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<div id="section-part-1---data-cleaning" class="section level2">
<h2>Part 1 - Data Cleaning</h2>
<div id="section-dataset" class="section level3">
<h3>Dataset</h3>
<p>To start with you will work on the following data set which contains
a number of variables for a list of 100 farmers.</p>
<pre class="r"><code>farmer_data</code></pre>
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<div id="section-exercise-1a---trim-whitespace" class="section level3">
<h3>Exercise 1a - Trim Whitespace</h3>
<p>Let’s start by checking for any whitespace, i suspect there may be
some in our name column.</p>
<p>Let’s perform a check by using the <code>unique</code> function to
look at all the unique values for name</p>
<pre class="r"><code>unique(farmer_data$name)</code></pre>
<pre><code>## [1] "Treven" "Brandon" "Kevin" "Erik" "Trusten"
## [6] "Saqr" "Bradford" "Jack" " Codie" "Frank"
## [11] "Tyler " "Joseph" "Royse" "Reece" "Michael"
## [16] "Paul" "Nathan" "Isaiah" "Taylor" "Matthew"
## [21] "Glen" "Marzooq" "Richard" "Justin" "Jonah"
## [26] "Zaahir" "Kabeer " "Lawrence" "Hamad" "Waseem"
## [31] "Sonim" "Hamza" "Zainuddeen" "Martin" " Ryan"
## [36] "Orran" "Geoffrey" "Zachery" "Lucas" "Cass"
## [41] "Luis" "Donald" " Hunter" "Devon" "Anthony"
## [46] "Carmen" "Kristin" "Briana" "Terra" " Priscilla "
## [51] "Danielle" "Vanessa " "Patricia" "Kiana" "Tammy"
## [56] "Astrid" "Savanna" "Waleeda" "Marilu" "Samantha"
## [61] "Amber" "Manaara" "Sabeeka" "Shamika" "Heaven"
## [66] "Tamanna" "Magan" "Lasha" "Sri" "Cheyenne"
## [71] "Yesenia" "Autum " "Aleksandrina" "Sonia" "Nicolette"
## [76] "Faseeha" "Alexis" "Ceara" "Danica" "Kendrer"
## [81] "Antoinette" "Kayleigh" "Micaela" "Amari" " Samantha"
## [86] "Olivia" "Breanna" "Geovana" "Najma" "Selena"
## [91] "Aanisa" "Charmaine"</code></pre>
<p>Seems our suspicions were correct, we have around 10 values for name
that have either a leading or trailing space in them.</p>
<p>Exercise 1a: Using <code>mutate</code> and <code>trimws</code>,
remove the whitespace present in the name column from both the beginning
and the ending of the variable entries</p>
<p><strong>REMEMBER</strong> You can inspect the results of your changes
at any time by writing the name of the data frame into the code chunk at
any time</p>
<div class="tutorial-exercise" data-label="ex1a" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate()
unique(farmer_data$name)</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1a-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate(name = trimws(name, which = "both"))
unique(farmer_data$name)</code></pre>
</div>
</div>
<div id="section-exercise-1b---missing-data" class="section level3">
<h3>Exercise 1b - Missing Data</h3>
<p>Recall that in R we need to be very explicit when it comes to
defining data. It <strong>MUST</strong> be coded as <code>NA</code></p>
<p>In our dataset, we have unfortunately not told R explicitly what our
missing data codes were when we imported the data. Therefore, there are
a few variables which have some values equalling -9. However this was a
missing data code, but R will see this value as literally -9.</p>
<p>Let’s check first which variables this issue applies to.</p>
<p>Exercise 1bi: Use <code>summary</code> to check which continuous
variables in the dataset are still using the missing data code
<code>-9</code> rather than being explicitly <code>NA</code>. Note that
-9 will be the minimum value in each of these cases.</p>
<div class="tutorial-exercise" data-label="ex1bi" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1bi-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>summary(farmer_data)
farmer_data
# Should have found that at least 1 value in each of hh_size, income and costs were equal to -9</code></pre>
</div>
