $ CD : Factor w/ 1 level "<0.0005": 1 1 1 1 1 1 1 1 1 1 ... $ CR6 : Factor w/ 1 level "<0.004": 1 1 1 1 1 1 1 1 1 1 ... $ PB : Factor w/ 1 level "<0.004": 1 1 1 1 1 1 1 1 1 1 ... $ CN : Factor w/ 1 level "<0.001": 1 1 1 1 1 1 1 1 1 1 ... $ VLPH : Factor w/ 5 levels "<0.0003","0.0005",..: 1 1 1 1 1 1 1 4 2 3 ... $ OIL : Factor w/ 1 level "<0.01": 1 1 1 1 1 1 1 1 1 1 ... $ LAS : Factor w/ 1 level "<0.05": 1 1 1 1 1 1 1 1 1 1 ... $ S2 : Factor w/ 1 level "<0.005": 1 1 1 1 1 1 1 1 1 1 ... $ FCG : Factor w/ 14 levels "<10","10","100",..: 8 1 1 2 1 1 13 1 12 7 ... $ SO4 : Factor w/ 16 levels "18.41","18.66",..: 14 6 1 13 11 7 10 16 16 16 ... $ CL : Factor w/ 16 levels "2.79","2.83",..: 15 10 7 14 2 5 13 16 16 16 ... $ NO3 : Factor w/ 14 levels "0.92","0.94",..: 4 2 2 2 1 5 10 14 14 14 ... $ FE : Factor w/ 15 levels "0.0536","0.054",..: 2 5 3 1 4 5 7 15 15 15 ... $ MN : Factor w/ 14 levels "0.002","0.0035",..: 2 6 3 4 1 2 12 14 14 14 ... $ CLARITY : Factor w/ 13 levels "1","1.2","1.5",..: 9 10 11 12 6 12 8 13 13 13 ... $ CHLA : Factor w/ 11 levels "0.39","1.13",..: 5 9 7 6 10 8 3 11 11 11 ...
You can see that there are numeric and factor variables in the data frame wq
2. Assign NA to all "not monitored" in the monitored data
Because the data in wq has factor type, there are two steps to achieve the purpose of conditional assignment: one is to modify the data type, and the other is to assign value
(1) Convert factor type data in dataset wq to numeric type
> wq[sapply(wq,is.factor)] <- lapply(wq[sapply(wq,is.factor)],as.character) > str(wq) 'data.frame': 20 obs. of 37 variables: $ Station name: chr "Baidu Beach" "Zhang Zhai, Xianghua town" "Danku Center" "Cangfang town-Zhao gou" ... $ Station code: int 99930128 99930133 99930129 99930130 99910136 99930134 99930131 99910124 99910126 99910125 ... $ Vertical line number: int 1 1 1 1 1 1 1 1 1 1 ... $ Layer number: int 1 1 1 1 1 1 1 1 1 1 ... $ Water type: int 1 1 1 1 3 1 3 1 1 NA ... $ Sampling time: chr "2021/1/1 0:00" "2021/1/1 0:00" "2021/1/1 0:00" "2021/1/1 0:00" ... $ WT : num 10.2 11.4 11.9 12 8.2 11 11.9 5.6 4.5 6.9 ... $ PH : num 8.2 7.7 7.7 7.7 8.1 7.7 7.6 8 8 8 ... $ DOX : num 9.4 9.5 9.6 9.7 11.7 10.1 9.2 14.1 12.8 13.2 ... $ CODMN : num 1.65 1.55 1.52 1.59 1.8 1.57 1.57 1.1 0.8 0.9 ... $ CODCR : chr "4.2" "7.2" "3.1" "7.2" ... $ BOD5 : chr "<0.5" "<0.5" "<0.5" "<0.5" ... $ NH3N : chr "0.022" "0.054" "0.045" "0.112" ... $ TP : chr "<0.01" "<0.01" "<0.01" "0.01" ... $ TN : num 1.03 1.05 1.09 1.08 1.04 1.15 1.44 3.91 1.8 2.24 ... $ CU : chr "<0.002" "<0.002" "<0.002" "<0.002" ... $ ZN : chr "<0.0006" "<0.0006" "<0.0006" "0.0028" ... $ F : num 0.15 0.21 0.13 0.19 0.13 0.19 0.2 0.19 0.13 0.12 ... $ SE : chr "<0.00041" "<0.00041" "<0.00041" "<0.00041" ... $ ARS : chr "0.00041" "0.00049" "0.00027" "0.00049" ... $ HG : chr "<0.00001" "<0.00001" "<0.00001" "<0.00001" ... $ CD : chr "<0.0005" "<0.0005" "<0.0005" "<0.0005" ... $ CR6 : chr "<0.004" "<0.004" "<0.004" "<0.004" ... $ PB : chr "<0.004" "<0.004" "<0.004" "<0.004" ... $ CN : chr "<0.001" "<0.001" "<0.001" "<0.001" ... $ VLPH : chr "<0.0003" "<0.0003" "<0.0003" "<0.0003" ... $ OIL : chr "<0.01" "<0.01" "<0.01" "<0.01" ... $ LAS : chr "<0.05" "<0.05" "<0.05" "<0.05" ... $ S2 : chr "<0.005" "<0.005" "<0.005" "<0.005" ... $ FCG : chr "30" "<10" "<10" "10" ... $ SO4 : chr "23.83" "20.27" "18.41" "22.69" ... $ CL : chr "5.66" "3.66" "3.27" "4.31" ... $ NO3 : chr "0.98" "0.94" "0.94" "0.94" ... $ FE : chr "0.054" "0.0637" "0.0547" "0.0536" ... $ MN : chr "0.0035" "0.0062" "0.0036" "0.0039" ... $ CLARITY : chr "2.7" "3" "3.7" "4" ... $ CHLA : chr "2.29" "3.05" "2.66" "2.32" ...
At this point, you can see through the str() function that all the factor type variables in the wq data frame have been converted to character type
(2) Make conditional assignment
> wq[wq=="Not monitored"]=NA
> wq
Station name station code vertical line number layer number water body type Sampling time WT PH DOX CODMN CODCR BOD5
1 Baidu beach 99930128 1 1 1 2021/1/1 0:00 10.2 8.2 9.4 1.65 4.2 <0.5
2 Xianghua town Zhangzhai 99930133 1 1 1 2021/1/1 0:00 11.4 7.7 9.5 1.55 7.2 <0.5
3 Danku center 99930129 1 1 1 2021/1/1 0:00 11.9 7.7 9.6 1.52 3.1 <0.5
4 Cangfang town-Zhaogou 99930130 1 1 1 2021/1/1 0:00 12.0 7.7 9.7 1.59 7.2 <0.5
5 Taocha 99910136 1 1 3 2021/1/1 0:00 8.2 8.1 11.7 1.80 2.6 0.9
6 Qingquangou 99930134 1 1 1 2021/1/1 0:00 11.0 7.7 10.1 1.57 5.3 0.4
7 Liangshui River-Taizishan 99930131 1 1 3 2021/1/1 0:00 11.9 7.6 9.2 1.57 4.1 0.1
8 Xianghe estuary 99910124 1 1 1 2021/1/1 0:00 5.6 8.0 14.1 1.10 5 0.9
9 Taohe estuary 99910126 1 1 1 2021/1/1 0:00 4.5 8.0 12.8 0.80 <2.3 0.8
10 Qihe estuary 99910125 1 1 NA 2021/1/1 0:00 6.9 8.0 13.2 0.90 <2.3 0.7
11 Laostork River Estuary 99910127 1 1 NA 2021/1/1 0:00 2.9 7.9 13.1 2.80 7.4 1
12 99930108 in front of the mountain in liupo town 1 1 NA 2021/1/1 0:00 9.1 7.9 9.5 1.49 <2.3 0.6
13 Hankou center 99930109 1 1 NA 2021/1/1 0:00 9.5 8.0 9.3 1.42 3 <0.5
14 lush mountain-Anyang 99930112 1 1 NA 2021/1/1 0:00 9.8 7.6 9.6 1.44 2.5 1.1
15 Yuanhe estuary 99930114 1 1 NA 2021/1/1 0:00 10.5 7.6 9.4 1.79 <2.3 <0.5
16 Wudang Mountain-Santang Bay 99930117 1 1 NA 2021/1/1 0:00 12.2 7.6 9.3 1.76 <2.3 0.5
17 Xiao Chuan-Longkou 99930118 1 1 NA 2021/1/1 0:00 11.7 7.6 9.0 1.88 5 1.6
18 Langhekou xia99030121 1 1 NA 2021/1/1 0:00 12.2 7.6 9.1 1.81 5.6 <0.5
19 Bashang (Longwang Temple) 99930123 1 1 NA 2021/1/1 0:00 12.2 7.5 7.8 1.88 4.4 <0.5
20 Baihe estuary (left) 99910102 1 1 NA 2021/1/1 0:00 8.4 8.2 13.4 1.50 4.7 0.8
