diff --git a/Makefile b/Makefile index 866f0d8..7f91904 100755 --- a/Makefile +++ b/Makefile @@ -3,7 +3,7 @@ HTML_FILES := $(filter-out docs/_sessionInfo.html, $(HTML_FILES)) R_FILES := $(patsubst %.R, docs/%.html, $(wildcard Part*.R)) .PHONY: all -all : cleanjunk html +all : clean html .PHONY : html html : docs/index.html docs/data.html $(HTML_FILES) $(R_FILES) @@ -23,11 +23,11 @@ docs/%.Rmd : %.R docs/knitopts.R R --slave -e "rmarkdown::render_site('$<')" .PHONY : clean -clean : +clean : cleanjunk $(RM) $(R_FILES) R --slave -e "rmarkdown::clean_site('docs')" .PHONY : cleanjunk -cleanjunk : +cleanjunk : $(RM) -r results/ $(RM) -r data/FungicideTidy.csv \ No newline at end of file diff --git a/Part2-Fig2Recreation.R b/Part2-Fig2Recreation.R index 5fce9eb..7ae9744 100644 --- a/Part2-Fig2Recreation.R +++ b/Part2-Fig2Recreation.R @@ -141,9 +141,14 @@ percents <- blocks %>% percents #' Because figure 2 plotted the average value, we want to summarize our data in #' averages. To do this, we need to convert our data back to tidy format by -#' using the *tidyr* function `gather()`: +#' using the *tidyr* function `gather()`. +#' +#' Additionally, because we observed some values that were missing or divided by +#' zero, we need to add a filter that removes these points. For that, we will +#' use the function `is.finite()`. percents <- percents %>% gather(key = Treatment, value = Area, -DAI, -Block) %>% + filter(is.finite(Area)) %>% # selecting all the finite values of Area mutate(Treatment = factor(Treatment, levels = unique(Treatment))) # reset factor percents #' @@ -154,7 +159,7 @@ percents #' to plot the data in the manner of Morini *et al.* 2017. avgs <- percents %>% group_by(DAI, Treatment) %>% - summarize(meanArea = mean(Area)) %>% + summarize(meanArea = mean(Area, na.rm = TRUE)) %>% ungroup() avgs #' diff --git a/Part2-Fig2Recreation.html b/Part2-Fig2Recreation.html deleted file mode 100644 index 23d0a16..0000000 --- a/Part2-Fig2Recreation.html +++ /dev/null @@ -1,777 +0,0 @@ - - - - - - - - - - - - - - -Figure 2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - -
-

Reading in Data and Example Analysis

-

In this exercise, we will learn how to read in data as an object in R, find and load packages into our library, and then re-create Figure 2 from the 2017 paper by Morini et al. paper “Control of white mold of dry bean and residual activity of fungicides applied by chemigation.”

-

We can use the read.table() function to read these data in to R. It’s important to remember that while in R, these data are simply a copy kept in memory, not on the disk, so we don’t have to worry too much about accidentally deleting the data :).

-

So, how do we actually USE the read.table() function? A good first step to figuring out how you can use a function is to look at it’s help page. The way you can do that is by typing either help(“function_name”) or ?function_name.

-
     # Type ?read.table and answer these three questions:
-     # 
-     # 1. What does it do? (Description)
-     # 2. What are the first three arguments and their defaults? (Usage/Arguments)
-     # 3. What does it return? (Value)
-

In order to read our data into R, we will need to provide three things:

-
    -
  1. The path to the data set : Results2017_combined-class.csv
    2. -
  2. If the first row are column names : yes
    3. -
  3. The separator for each cell in the data : comma
    4. -
-

Now that we have these elements, we can read our data into a variable, which we can call “res” because it is short for “results”. Once we do this, we should check the dimensions to make sure that we have all of the data.

-

Now we can read in the data and inspect to make sure it is what we expect.

-
res <- read.csv("Results2017_combined-class.csv", head = TRUE, stringsAsFactors = FALSE)
-head(res)
-
##   DAI Number Your.name   Area      Treatment Block
-## 1   1      1      Noel 19.808        control     1
-## 2   1      2      Noel  9.371  2.53 mm water     1
-## 3   1      3      Noel  0.000  5.07 mm water     1
-## 4   1      4      Noel  0.531 10.13 mm water     1
-## 5   1      5       Lee  0.000 10.13 mm water     2
-## 6   1      6       Lee 20.914        control     2
-
dim(res)
-
## [1] 288   6
-

This shows the data in columns named DAI, Number, Your.name, Area, and Treatment.

-
# Thinking before doing: Your data and the analysis
-
# 
-# The example data presented here were collected by students in the 2017
-# PLPT802 class and should represent the same data that this year's class collected.
-# This data consists of six columns of data: DAI, Number, Your.name, Area, Treatment, and Block.
-# With these data, we want to answer the following questions:
-# 
-#  1. Can we convert lesion areas to a measurement that is relative to the control? 
-#  2. What do we want to plot in order to demonstrate the residual disease control over
-#     time by treatment?
-# 
-# To answer these questions, we will need to manipulate the layout of the data and will also
-# need to write a function to perform a calculation and then plot the results of the 
-# calculations over time.  Not all data manipulations will be described in detail here and
-# are shown so that you can re-run this analysis using data collected in class.  We will
-# do this using the following four steps:
-#
-#    1. Create a function
-#    2. Spread the data in different columns by treatment
-#    3. Calculate percent disease control
-#    4. Summarize the average lesion area in a plot
-#
-#
-
-
-

Preparing Data and Packages

-

We will re-create the figure using three packages dplyr, tidyr, and ggplot2. These packages add flexibility to data and figure manipulations. We will not go into these in detail. Refer to the tab in this website called “Other Exercises” to get started learning more.

-
library("dplyr")
-library("tidyr")
-library("ggplot2")
-
-

Reordering factors

-

The factors in the treatments will be out of order when plotting because it’s always alphabetical by default (and 1 comes before 2). Here we are reordering the factors:

-
unique(res$Treatment) # The correct order
-
## [1] "control"        "2.53 mm water"  "5.07 mm water"  "10.13 mm water"
-
res <- mutate(res, Treatment = factor(Treatment, levels = unique(Treatment)))
-levels(res$Treatment)
-
## [1] "control"        "2.53 mm water"  "5.07 mm water"  "10.13 mm water"
-
-
-
-

Calculating Percent Disease Control

-

Percent disease control is “estimated as the difference in lesion area of the control and treatment, divided by lesion area of the control and expressed as percent.” Because we will have to make this calculation many times, it’s best to write a function for it.

-
-

Step 1: Create a function

-

Our function will take in two numbers, the lesion area of the control and the lesion area of the treatment.

-
percent_control <- function(control, treatment){
-  res <- (control - treatment)/control # estimate the disease control
-  return(res*100)                      # express as percent
-}
-

We can show that this works:

-
percent_control(control = 10, treatment = 5)
-
## [1] 50
-
-
-

Step 2: Spread the data in different columns by treatment

-

Our data were recorded in a “tidy” fashion in which we had one observation per row. In order to calculate the percent control, we’ll need to rehshape our data so that we have one column per treatment such that each row will represent a single block per day after application. We’ll use the tidyr function spread() to do this.