<p>You should have identified a few variables unfortunately using this
code.</p>
<p>Exercise 1bii: Using <code>mutate</code> and <code>na_if</code>,
correct the variables that are still using <code>-9</code> so that these
are instead coded as <code>NA</code></p>
<p><strong>NOTE</strong> Please take note of how many variables you need
to correct during this question. The answer will be part of the brief
assessment quiz which will be used to register you for the module
certificate.</p>
<div class="tutorial-exercise" data-label="ex1bii" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate()
summary(farmer_data)</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1bii-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate(hh_size = na_if(hh_size, -9),
income = na_if(income, -9),
costs = na_if(costs, -9))
summary(farmer_data)
# OR
farmer_data <- farmer_data %>%
mutate_all(na_if, -9)
summary(farmer_data)
#This would only be useful if -9 will ALWAYS be a missing code and never a true value of a variable
#Recommend looking into mutate_at or mutate_if to see how you can make the same edit across multiple but not all columns</code></pre>
</div>
</div>
<div id="section-exercise-1c---correcting-labels"
class="section level3">
<h3>Exercise 1c - Correcting labels</h3>
<p>Now something else looked off about our sex variable. Let’s use
<code>unique</code> to have a look at all of the unique values of
sex</p>
<pre class="r"><code>unique(farmer_data$sex)</code></pre>
<pre><code>## [1] "1" "Male" "m" "M" "f" "Female" "2" "F"</code></pre>
<p>Hmm, seems we have multiple values to mean the same thing
unfortunately. Male and Female have both been coded four different ways
each.</p>
<p>Exercise 1c: Using <code>mutate</code> and <code>case_when</code>,
standardise the sex variable such that</p>
<p>Male, m, M & “1” are all recoded to equal “Male”</p>
<p>Female, f, F & “2” are all recoded to equal “Female”</p>
<p><strong>Hint</strong> Think back to how we used the <code>%in%</code>
operator</p>
<div class="tutorial-exercise" data-label="ex1c" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate()</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1c-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data %>%
mutate(sex = case_when(
sex %in% c("Male", "m", "M", "1") ~ "Male",
sex %in% c("Female", "f", "F", "2") ~ "Female"
))
table(farmer_data$sex)</code></pre>
</div>
</div>
<div id="section-exercise-1d---correcting-outlier-values"
class="section level3">
<h3>Exercise 1d - Correcting outlier values</h3>
<p>Let’s use a histogram to inspect one of our continuous variables for
possible data entry errors</p>
<pre class="r"><code>hist(farmer_data$age)</code></pre>
<p><img src="index_files/figure-html/unnamed-chunk-4-1.png" width="624" /></p>
<p>Looks like we have one implausibly high outlying value for age.</p>
<p>Upon contacting the enumerator who collected data for this farmer,
you find the true value was meant to be 18 years It was just inputted
wrongly.</p>
<p>Exercise 1d: Using any method demonstrated in the workbook, correct
this implausibly high value of 180 years old to the correct value of 18
years old.</p>
<div class="tutorial-exercise" data-label="ex1d" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<p>Remember you can use the histogram to again plot the value and check
your changes</p>
<div class="tutorial-exercise" data-label="ex1db" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>hist(farmer_data$age)</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1d-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data$age[farmer_data$age == 180] <- 19
#OR
farmer_data <- farmer_data%>%
mutate(age = ifelse(age == 180, 18, age))</code></pre>
</div>
</div>
<div id="section-exercise-1e---creating-a-new-variable"
class="section level3">