NH3N TP TN CU ZN F SE ARS HG CD CR6 PB CN VLPH
1 0.022 <0.01 1.03 <0.002 <0.0006 0.15 <0.00041 0.00041 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
2 0.054 <0.01 1.05 <0.002 <0.0006 0.21 <0.00041 0.00049 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
3 0.045 <0.01 1.09 <0.002 <0.0006 0.13 <0.00041 0.00027 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
4 0.112 0.01 1.08 <0.002 0.0028 0.19 <0.00041 0.00049 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
5 0.04 0.02 1.04 <0.002 <0.0006 0.13 <0.00041 0.00036 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
6 0.112 <0.01 1.15 <0.002 <0.0006 0.19 <0.00041 0.00095 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
7 0.141 <0.01 1.44 <0.002 <0.0006 0.20 <0.00041 0.00029 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
8 <0.025 0.04 3.91 0.003 0.0011 0.19 <0.00041 0.001 <0.00001 <0.0005 <0.004 <0.004 <0.001 0.001
9 <0.025 0.03 1.80 0.002 0.0006 0.13 <0.00041 0.0004 <0.00001 <0.0005 <0.004 <0.004 <0.001 0.0005
10 <0.025 0.02 2.24 0.002 0.0016 0.12 <0.00041 0.0004 <0.00001 <0.0005 <0.004 <0.004 <0.001 0.0006
11 0.118 0.08 5.94 0.003 0.002 0.17 <0.00041 0.0005 <0.00001 <0.0005 <0.004 <0.004 <0.001 0.0017
12 0.048 0.02 1.45 <0.002 0.0015 0.15 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
13 0.129 0.02 1.58 <0.002 <0.0006 0.18 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
14 0.1 0.02 1.46 <0.002 0.0022 0.14 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
15 0.06 0.02 1.44 <0.002 <0.0006 0.21 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
16 0.141 0.02 1.42 <0.002 <0.0006 0.13 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
17 0.118 0.01 1.42 <0.002 <0.0006 0.11 <0.00041 <0.00012 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
18 0.094 0.02 1.26 <0.002 0.0012 0.13 <0.00041 0.00016 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
19 0.1 0.01 1.06 <0.002 0.0021 0.17 <0.00041 0.00044 <0.00001 <0.0005 <0.004 <0.004 <0.001 <0.0003
20 0.163 0.04 1.50 0.002 0.0041 0.15 <0.00041 0.0007 <0.00001 <0.0005 <0.004 <0.004 <0.001 0.0005
OIL LAS S2 FCG SO4 CL NO3 FE MN CLARITY CHLA
1 <0.01 <0.05 <0.005 30 23.83 5.66 0.98 0.054 0.0035 2.7 2.29
2 <0.01 <0.05 <0.005 <10 20.27 3.66 0.94 0.0637 0.0062 3 3.05
3 <0.01 <0.05 <0.005 <10 18.41 3.27 0.94 0.0547 0.0036 3.7 2.66
4 <0.01 <0.05 <0.005 10 22.69 4.31 0.94 0.0536 0.0039 4 2.32
5 <0.01 <0.05 <0.005 <10 21.63 2.83 0.92 0.0582 0.002 2 3.45
6 <0.01 <0.05 <0.005 <10 20.48 3.11 0.99 0.0637 0.0035 4 2.71
7 <0.01 <0.05 <0.005 60 21.48 4.14 1.27 0.0828 0.0131 2.5 1.53
8 <0.01 <0.05 <0.005 <10 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
9 <0.01 <0.05 <0.005 52 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