-
blocks <- res %>%
-  select(DAI, Block, Treatment, Area) %>% # We don't need name or number here
-  spread(Treatment, Area) # make new columns from treatment, and fill with Area
-blocks
-
##    DAI Block control 2.53 mm water 5.07 mm water 10.13 mm water
-## 1    1     1  19.808         9.371         0.000          0.531
-## 2    1     2  20.914         0.000         0.173          0.000
-## 3    1     3  15.206        21.173         0.000         18.270
-## 4    1     4  11.598         0.000         5.736          9.524
-## 5    1     5   3.034         0.000         0.458          1.943
-## 6    1     6   8.770        12.053        13.148          0.000
-## 7    1     7  12.951         0.000        15.283         14.839
-## 8    1     8  20.537         0.000        12.080          1.737
-## 9    1     9  25.941         0.000         7.428          1.592
-## 10   1    10  22.998         3.106         0.000          7.679
-## 11   1    11  15.302         0.000        20.567          0.000
-## 12   1    12  23.496         0.000        14.778          0.000
-## 13   3     1   4.853        18.860         1.632         10.572
-## 14   3     2  20.540         0.000         5.032          0.000
-## 15   3     3  20.577        13.730         0.000         10.990
-## 16   3     4  20.374         8.404         0.000          0.000
-## 17   3     5  18.757         2.168         0.000          8.969
-## 18   3     6   0.000         9.768         0.000          0.000
-## 19   3     7  21.004         0.000        18.070         18.346
-## 20   3     8  27.562         3.396         3.715          0.000
-## 21   3     9   4.378         0.922         0.000          0.000
-## 22   3    10   5.200         1.206         0.000          0.000
-## 23   3    11   3.249         0.000         0.565          1.558
-## 24   3    12   4.874         0.000         1.989          1.954
-## 25   5     1   4.883         0.043         0.790          0.000
-## 26   5     2   3.056         1.605         3.636          5.114
-## 27   5     3   5.618         3.883         5.844          0.156
-## 28   5     4   6.982         3.231         1.904          3.178
-## 29   5     5   5.009         1.302         1.713          3.706
-## 30   5     6   7.928         0.000         1.283          3.645
-## 31   5     7   3.008         2.018         0.000          2.141
-## 32   5     8   6.897         0.409         2.970          0.308
-## 33   5     9   7.436         3.427         2.257          4.952
-## 34   5    10   3.220         2.375         6.440          5.177
-## 35   5    11   0.216         1.559         3.864          0.000
-## 36   5    12   4.263         0.000         2.708          7.763
-## 37   7     1   9.142         7.791         6.630          5.999
-## 38   7     2   5.434         1.156        10.471          8.931
-## 39   7     3   8.585         0.000         6.271          5.393
-## 40   7     4  10.235         4.716         5.185          6.353
-## 41   7     5  55.971        13.891         1.525         12.636
-## 42   7     6  31.311        12.637        14.733         24.960
-## 43   7     7  21.758         0.000        23.765         12.978
-## 44   7     8  37.599         0.131         6.613          4.550
-## 45   7     9   7.359         1.795         1.759          6.350
-## 46   7    10   5.552         0.000         1.268          5.412
-## 47   7    11   5.069         8.384         6.041          3.176
-## 48   7    12   7.591         0.000         0.000          5.515
-## 49   9     1   8.919         8.796         7.081          4.643
-## 50   9     2  10.657         8.862         6.634          4.739
-## 51   9     3   9.730         2.427         1.883          7.845
-## 52   9     4  10.671         6.912         7.440          8.794
-## 53   9     5  10.393         2.631         5.389          3.524
-## 54   9     6   8.071         6.992         8.032          2.958
-## 55   9     7   6.356         4.054         6.538          5.624
-## 56   9     8   8.292         3.227         8.240          8.555
-## 57   9     9   6.113         3.772         7.535          6.817
-## 58   9    10   8.865         5.242         5.905          8.144
-## 59   9    11   5.845         2.444         7.000          8.436
-## 60   9    12   7.474         1.745         6.439          5.909
-## 61  11     1   7.050         7.796         6.499          3.263
-## 62  11     2  11.174         6.813         4.792          4.166
-## 63  11     3   5.434         2.679         2.493          7.671
-## 64  11     4   9.026         9.877         7.636          8.345
-## 65  11     5   9.134         2.167         4.587          2.911
-## 66  11     6   8.089         7.029         9.049          2.460
-## 67  11     7   5.497         3.430         7.416          4.440
-## 68  11     8   7.777         2.426         6.300          6.878
-## 69  11     9   8.235         7.302         8.213          9.184
-## 70  11    10   8.933         4.668         6.943         10.742
-## 71  11    11  21.237         7.896        42.692         20.002
-## 72  11    12  25.596         8.952        26.063         18.278
-
-
-

Step 3: Calculate percent disease control

-

Now that we have reshaped our data, we can manipulate each treatment column to give us the percent control.

-
# Note: the backtics "`" allow R to recognize a variable with spaces.
-percents <- blocks %>%
-  mutate(`2.53 mm water`  = percent_control(control, `2.53 mm water`)) %>%
-  mutate(`5.07 mm water`  = percent_control(control, `5.07 mm water`)) %>%
-  mutate(`10.13 mm water` = percent_control(control, `10.13 mm water`)) %>%
-  mutate(control          = percent_control(control, 0))
-percents
-
##    DAI Block control 2.53 mm water 5.07 mm water 10.13 mm water
-## 1    1     1     100     52.690832   100.0000000      97.319265
-## 2    1     2     100    100.000000    99.1728029     100.000000
-## 3    1     3     100    -39.241089   100.0000000     -20.149941
-## 4    1     4     100    100.000000    50.5431971      17.882394
-## 5    1     5     100    100.000000    84.9044166      35.959130
-## 6    1     6     100    -37.434436   -49.9201824     100.000000
-## 7    1     7     100    100.000000   -18.0063316     -14.578025
-## 8    1     8     100    100.000000    41.1793349      91.542095
-## 9    1     9     100    100.000000    71.3657916      93.862997
-## 10   1    10     100     86.494478   100.0000000      66.610140
-## 11   1    11     100    100.000000   -34.4072670     100.000000
-## 12   1    12     100    100.000000    37.1041879     100.000000
-## 13   3     1     100   -288.625592    66.3713167    -117.844632
-## 14   3     2     100    100.000000    75.5014606     100.000000
-## 15   3     3     100     33.275016   100.0000000      46.590854
-## 16   3     4     100     58.751350   100.0000000     100.000000
-## 17   3     5     100     88.441648   100.0000000      52.183185
-## 18   3     6     NaN          -Inf           NaN            NaN
-## 19   3     7     100    100.000000    13.9687679      12.654732
-## 20   3     8     100     87.678688    86.5212974     100.000000
-## 21   3     9     100     78.940155   100.0000000     100.000000
-## 22   3    10     100     76.807692   100.0000000     100.000000
-## 23   3    11     100    100.000000    82.6100339      52.046784
-## 24   3    12     100    100.000000    59.1916291      59.909725
-## 25   5     1     100     99.119394    83.8214213     100.000000
-## 26   5     2     100     47.480366   -18.9790576     -67.342932
-## 27   5     3     100     30.882876    -4.0227839      97.223211
-## 28   5     4     100     53.723861    72.7298768      54.482956
-## 29   5     5     100     74.006788    65.8015572      26.013176
-## 30   5     6     100    100.000000    83.8168517      54.023713
-## 31   5     7     100     32.912234   100.0000000      28.823138
-## 32   5     8     100     94.069885    56.9377990      95.534290
-## 33   5     9     100     53.913394    69.6476600      33.405056
-## 34   5    10     100     26.242236  -100.0000000     -60.776398
-## 35   5    11     100   -621.759259 -1688.8888889     100.000000
-## 36   5    12     100    100.000000    36.4766596     -82.101806
-## 37   7     1     100     14.777948    27.4775760      34.379786
-## 38   7     2     100     78.726537   -92.6941480     -64.354067
-## 39   7     3     100    100.000000    26.9539895      37.181130
-## 40   7     4     100     53.922814    49.3404983      37.928676
-## 41   7     5     100     75.181791    97.2753747      77.424023
-## 42   7     6     100     59.640382    52.9462489      20.283606
-## 43   7     7     100    100.000000    -9.2241934      40.352974
-## 44   7     8     100     99.651586    82.4117663      87.898614
-## 45   7     9     100     75.608099    76.0972958      13.711102
-## 46   7    10     100    100.000000    77.1613833       2.521614
-## 47   7    11     100    -65.397514   -19.1753798      37.344644
-## 48   7    12     100    100.000000   100.0000000      27.348175
-## 49   9     1     100      1.379078    20.6076914      47.942594
-## 50   9     2     100     16.843389    37.7498358      55.531575
-## 51   9     3     100     75.056526    80.6474820      19.373073
-## 52   9     4     100     35.226314    30.2783244      17.589729
-## 53   9     5     100     74.684884    48.1477918      66.092562
-## 54   9     6     100     13.368851     0.4832115      63.350266
-## 55   9     7     100     36.217747    -2.8634361      11.516677
-## 56   9     8     100     61.082972     0.6271105      -3.171732
-## 57   9     9     100     38.295436   -23.2619009     -11.516440
-## 58   9    10     100     40.868584    33.3897349       8.133108
-## 59   9    11     100     58.186484   -19.7604790     -44.328486
-## 60   9    12     100     76.652395    13.8480064      20.939256
-## 61  11     1     100    -10.581560     7.8156028      53.716312
-## 62  11     2     100     39.028101    57.1147306      62.717022
-## 63  11     3     100     50.699301    54.1221936     -41.166728
-## 64  11     4     100     -9.428318    15.3999557       7.544870
-## 65  11     5     100     76.275454    49.7810379      68.130063
-## 66  11     6     100     13.104216   -11.8679688      69.588330
-## 67  11     7     100     37.602329   -34.9099509      19.228670
-## 68  11     8     100     68.805452    18.9918992      11.559727
-## 69  11     9     100     11.329690     0.2671524     -11.523983
-## 70  11    10     100     47.744319    22.2769506     -20.250756
-## 71  11    11     100     62.819607  -101.0265103       5.815322
-## 72  11    12     100     65.025785    -1.8245038      28.590405
-