<h3>Exercise 1e - Creating a new variable</h3>
<p>As part of the data analysis plan you notice that part of the
analysis will include an evaluation of the gross income of farmers, a
calculation of income minus costs. However, you find this was not
calculated directly as part of the data collection.</p>
<p>Exercise 1e: Using <code>mutate</code> create a new variable called
<code>gross_income</code> which is the income minus the costs.</p>
<div class="tutorial-exercise" data-label="ex1e" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1e-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data%>%
mutate(gross_income = income-costs)
farmer_data$gross_income</code></pre>
</div>
</div>
<div id="section-exercise-1f---sorting-data" class="section level3">
<h3>Exercise 1f - Sorting data</h3>
<p>Finally, you may have noticed that there is currently no particular
order to our data. While not mandatory, sorting data can be useful for
presentation, readability and help users easily find certain pieces of
information.</p>
<p>We have a natural sorting variable in our unique identifier “id”</p>
<p>Exercise 1f: Using <code>arrange</code>, sort the data using the id
variable so that the rows are in order from 1 to 100 by id.</p>
<div class="tutorial-exercise" data-label="ex1f" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex1f-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data <- farmer_data%>%
arrange(id)</code></pre>
</div>
</div>
</div>
<div id="section-part-2---data-reshaping" class="section level2">
<h2>Part 2 - Data Reshaping</h2>
<div id="section-datasets" class="section level3">
<h3>Datasets</h3>
<p>Before we test your skills in pivoting data, let’s look at some
additional data we have for this project. It is at the level of the plot
while our previous dataset was at the level of the farmer.</p>
<p>Each farmer can have up to 8 plots, they are labelled id_1, id_2 and
so on. You will notice that many of the values in these columns are NA
as the farmers do not all have this many plots.`</p>
<pre class="r"><code>plot_data</code></pre>
<div data-pagedtable="false">
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<p>Secondly, you have been provided by an outside source with some data
about the villages that have been used in this project. However, it has
unfortunately been passed along in long format with individual rows for
the villages’ population, sampling weights and whether or not they
received support from an NGO.</p>
<pre class="r"><code>village_data</code></pre>
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</div>
<div id="section-exercise-2a---pivot-longer" class="section level3">
<h3>Exercise 2a - Pivot longer</h3>
<p>Firstly, let’s get our plot data into a more useful shape. When
conducting analysis it will be much simpler to keep comparable
information into the same column. i.e keep all values for the area of
plot in just the single column.</p>
<p>Exercise 2a: Using <code>pivot_longer</code> reshape the wide plot
data into a new long data set, save this output as
<code>plot_data_long</code> and call the new variable, that will list
the plot number within a farmer, “plot_num”.</p>
<p>Please include the <code>values_drop_na = TRUE</code> argument into
your answer. This will stop unnecessary rows being created for where all
variables will be <code>NA</code>. In other words, all farmers will only
have as many rows as they will plots.</p>
<p><strong>Hint</strong>: If you are stuck at all, look back at the
session 2 workbook and how we utilised the special string of
<code>".value"</code>.</p>
<p><strong>NOTE</strong> For the following steps you can use
<code>dim()</code> to check the number of rows and columns in your data
to make sure you have the right amount. Please take note of how many
rows are in the resulting data frame. The answer will be part of the