10 <0.01 <0.05 <0.005 20 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
11 <0.01 <0.05 <0.005 390 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
12 <0.01 <0.05 <0.005 40 18.95 3.69 1.3 0.067 0.0119 2.5 2.32
13 <0.01 <0.05 <0.005 110 18.66 2.86 1.39 0.1099 0.0119 2.1 2.09
14 <0.01 <0.05 <0.005 160 20.24 3.44 1.32 0.1335 0.009 1.5 1.53
15 <0.01 <0.05 <0.005 80 26.38 2.79 1.2 0.1434 0.0162 1.8 1.53
16 <0.01 <0.05 <0.005 <10 21.28 3.21 1.19 0.167 0.0081 1.2 0.39
17 <0.01 <0.05 <0.005 100 18.85 3.89 1.24 0.1158 0.0045 1.5 1.13
18 <0.01 <0.05 <0.005 40 20.52 3.28 1.13 0.1415 0.0078 1 1.13
19 <0.01 <0.05 <0.005 190 22.07 2.84 0.97 0.1034 0.0122 1.7 1.13
20 <0.01 <0.05 <0.005 380 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
It can be seen that all "not monitored" have become NA, so you can make another judgment if you are not sure
> is.na(wq)
Station name station code vertical line number layer number water body type sampling time WT PH DOX CODMN CODCR BOD5 NH3N
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[11,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[16,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[17,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[19,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[20,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
TP TN CU ZN F SE ARS HG CD CR6 PB CN VLPH OIL LAS S2
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[11,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[16,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[17,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[19,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[20,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
FCG SO4 CL NO3 FE MN CLARITY CHLA
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[8,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[9,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[10,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[11,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[12,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[16,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[17,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[19,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[20,] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
3. Process data less than the detection limit (i.e. "< XX") and replace all such data with 0
Use sapply() function and gsub() function to replace text in batch
The sapply () function functions as a loop traversal to obtain a vector or matrix data set with a simpler structure
The gsub () function is a function used to replace the text in all vectors in the regular expression gift bag, which is used to construct conditional substitution