Because figure 2 plotted the average value, we want to summarize our data in averages. To do this, we need to convert our data back to tidy format by using the tidyr function gather():

-
percents <- percents %>%
-  gather(key = Treatment, value = Area, -DAI, -Block) %>%
-  mutate(Treatment = factor(Treatment, levels = unique(Treatment))) # reset factor
-percents
-
##     DAI Block      Treatment          Area
-## 1     1     1        control   100.0000000
-## 2     1     2        control   100.0000000
-## 3     1     3        control   100.0000000
-## 4     1     4        control   100.0000000
-## 5     1     5        control   100.0000000
-## 6     1     6        control   100.0000000
-## 7     1     7        control   100.0000000
-## 8     1     8        control   100.0000000
-## 9     1     9        control   100.0000000
-## 10    1    10        control   100.0000000
-## 11    1    11        control   100.0000000
-## 12    1    12        control   100.0000000
-## 13    3     1        control   100.0000000
-## 14    3     2        control   100.0000000
-## 15    3     3        control   100.0000000
-## 16    3     4        control   100.0000000
-## 17    3     5        control   100.0000000
-## 18    3     6        control           NaN
-## 19    3     7        control   100.0000000
-## 20    3     8        control   100.0000000
-## 21    3     9        control   100.0000000
-## 22    3    10        control   100.0000000
-## 23    3    11        control   100.0000000
-## 24    3    12        control   100.0000000
-## 25    5     1        control   100.0000000
-## 26    5     2        control   100.0000000
-## 27    5     3        control   100.0000000
-## 28    5     4        control   100.0000000
-## 29    5     5        control   100.0000000
-## 30    5     6        control   100.0000000
-## 31    5     7        control   100.0000000
-## 32    5     8        control   100.0000000
-## 33    5     9        control   100.0000000
-## 34    5    10        control   100.0000000
-## 35    5    11        control   100.0000000
-## 36    5    12        control   100.0000000
-## 37    7     1        control   100.0000000
-## 38    7     2        control   100.0000000
-## 39    7     3        control   100.0000000
-## 40    7     4        control   100.0000000
-## 41    7     5        control   100.0000000
-## 42    7     6        control   100.0000000
-## 43    7     7        control   100.0000000
-## 44    7     8        control   100.0000000
-## 45    7     9        control   100.0000000
-## 46    7    10        control   100.0000000
-## 47    7    11        control   100.0000000
-## 48    7    12        control   100.0000000
-## 49    9     1        control   100.0000000
-## 50    9     2        control   100.0000000
-## 51    9     3        control   100.0000000
-## 52    9     4        control   100.0000000
-## 53    9     5        control   100.0000000
-## 54    9     6        control   100.0000000
-## 55    9     7        control   100.0000000
-## 56    9     8        control   100.0000000
-## 57    9     9        control   100.0000000
-## 58    9    10        control   100.0000000
-## 59    9    11        control   100.0000000
-## 60    9    12        control   100.0000000
-## 61   11     1        control   100.0000000
-## 62   11     2        control   100.0000000
-## 63   11     3        control   100.0000000
-## 64   11     4        control   100.0000000
-## 65   11     5        control   100.0000000
-## 66   11     6        control   100.0000000
-## 67   11     7        control   100.0000000
-## 68   11     8        control   100.0000000
-## 69   11     9        control   100.0000000
-## 70   11    10        control   100.0000000
-## 71   11    11        control   100.0000000
-## 72   11    12        control   100.0000000
-## 73    1     1  2.53 mm water    52.6908320
-## 74    1     2  2.53 mm water   100.0000000
-## 75    1     3  2.53 mm water   -39.2410890
-## 76    1     4  2.53 mm water   100.0000000
-## 77    1     5  2.53 mm water   100.0000000
-## 78    1     6  2.53 mm water   -37.4344356
-## 79    1     7  2.53 mm water   100.0000000
-## 80    1     8  2.53 mm water   100.0000000
-## 81    1     9  2.53 mm water   100.0000000
-## 82    1    10  2.53 mm water    86.4944778
-## 83    1    11  2.53 mm water   100.0000000
-## 84    1    12  2.53 mm water   100.0000000
-## 85    3     1  2.53 mm water  -288.6255924
-## 86    3     2  2.53 mm water   100.0000000
-## 87    3     3  2.53 mm water    33.2750158
-## 88    3     4  2.53 mm water    58.7513498
-## 89    3     5  2.53 mm water    88.4416485
-## 90    3     6  2.53 mm water          -Inf
-## 91    3     7  2.53 mm water   100.0000000
-## 92    3     8  2.53 mm water    87.6786880
-## 93    3     9  2.53 mm water    78.9401553
-## 94    3    10  2.53 mm water    76.8076923
-## 95    3    11  2.53 mm water   100.0000000
-## 96    3    12  2.53 mm water   100.0000000
-## 97    5     1  2.53 mm water    99.1193938
-## 98    5     2  2.53 mm water    47.4803665
-## 99    5     3  2.53 mm water    30.8828765
-## 100   5     4  2.53 mm water    53.7238614
-## 101   5     5  2.53 mm water    74.0067878
-## 102   5     6  2.53 mm water   100.0000000
-## 103   5     7  2.53 mm water    32.9122340
-## 104   5     8  2.53 mm water    94.0698855
-## 105   5     9  2.53 mm water    53.9133943
-## 106   5    10  2.53 mm water    26.2422360
-## 107   5    11  2.53 mm water  -621.7592593
-## 108   5    12  2.53 mm water   100.0000000
-## 109   7     1  2.53 mm water    14.7779479
-## 110   7     2  2.53 mm water    78.7265366
-## 111   7     3  2.53 mm water   100.0000000
-## 112   7     4  2.53 mm water    53.9228139
-## 113   7     5  2.53 mm water    75.1817906
-## 114   7     6  2.53 mm water    59.6403820
-## 115   7     7  2.53 mm water   100.0000000
-## 116   7     8  2.53 mm water    99.6515865
-## 117   7     9  2.53 mm water    75.6080989
-## 118   7    10  2.53 mm water   100.0000000
-## 119   7    11  2.53 mm water   -65.3975143
-## 120   7    12  2.53 mm water   100.0000000
-## 121   9     1  2.53 mm water     1.3790784
-## 122   9     2  2.53 mm water    16.8433893
-## 123   9     3  2.53 mm water    75.0565262
-## 124   9     4  2.53 mm water    35.2263143
-## 125   9     5  2.53 mm water    74.6848841
-## 126   9     6  2.53 mm water    13.3688514
-## 127   9     7  2.53 mm water    36.2177470
-## 128   9     8  2.53 mm water    61.0829715
-## 129   9     9  2.53 mm water    38.2954360
-## 130   9    10  2.53 mm water    40.8685843
-## 131   9    11  2.53 mm water    58.1864842
-## 132   9    12  2.53 mm water    76.6523950
-## 133  11     1  2.53 mm water   -10.5815603
-## 134  11     2  2.53 mm water    39.0281009
-## 135  11     3  2.53 mm water    50.6993007
-## 136  11     4  2.53 mm water    -9.4283182
-## 137  11     5  2.53 mm water    76.2754543
-## 138  11     6  2.53 mm water    13.1042156
-## 139  11     7  2.53 mm water    37.6023285
-## 140  11     8  2.53 mm water    68.8054520
-## 141  11     9  2.53 mm water    11.3296903
-## 142  11    10  2.53 mm water    47.7443188
-## 143  11    11  2.53 mm water    62.8196073
-## 144  11    12  2.53 mm water    65.0257853
-## 145   1     1  5.07 mm water   100.0000000
-## 146   1     2  5.07 mm water    99.1728029
-## 147   1     3  5.07 mm water   100.0000000
-## 148   1     4  5.07 mm water    50.5431971
-## 149   1     5  5.07 mm water    84.9044166
-## 150   1     6  5.07 mm water   -49.9201824
-## 151   1     7  5.07 mm water   -18.0063316
-## 152   1     8  5.07 mm water    41.1793349
-## 153   1     9  5.07 mm water    71.3657916
-## 154   1    10  5.07 mm water   100.0000000
-## 155   1    11  5.07 mm water   -34.4072670
-## 156   1    12  5.07 mm water    37.1041879
-## 157   3     1  5.07 mm water    66.3713167
-## 158   3     2  5.07 mm water    75.5014606
-## 159   3     3  5.07 mm water   100.0000000
-## 160   3     4  5.07 mm water   100.0000000
-## 161   3     5  5.07 mm water   100.0000000
-## 162   3     6  5.07 mm water           NaN
-## 163   3     7  5.07 mm water    13.9687679
-## 164   3     8  5.07 mm water    86.5212974
-## 165   3     9  5.07 mm water   100.0000000
-## 166   3    10  5.07 mm water   100.0000000
-## 167   3    11  5.07 mm water    82.6100339
-## 168   3    12  5.07 mm water    59.1916291
-## 169   5     1  5.07 mm water    83.8214213
-## 170   5     2  5.07 mm water   -18.9790576
-## 171   5     3  5.07 mm water    -4.0227839
-## 172   5     4  5.07 mm water    72.7298768
-## 173   5     5  5.07 mm water    65.8015572
-## 174   5     6  5.07 mm water    83.8168517
-## 175   5     7  5.07 mm water   100.0000000
-## 176   5     8  5.07 mm water    56.9377990
-## 177   5     9  5.07 mm water    69.6476600
-## 178   5    10  5.07 mm water  -100.0000000
-## 179   5    11  5.07 mm water -1688.8888889
-## 180   5    12  5.07 mm water    36.4766596
-## 181   7     1  5.07 mm water    27.4775760
-## 182   7     2  5.07 mm water   -92.6941480
-## 183   7     3  5.07 mm water    26.9539895
-## 184   7     4  5.07 mm water    49.3404983
-## 185   7     5  5.07 mm water    97.2753747
-## 186   7     6  5.07 mm water    52.9462489
-## 187   7     7  5.07 mm water    -9.2241934
-## 188   7     8  5.07 mm water    82.4117663
-## 189   7     9  5.07 mm water    76.0972958
-## 190   7    10  5.07 mm water    77.1613833
-## 191   7    11  5.07 mm water   -19.1753798
-## 192   7    12  5.07 mm water   100.0000000
-## 193   9     1  5.07 mm water    20.6076914
-## 194   9     2  5.07 mm water    37.7498358
-## 195   9     3  5.07 mm water    80.6474820
-## 196   9     4  5.07 mm water    30.2783244
-## 197   9     5  5.07 mm water    48.1477918
-## 198   9     6  5.07 mm water     0.4832115
-## 199   9     7  5.07 mm water    -2.8634361
-## 200   9     8  5.07 mm water     0.6271105
-## 201   9     9  5.07 mm water   -23.2619009
-## 202   9    10  5.07 mm water    33.3897349
-## 203   9    11  5.07 mm water   -19.7604790
-## 204   9    12  5.07 mm water    13.8480064
-## 205  11     1  5.07 mm water     7.8156028
-## 206  11     2  5.07 mm water    57.1147306
-## 207  11     3  5.07 mm water    54.1221936
-## 208  11     4  5.07 mm water    15.3999557
-## 209  11     5  5.07 mm water    49.7810379
-## 210  11     6  5.07 mm water   -11.8679688
-## 211  11     7  5.07 mm water   -34.9099509
-## 212  11     8  5.07 mm water    18.9918992
-## 213  11     9  5.07 mm water     0.2671524
-## 214  11    10  5.07 mm water    22.2769506
-## 215  11    11  5.07 mm water  -101.0265103
-## 216  11    12  5.07 mm water    -1.8245038
-## 217   1     1 10.13 mm water    97.3192649
-## 218   1     2 10.13 mm water   100.0000000
-## 219   1     3 10.13 mm water   -20.1499408
-## 220   1     4 10.13 mm water    17.8823935
-## 221   1     5 10.13 mm water    35.9591299
-## 222   1     6 10.13 mm water   100.0000000
-## 223   1     7 10.13 mm water   -14.5780249
-## 224   1     8 10.13 mm water    91.5420948
-## 225   1     9 10.13 mm water    93.8629968
-## 226   1    10 10.13 mm water    66.6101400
-## 227   1    11 10.13 mm water   100.0000000
-## 228   1    12 10.13 mm water   100.0000000
-## 229   3     1 10.13 mm water  -117.8446322
-## 230   3     2 10.13 mm water   100.0000000
-## 231   3     3 10.13 mm water    46.5908539
-## 232   3     4 10.13 mm water   100.0000000
-## 233   3     5 10.13 mm water    52.1831849
-## 234   3     6 10.13 mm water           NaN
-## 235   3     7 10.13 mm water    12.6547324
-## 236   3     8 10.13 mm water   100.0000000
-## 237   3     9 10.13 mm water   100.0000000
-## 238   3    10 10.13 mm water   100.0000000
-## 239   3    11 10.13 mm water    52.0467836
-## 240   3    12 10.13 mm water    59.9097251
-## 241   5     1 10.13 mm water   100.0000000
-## 242   5     2 10.13 mm water   -67.3429319
-## 243   5     3 10.13 mm water    97.2232111
-## 244   5     4 10.13 mm water    54.4829562
-## 245   5     5 10.13 mm water    26.0131763
-## 246   5     6 10.13 mm water    54.0237134
-## 247   5     7 10.13 mm water    28.8231383
-## 248   5     8 10.13 mm water    95.5342903
-## 249   5     9 10.13 mm water    33.4050565
-## 250   5    10 10.13 mm water   -60.7763975
-## 251   5    11 10.13 mm water   100.0000000
-## 252   5    12 10.13 mm water   -82.1018062
-## 253   7     1 10.13 mm water    34.3797856
-## 254   7     2 10.13 mm water   -64.3540670
-## 255   7     3 10.13 mm water    37.1811299
-## 256   7     4 10.13 mm water    37.9286761
-## 257   7     5 10.13 mm water    77.4240232
-## 258   7     6 10.13 mm water    20.2836064
-## 259   7     7 10.13 mm water    40.3529736
-## 260   7     8 10.13 mm water    87.8986143
-## 261   7     9 10.13 mm water    13.7111021
-## 262   7    10 10.13 mm water     2.5216138
-## 263   7    11 10.13 mm water    37.3446439
-## 264   7    12 10.13 mm water    27.3481755
-## 265   9     1 10.13 mm water    47.9425945
-## 266   9     2 10.13 mm water    55.5315755
-## 267   9     3 10.13 mm water    19.3730730
-## 268   9     4 10.13 mm water    17.5897292
-## 269   9     5 10.13 mm water    66.0925623
-## 270   9     6 10.13 mm water    63.3502664
-## 271   9     7 10.13 mm water    11.5166772
-## 272   9     8 10.13 mm water    -3.1717318
-## 273   9     9 10.13 mm water   -11.5164404
-## 274   9    10 10.13 mm water     8.1331077
-## 275   9    11 10.13 mm water   -44.3284859
-## 276   9    12 10.13 mm water    20.9392561
-## 277  11     1 10.13 mm water    53.7163121
-## 278  11     2 10.13 mm water    62.7170217
-## 279  11     3 10.13 mm water   -41.1667280
-## 280  11     4 10.13 mm water     7.5448704
-## 281  11     5 10.13 mm water    68.1300635
-## 282  11     6 10.13 mm water    69.5883298
-## 283  11     7 10.13 mm water    19.2286702
-## 284  11     8 10.13 mm water    11.5597274
-## 285  11     9 10.13 mm water   -11.5239830
-## 286  11    10 10.13 mm water   -20.2507556
-## 287  11    11 10.13 mm water     5.8153223
-## 288  11    12 10.13 mm water    28.5904048
-
-
-