brief assessment quiz which will be used to register you for the module
certificate.</p>
<div class="tutorial-exercise" data-label="ex2a" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>plot_data_long <-plot_data%>%
pivot_longer(
values_drop_na = TRUE
)</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex2a-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>plot_data_long <-plot_data%>%
pivot_longer(
cols = id_1:fertilisers_8,
names_to = c(".value", "plot_num"),
names_sep = "_",
values_drop_na = TRUE
)
plot_data_long
dim(plot_data_long)</code></pre>
</div>
</div>
<div id="section-exercise-2b---pivot-wider" class="section level3">
<h3>Exercise 2b - Pivot wider</h3>
<p>Next let’s do the opposite to that village data.</p>
<p>Exercise 2b: Using <code>pivot_wider</code>, transform the data into
wide format such that you only have one row per village. Save this to a
new object titled <code>village_data_wide</code></p>
<div class="tutorial-exercise" data-label="ex2b" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>village_data_wide <- village_data%>%
pivot_wider()</code></pre>
<script type="application/json" data-opts-chunk="1">{"fig.width":6.5,"fig.height":4,"fig.retina":2,"fig.align":"default","fig.keep":"high","fig.show":"asis","out.width":624,"warning":true,"error":false,"message":true,"exercise.df_print":"paged","exercise.checker":"NULL"}</script>
</div>
<div class="tutorial-exercise-support" data-label="ex2b-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>village_data_wide <- village_data%>%
pivot_wider(
names_from = information,
values_from = value
)
village_data_wide
dim(village_data_wide)</code></pre>
</div>
</div>
</div>
<div id="section-part-3---data-merging" class="section level2">
<h2>Part 3 - Data Merging</h2>
<div id="section-datasets-1" class="section level3">
<h3>Datasets</h3>
<p>For the first exercise of this final section, we are going to bind
our previous data sets with some additional observations for our farmers
and plots. 20 new farmers with 45 plots between them.</p>
<pre class="r"><code>farmer_data_additional</code></pre>
<div data-pagedtable="false">
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</script>
</div>
<pre class="r"><code>plot_data_additional</code></pre>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["farmer_id"],"name":[1],"type":["int"],"align":["right"]},{"label":["id"],"name":[2],"type":["int"],"align":["right"]},{"label":["area"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["pesticides"],"name":[4],"type":["int"],"align":["right"]},{"label":["fertilisers"],"name":[5],"type":["int"],"align":["right"]},{"label":["plot_num"],"name":[6],"type":["chr"],"align":["left"]}],"data":[{"1":"101","2":"306","3":"4.8","4":"1","5":"0","6":"1"},{"1":"101","2":"307","3":"2.4","4":"0","5":"0","6":"2"},{"1":"101","2":"312","3":"4.8","4":"0","5":"1","6":"3"},{"1":"101","2":"321","3":"0.8","4":"1","5":"0","6":"4"},{"1":"101","2":"327","3":"2.8","4":"0","5":"1","6":"5"},{"1":"101","2":"333","3":"0.4","4":"1","5":"0","6":"6"},{"1":"104","2":"313","3":"6.0","4":"0","5":"0","6":"1"},{"1":"104","2":"320","3":"3.2","4":"1","5":"0","6":"2"},{"1":"105","2":"332","3":"3.2","4":"1","5":"0","6":"1"},{"1":"106","2":"325","3":"5.2","4":"1","5":"0","6":"1"},{"1":"106","2":"328","3":"6.0","4":"0","5":"0","6":"2"},{"1":"106","2":"339","3":"6.4","4":"0","5":"0","6":"3"},{"1":"107","2":"301","3":"5.6","4":"0","5":"0","6":"1"},{"1":"107","2":"302","3":"3.6","4":"0","5":"0","6":"2"},{"1":"107","2":"305","3":"4.4","4":"0","5":"0","6":"3"},{"1":"107","2":"329","3":"8.0","4":"1","5":"1","6":"4"},{"1":"108","2":"308","3":"0.4","4":"0","5":"0","6":"1"},{"1":"108","2":"316","3":"7.6","4":"1","5":"0","6":"2"},{"1":"109","2":"336","3":"5.2","4":"0","5":"1","6":"1"},{"1":"110","2":"314","3":"7.6","4":"0","