> sapply(wq,gsub,pattern="<[0-9]+.?[0-9]+",replacement=0)
Station name Station code vertical line number layer number water body type sampling time WT PH DOX CODMN
[1,] "Baidu Beach" "99930128" "1" "1" "1" "2021/1/1 0:00" "10.2" "8.2" "9.4" "1.65"
[2,] "Zhang Zhai, Xianghua town" "99930133" "1" "1" "1" "2021/1/1 0:00" "11.4" "7.7" "9.5" "1.55"
[3,] "Danku Center" "99930129" "1" "1" "1" "2021/1/1 0:00" "11.9" "7.7" "9.6" "1.52"
[4,] "Cangfang town-Zhao gou" "99930130" "1" "1" "1" "2021/1/1 0:00" "12" "7.7" "9.7" "1.59"
[5,] "Taocha" "99910136" "1" "1" "3" "2021/1/1 0:00" "8.2" "8.1" "11.7" "1.8"
[6,] "Qingquangou" "99930134" "1" "1" "1" "2021/1/1 0:00" "11" "7.7" "10.1" "1.57"
[7,] "Liangshui River-Taizi mountain" "99930131" "1" "1" "3" "2021/1/1 0:00" "11.9" "7.6" "9.2" "1.57"
[8,] "Xianghe Estuary" "99910124" "1" "1" "1" "2021/1/1 0:00" "5.6" "8" "14.1" "1.1"
[9,] "Taohe Estuary" "99910126" "1" "1" "1" "2021/1/1 0:00" "4.5" "8" "12.8" "0.8"
[10,] "Qihe Estuary" "99910125" "1" "1" NA "2021/1/1 0:00" "6.9" "8" "13.2" "0.9"
[11,] "Laostork River Estuary" "99910127" "1" "1" NA "2021/1/1 0:00" "2.9" "7.9" "13.1" "2.8"
[12,] "In front of the mountain in liupo town" "99930108" "1" "1" NA "2021/1/1 0:00" "9.1" "7.9" "9.5" "1.49"
[13,] "Hanku Center" "99930109" "1" "1" NA "2021/1/1 0:00" "9.5" "8" "9.3" "1.42"
[14,] "lush mountain-Anyang" "99930112" "1" "1" NA "2021/1/1 0:00" "9.8" "7.6" "9.6" "1.44"
[15,] "Yuanhe Estuary" "99930114" "1" "1" NA "2021/1/1 0:00" "10.5" "7.6" "9.4" "1.79"
[16,] "Wudang Mountain-Santang Bay" "99930117" "1" "1" NA "2021/1/1 0:00" "12.2" "7.6" "9.3" "1.76"
[17,] "Xiao Chuan-Longkou" "99930118" "1" "1" NA "2021/1/1 0:00" "11.7" "7.6" "9" "1.88"
[18,] "Under langhekou" "99930121" "1" "1" NA "2021/1/1 0:00" "12.2" "7.6" "9.1" "1.81"
[19,] "Bashang (Longwang Temple)" "99930123" "1" "1" NA "2021/1/1 0:00" "12.2" "7.5" "7.8" "1.88"
[20,] "Baihe estuary (left)" "99910102" "1" "1" NA "2021/1/1 0:00" "8.4" "8.2" "13.4" "1.5"
CODCR BOD5 NH3N TP TN CU ZN F SE ARS HG CD CR6 PB CN
[1,] "4.2" "0" "0.022" "0" "1.03" "0" "0" "0.15" "0" "0.00041" "0" "0" "0" "0" "0"
[2,] "7.2" "0" "0.054" "0" "1.05" "0" "0" "0.21" "0" "0.00049" "0" "0" "0" "0" "0"
[3,] "3.1" "0" "0.045" "0" "1.09" "0" "0" "0.13" "0" "0.00027" "0" "0" "0" "0" "0"
[4,] "7.2" "0" "0.112" "0.01" "1.08" "0" "0.0028" "0.19" "0" "0.00049" "0" "0" "0" "0" "0"
[5,] "2.6" "0.9" "0.04" "0.02" "1.04" "0" "0" "0.13" "0" "0.00036" "0" "0" "0" "0" "0"
[6,] "5.3" "0.4" "0.112" "0" "1.15" "0" "0" "0.19" "0" "0.00095" "0" "0" "0" "0" "0"
[7,] "4.1" "0.1" "0.141" "0" "1.44" "0" "0" "0.2" "0" "0.00029" "0" "0" "0" "0" "0"
[8,] "5" "0.9" "0" "0.04" "3.91" "0.003" "0.0011" "0.19" "0" "0.001" "0" "0" "0" "0" "0"