Step 4: Summarize the average lesion area

-

We can summarize the average area per DAI and Treatment, which will allow us to plot the data in the manner of Morini et al. 2017.

-
avgs <- percents %>%
-  group_by(DAI, Treatment) %>%
-  summarize(meanArea = mean(Area)) %>%
-  ungroup()
-avgs
-
## # A tibble: 24 x 3
-##      DAI Treatment      meanArea
-##    <int> <fct>             <dbl>
-##  1     1 control          100   
-##  2     1 2.53 mm water     71.9 
-##  3     1 5.07 mm water     48.5 
-##  4     1 10.13 mm water    64.0 
-##  5     3 control          NaN   
-##  6     3 2.53 mm water   -Inf   
-##  7     3 5.07 mm water    NaN   
-##  8     3 10.13 mm water   NaN   
-##  9     5 control          100   
-## 10     5 2.53 mm water      7.55
-## # ... with 14 more rows
-

Now that we have our averages, we can plot it using ggplot2

-
ggplot(avgs, aes(x = DAI, y = meanArea, group = Treatment)) +
-  geom_point(aes(pch = Treatment), size = 3) + # plot the points
-  stat_smooth(aes(lty = Treatment), method = "lm", se = FALSE, color = "black") + # plot the regression
-  theme_classic() + # change the appearance
-  ylim(0, 100) + # set the limits on the y axis
-  theme(text = element_text(size = 14)) + # increase the text size
-  labs(list( # Set the labels
-    x = "Days after fluazinam application",
-    y = "Percent disease control",
-    pch = "Irrigation levels",
-    lty = "Irrigation levels"))
-
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
-
## Warning: Removed 4 rows containing missing values (geom_point).
-

-
-
-
-

Additional visualizations

-

When we plot the averages, we inadvertently hide the data. Of course, if we tried to plot all the data in one graph, it would look a bit messy:

-
ggplot(percents, aes(x = DAI, y = Area, group = Treatment)) +
-  geom_point(aes(pch = Treatment), size = 3) + # plot the points
-  stat_smooth(aes(lty = Treatment), method = "lm", se = FALSE, color = "black") + # plot the regression
-  theme_classic() + # change the appearance
-  ylim(0, 100) + # set the limits on the y axis
-  theme(text = element_text(size = 14)) + # increase the text size
-  labs(list( # Set the labels
-    x = "Days after fluazinam application",
-    y = "Percent disease control",
-    pch = "Irrigation levels",
-    lty = "Irrigation levels"))
-
## Warning: Removed 41 rows containing non-finite values (stat_smooth).
-
## Warning: Removed 40 rows containing missing values (geom_point).
-
## Warning: Removed 80 rows containing missing values (geom_smooth).
-

-

With ggplot2, we can spread the data out into “facets”:

-
ggplot(percents, aes(x = DAI, y = Area, group = Treatment)) +
-  geom_point(aes(pch = Treatment), size = 2) + # plot the points
-  stat_smooth(aes(lty = Treatment), method = "lm", se = FALSE, color = "black") + # plot the regression
-  theme_classic() + # change the appearance
-  ylim(0, 100) + # set the limits on the y axis
-  theme(text = element_text(size = 14)) + # increase the text size
-  facet_wrap(~Treatment, nrow = 1) +
-  theme(aspect.ratio = 1) +
-  labs(list( # Set the labels
-    x = "Days after fluazinam application",
-    y = "Percent disease control",
-    pch = "Irrigation levels",
-    lty = "Irrigation levels"))
-
## Warning: Removed 41 rows containing non-finite values (stat_smooth).
-
## Warning: Removed 40 rows containing missing values (geom_point).
-
## Warning: Removed 80 rows containing missing values (geom_smooth).
-

-
- - - - -
- - - - - - - - diff --git a/README.html b/README.html deleted file mode 100644 index 33cb208..0000000 --- a/README.html +++ /dev/null @@ -1,178 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - -
-

R for Plant Pathologists

- -

Binder

-

This introduction to R for plant pathology was originally written as a workshop by Drs. Sydney E. Everhart and Zhian N. Kamvar, and modified to the current form for the 2018 course PLPT 802: Ecology & Management of Plant Pathogens.

-

The source code can be found at: https://github.com/everhartlab/IntroR-for-PLPT802.

-
-

Goals

-

As a result of taking this workshop you should be able to:

-
    -
  • find, download, and load necessary packages for analysis
    • -
  • load tabular data into R
    • -
  • understand the basics of data manipulation in R
    • -
  • know what a data frame, vector, and function are
    • -
  • summarize data
    • -
  • visualize data
    • -
  • troubleshoot commmon problems
    • -
-
-
- - - - -
- - - - - - - - diff --git a/docs/Part2-Fig2Recreation.html b/docs/Part2-Fig2Recreation.html index 98f60a6..f569b8b 100644 --- a/docs/Part2-Fig2Recreation.html +++ b/docs/Part2-Fig2Recreation.html @@ -590,9 +590,11 @@

Step 3: Calculate percent disease control

70 11 10 100 47.744319 22.2769506 -20.250756 71 11 11 100 62.819607 -101.0265103 5.815322 72 11 12 100 65.025785 -1.8245038 28.590405 -

Because figure 2 plotted the average value, we want to summarize our data in averages. To do this, we need to convert our data back to tidy format by using the tidyr function gather():

+

Because figure 2 plotted the average value, we want to summarize our data in averages. To do this, we need to convert our data back to tidy format by using the tidyr function gather().

+

Additionally, because we observed some values that were missing or divided by zero, we need to add a filter that removes these points. For that, we will use the function is.finite().

> percents <- percents %>%
 +   gather(key = Treatment, value = Area, -DAI, -Block) %>%
++   filter(is.finite(Area)) %>%  # selecting all the finite values of Area
 +   mutate(Treatment = factor(Treatment, levels = unique(Treatment))) # reset factor
 > percents
    DAI Block      Treatment          Area
@@ -613,298 +615,294 @@ 
Step 3: Calculate percent disease control