5":"0","6":"1"},{"1":"111","2":"326","3":"0.4","4":"1","5":"1","6":"1"},{"1":"111","2":"340","3":"3.2","4":"1","5":"1","6":"2"},{"1":"111","2":"343","3":"3.2","4":"1","5":"1","6":"3"},{"1":"112","2":"304","3":"5.2","4":"1","5":"1","6":"1"},{"1":"112","2":"315","3":"5.2","4":"0","5":"1","6":"2"},{"1":"112","2":"335","3":"8.0","4":"1","5":"1","6":"3"},{"1":"113","2":"334","3":"3.2","4":"1","5":"0","6":"1"},{"1":"114","2":"318","3":"7.2","4":"1","5":"1","6":"1"},{"1":"114","2":"331","3":"1.6","4":"1","5":"1","6":"2"},{"1":"115","2":"317","3":"1.6","4":"1","5":"0","6":"1"},{"1":"115","2":"319","3":"4.8","4":"1","5":"1","6":"2"},{"1":"115","2":"337","3":"6.0","4":"1","5":"0","6":"3"},{"1":"115","2":"345","3":"5.2","4":"1","5":"1","6":"4"},{"1":"116","2":"322","3":"6.8","4":"1","5":"0","6":"1"},{"1":"116","2":"323","3":"2.8","4":"1","5":"1","6":"2"},{"1":"117","2":"310","3":"8.0","4":"0","5":"0","6":"1"},{"1":"117","2":"344","3":"6.8","4":"1","5":"1","6":"2"},{"1":"118","2":"303","3":"0.4","4":"1","5":"0","6":"1"},{"1":"119","2":"309","3":"4.4","4":"1","5":"1","6":"1"},{"1":"119","2":"311","3":"2.4","4":"0","5":"1","6":"2"},{"1":"119","2":"324","3":"8.0","4":"1","5":"1","6":"3"},{"1":"119","2":"330","3":"2.0","4":"1","5":"0","6":"4"},{"1":"119","2":"341","3":"0.8","4":"1","5":"1","6":"5"},{"1":"119","2":"342","3":"2.0","4":"1","5":"1","6":"6"},{"1":"120","2":"338","3":"0.8","4":"0","5":"1","6":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
</div>
<div id="section-exercise-3a---binding" class="section level3">
<h3>Exercise 3a - Binding</h3>
<p>As the datasets for our additional data sets which use the same
formats that we have developed from our tidied up data. We can move
straight to appending them onto our existing data frames.</p>
<p>Exercise 3a: Using <code>bind_rows</code>, append on the new
observations to farmer_data and plot_data_long.</p>
<p><strong>NOTE</strong>: The cleaned version of the farmer data has
been saved to a new object called <code>farmer_data_clean</code>. Please
use this dataset instead of <code>farmer_data</code></p>
<p><strong>NOTE</strong> For the following steps you can use
<code>dim()</code> to check the number of rows and columns in your data
to make sure you have the right amount. Please take note of how many
rows are in the <code>farmer_data_full</code> data set. The answer will
be part of the brief assessment quiz which will be used to register you
for the module certificate.</p>
<div class="tutorial-exercise" data-label="ex3a" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>farmer_data_full <-
plot_data_full
dim(plot_data_full)
dim(farmer_data_full)</code></pre>
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</div>
<div class="tutorial-exercise-support" data-label="ex3a-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data_full <- bind_rows(farmer_data_clean, farmer_data_additional)
plot_data_full <- bind_rows(plot_data_long, plot_data_additional)
dim(plot_data_full)
dim(farmer_data_full)</code></pre>
</div>
</div>
<div id="section-exercise-3b---summarising" class="section level3">
<h3>Exercise 3b - Summarising</h3>
<p>Now we will move onto first summarising some data from a lower level
to then then join it up with data at a higher level.</p>
<p>So as part of our data analysis plan we need a total farm area which
is the sum of the individual plot areas for each individual farmer. We
then want to merge this with our farmer data set.</p>
<p>Exercise 3b: Using <code>group_by</code> and <code>summarise</code>,
create a new data set which is a summary of the plot level data. Call
this <code>plot_data_summary</code>. Create summary variables for the
total area of a farmer’s plots using <code>sum</code> and then also
create a variable which counts the number of plots a farmer owns.</p>