[9,] "0" "0.8" "0" "0.03" "1.8" "0.002" "0.0006" "0.13" "0" "0.0004" "0" "0" "0" "0" "0"
[10,] "0" "0.7" "0" "0.02" "2.24" "0.002" "0.0016" "0.12" "0" "0.0004" "0" "0" "0" "0" "0"
[11,] "7.4" "1" "0.118" "0.08" "5.94" "0.003" "0.002" "0.17" "0" "0.0005" "0" "0" "0" "0" "0"
[12,] "0" "0.6" "0.048" "0.02" "1.45" "0" "0.0015" "0.15" "0" "0" "0" "0" "0" "0" "0"
[13,] "3" "0" "0.129" "0.02" "1.58" "0" "0" "0.18" "0" "0" "0" "0" "0" "0" "0"
[14,] "2.5" "1.1" "0.1" "0.02" "1.46" "0" "0.0022" "0.14" "0" "0" "0" "0" "0" "0" "0"
[15,] "0" "0" "0.06" "0.02" "1.44" "0" "0" "0.21" "0" "0" "0" "0" "0" "0" "0"
[16,] "0" "0.5" "0.141" "0.02" "1.42" "0" "0" "0.13" "0" "0" "0" "0" "0" "0" "0"
[17,] "5" "1.6" "0.118" "0.01" "1.42" "0" "0" "0.11" "0" "0" "0" "0" "0" "0" "0"
[18,] "5.6" "0" "0.094" "0.02" "1.26" "0" "0.0012" "0.13" "0" "0.00016" "0" "0" "0" "0" "0"
[19,] "4.4" "0" "0.1" "0.01" "1.06" "0" "0.0021" "0.17" "0" "0.00044" "0" "0" "0" "0" "0"
[20,] "4.7" "0.8" "0.163" "0.04" "1.5" "0.002" "0.0041" "0.15" "0" "0.0007" "0" "0" "0" "0" "0"
VLPH OIL LAS S2 FCG SO4 CL NO3 FE MN CLARITY CHLA
[1,] "0" "0" "0" "0" "30" "23.83" "5.66" "0.98" "0.054" "0.0035" "2.7" "2.29"
[2,] "0" "0" "0" "0" "0" "20.27" "3.66" "0.94" "0.0637" "0.0062" "3" "3.05"
[3,] "0" "0" "0" "0" "0" "18.41" "3.27" "0.94" "0.0547" "0.0036" "3.7" "2.66"
[4,] "0" "0" "0" "0" "10" "22.69" "4.31" "0.94" "0.0536" "0.0039" "4" "2.32"
[5,] "0" "0" "0" "0" "0" "21.63" "2.83" "0.92" "0.0582" "0.002" "2" "3.45"
[6,] "0" "0" "0" "0" "0" "20.48" "3.11" "0.99" "0.0637" "0.0035" "4" "2.71"
[7,] "0" "0" "0" "0" "60" "21.48" "4.14" "1.27" "0.0828" "0.0131" "2.5" "1.53"
[8,] "0.001" "0" "0" "0" "0" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored"
[9,] "0.0005" "0" "0" "0" "52" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored"
[10,] "0.0006" "0" "0" "0" "20" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored"
[11,] "0.0017" "0" "0" "0" "390" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored"
[12,] "0" "0" "0" "0" "40" "18.95" "3.69" "1.3" "0.067" "0.0119" "2.5" "2.32"
[13,] "0" "0" "0" "0" "110" "18.66" "2.86" "1.39" "0.1099" "0.0119" "2.1" "2.09"
[14,] "0" "0" "0" "0" "160" "20.24" "3.44" "1.32" "0.1335" "0.009" "1.5" "1.53"
[15,] "0" "0" "0" "0" "80" "26.38" "2.79" "1.2" "0.1434" "0.0162" "1.8" "1.53"
[16,] "0" "0" "0" "0" "0" "21.28" "3.21" "1.19" "0.167" "0.0081" "1.2" "0.39"
[17,] "0" "0" "0" "0" "100" "18.85" "3.89" "1.24" "0.1158" "0.0045" "1.5" "1.13"
[18,] "0" "0" "0" "0" "40" "20.52" "3.28" "1.13" "0.1415" "0.0078" "1" "1.13"
[19,] "0" "0" "0" "0" "190" "22.07" "2.84" "0.97" "0.1034" "0.0122" "1.7" "1.13"
[20,] "0.0005" "0" "0" "0" "380" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored" "Not monitored"