 15    3     3        control   100.0000000
 16    3     4        control   100.0000000
 17    3     5        control   100.0000000
-18    3     6        control           NaN
-19    3     7        control   100.0000000
-20    3     8        control   100.0000000
-21    3     9        control   100.0000000
-22    3    10        control   100.0000000
-23    3    11        control   100.0000000
-24    3    12        control   100.0000000
-25    5     1        control   100.0000000
-26    5     2        control   100.0000000
-27    5     3        control   100.0000000
-28    5     4        control   100.0000000
-29    5     5        control   100.0000000
-30    5     6        control   100.0000000
-31    5     7        control   100.0000000
-32    5     8        control   100.0000000
-33    5     9        control   100.0000000
-34    5    10        control   100.0000000
-35    5    11        control   100.0000000
-36    5    12        control   100.0000000
-37    7     1        control   100.0000000
-38    7     2        control   100.0000000
-39    7     3        control   100.0000000
-40    7     4        control   100.0000000
-41    7     5        control   100.0000000
-42    7     6        control   100.0000000
-43    7     7        control   100.0000000
-44    7     8        control   100.0000000
-45    7     9        control   100.0000000
-46    7    10        control   100.0000000
-47    7    11        control   100.0000000
-48    7    12        control   100.0000000
-49    9     1        control   100.0000000
-50    9     2        control   100.0000000
-51    9     3        control   100.0000000
-52    9     4        control   100.0000000
-53    9     5        control   100.0000000
-54    9     6        control   100.0000000
-55    9     7        control   100.0000000
-56    9     8        control   100.0000000
-57    9     9        control   100.0000000
-58    9    10        control   100.0000000
-59    9    11        control   100.0000000
-60    9    12        control   100.0000000
-61   11     1        control   100.0000000
-62   11     2        control   100.0000000
-63   11     3        control   100.0000000
-64   11     4        control   100.0000000
-65   11     5        control   100.0000000
-66   11     6        control   100.0000000
-67   11     7        control   100.0000000
-68   11     8        control   100.0000000
-69   11     9        control   100.0000000
-70   11    10        control   100.0000000
-71   11    11        control   100.0000000
-72   11    12        control   100.0000000
-73    1     1  2.53 mm water    52.6908320
-74    1     2  2.53 mm water   100.0000000
-75    1     3  2.53 mm water   -39.2410890
-76    1     4  2.53 mm water   100.0000000
-77    1     5  2.53 mm water   100.0000000
-78    1     6  2.53 mm water   -37.4344356
-79    1     7  2.53 mm water   100.0000000
-80    1     8  2.53 mm water   100.0000000
-81    1     9  2.53 mm water   100.0000000
-82    1    10  2.53 mm water    86.4944778
-83    1    11  2.53 mm water   100.0000000
-84    1    12  2.53 mm water   100.0000000
-85    3     1  2.53 mm water  -288.6255924
-86    3     2  2.53 mm water   100.0000000
-87    3     3  2.53 mm water    33.2750158
-88    3     4  2.53 mm water    58.7513498
-89    3     5  2.53 mm water    88.4416485
-90    3     6  2.53 mm water          -Inf
-91    3     7  2.53 mm water   100.0000000
-92    3     8  2.53 mm water    87.6786880
-93    3     9  2.53 mm water    78.9401553
-94    3    10  2.53 mm water    76.8076923
-95    3    11  2.53 mm water   100.0000000
-96    3    12  2.53 mm water   100.0000000
-97    5     1  2.53 mm water    99.1193938
-98    5     2  2.53 mm water    47.4803665
-99    5     3  2.53 mm water    30.8828765
-100   5     4  2.53 mm water    53.7238614
-101   5     5  2.53 mm water    74.0067878
-102   5     6  2.53 mm water   100.0000000
-103   5     7  2.53 mm water    32.9122340
-104   5     8  2.53 mm water    94.0698855
-105   5     9  2.53 mm water    53.9133943
-106   5    10  2.53 mm water    26.2422360
-107   5    11  2.53 mm water  -621.7592593
-108   5    12  2.53 mm water   100.0000000
-109   7     1  2.53 mm water    14.7779479
-110   7     2  2.53 mm water    78.7265366
-111   7     3  2.53 mm water   100.0000000
-112   7     4  2.53 mm water    53.9228139
-113   7     5  2.53 mm water    75.1817906
-114   7     6  2.53 mm water    59.6403820
-115   7     7  2.53 mm water   100.0000000
-116   7     8  2.53 mm water    99.6515865
-117   7     9  2.53 mm water    75.6080989
-118   7    10  2.53 mm water   100.0000000
-119   7    11  2.53 mm water   -65.3975143
-120   7    12  2.53 mm water   100.0000000
-121   9     1  2.53 mm water     1.3790784
-122   9     2  2.53 mm water    16.8433893
-123   9     3  2.53 mm water    75.0565262
-124   9     4  2.53 mm water    35.2263143
-125   9     5  2.53 mm water    74.6848841
-126   9     6  2.53 mm water    13.3688514
-127   9     7  2.53 mm water    36.2177470
-128   9     8  2.53 mm water    61.0829715
-129   9     9  2.53 mm water    38.2954360
-130   9    10  2.53 mm water    40.8685843
-131   9    11  2.53 mm water    58.1864842
-132   9    12  2.53 mm water    76.6523950
-133  11     1  2.53 mm water   -10.5815603
-134  11     2  2.53 mm water    39.0281009
-135  11     3  2.53 mm water    50.6993007
-136  11     4  2.53 mm water    -9.4283182
-137  11     5  2.53 mm water    76.2754543
-138  11     6  2.53 mm water    13.1042156
-139  11     7  2.53 mm water    37.6023285
-140  11     8  2.53 mm water    68.8054520
-141  11     9  2.53 mm water    11.3296903
-142  11    10  2.53 mm water    47.7443188
-143  11    11  2.53 mm water    62.8196073
-144  11    12  2.53 mm water    65.0257853
-145   1     1  5.07 mm water   100.0000000
-146   1     2  5.07 mm water    99.1728029
-147   1     3  5.07 mm water   100.0000000
-148   1     4  5.07 mm water    50.5431971
-149   1     5  5.07 mm water    84.9044166
-150   1     6  5.07 mm water   -49.9201824
-151   1     7  5.07 mm water   -18.0063316
-152   1     8  5.07 mm water    41.1793349
-153   1     9  5.07 mm water    71.3657916
-154   1    10  5.07 mm water   100.0000000
-155   1    11  5.07 mm water   -34.4072670
-156   1    12  5.07 mm water    37.1041879
-157   3     1  5.07 mm water    66.3713167
-158   3     2  5.07 mm water    75.5014606
-159   3     3  5.07 mm water   100.0000000
-160   3     4  5.07 mm water   100.0000000
-161   3     5  5.07 mm water   100.0000000
-162   3     6  5.07 mm water           NaN
-163   3     7  5.07 mm water    13.9687679
-164   3     8  5.07 mm water    86.5212974
-165   3     9  5.07 mm water   100.0000000
-166   3    10  5.07 mm water   100.0000000
-167   3    11  5.07 mm water    82.6100339
-168   3    12  5.07 mm water    59.1916291
-169   5     1  5.07 mm water    83.8214213
-170   5     2  5.07 mm water   -18.9790576
-171   5     3  5.07 mm water    -4.0227839
-172   5     4  5.07 mm water    72.7298768
-173   5     5  5.07 mm water    65.8015572