<div class="tutorial-exercise" data-label="ex3b" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>plot_data_summary <- plot_data_full%>%</code></pre>
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</div>
<div class="tutorial-exercise-support" data-label="ex3b-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>plot_data_summary <- plot_data_full%>%
group_by(farmer_id)%>%
summarise(total_area = sum(area, na.rm = TRUE),
nplots = n())
plot_data_summary</code></pre>
</div>
</div>
<div id="section-exercise-3c---joining-data" class="section level3">
<h3>Exercise 3c - Joining data</h3>
<p>Join the plot_data_summary data set onto the farmer_data_full
dataset. Now we actually have a few farmers who we have no plot data
available. We have made the decision for analysis that we only want
farmers who own at least one plot.</p>
<p>Exercise 3c: Using a type of join that will only keep the farmers who
have plot data that they can be matched to, join plot_data_summary onto
farmer_data_full.</p>
<p><strong>HINT</strong> Remember that you can use <code>c()</code> to
match variables where they have different names in the two datasets</p>
<div class="tutorial-exercise" data-label="ex3c" data-caption="Code"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0">
<pre class="text"><code>farmer_data_full <- </code></pre>
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</div>
<div class="tutorial-exercise-support" data-label="ex3c-solution"
data-caption="Code" data-completion="1" data-diagnostics="1"
data-startover="1" data-lines="0">
<pre class="text"><code>farmer_data_full <- inner_join(
farmer_data_full, plot_data_summary, by = c("id" = "farmer_id")
)</code></pre>
</div>
<p>Thank you for completing this assignment.</p>
Please now go to the Module 3 quiz where you will be asked a couple
questions about the assignment. This quiz will be used to mark
completion of the module. Once you have completed the quiz, you can
expect to receive your certificate within a few working days.
<script type="application/shiny-prerendered" data-context="server-start">
knitr::opts_chunk$set(echo = TRUE)
library(learnr)
library(tidyverse)
library(randomNames)
set.seed(1)
farmer_data <- data.frame(
id = sample(1:100, size = 100),
village_id = sample(1:8, size = 100, replace = TRUE),
name = c(randomNames(50, gender = 0, which.names = "first"), randomNames(50, gender = 1, which.names = "first")),
sex = c(sample(c("1", "M", "m", "Male"), 50, replace = TRUE), sample(c("2", "F", "f", "Female"), 50, replace = TRUE)),
age = c(sample(seq(from = 18, to = 65, by = 1), 99, replace = TRUE), 180),
hh_size = sample(c(1:10, -9), size = 100, replace = TRUE),
income = sample(c(seq(from = 100, to = 900, by = 60), 0, -9), size = 100, replace = TRUE),
costs = sample(c(seq(from = 50, to = 500, by = 20), -9), size = 100, replace = TRUE)
)
xx <- sample(1:100, 5)
for(i in 1:5){
farmer_data$name[xx[i]] <- paste0(" ", farmer_data$name[xx[i]])
}
yy <- 1:100
yy <- yy[-xx]
yy <- sample(1:100, 5)
for(i in 1:5){
farmer_data$name[yy[i]] <- paste0(farmer_data$name[yy[i]], " ")
}
plot_data <- data.frame(
id = 1:300,
farmer_id = sample(1:100, 300, replace = TRUE),
area = sample(seq(from = 0.4, to = 8, by = 0.4), 300, replace = TRUE),
pesticides = sample(0:1, 300, replace = TRUE),
fertilisers = sample(0:1, 300, replace = TRUE)
)
plot_data <- plot_data%>%
group_by(farmer_id)%>%
mutate(plot_num = row_number())
plot_data <- pivot_wider(plot_data,
id_cols = farmer_id,
names_from = plot_num,
values_from = c(id, area:fertilisers),
names_sep = "_")
village_data <- data.frame(
id = rep(c(1:8),3),
name = rep(c("A","B","C","D","E","F","G","H"),3),
information = c(rep("population", 8), rep("ngo_supported",8), rep("weight",8))
)
for(i in 1:nrow(village_data)){
village_data$value[i] <- ifelse(
village_data$information[i] == "population", sample(1500:10000,size = 1),