-174   5     6  5.07 mm water    83.8168517
-175   5     7  5.07 mm water   100.0000000
-176   5     8  5.07 mm water    56.9377990
-177   5     9  5.07 mm water    69.6476600
-178   5    10  5.07 mm water  -100.0000000
-179   5    11  5.07 mm water -1688.8888889
-180   5    12  5.07 mm water    36.4766596
-181   7     1  5.07 mm water    27.4775760
-182   7     2  5.07 mm water   -92.6941480
-183   7     3  5.07 mm water    26.9539895
-184   7     4  5.07 mm water    49.3404983
-185   7     5  5.07 mm water    97.2753747
-186   7     6  5.07 mm water    52.9462489
-187   7     7  5.07 mm water    -9.2241934
-188   7     8  5.07 mm water    82.4117663
-189   7     9  5.07 mm water    76.0972958
-190   7    10  5.07 mm water    77.1613833
-191   7    11  5.07 mm water   -19.1753798
-192   7    12  5.07 mm water   100.0000000
-193   9     1  5.07 mm water    20.6076914
-194   9     2  5.07 mm water    37.7498358
-195   9     3  5.07 mm water    80.6474820
-196   9     4  5.07 mm water    30.2783244
-197   9     5  5.07 mm water    48.1477918
-198   9     6  5.07 mm water     0.4832115
-199   9     7  5.07 mm water    -2.8634361
-200   9     8  5.07 mm water     0.6271105
-201   9     9  5.07 mm water   -23.2619009
-202   9    10  5.07 mm water    33.3897349
-203   9    11  5.07 mm water   -19.7604790
-204   9    12  5.07 mm water    13.8480064
-205  11     1  5.07 mm water     7.8156028
-206  11     2  5.07 mm water    57.1147306
-207  11     3  5.07 mm water    54.1221936
-208  11     4  5.07 mm water    15.3999557
-209  11     5  5.07 mm water    49.7810379
-210  11     6  5.07 mm water   -11.8679688
-211  11     7  5.07 mm water   -34.9099509
-212  11     8  5.07 mm water    18.9918992
-213  11     9  5.07 mm water     0.2671524
-214  11    10  5.07 mm water    22.2769506
-215  11    11  5.07 mm water  -101.0265103
-216  11    12  5.07 mm water    -1.8245038
-217   1     1 10.13 mm water    97.3192649
-218   1     2 10.13 mm water   100.0000000
-219   1     3 10.13 mm water   -20.1499408
-220   1     4 10.13 mm water    17.8823935
-221   1     5 10.13 mm water    35.9591299
-222   1     6 10.13 mm water   100.0000000
-223   1     7 10.13 mm water   -14.5780249
-224   1     8 10.13 mm water    91.5420948
-225   1     9 10.13 mm water    93.8629968
-226   1    10 10.13 mm water    66.6101400
-227   1    11 10.13 mm water   100.0000000
-228   1    12 10.13 mm water   100.0000000
-229   3     1 10.13 mm water  -117.8446322
-230   3     2 10.13 mm water   100.0000000
-231   3     3 10.13 mm water    46.5908539
-232   3     4 10.13 mm water   100.0000000
-233   3     5 10.13 mm water    52.1831849
-234   3     6 10.13 mm water           NaN
-235   3     7 10.13 mm water    12.6547324
-236   3     8 10.13 mm water   100.0000000
-237   3     9 10.13 mm water   100.0000000
-238   3    10 10.13 mm water   100.0000000
-239   3    11 10.13 mm water    52.0467836
-240   3    12 10.13 mm water    59.9097251
-241   5     1 10.13 mm water   100.0000000
-242   5     2 10.13 mm water   -67.3429319
-243   5     3 10.13 mm water    97.2232111
-244   5     4 10.13 mm water    54.4829562
-245   5     5 10.13 mm water    26.0131763
-246   5     6 10.13 mm water    54.0237134
-247   5     7 10.13 mm water    28.8231383
-248   5     8 10.13 mm water    95.5342903
-249   5     9 10.13 mm water    33.4050565
-250   5    10 10.13 mm water   -60.7763975
-251   5    11 10.13 mm water   100.0000000
-252   5    12 10.13 mm water   -82.1018062
-253   7     1 10.13 mm water    34.3797856
-254   7     2 10.13 mm water   -64.3540670
-255   7     3 10.13 mm water    37.1811299
-256   7     4 10.13 mm water    37.9286761
-257   7     5 10.13 mm water    77.4240232
-258   7     6 10.13 mm water    20.2836064
-259   7     7 10.13 mm water    40.3529736
-260   7     8 10.13 mm water    87.8986143
-261   7     9 10.13 mm water    13.7111021
-262   7    10 10.13 mm water     2.5216138
-263   7    11 10.13 mm water    37.3446439
-264   7    12 10.13 mm water    27.3481755
-265   9     1 10.13 mm water    47.9425945
-266   9     2 10.13 mm water    55.5315755
-267   9     3 10.13 mm water    19.3730730
-268   9     4 10.13 mm water    17.5897292
-269   9     5 10.13 mm water    66.0925623
-270   9     6 10.13 mm water    63.3502664
-271   9     7 10.13 mm water    11.5166772
-272   9     8 10.13 mm water    -3.1717318
-273   9     9 10.13 mm water   -11.5164404
-274   9    10 10.13 mm water     8.1331077
-275   9    11 10.13 mm water   -44.3284859
-276   9    12 10.13 mm water    20.9392561
-277  11     1 10.13 mm water    53.7163121
-278  11     2 10.13 mm water    62.7170217
-279  11     3 10.13 mm water   -41.1667280
-280  11     4 10.13 mm water     7.5448704
-281  11     5 10.13 mm water    68.1300635
-282  11     6 10.13 mm water    69.5883298
-283  11     7 10.13 mm water    19.2286702
-284  11     8 10.13 mm water    11.5597274
-285  11     9 10.13 mm water   -11.5239830
-286  11    10 10.13 mm water   -20.2507556
-287  11    11 10.13 mm water     5.8153223
-288  11    12 10.13 mm water    28.5904048
+18 3 7 control 100.0000000 +19 3 8 control 100.0000000 +20 3 9 control 100.0000000 +21 3 10 control 100.0000000 +22 3 11 control 100.0000000 +23 3 12 control 100.0000000 +24 5 1 control 100.0000000 +25 5 2 control 100.0000000 +26 5 3 control 100.0000000 +27 5 4 control 100.0000000 +28 5 5 control 100.0000000 +29 5 6 control 100.0000000 +30 5 7 control 100.0000000 +31 5 8 control 100.0000000 +32 5 9 control 100.0000000 +33 5 10 control 100.0000000 +34 5 11 control 100.0000000 +35 5 12 control 100.0000000 +36 7 1 control 100.0000000 +37 7 2 control 100.0000000 +38 7 3 control 100.0000000 +39 7 4 control 100.0000000 +40 7 5 control 100.0000000 +41 7 6 control 100.0000000 +42 7 7 control 100.0000000 +43 7 8 control 100.0000000 +44 7 9 control 100.0000000 +45 7 10 control 100.0000000 +46 7 11 control 100.0000000 +47 7 12 control 100.0000000 +48 9 1 control 100.0000000 +49 9 2 control 100.0000000 +50 9 3 control 100.0000000 +51 9 4 control 100.0000000 +52 9 5 control 100.0000000 +53 9 6 control 100.0000000 +54 9 7 control 100.0000000 +55 9 8 control 100.0000000 +56 9 9 control 100.0000000 +57 9 10 control 100.0000000 +58 9 11 control 100.0000000 +59 9 12 control 100.0000000 +60 11 1 control 100.0000000 +61 11 2 control 100.0000000 +62 11 3 control 100.0000000 +63 11 4 control 100.0000000 +64 11 5 control 100.0000000 +65 11 6 control 100.0000000 +66 11 7 control 100.0000000 +67 11 8 control 100.0000000 +68 11 9 control 100.0000000 +69 11 10 control 100.0000000 +70 11 11 control 100.0000000 +71 11 12 control 100.0000000 +72 1 1 2.53 mm water 52.6908320 +73 1 2 2.53 mm water 100.0000000 +74 1 3 2.53 mm water -39.2410890 +75 1 4 2.53 mm water 100.0000000 +76 1 5 2.53 mm water 100.0000000 +77 1 6 2.53 mm water -37.4344356 +78 1 7 2.53 mm water 100.0000000 +79 1 8 2.53 mm water 100.0000000 +80 1 9 2.53 mm water 100.0000000 +81 1 10 2.53 mm water 86.4944778 +82 1 11 2.53 mm water 100.0000000 +83 1 12 2.53 mm water 100.0000000 +84 3 1 2.53 mm water -288.6255924 +85 3 2 2.53 mm water 100.0000000 +86 3 3 2.53 mm water 33.2750158 +87 3 4 2.53 mm water 58.7513498 +88 3 5 2.53 mm water 88.4416485 +89 3 7 2.53 mm water 100.0000000 +90 3 8 2.53 mm water 87.6786880 +91 3 9 2.53 mm water 78.9401553 +92 3 10 2.53 mm water 76.8076923 +93 3 11 2.53 mm water 100.0000000 +94 3 12 2.53 mm water 100.0000000 +95 5 1 2.53 mm water 99.1193938 +96 5 2 2.53 mm water 47.4803665 +97 5 3 2.53 mm water 30.8828765 +98 5 4 2.53 mm water 53.7238614 +99 5 5 2.53 mm water 74.0067878 +100 5 6 2.53 mm water 100.0000000 +101 5 7 2.53 mm water 32.9122340 +102 5 8 2.53 mm water 94.0698855 +103 5 9 2.53 mm water 53.9133943 +104 5 10 2.53 mm water 26.2422360 +105 5 11 2.53 mm water -621.7592593 +106 5 12 2.53 mm water 100.0000000 +107 7 1 2.53 mm water 14.7779479 +108 7 2 2.53 mm water 78.7265366 +109 7 3 2.53 mm water 100.0000000 +110 7 4 2.53 mm water 53.9228139 +111 7 5 2.53 mm water 75.1817906 +112 7 6 2.53 mm water 59.6403820 +113 7 7 2.53 mm water 100.0000000 +114 7 8 2.53 mm water 99.6515865 +115 7 9 2.53 mm water 75.6080989 +116 7 10 2.53 mm water 100.0000000 +117 7 11 2.53 mm water -65.3975143 +118 7 12 2.53 mm water 100.0000000 +119 9 1 2.53 mm water 1.3790784 +120 9 2 2.53 mm water 16.8433893 +121 9 3 2.53 mm water 75.0565262 +122 9 4 2.53 mm water 35.2263143 +123 9 5 2.53 mm water 74.6848841 +124 9 6 2.53 mm water 13.3688514 +125 9 7 2.53 mm water 36.2177470 +126 9 8 2.53 mm water 61.0829715 +127 9 9 2.53 mm water 38.2954360 +128 9 10 2.53 mm water 40.8685843 +129 9 11 2.53 mm water 58.1864842 +130 9 12 2.53 mm water 76.6523950 +131 11 1 2.53 mm water -10.5815603 +132 11 2 2.53 mm water 39.0281009 +133 11 3 2.53 mm water 50.6993007 +134 11 4 2.53 mm water -9.4283182 +135 11 5 2.53 mm water 76.2754543 +136 11 6 2.53 mm water 13.1042156 +137 11 7 2.53 mm water 37.6023285 +138 11 8 2.53 mm water 68.8054520 +139 11 9 2.53 mm water 11.3296903 +140 11 10 2.53 mm water 47.7443188 +141 11 11 2.53 mm water 62.8196073 +142 11 12 2.53 mm water 65.0257853 +143 1 1 5.07 mm water 100.0000000 +144 1 2 5.07 mm water 99.1728029 +145 1 3 5.07 mm water 100.0000000 +146 1 4 5.07 mm water 50.5431971 +147 1 5 5.07 mm water 84.9044166 +148 1 6 5.07 mm water -49.9201824 +149 1 7 5.07 mm water -18.0063316 +150 1 8 5.07 mm water 41.1793349 +151 1 9 5.07 mm water 71.3657916 +152 1 10 5.07 mm water 100.0000000 +153 1 11 5.07 mm water -34.4072670 +154 1 12 5.07 mm water 37.1041879 +155 3 1 5.07 mm water 66.3713167 +156 3 2 5.07 mm water 75.5014606 +157 3 3 5.07 mm water 100.0000000 +158 3 4 5.07 mm water 100.0000000 +159 3 5 5.07 mm water 100.0000000 +160 3 7 5.07 mm water 13.9687679 +161 3 8 5.07 mm water 86.5212974 +162 3 9 5.07 mm water 100.0000000 +163 3 10 5.07 mm water 100.0000000 +164 3 11 5.07 mm water 82.6100339 +165 3 12 5.07 mm water 59.1916291 +166 5 1 5.07 mm water 83.8214213 +167 5 2 5.07 mm water -18.9790576 +168 5 3 5.07 mm water -4.0227839 +169 5 4 5.07 mm water 72.7298768 +170 5 5 5.07 mm water 65.8015572 +171 5 6 5.07 mm water 83.8168517 +172 5 7 5.07 mm water 100.0000000 +173 5 8 5.07 mm water 56.9377990 +174 5 9 5.07 mm water 69.6476600 +175 5 10 5.07 mm water -100.0000000 +176 5 11 5.07 mm water -1688.8888889 +177 5 12 5.07 mm water 36.4766596 +178 7 1 5.07 mm water 27.4775760 +179 7 2 5.07 mm water -92.6941480 +180 7 3 5.07 mm water 26.9539895 +181 7 4 5.07 mm water 49.3404983 +182 7 5 5.07 mm water 97.2753747 +183 7 6 5.07 mm water 52.9462489 +184 7 7 5.07 mm water -9.2241934 +185 7 8 5.07 mm water 82.4117663 +186 7 9 5.07 mm water 76.0972958 +187 7 10 5.07 mm water 77.1613833 +188 7 11 5.07 mm water -19.1753798 +189 7 12 5.07 mm water 100.0000000 +190 9 1 5.07 mm water 20.6076914 +191 9 2 5.07 mm water 37.7498358 +192 9 3 5.07 mm water 80.6474820 +193 9 4 5.07 mm water 30.2783244 +194 9 5 5.07 mm water 48.1477918 +195 9 6 5.07 mm water 0.4832115 +196 9 7 5.07 mm water -2.8634361 +197 9 8 5.07 mm water 0.6271105 +198 9 9 5.07 mm water -23.2619009 +199 9 10 5.07 mm water 33.3897349 +200 9 11 5.07 mm water -19.7604790 +201 9 12 5.07 mm water 13.8480064 +202 11 1 5.07 mm water 7.8156028 +203 11 2 5.07 mm water 57.1147306 +204 11 3 5.07 mm water 54.1221936 +205 11 4 5.07 mm water 15.3999557 +206 11 5 5.07 mm water 49.7810379 +207 11 6 5.07 mm water -11.8679688 +208 11 7 5.07 mm water -34.9099509 +209 11 8 5.07 mm water 18.9918992 +210 11 9 5.07 mm water 0.2671524 +211 11 10 5.07 mm water 22.2769506 +212 11 11 5.07 mm water -101.0265103 +213 11 12 5.07 mm water -1.8245038 +214 1 1 10.13 mm water 97.3192649 +215 1 2 10.13 mm water 100.0000000 +216 1 3 10.13 mm water -20.1499408 +217 1 4 10.13 mm water 17.8823935 +218 1 5 10.13 mm water 35.9591299 +219 1 6 10.13 mm water 100.0000000 +220 1 7 10.13 mm water -14.5780249 +221 1 8 10.13 mm water 91.5420948 +222 1 9 10.13 mm water 93.8629968 +223 1 10 10.13 mm water 66.6101400 +224 1 11 10.13 mm water 100.0000000 +225 1 12 10.13 mm water 100.0000000 +226 3 1 10.13 mm water -117.8446322 +227 3 2 10.13 mm water 100.0000000 +228 3 3 10.13 mm water 46.5908539 +229 3 4 10.13 mm water 100.0000000 +230 3 5 10.13 mm water 52.1831849 +231 3 7 10.13 mm water 12.6547324 +232 3 8 10.13 mm water 100.0000000 +233 3 9 10.13 mm water 100.0000000 +234 3 10 10.13 mm water 100.0000000 +235 3 11 10.13 mm water 52.0467836 +236 3 12 10.13 mm water 59.9097251 +237 5 1 10.13 mm water 100.0000000 +238 5 2 10.13 mm water -67.3429319 +239 5 3 10.13 mm water 97.2232111 +240 5 4 10.13 mm water 54.4829562 +241 5 5 10.13 mm water 26.0131763 +242 5 6 10.13 mm water 54.0237134 +243 5 7 10.13 mm water 28.8231383 +244 5 8 10.13 mm water 95.5342903 +245 5 9 10.13 mm water 33.4050565 +246 5 10 10.13 mm water -60.7763975 +247 5 11 10.13 mm water 100.0000000 +248 5 12 10.13 mm water -82.1018062 +249 7 1 10.13 mm water 34.3797856 +250 7 2 10.13 mm water -64.3540670 +251 7 3 10.13 mm water 37.1811299 +252 7 4 10.13 mm water 37.9286761 +253 7 5 10.13 mm water 77.4240232 +254 7 6 10.13 mm water 20.2836064 +255 7 7 10.13 mm water 40.3529736 +256 7 8 10.13 mm water 87.8986143 +257 7 9 10.13 mm water 13.7111021 +258 7 10 10.13 mm water 2.5216138 +259 7 11 10.13 mm water 37.3446439 +260 7 12 10.13 mm water 27.3481755 +261 9 1 10.13 mm water 47.9425945 +262 9 2 10.13 mm water 55.5315755 +263 9 3 10.13 mm water 19.3730730 +264 9 4 10.13 mm water 17.5897292 +265 9 5 10.13 mm water 66.0925623 +266 9 6 10.13 mm water 63.3502664 +267 9 7 10.13 mm water 11.5166772 +268 9 8 10.13 mm water -3.1717318 +269 9 9 10.13 mm water -11.5164404 +270 9 10 10.13 mm water 8.1331077 +271 9 11 10.13 mm water -44.3284859 +272 9 12 10.13 mm water 20.9392561 +273 11 1 10.13 mm water 53.7163121 +274 11 2 10.13 mm water 62.7170217 +275 11 3 10.13 mm water -41.1667280 +276 11 4 10.13 mm water 7.5448704 +277 11 5 10.13 mm water 68.1300635 +278 11 6 10.13 mm water 69.5883298 +279 11 7 10.13 mm water 19.2286702 +280 11 8 10.13 mm water 11.5597274 +281 11 9 10.13 mm water -11.5239830 +282 11 10 10.13 mm water -20.2507556 +283 11 11 10.13 mm water 5.8153223 +284 11 12 10.13 mm water 28.5904048