ifelse(village_data$information[i] == "ngo_supported", sample(c("Yes", "No")),
sample(seq(0.02,1.98, by = 0.02)))
)
}
farmer_data_additional <- data.frame(
id = 101:120,
village_id = sample(1:8, size = 20, replace = TRUE),
name = c(randomNames(10, gender = 0, which.names = "first"), randomNames(10, gender = 1, which.names = "first")),
sex = c(rep("Male", 10), rep("Female", 10)),
age = c(sample(seq(from = 18, to = 65, by = 1), 20, replace = TRUE)),
hh_size = sample(1:10, size = 20, replace = TRUE),
income = sample(c(seq(from = 100, to = 900, by = 60), 0), size = 20, replace = TRUE),
costs = sample(seq(from = 50, to = 500, by = 20), size = 20, replace = TRUE)
)
farmer_data_additional$gross_income = farmer_data_additional$income - farmer_data_additional$costs
plot_data_additional <- data.frame(
farmer_id = sample(101:120, size = 45, replace = TRUE),
id = 301:345,
area = sample(seq(from = 0.4, to = 8, by = 0.4), 45, replace = TRUE),
pesticides = sample(0:1, 45, replace = TRUE),
fertilisers = sample(0:1, 45, replace = TRUE)
)
plot_data_additional <- plot_data_additional%>%
group_by(farmer_id)%>%
mutate(plot_num = as.character(row_number()))%>%
arrange(farmer_id, plot_num)
plot_data_long <-plot_data%>%
pivot_longer(
cols = id_1:fertilisers_8,
names_to = c(".value", "plot_num"),
names_sep = "_",
values_drop_na = TRUE
)
village_data_wide <- village_data%>%
pivot_wider(
names_from = information,
values_from = value
)
farmer_data_clean <- farmer_data%>%
mutate(name = trimws(name, which = "both"),
hh_size = na_if(hh_size, -9),
income = na_if(income, -9),
costs = na_if(costs, -9),
sex = case_when(
sex %in% c("Male", "m", "M", "1") ~ "Male",
sex %in% c("Female", "f", "F", "2") ~ "Female"
),
age = ifelse(age == 180, 18, age),
gross_income = income-costs)%>%
arrange(id)
farmer_data_full <- bind_rows(farmer_data_clean, farmer_data_additional)
plot_data_full <- bind_rows(plot_data_long, plot_data_additional)
plot_data_summary <- plot_data_full%>%
group_by(farmer_id)%>%
summarise(total_area = sum(area, na.rm = TRUE),
nplots = n())
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`tutorial-exercise-ex1e-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex1e-code-editor`)), session)
output$`tutorial-exercise-ex1e-output` <- renderUI({
`tutorial-exercise-ex1e-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex1f-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex1f-code-editor`)), session)
output$`tutorial-exercise-ex1f-output` <- renderUI({
`tutorial-exercise-ex1f-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex2a-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex2a-code-editor`)), session)
output$`tutorial-exercise-ex2a-output` <- renderUI({
`tutorial-exercise-ex2a-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex2b-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex2b-code-editor`)), session)
output$`tutorial-exercise-ex2b-output` <- renderUI({
`tutorial-exercise-ex2b-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex3a-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex3a-code-editor`)), session)
output$`tutorial-exercise-ex3a-output` <- renderUI({
`tutorial-exercise-ex3a-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex3b-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex3b-code-editor`)), session)
output$`tutorial-exercise-ex3b-output` <- renderUI({
`tutorial-exercise-ex3b-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-ex3c-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-ex3c-code-editor`)), session)
output$`tutorial-exercise-ex3c-output` <- renderUI({
`tutorial-exercise-ex3c-result`()
})
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<h2 class="title toc-ignore" style="display:none;">Module 3 -
Assignment</h2>
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