Step 4: Summarize the average lesion area

We can summarize the average area per DAI and Treatment, which will allow us to plot the data in the manner of Morini et al. 2017.

> avgs <- percents %>%
 +   group_by(DAI, Treatment) %>%
-+   summarize(meanArea = mean(Area)) %>%
++   summarize(meanArea = mean(Area, na.rm = TRUE)) %>%
 +   ungroup()
 > avgs
# A tibble: 24 x 3
      DAI Treatment      meanArea
    <int> <fct>             <dbl>
- 1     1 control          100   
+ 1     1 control          100.  
  2     1 2.53 mm water     71.9 
  3     1 5.07 mm water     48.5 
  4     1 10.13 mm water    64.0 
- 5     3 control          NaN   
- 6     3 2.53 mm water   -Inf   
- 7     3 5.07 mm water    NaN   
- 8     3 10.13 mm water   NaN   
- 9     5 control          100   
+ 5     3 control          100.  
+ 6     3 2.53 mm water     48.7 
+ 7     3 5.07 mm water     80.4 
+ 8     3 10.13 mm water    55.0 
+ 9     5 control          100.  
 10     5 2.53 mm water      7.55
 # ... with 14 more rows

Now that we have our averages, we can plot it using ggplot2

@@ -919,8 +917,8 @@

Step 4: Summarize the average lesion area

+ y = "Percent disease control", + pch = "Irrigation levels", + lty = "Irrigation levels")) -
Warning: Removed 5 rows containing non-finite values (stat_smooth).
-
Warning: Removed 4 rows containing missing values (geom_point).
+
Warning: Removed 1 rows containing non-finite values (stat_smooth).
+
Warning: Removed 1 rows containing missing values (geom_point).

@@ -938,8 +936,8 @@

Additional visualizations

+ y = "Percent disease control", + pch = "Irrigation levels", + lty = "Irrigation levels")) -
Warning: Removed 41 rows containing non-finite values (stat_smooth).
-
Warning: Removed 40 rows containing missing values (geom_point).
+
Warning: Removed 37 rows containing non-finite values (stat_smooth).
+
Warning: Removed 37 rows containing missing values (geom_point).
Warning: Removed 80 rows containing missing values (geom_smooth).

With ggplot2, we can spread the data out into “facets”:

@@ -956,8 +954,8 @@

Additional visualizations

+ y = "Percent disease control", + pch = "Irrigation levels", + lty = "Irrigation levels")) -
Warning: Removed 41 rows containing non-finite values (stat_smooth).
-
Warning: Removed 40 rows containing missing values (geom_point).
+
Warning: Removed 37 rows containing non-finite values (stat_smooth).
+
Warning: Removed 37 rows containing missing values (geom_point).
Warning: Removed 80 rows containing missing values (geom_smooth).

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